extreme manufacturing flexibility: organizational levers ... · keywords: flexibility; lean...

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POLITECNICO DI MILANO DEPARTMENT of Management, Economics and Industrial Engineering (DIG)

DOCTORAL PROGRAMME IN Management, Economics and Industrial Engineering

Extreme Manufacturing Flexibility: Organizational levers to cope with extreme

planning, volume and mix flexibility requirements.

Doctoral Dissertation of: Giorgio Fantino

Supervisor: Prof. Giovanni Miragliotta

Tutor: Prof. Mariano Corso

The Chair of the Doctoral Program: Prof. Paolo Trucco

2017 – XXVII°

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Abstract

In a world where the “economics” seem to be everything and all other knowledge fields are in background, where “growth” among all the economic matters seems to be the one and the only one measure of the systems health, where all the economic levers are bent or “disfigured” by the “growth”, where the “speed” change is always increasing but even the change does not produce the past growth rate, where also the time has no value (even a negative value if we translate it into interest rate or negative interest rate), where the planning, viewed as driving the economy, is not really a discussion argument as in 1970s, due to the socialism crisis and the growing rate of transformation, this paper, on the contrary, seeks to focus on a medium or long term issue:

Is it an advantage to invest in developing soft skills or flexibility through specializations? How can we measure the existing gaps between actual level of knowledge and the possible one? How can we explain or simply understand these gaps not only as actual losses or future gains?

Considering some important concepts studied during the second part of the last century, 1900s, and probably forgotten after the socialism crisis, the paper tries to focus on workers skills as a competitive asset versus countries which seem to have an unbridgeable cost advantage: BRICs countries didn’t evolve versus the levels reached by western economies, probably because their raw materials or worker markets are too “big” and they didn’t even approach a maturity “age”; too much offer, or a never‐ending offer made the demand unable to push salaries and worker conditions higher. Italian medium or small size companies inherited the larger companies’ knowledge in the past when these last ones, the large companies, could compete worldwide but nowadays the Italian paradigm seems to shift versus small or medium actors playing on some sectors without any historical large players. Italian players seem to find difficulties in bridging from medium to large size.

In this context, the new actors have to understand and measure the advantage of investing in

distinguished skills of their workers and translate this investment into competitive advantage.We

will discuss how this investment was treated by literature, how we can measure the gaps that have

to be filled, how these gaps can be seen as the investment we want to afford to reach a better

system “status”, how this approach could be reflected in an “on field” study based on a ten-year

simulation.

This paper is based on the professional experience of its author: a general manager who wanted to

drive the change in a typical medium Italian company. A growing company with well-known fashion

customers, operating in a partner relationship with these customers, characterized by a negative

attitude to change: managers, structure loved the past success tracks and Unions don’t want to

modify people working rhythms. In this context the Action Research Project initially studied

transformed itself in a simulation project useful to define the “efficiency – flexibility” gaps and risks

to be covered: to study a “social” environment, to define the indicators useful to drive the change

along a developed soft skill knowledge and a more flexible working specialization and to draw a

clear future path to follow.

Keywords: Flexibility; Lean Approach; Organization; Performance measurement; Simulation

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Research Design

In this section, we want to describe the “guidelines” of this paper in order to immediately give a

clear picture of the project

Objectives and Research Questions

In a changing world with more and more structural differences between “western” and new

countries or economies, the competitive arena obliged every actor to find the right competences

mix to offset competitors’ advantage, especially when these competitors have an important cost

advantage. Even if the Schumpeterian innovative disruption has always played a major role in

changing competition, we want to study how the education and training could also be an important

change factor pushing on a new “service” level offered to customers in the place of the competitors’

lower cost.

The base Research Question could be: “How can we measure and judge the effects of new

organizational levers on a company structure? How far can we push the system flexibility if we have

to consider benefits and costs?” As we present in the following sections, this paper wants to

measure the benefits and/or the related costs that

more training and educational expenses could have on the structure

more accurate and fast reacting planning and scheduling could have on the structure

These two areas are investigated through the two following axes:

the “workers flex” as the ability of the worker to accomplish two or more different

operational tasks

the “system flex” as the ability of the people in charge of driving the operations to perfectly

organize the single operations in the most efficient and effective way

The proposed target variables useful to measure the effects are:

“FTE” defined as number of workers, theoretical, employed on a full-time basis necessary to

fulfil the demand received, given all the system conditions and axes, here-above, simulated

Cost of the FTE requested

“LFTE” defined as the number of theoretical equipment lines, given all the system conditions

and levers simulated, requested to fulfil the demand received

Cost of the “LFTE” with the double definition of space, in squared meters, necessary to install

the theoretical full occupied lines and the investment cost, based on a standard cost per line

or macro-phase line

A set of technical indicators able to suggest the system answer in term of hours needed to

complete the production process

The results are inferred as difference between the targets variables calculated in several scenarios.

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Case Study

The paper is based on ICR Spa data. With a different viewpoint, the paper seeks to test the system,

improve the model, with the real data recorded by the company. ICR Spa is, probably, a typical

example of medium size Italian company working in a well-known sector: luxury, fashion, luxury

perfumes in particular.

Established in 1975, the company passed through all the recent Italian events and this is a good

example of the evolution that the industrial system and many small medium size companies had in

Italy over these last decades. ICR Spa is today a 50 million EURO turnover company with more than

500 employees. High quality products, with high value added into the product, partnerships not

supply contracts with famous Italian brands. These are some of the company characteristics which

every day has to study for a better efficiency, to face a stronger competitive arena, to change and

adapt itself to the new market forces.

Please refer to the Context section for an in-depth description of the company and historical

evolution of the Italian background.

Literature review

The literature review sets out to look at the research fields useful to understand if this approach has

already been studied.Starting with some key words searched (agility, flexibility, luxury, training, education, performance measurement) the paper tries to develop the different approaches followed by different studies and the types of interest covered by these researches.

This was initially an introduction to all the arguments that a “change” process pushes to consider: a manager driving a small or medium size company, especially in Italy but abroad it is not so different, has to study a “complex” social environment always changing over the time. Technology, theories, social and company culture and history, market and products characteristics or life-cycle step, …. The literature review is necessary:

to know the “state-of-the-art” concerning all or most of the issues influencing the studied environment,

to restrain the interesting arguments and to focus a research target attainable and useful

to understand which aspects of the target are already studied and from which viewpoint

For each study trend line the paper shows some notes and the connection with the argument:

sometimes only to present the different target of the analysis followed by some researchers.

The following (Figure 1) shows how the papers were grouped by the author. In (Annex Figure 51)

the detailed list of papers each one affected to the groups identified.

Figure 1 - Papers viewed

— page 5 — All the papers were significant but no one covered the need to measure the interest to implement

a new structure based on new skills. Many arguments with a focus on each effect or side-effect that

the issue could have on targets (flexibility, agility, lean, …) but not really from the value-measuring

viewpoint.

Case Study Objectives and Research Questions in the context

The general objectives presented as well as the research questions have to be “dropped down” in

the case study. A company, working with a high value added product forced by the competition to

reduce its prices, naturally tries to enrich the product itself with a set of services: the focus shifts

from the physical product to the service conditions.

To focus on the simulation, the paper seeks to measure a set of indicators (FTE, FTE cost, LFTE, LFTE

cost, technical indicators) in different scenarios. These different scenarios represent several statuses

in which the company operates or could operate. The differences between the indicators calculated

in these scenarios could be viewed as

the benefits/costs to face if we pass from the first status to the second one

the theoretical costs affordable if we seek to reach the second status starting from the first

one

the answer of the system to different customers’ request (especially for technical indicators)

With this previous approach ICR could be in better position gaining the cost differential (or,

alternatively, using the gain as training or educational cost: in this manner the benefit could be

viewed as workers with higher competences while the cost of this training could be covered by the

cost differential)

Methodology

Initially the project should have to be a real action-research with constant interaction with the

company plant and structure. But this project was immediately dismissed because the Unions’

negotiation was so difficult that every proposal was doomed to failure. Moreover, as in every social

environment, the system adaptability was so strictly connected to the project’s author that many

levers or tests could be absorbed in advance with modified and not completely relevant results.

“Creating productive collaborative research partnerships that produce mutual benefits for scientists

and practitioners requires that a great deal of effort be put into the relationship between the

parties, the formulation of research plans and methods, and the interpretation, application, and

diffusion of results. As it turns out, these things are more easily said than done.” (Shani & Others,

2008)

The model, and the possible simulation environment built around the model, was judged more

“external” to the loop system-author and this independency was also judged critical.

This approach reflects what was studied by Herbert A. Simon about his “bounded rationality”. All

the limits quoted and concerning the environment, internal and external, could be investigated with

this approach knowing that the “pure” economic rationality does not exist in the real world. And

the conclusion reached by Herbert A. Simon was to study these effects through computer simulation

modelling.

— page 6 — The data concerning the last years, 125 thousand records, available from the company system, were

considered as statistically relevant and were subjected to a first series of analysis. These analysis

were made with the software Knime.

The simulation model was developed on a Software Vensim DSS version 5.9, Ventana System

Development, considering a 10 year horizon. The model considers a set of more than 500 variables;

many variables are arrays with multidimensional vectors.

On a double set, system model and statistical relevant data, the paper defines the boundaries,

components and rules of the model. The model takes into account a series of scenarios able to be

used for the testing as well as the simulation activity.

Hypothesis

Two sets of scenarios were ideally built: the first tried to replicate the real situation all along the ten

year horizon, the second wanted to imagine an ideal world where the past was imagined in line with

the actual and future conditions.

While the first set of hypothesis, named “Base”, was used to test the model validity comparing some

real set of data with the simulated one to confirm the model, the second set, called “All Intern”,

wanted to inherit the parameters validated with the “Base” set to re-build the data series as if

everything, even in the past, was produced internally.This second set lets the simulation imagine

the simulated future comparable with the past.

A second group of scenarios, a subset, comparable across the two main sets was imagined to draw

several different conditions. In the “Base” main group, the actual/real scenarios group, we find:

a first “actual” set which tries to replicate the real situation with quantities as per records,

optimum planning ability, no reduction of days inside the time batch due to customers’ order

transmission, no concurrent operations and no flexibility through specialization (workers

versus setup operators or vice-versa)

a second “actual optimum” like the “actual” but with perfect flexibility through specialization

a third “actual reduction days” like the “actual” but without perfect planning and an

estimation of reduction days inside the time batch (this reduction estimated externally to

explain the real setup operators versus the simulated one)

a fourth “actual reduction days’ optimum” that starting from the third one tries to calculate

the effect of a perfect flexibility through specialization based on this hypothesis

a fifth one, the last one, that forgets the reduction in days inside the time batch and searches

the same effect modifying the concurrent rate among operations to explain the setup

operators recorded versus the simulated ones.

This set of five scenarios is replicated for the “all Intern” group that considers all the quantities as

made internally.

All the five options, sets are replicated for this second group with only one great exception: the fifth

set, the “All Intern Concurrent Reduction Days” scenario, considers a mix between the

independently estimated parameters, reduction days and concurrent operations rates, of the

“base” set. This fifth “All Intern” scenario tries to combine all the actual conditions imagining the

“most” real situation applied to the all intern quantities picture.

— page 7 — Finally some words about the imagined “sensitivity” options: the two levers considered were the

order batch size and the average working rate. These two levers define the two main conditions

able to modify structurally the production system.

The order batch size could be seen as a discriminant between the handcraft and industrial demand

of every customer but it has great effect on the number of orders treated by the company as well

as the number or production changes to be made, the importance of setup on the global

coordinated operations.

The average working rate, linked in some way to the “industrial” level of the customer, has direct

effect on the time spent for the same order size, the industrial character of the industrial level of

the equipment, the setup time requested by the average equipment, the team requested by setup

or general working operations …

As you can see the two levers chosen for the sensitivity have many effects on the system and these

effects have to be described and evaluated. Some parameters are directly taken from the company

dataset, others are generated by the “base” set and others are generated by the model inside the

new scenarios.

Testing

The real data were compared with the “base” set scenarios. This group of scenarios was used to

validate the data which tried to replicate the simulation that runs the records of the company.

Inside this set, once validated some main data, the model calibrates some other parameters that

were not detailed by the company records: concurrent operations rate or the reduction days rate

inside the time batch were inferred trying to adjust the number of setup operators simulated by the

model to the real number of setup operators enrolled by the company.

Logically this kind of calibration is not able to discriminate between inefficiency of the real choices

and the theoretical number requested by an unknown internal rule of the system but the result is

relevant even if not completely understood.

Results: the “general” as well as “specific” implications to the context

Finally, the results and their interpretation, based on the Case Study, they are, naturally, specific to

the environment studied.

We will describe the results that are interesting in terms of absolute value, either on the total all

along the time schedule or on every single time step, or in percentage. They are interesting either

comparing the main scenarios using them to explain the gains available choosing different

organizations or evaluating the sensitivity variations in each scenario, moving one or both the

technical levers described. It is also interesting to evaluate the sensitivity results comparing the

different scenarios.

But if the results are interesting for the Case Study, ICR Spa situation, they could be generalized if

we consider ICR as a model for small medium size Italian companies.

It needs to be highlighted that it is linked further to the initial situation: as per many differential

equations systems in mathematics, the result depends directly on the system’s initial status.

But again the model description is useful in itself to think about the system and its internal rules or

its boundaries (moving the boundaries we could describe a totally different system: imagine, for

example, a system where not only an operation setup but indirect functions could be evaluated with

a higher degree of flexibility through specialization … With this different horizon, many other

considerations could be made and many issues claimed and studied by quoted literature review

— page 8 — begin to take importance: the “forgetting, attrition” costs quoted for training activities could have

new relevant roles while in our model were completely forgotten.

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Summary

Abstract ................................................................................................................................................ 2

Research Design ................................................................................................................................... 3

Objectives and Research Questions ............................................................................................. 3

Case Study ..................................................................................................................................... 4

Literature review .......................................................................................................................... 4

Case Study Objectives and Research Questions in the context ................................................... 5

Methodology ................................................................................................................................ 5

Hypothesis .................................................................................................................................... 6

Testing ........................................................................................................................................... 7

Results: the “general” as well as “specific” implications to the context ...................................... 7

Summary .............................................................................................................................................. 9

1 Introduction .................................................................................................................................... 11

2 Research Objectives ........................................................................................................................ 12

3 Research Limitations ....................................................................................................................... 14

4 Context ............................................................................................................................................ 17

4.1 Case Study: ICR Spa ............................................................................................................... 20

4.1.1 ICR background .............................................................................................................. 20

4.1.2 Some data to understand the trends ............................................................................. 21

4.1.3 Data Context Analysis: Market data .............................................................................. 22

4.1.4 Data Context Analysis: Industrial Data .......................................................................... 28

4.1.5 A different Viewpoint: Time Series from Launch Time .................................................. 34

4.1.6 Series characteristics as Customers’ requests and their effects on the industrial system

................................................................................................................................................. 42

4.1.7 Some Industrial trend lines ............................................................................................ 45

5 Literature Review ............................................................................................................................ 48

6 Research Approach ......................................................................................................................... 59

6.1 Theoretical Approach ........................................................................................................... 59

6.2 Methodological Approach .................................................................................................... 59

6.2.1 Simulation ...................................................................................................................... 60

6.2.1.1 Why? ........................................................................................................................ 60

6.2.1.2 Context .................................................................................................................... 61

6.2.1.3 Structure .................................................................................................................. 62

Environment Description ................................................................................................. 62

Model entities description ............................................................................................... 63

Model validation .............................................................................................................. 65

Scenarios Hypothesis ....................................................................................................... 66

Scenarios Results ............................................................................................................. 73

Sensitivity ......................................................................................................................... 87

Sensitivity Scenarios ..................................................................................................... 87

Sensitivity levers .......................................................................................................... 88

Sensitivity Results ........................................................................................................ 92

“All Intern Base” Sensitivity ...................................................................................... 93

FTE .................................................................................................................. 93

COST ................................................................................................................ 96

Lines ................................................................................................................ 98

Technical Indicators ...................................................................................... 101

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- Order Size Sensitivity Analysis ................................................................. 101

- Working Rate Sensitivity Analysis ............................................................ 104

- Working Rate and Order Size Sensitivity Analysis ................................... 106

“All Intern Base Optimum” Sensitivity ................................................................... 109

FTE ................................................................................................................ 109

COST .............................................................................................................. 111

Lines .............................................................................................................. 113





“All Intern Reduction Days, Concurrent” Sensitivity .............................................. 124

FTE ................................................................................................................ 124

COST .............................................................................................................. 125

Lines .............................................................................................................. 127





7 Discussion ...................................................................................................................................... 139

7.1 Some notes and remarks about the model ........................................................................ 140

7.2 Some notes and remarks about the environment ............................................................. 140

7.3 Some notes and remarks about the simulation results ..................................................... 141

7.3.1 Scenarios comparison .................................................................................................. 142

7.3.1.1 “Starting point” scenario: “All Intern Reduction Days Concurrent Flexibility” ..... 145

7.3.1.2 “Final target” scenario: “All Intern base Optimum” ............................................. 147

7.3.1.3 “Mid-Point” scenario: “All Intern Base” ................................................................ 149

7.3.2 Scenarios Sensitivity ..................................................................................................... 151

7.3.2.1 FTE ......................................................................................................................... 152

7.3.2.2 Lines ....................................................................................................................... 162

7.3.2.3 Technical Indicators ............................................................................................... 169

8 Conclusion ..................................................................................................................................... 171

Aknowledgement ............................................................................................................................. 184

Bibliography ..................................................................................................................................... 185

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CHAPTER 1

1 Introduction

This research project wants to reflect the extreme flexible conditions that most of the Italian

companies have to consider today facing the competitive arena and a business world which is really

“fast and furious”, mentioning a series of well-known films among the younger generation.

As most of the cutting-edge technological innovation and, in a more general way, the technical

progress, the business world shows a constant accelerating “walk”: everything is speeding up and

Schumpeterian concept “creative destruction” (Schumpeter, 1942 - 1994) is on the news every day.

Most of the case studies used thirty years ago no longer exist or have completely changed (Kodak

or Olivetti, for example). We are witness of a changed, changing and rapidly changing world.

Starting from the Italian industrial environment and analysing the ICR Spa case study, the research

wants to focus on the organizational levers available to business actors to cope with extreme

flexibility that the market demands. The research wants to put forward how to evaluate the gaps to

be covered between the actual real situation and the targeted one: it’s important to measure the

difference because the same gap could be seen as possible gain as well as, on the other hand, the

available amount of resources that could be invested, without loss, to reach a better situation.

Quoting an overused economic term, we could also say a “better” equilibrium state: equilibrium

because what was gained, during the “passage” from actual to target, was also “invested” to buy

the conditions to reach the new “state”; the new situation, the final state, has new conditions which

could grant a “superior” answer to market turbulence and new “fast pace” requests.

The approach proposed could be useful for professionals that have to drive business realities from

small to medium size with all the changes that generally we associate to terms like “small” and

“medium”, hoping that the external conditions which could be the same as those in which ICR Spa

works in.

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CHAPTER 2

2 Research Objectives

This paper wants to measure the benefits or the costs that

more training and educational expenses could have on the structure

more accurate and fast reacting planning and scheduling could have on the structure

The following scheme (Figure 2 Framework and objectives) could be a summary of the two axis that

the study could investigate measuring the change effects on some indicators useful for management

choices.

Figure 2 Framework and objectives

We could define

the “workers flex” as the ability of the worker to accomplish two or more different tasks:

o inside the case study we focus on the two main duties inside a production

department, the “set-up” and the “working operations”, and

o also on the “specialization” degree that the two quoted main duties, “set-up” and

“working operations”, could have as function of other production characteristics -

customers or product family or macro-phase requests

the “scheduling flex” as the ability of the people in charge of planning and scheduling to

override

o the “time batch” compression requested by customers and/or

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o the “concurrent” degree that independent customers operating in the same sector

with a similar approach could cause to the production department.

By operating on these previous levers, we want to measure the effects on the system, especially on:

“full time equivalent” (FTE) worker, considered with a double viewpoint: internal and total

“full time equivalent”. This double approach wants to stress the effect of different industrial

organizations: outsourced more or less. “Total FTE” considers internal plus outsourced

workers. The ICR model “loses” only the “external” orders which are considered as not

“made” but “bought”.

cost of “full time equivalent” always in the double view, internal and total, already described

“full time equivalent production lines” per production phase or “production step”. This

concept tries to apply the same theoretical approach used for workers to the equipment

needed, the production lines.

Cost of “full time equivalent production lines”, measured in terms of space (m²) and

investment amount, using the average historical cost of last equipment installed by ICR

Furthermore, the system has to measure all the answers that it is able to provide in different

configurations. With this viewpoint, the technical answer could be measured in terms of:

Technical operation flow time, also defined as “TechOpsFlowTime”: the average time

requested by one piece of product to pass through the production plant. This measure

depends on the customer and product family mix. This measure is also summarized at

customer or product family level, to obtain a “higher” level indicator.

Frozen Time: the average time requested since the decision which considers the order

producible to the product availability time. It is the sum of the technical operation flow time

and some extra periods needed by the components and order preparation (technical

operation frozen time) and the “bulk preparation time” (total frozen time). Both of these

indicators could be presented at a “higher” level, as an average per customer or product

family. Please note that the “total frozen time” considers a preparation time for the bulk

which is not really under the technical control: this “waiting” time for “maceration” is strictly

prescribed by the fragrance supplier.

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CHAPTER 3

3 Research Limitations

The project should have been put in place as an action research, with a focus on the ICR case study

and ICR future development. As most of the “economics” search, it is based on a “social” environment; the most important limitation is due to the effect that just only the idea of the research project could have in the system itself: during the period of study many “investigations” were carried out on the target environment (ICR system), trying to define the boundaries of the system and drawing the general rules of the system itself. Most of these studies became a clear signal to different bodies:

Unions to discuss the future of the company to push the “internalization” of the outsourced

activities

ICR Employees to obtain different roles inside the structure and change working position

along the structure

Directors and officers to elaborate new industrial structure and try new solutions

(continuous production lines, product family dedicated lines,

….

A social environment, always refraining great changes if not actually compelled by survival reasons,

could adapt itself and anticipate some trending line or evolutions, influencing the results of the tests

and creating different conditions “in” or “around” the “test area”. This situation generates a loop or

a series of feed-backs between the project itself and the environment studied: it is important to

understand which of these inter-actions could be specific of the analysed system and which ones

have a more general and overall character.

Some examples could be viewed in the continuous production lines improvement that started as a

matter of theoretical discussion, encouraged by new and extraordinary product requests made by

important customers. Nevertheless, it became a “wider” test applied through all the plant, although

it hasn’t been sponsored by the directors and, sometimes, in fact rejected by the department chiefs.

The positive and negative feed-backs, regarding this new solution, were confused and stressed: the

test line was used over such a long period of time and in so many different situations that every

judgement was possible.

Many similar situations could be shown as a result of internal investigation concerning the change

attitude of ICR people through many new projects started and developed during the last ten years.

But the ICR attitude is not only a “specific” attribute of ICR system, it could be seen as a

generalization to many other company environments.

— page 15 —

Figure 3 ICR people "Change" attitude

• The real forces working on a production context move all together or simply don’t move at all: it’s difficult to

isolate a set or a line with a checking alternative just to compare the results obtained by changing the lever

chosen

• When I tried to apply some new force onto the context I obtained a “contrary” vector equal to the force I

applied

• As per many “social” environment is really difficult not to influence the context with the changing force

applied even the “checking” set defined to control the results

Research Outline

Action research vs. Simulation model

(Figure 3 ICR people "Change" attitude) underlines the results checked inside the company:

Directors and officers have a higher perception of change gap but a real low sense of urgency in

changing.

While generally the analysis is made comparing two sets, one presenting the “historical” conditions

and the second one with the new attributes to be tested, drawing the conclusions on the outputs

obtained by the same inputs applied at the same time on the two sets, in this case we have to

consider:

the influences between the two sets which are not completely separated and independent

and

the effects that the same people working on the two sets could cause on the same sets all

along the test period (contemporaneity is not possible) and

the distortions that the prejudices, or simply the knowledge of the project, absorbed by the

key people of the test could cause on the two whole test sets

In short we could say that:

The “social” environment is specific of the study area, even if some general characteristic

could be inferred

This “social” environment is not static and some studies or projects, even if only announced

or discussed, could have feed-back on the “test” environments

Bodies could try to prevent or simply adapt themselves to probable future changes: student

and his studies could have effect on the study-object

A “social” environment is always dynamic: internal and external “forces” could have

different effects, throughout and as a function of the initial conditions. Some “minor”

relationships studied could have “major” impact in different conditions. Events, or “minor”

initial forces, might push the system to come back to the initial state after some time. People

in disagreement, even not initially declared, could increase during the time retaking the

system to the starting point

— page 16 —

A “social” system could always show some general aspects but it is always composed by

people who transform the information: the micro seems not to be relevant from a static

system overview but the internal rules and the interactions among each group components

could setup completely different paths for apparent similar data causing totally different

final results. Quoting Edward N. Lorenz, an MIT researcher and a pioneer in chaos theory, in

a complex system we could always find a “butterfly effect” with an unexpected important

effect caused by misunderstood or unknown remote causes (Gleick, 1987)

A second limit is represented by the case study: ICR environment. The description given of the

production environment and people structure is representing a simple process flow. Nevertheless,

it is a single case study based on a real situation. In order to generalize the results it might be useful

to find other case studies. The case study and the simulation is based on a specific sector and the

ICR industrial process: this limit restrains the results to direct operators (line workers and setup

operators) with a possible broader horizon covering other indirect operators (quality control,

industrial coordination, in and out handlers). A broader boundary could be affected by the

organizational “confusion” which could cause a lower level of possible “gain” due to new inter-

functional operator skills.

With this limit we have to return immediately to the literature review: especially at the final part of

the literature review referring to the limit that the training and education could show in a project

which wants to “enlarge” the flexibility through all the industrial areas.

A third limit it is linked to the author’s function inside the organization: as per the “social” loops and

feed-back already described, its position might directly influence the structure under observation,

which adapts itself to its members and, especially, to its managers. The author’s viewpoint is not

really “external” and the loops between his studies and his decisions could themselves modify the

“target” or his analysis.

— page 17 —

CHAPTER 4

4 Context

Before exploring the ICR Spa case study, it could be useful to sketch the Italian industrial

environment just to describe the “general” trends that influenced the actors working in our

geographical area all along the time. Understanding where we were and how we got here and now,

it could give us a clear perception of the path followed during the years and, probably, some idea of

where our “road”, with its limits or boundaries, is leading us to.

Starting from the Italian unification, in 1860-70, the country’s “industrialization” showed different

situations and very interesting solutions to all the drawbacks that the last 150 years registered.

The country came out from feudalism and renaissance with the beautiful arts aspects everybody

knows, but, surely, with an economic situation tremendously delayed compared to the other main

European countries. The differences inside the country, between the north and the south or the

north-west and the north-east, were very important: the north-west, with the area between Milan-

Turin-Genoa, started a fast growing industrial process with well-known companies (Fiat in Turin

founded in 1899, Ansaldo & Co. in Genoa founded in 1853, Pirelli in Milan founded in 1872, …) while

the other regions, with some exceptions (the industrial history of the Naples area is significantly

interesting), remained more agricultural.

Italian territory was not rich in raw materials but the “will” to be present among the most important

European nations pushed the new Kingdom to develop a new processing industry: this industry was

necessary to make Italy independent and able to face war requirements. Most of the big companies

born in these years were involved in primary market or sectors like steel, mechanical, automotive,

railways, electricity… It’s interesting to note how this industrialization process was led “by the hand”

of the new financial and bank system: Credito Italiano and Banca Commerciale were founded in

1870s and promoted this first industrial growth.

Crisis and euphoria are always part of the economic cycles but even inside a well-defined market

trend every single company history could show results not in line with the general situation;

nevertheless, this developing phase drove Italy toward WWI and the international and national

problems of the first post-war period: end of the war needs, industrial re-conversion problems from

the war-industry to the civil-industry, social problems mostly all around the European countries but

also around the world, weak economy in important countries like Germany that lost the war, too

high inflation rate and last but not least the 1929 world crisis, …

Without considering the political effect of this situation in our world history (Nazism in Germany,

Japanese imperialism, Soviet socialism, Fascism in Italy, …) or the macro-economic effects

(depression, even named Great Depression after the 1929, protectionism, colonial economies and

their effect on countries economic system, …), it is important to note how the parallel crisis of the

industrial and bank systems, really interdependent at this time, caused the Great Depression period

with a constant negative loop that increased the crisis effects (Avagliano, 1991). Here, we do not

— page 18 — want here to discuss the interpretation and the receipts given by the two main economic

approaches, Neo-classical and Keynesian (see (Galbraith, 1988),(von Hayek, 1988), (Galbraith J. ,

2002), (Keynes, 1931), (Nicholas Wapshott (Author), 2015)), which confronted each other all along

the years in this sad period of time, but we note that Italy, through the fascist government, chose

to directly act on the market. In 1933 the IRI (Amatori, 2013) (Franzinelii & Magnani, 2009),

“Institute for Industrial Reconstruction”, was established to rescue, restructure and finance banks

(Credito Italiano and Banca commercial, among others) and private companies (in practically all the

Italian markets) went bankrupt during the Great Depression ( (Franzinelii & Magnani, 2009). This

operation was a success and although initially though as a temporary holding company, IRI led the

Italian economy versus the WWII. By the end of 1930s, IRI activities on the Italian economy was so

important that no other country all over the world except the USSR could say the same. We want

to remember that in 1936 the “bank law” prohibited the mixture of commercial and finance

investments in commercial bank hands: the two activities were separated by law in two different

financial institutions with different targets and rules. This law wanted to prevent the Great

Depression causes: the commingling between financial and industrial interests, with credit

completely absorbed by ownership relations existing through credit institutions and industrial

companies or vice versa.

But this “private nationalization”, state ownership ruled with and by private laws, pushed all the

industrial system to expand and continued its opera well beyond the WWII end. The “Italian

economic miracle” after the war was mainly due to the “public” hand represented in Italy by IRI: but

all over the world, except probably USA, this formula was copied (Great Britain or France, for

example, were “champions” of the “public” hand). Public discussion imagined this formula like a

“third” way between pure liberalism/capitalism and socialism (URSS and its system).

The second post-war, with the well-known Cold War period, the Marshall Plan help at the outset,

with the IRI in a primary position (Pini, 2000), was characterized by expanding size companies,

operating in the industrials sectors and, in a second phase, in services: the trends were constantly

expanding and the economic cycles were, at least, several years long.

Macro-economic context was really positive and the reconstruction after the war initially and the

economic boom, the quoted Italian miracle, during the years 50s and 60s, pushed the industrial

sectors and, in a general way, all the infrastructure operators: companies began to believe in

constant and never-ending growth with focus and studies on economies of scale, statistical method

to forecast the growth, “improper charges” discussions (this matter, today completely forgotten,

anticipated the today well-known “sustainability” issues). The “IRI public” companies, operating

mostly in “base” and “infrastructure” sectors, pull also the growth of the “private” actors, which had

the same approach to the market here-above described with the exception of the “improper

charges” (La Bella, 1983) not applicable to “private” companies.

Since the beginning of 70s the macro-economic scenario completely changed with the first oil-shock,

the beginning of the base sectors crisis (steel, metals, infrastructures – these ones perhaps more in

late 70s or during the 80s, …), the growing inflation and tensions on the labour market, the

increasing state debts and the decreasing GDP growth of western countries, the new exchange rate

market with the fluctuation and devaluation of some currencies, the new industrial expanding

countries and their new paradigm (Japan with a new role and the new eastern countries in the

beginning , “BRICS” countries in a more general way all along the time) ….. Even the economic

approach to these new conditions changes: from a Keynesian approach, with the public intervention

— page 19 — judged more and more negatively for the losses and debts registered, to a new classical or liberal

approach with the monetarism viewpoint and the Chicago school theory (Friedman, 1987). This

change was also reflected in some political choices: President Ronald Regan in USA and Mrs

Margaret Thatcher in Great Britain, Republicans in USA and Tories in GB. Last but not least the Cold

War that drove the entire World with some kind of race between URSS and USA. And this race on

all the fields, from the macro-economic philosophy (state planning economy versus market

“invisible hand”, quoting Adam Smith, (Smith, 1776)) to sports or Space Race …, drove URSS to its

demise.

In Italy all these conditions caused the crisis of “big” industrial companies (most of all inside the IRI

group (Troillo, 2008) or other State owned holdings) and the dire situation concerning the labour

market and investments. Many Italian companies were financed by the Bank system and not by their

shareholders: the debt leverage, (Modigliani & Miller, The Cost of Capital, Corporation Finance and

the Theory of Investment, 1958; Modigliani & Miller, Corporate income taxes and the cost of capital:

a correction, 1963), played a reverse role decreasing the companies’ results and weakening the

company financial structure. State owned companies, characterized by weaker financial structures

due to always delayed contributions, suffered the industrial crisis and the financial restrains more

than the private sectors. The economic principal role switched from the “public” companies to the

private ones, big ones but also small and medium entrepreneurs who since then represented the

backbone of the Italian economy. In these years the markets and their expansion rate changed

completely; the “scale” with the “simple statistical approach”, used by large “process” plants, lost

the prominence against new paradigms: just in time, flexibility, product innovation ... All the insights

due to more mature markets became important: attention to costs and results, role and impact of

new technologies, service and “servitization” of the activities, new approaches based on the activity,

attention to indirect functions and costs, ….

Two are the base lines clearly visible from this period on:

crisis of big “base” plants and activities, with a shifting to private sectors, perhaps more able

or swifter to change and

a growing role of small and medium entrepreneurial structures, more willing to change

according to the market and to adapt themselves to new technologies

Difficulties always push hard to find new solutions. Italian economy had to combine either the

internal restrains (“bureaucracy”, growing state debt, stop to infrastructure investments, large

companies crisis, geographical strong differences, high inflation and labour costs, …) or the

international trends (growing globalization, new horizon open by the IT and software development,

different new markets, strong concurrence from new actors like BRICs …). “Old economy” players

tried to find new products or at least new characteristics of their products (FIAT, for example,

studied new engines with low consumption) and new players discovered some new way to provide

existing products (Benetton for examples with the new production and logistic system applying a

“just in time” approach).

In this context, after a first period during the 80s when the large private Italian groups seemed to

conquer international markets trying to buy international competitors, the trends confirmed

difficulties for big Italian players and better results for the more agile small and medium Italian

actors (always with exceptions) able to cope with the even faster international world and cheaper

competitors in traditional sectors. The Italian way found a new life in new markets well known for

the Italian style (fashion, luxury, design, restaurant and food …) or for the ability of our workers,

often entrepreneurs (mechanical automotive, engine, mechanical automation …). Not all the players

have small or medium size (Finmeccanica for example was, and even today is, a global relevant

— page 20 — player) but the attention of the Italian economy shifted to reduced size operators able to provide

more service inside or within the products and to defend internationally their image, products and

services.

This context description could leave several open discussions with different viewpoints and different

issues but it is surely the milieu where ICR was established and started to expand.

4.1 Case Study: ICR Spa

New sector, fashion luxury fragrances, probably discovered or invented in Italy by ICR Spa and its

founder. A sector characterized by a “servitization” trend: born as producer of perfumes ICR became

more and more involved as a service provider for its customer/partners. Today well-known in the

market for the high quality served ICR declines the quality offered not only in terms of product

characteristics but also in terms of services complementary to the “physical” product. A mix of “old”

and “new” economy.

4.1.1 ICR background

ICR, Industrie Cosmetiche Riunite Spa, is a leading company in luxury perfumes production and

logistics, with 93 million pieces filled and more than 100.000 shipping orders prepared for the most

famous Italian fashion brands.

The company established in 1975 by Mr. Roberto Martone maintained the pharmaceutical aim of

MARVIN, the company founded by Mr. Vincenzo Martone, Roberto Martone’s father, during the

40s and specialized in antibiotic, sulphonamide and penicillin products. Mr. Roberto Martone’s

business vision was to provide “everything” the customer might want, from the idea/concept

through to the laboratory research, bulk preparation, filling, packaging … to logistic services:

customers could find all the answers to their needs and organise their own supply chain as they

wished.

Throughout the years, the new Lodi factory and warehouse, bought during the 80s and empowered

all over the years, saw many of the most famous Italian brands launch their perfumes lines

worldwide (80s Trussardi, Gigli e Nazareno Gabrielli, 90s Versace, Bulgari, Ferragamo and Ungaro,

2000s Roberto Cavalli, Ferré, Gai Mattiolo, DSquared2, Blumarine, Exté). Some of these famous

brands created a joint venture directly with Mr. Roberto Martone and they always based their

products into ICR structure. The 2003 was a turn-around year in ICR history: Mr. Roberto Martone

bought the full ownership of ITF, a joint venture he created in 2001, as minority shareholder, with

the partnership of ITHolding, and this operation was completely fulfilled “inside” ICR and not directly

by the entrepreneur, as, on the contrary, always in the past. By 2003 all the operations were slowly

brought inside a new ICR Group with a sequence of company mergers. Even the personnel

organization was newly structured: from an informal entrepreneurial framework with 2-3 directors

to a firmly structured organization with new directors (6 directors plus 2 other officers) and all the

line and staff positions filled by managers. The group reorganized itself reducing the factories from

2 to 1, with the merger of the subsidiary Beauty 2000, and improved the production from 37 million

pieces in 2003 to more than 93 million in 2011; the personnel increased from 160 persons in 2003

to more than 330 in 2011 and a new corporate agreement was discussed with the Unions to

reorganize all the operations in Lodi: this new structure will absorb most of the external workers

and will take the direct personnel up to 470 persons in 2017 (with other 150 workers working on a

long seasonal base).

— page 21 —

4.1.2 Some data to understand the trends

The ICR structure changed following strictly the customers’ increasing demand.

Tables containing data that could show you the main trends registered by the industrial area of ICR

Group are shown below.

Behind the operational and financial data, the environment all around the company changed

completely during the years.

In 2004 ICR Spa bought the commercial company ITF Spa becoming an integrated group that was

able to cover all the supply chain operations for fragrances, from marketing and conceptual idea for

a new project to the final shipment versus little selling point. In 2005/2006 ICR merged the

subsidiary Beauty 2000 Srl and concentrated all the industrial operations inside the Lodi plant,

closing the San Giuliano Milanese factory. By 2007 the brand portfolio directly kept inside the

subsidiary ITF Spa, it shifted completely because the litigation with Cavalli brand obliged to search

new market opportunities; meanwhile Bulgari and Ferragamo, the two main industrial customers,

started a strong expansion strategy.

Table 1 - ICR Key figures

Year 2002 2003 2004 2005 2006 2007 2008

Production (K pcs) 45.811 55.494 64.616 62.148 73.037 80.005 84.398

Shipping Orders 27.764 58.258 65.235 59.653 65.883 68.809 67.832

Turnover (Keuro) 36.952 46.100 62.039 63.333 76.654 76.486 81.467

EBITDA 4.846 3.908 7.458 2.193 10.024 8.117 11.247

EBIT 3.311 1.804 5.793 548 8.138 5.880 8.837

Net Result 2.630 476 806 -2.008 943 924 2.750

Net Current Assets 12.050 20.175 13.736 13.040 6.593 14.713 12.280

Intangible assets 854 867 1.402 1.809 2.107 2.071 1.373

Tangible assets 3.327 4.709 4.533 4.296 4.918 4.565 4.938

Investments 1.891 1.579 34.136 34.328 47.150 47.049 47.046

NET ASSETS 18.122 27.330 53.807 53.473 60.768 68.398 65.637

Net Financial Position (euro /000) 6.440 11.328 34.948 29.433 39.371 45.190 39.293

Capital and Reserves 13.885 14.331 14.385 16.877 17.825 19.174 21.498

Year 2009 2010 2011 2012 2013 2014 2015

Production (pcs * 000) 54.038 62.810 93.075 81.791 75.962 83.722 86.508

Shipping Orders 65.485 96.070 100.800 92.922 88.017 95.920 100.800

Turnover (euro * 000) 67.045 91.315 107.931 99.352 103.457 86.166 58.683

EBITDA 6.320 10.224 16.883 14.932 8.190 10.550 12.000

EBIT 4.370 7.458 11.083 12.946 5.958 8.371 9.715

Net Result 2.505 3.345 6.034 7.365 6.479 5.898 7.169

- - - - - - -

- - - - - - -

Net Current Assets 12.201 5.598 13.950 17.237 15.611 8.118 12.530

Intangible assets 835 844 1.491 1.745 1.815 2.020 1.802

Tangible assets 4.665 6.101 6.187 7.697 6.959 6.164 5.840

Investments 47.441 35.206 36.442 36.482 15.734 16.025 8.423

NET ASSETS 65.142 47.749 58.070 63.161 40.120 32.326 28.595

Net Financial Position (euro /000) 37.444 12.515 14.262 11.177 4.979- 18.670- 29.571-

Capital and Reserves 24.004 31.099 36.310 43.676 45.098 50.996 58.166

— page 22 —

Figure 4 ICR Key Figures (turnover vs production pcs)

-

20.000

40.000

60.000

80.000

100.000

120.000

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Key Figures

Turnover (Keuro) Production (K pcs)

It is really interesting to note (see the graph here above) that the growing trend in terms of turnover

is only partially reflected by the number of pieces: this is somewhat due to the different customer

mix served (in the beginning the internal brands were much less important than the industrial

customers) and moderately due to the different product family mix requested (the main important

industrial customers launched more hotel line products and requested an increasingly number of

little size pieces).

Figure 5 ICR Key Figures (turnover vs net financial position)

-40.000

-20.000

-

20.000

40.000

60.000

80.000

100.000

120.000

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Key Figures

Turnover (Keuro) Net Financial Position (euro /000)

(Figure 5 ICR Key Figures (turnover vs net financial position)) describes the ICR capacity to generate

cash flows: the increase in net financial position registered between 2004 and 2007 was linked to

the buy-out of the commercial subsidiary (ITF Spa) and the commercial net asset funding necessary

to support the “internal” commercial expansion. Since 2007 the more stable commercial needs were

offset by the industrial capacity to generate an important positive cash flow. Between 2012 and

2015 the industrial positive cash flow was busted by the spin-off of the commercial subsidiary, sold

to Angelini group.

4.1.3 Data Context Analysis: Market data

The ICR activity is a meaningful example of a market characterized by a high level of uncertainty and

volatility.

The ICR operational data used into the model was recorded by 2002 until 2013. It is either

production/industrial data or commercial data recorded by the sale division that covered only a

— page 23 — minor part of what worked by the industrial division. In fact, the industrial activity covers not only

the supply chain requested by the group commercial division but also the needs and requests

expressed by some major Italian fashions “maisons” that didn’t use the commercial services of the

ICR Group but only the industrial and logistic ones. They internally organized the first part of their

supply chain (marketing with the concept idea) and the last part (commercial organization),

outsourcing the industrial and logistics to ICR.

The data available consist of more or less 125 thousand lines, organized by year, brand, line, country

served and product reference; they are divided into two main sets: Production and Commercial.

The following graph (Figure 2), shows the Production and Commercial data compared to an estimate

of the trends (Moving Averages on 12 months which let you smooth over the seasonal shifts as well

as the differences among the years due to different new lines launch time). The Product/Line life

cycle gets shorter and shorter: every Brand owner needs to renew the product mix periodically

Please note that we are analysing the real number of pieces recorded for every month.

Figure 6 Total quantity

Due to the different product size and the industrial “impact” that the size could have, the market

could also be measured in 50ml equivalent: like in the oil market that uses the BOE as a standard

statistical measure, the fragrance/parfums market uses the 50ml size like a standard and the 50ml

equivalence is a statistical way to balance very different sizes (from 1 ml or 5 ml, promotional

materials like vials, to 200 ml or 400 ml, a very exclusive size or accessories line size).

As you can see in Figure 3, transformed into 50ml equivalent show different paths and scales but

they always present an important volatility around the trends line (always measured as 12 month

moving average).

— page 24 — Figure 7 Total Quantity in 50ml Equivalent

The correlation rates among the different series,as demonstrated in Figure 4, show only a high rate

between the corresponding series original and transformed into 50ml. This correlation analysis does

not give us any useful information to identify a path or a rule.

Only a high correlation rate between the commercial and industrial moving averages, either in 50ml

equivalent or the original size, could suggest a new viewpoint : the suggestion offered by this high

correlation rate could be investigated considering the production series as naturally anticipating the

commercial one ; the smoothing of the moving averages could also eliminate the disturbing

elements due to different launch approaches along the years. It’s similar to a production series that

has to be compared to a commercial series with an unknow time shift (some months due to the

product availability in logistic center to dispatch the product in shops): the 50ml equivalent could

minimize the different brand launches, considering the existing proportion among promotional and

sales products during the launch.

Table 2 Correlation rates Row ID Comm Qty Prod Qty 12 months MA

Prod

12 months MA

Comm

Comm Qty in

50ML

Prod Qty in 50ML 12 months MA

Comm 50ML

12 months MA

Prod 50ML

Comm Qty 1,00 0,44 0,10 0,37 0,82 0,44 0,24 0,16

Prod Qty 0,44 1,00 0,56 0,47 0,49 0,89 0,55 0,55

12 months MA Prod 0,10 0,56 1,00 0,60 0,26 0,54 0,74 0,92

12 months MA Comm 0,37 0,47 0,60 1,00 0,41 0,50 0,78 0,66

Comm Qty in 50ML 0,82 0,49 0,26 0,41 1,00 0,57 0,52 0,40

Prod Qty in 50ML 0,44 0,89 0,54 0,50 0,57 1,00 0,64 0,62

12 months MA Comm 50ML 0,24 0,55 0,74 0,78 0,52 0,64 1,00 0,88

12 months MA Prod 50ML 0,16 0,55 0,92 0,66 0,40 0,62 0,88 1,00

The data shown previously in Figure 3 could be better appreciated splitting the graphs between

Production and Commercial series as well as in Licensed Product Series (part of Commercial in

previous graphs). These different sets have a completely different scale but more importantly a

different “origin”.

— page 25 — Figure 8 Production versus its 50ml equivalent

As represented above in Figure 4, we could see the production series considering:

- The real pieces produced

- The equivalent to 50 ML pieces produced

- The two moving averages on 12 months of each one of the previous series

The correlation ratios already described are much clearer with the graphical approach. The gap

between the two pieces’ series represents the different mix produced in each period. The moving

average gap underlines the same mix effect smoothed over 12 months’ time.

Figure 9 Commercial versus its 50ml equivalent

— page 26 — As partially already described, the "commercial" graph (Figure 5) considers only the products that

the group produces and commercializes. The worldwide sales show a greater volatility and a

different ratio in terms of 50ML equivalent.

If we consider only the "commercial" figures (defined as Production and Commercial of Licenced

Products) the range of scale is significantly different: a maximum of 2.1 million pieces per month

produced against the 10 million showed in the Production Graph.

The following graph (Figure 10) compares the production level and the commercial side of the same

product lines owned by the ICR Group.

Figure 10 Production and commercial for licensed lines

By reviewing the data over the years, it has been noted that the high volatility of the two main series

is not correlated.

It is also interesting to consider the trends showed by the two moving averages which naturally

smooth over the volatility over the 12 months. Depending on the time period, the two MA move

with different patterns: sometimes between 2003 and 2006, the production trend tries to anticipate

the commercial side; sometimes from 2007 to 2008 or from 2011 to 2012, the commercial side tries

to reduce the stock and the production side seems to cover the needs only.

This simple analysis based on the graph review (Figure 10), review could also be linked to some

external explanation: up to 2007, with some exceptions and market issues, the sales approach, or

the salesforce feeling versus the market trend, was in fact positive with a real expansion rate

forecast. In this way, all the planning issues were faced and solved applying a simple formula: it is

better to have the products in our warehouse, with the unsold risk and the possible destruction

costs, than being short on products when the market asks for them. When in doubt, the philosophy

was to produce more than what’s expected as well as for their security levels.

— page 27 — Figure 11 Licensed lines in 50ml equivalent

The quantity transformation into a standard 50ml equivalent does not provide any useful extra

information. There is little difference between the lines but no clear path is shown by the

transformation. Even the moving average lines that smooth over the annual seasonal components

do not provide extra information: all the reasoning made for the real series find a mirror into the

transformed data.

Conclusion: the series present a random path due to the sum of various “sub-series” characterized

by different basic points. Some points to be considered are:

Some series are only “industrial” (the customers didn’t show the commercial side of their

forecasting and sale process),

Some others are really “commercial” (due to the fact that between 2001 and 2012 the

Customer 3 was a Group subsidiary and his forecasting and sale process were internally

regulated),

Lastly, some others are “industrial due to commercial” (the industrial series directly caused

by the quoted internal forecast and sale process).

Furthermore the different Customers could have

a different “Brand” awareness on the market and this lever with the marketing effect

induced is a “push” difference applied to the sales (and indirectly or in an expected way on

the industrial demand)

a different “advertising” approach: a well-known brand probably pushes the sales with a

direct advertising covering all the brand products; a “niche” brand generally prefers

“promotional materials” tactics using more the price and free of charges products to

generate the demand

a different approach to the “accessories” and complementary lines (bath lines, creams,

coffrets/gift sets) used as different product lines or in a “promotional” way

— page 28 — Alongside all these effects, it is important to consider that the time and the consequent life cycle of

the market could affect in different ways some series that show common or similar characteristics.

4.1.4 Data Context Analysis: Industrial Data

The structure of the company is simple: the production flow is linear and the process can be

continuous or “split” through the production sections.

As described into the public consolidated financial statements, ICR covers all the supply chain

operations starting from the laboratories to the logistic services.

Figure 12 Industrial process description

(Figure 12) describes the industrial structure of the company/group.

It’s important to note that the process flow can be stopped/left at any point from one line and

continued, entering the following step, on a different line. It is also possible to move along two or

three different lines at the same step if the planning or scheduling requirements need to operate

simultaneously. This way of operating is in fact exceptional but it is possible. Finally, it is important

to note that two steps are not recorded into the previous scheme:

a marketing/conceptualization moment just before the first step (laboratory)

a sales moment just after the “coding” step and just before the “logistic services”

These two steps are generally covered by the customers even if in the past the company also had

an internal marketing/sales department that generally was managed by a dedicated subsidiary.

(Figure 12) shows the structure of the process that does not cover all the support operations

provided also by the structure departments: purchasing, planning, finance and administration,

general services...

The first support service quoted, the purchasing department, could be put among the “process”

services: the company could provide a “turnkey” service including the purchase of the materials and

components (included into the product price or as an external service) or a “pure” service that

transforms materials and components owned by the customers.

Over the last forty years, the company experimented a expanding trend of “servitization”, shifting

its focus from the products provided to the “production and logistic” services tailored for each

customer’s need (it might have changed during the time as function of the customer’ successful

rate, increasing or decreasing levels of each customer’ market “saturation” rate, more or less

internalization of each customer’ activity, …)

— page 29 —

In this scenario the company structure was drawn by searching the maximum flexibility in terms of

workforce: the outsourced services were common in most of the Italian industries especially all the

“handcraft” sectors where the production flow was not stable and controlled by the actors. The

“evolution” path followed by the company was determined by this strategic initial choice and even

in completely different scenarios, time and customers’ requests, tried to privilege the manpower

solutions instead of different machinery and equipment.

We will review briefly the industrial structure to understand the model applied by the company.

In this analysis we will not consider the first part of the process described in the previous (Figure 12

Industrial process description) because the “laboratory” and “bulk preparation” are considered

more “specialized” departments: laboratory people were and are people with higher knowledge

generally much more involved into formula conceptualization and test; bulk preparation team

operate more on fixed equipment in an alcohol risky environment and they were not requested to

work on other process steps.

Our analysis will face and focus the production steps, from filling to coding, which are more

“manpower” demanding and were historically more “interdependent”.

In the following table (Table 3 Production data 2010 2013), we appreciate the differences among

the ICR customers. And, in a more general way, the production flow applied to different demand

requests.

Not all the products transformed into the main production “step” (filling) are running through the

following steps; and the “proportion” of the products is different among the customers.

Some points to be noted:

- customer 1 with a real important number of pieces “filled” compared to other customer

- customer 1 with the same number of production orders as the third customer but a different

number of pieces: batch per order is sensibly different

- proportion among the product families differs throughout the years and in function of the

customer

- proportion of worked pieces through the different production steps in a random distribution

even inside the same customer all along the years

— page 30 — Table 3 Production data 2010 2013

2010 2011

N° Orders Filling

QTY

Packaging

QTY

Wrapping

QTY

Coding

QTY

N° Orders Filling

QTY

Packaging

QTY

Wrapping

QTY

Coding

QTY

CUSTOMER 1 Prod Fam 1 894 14.477.471 14.442.988 11.948.951 9.524.343 962 17.090.040 16.994.421 13.823.102 11.562.446

Prod Fam 2 183 1.581.942 2.044.363 0 0 255 5.030.676 3.725.219 0 0

Prod Fam 3 79 215.437 447.828 0 0 80 788.709 805.119 0 0

Prod Fam A 61 9.688 139.110 44.060 5.058 93 504 341.051 894 0

Prod Fam 4 191 7.118.997 0 0 0 206 13.343.485 0 0 0

Prod Fam 5 361 4.676.177 702.599 451.799 0 316 6.459.856 1.418.817 576.098 0

Prod Fam 6 2 5.037 5.051 5.050 0

Prod Fam 7 14 3.722.869 0 0 0 28 6.982.937 0 0 0

Prod Fam 8 14 6.350 7.679 0 0 13 21.275 21.273 0 0

CUSTOMER 1 TOTAL 1.799 31.813.968 17.789.618 12.449.860 9.529.401 1.953 49.717.482 23.305.900 14.400.094 11.562.446


Prod Fam 2 24 557.513 545.108 0 0 37 811.331 765.279 0 0

Prod Fam 3 1 5.044 4.855 0 0

Prod Fam A 16 0 84.811 53.436 0 4 0 18.584 18.579 0

Prod Fam 4 2 13.603 8.418 0 0

Prod Fam 5 132 1.372.684 285.187 163.197 0 171 2.839.173 346.964 267.830 0

Prod Fam 7 37 1.970.163 1.945.593 0 0 73 4.465.647 4.305.555 0 0

Prod Fam 8 4 950 981 0 0 6 4.048 3.946 0 0

CUSTOMER 2 TOTAL 776 8.590.884 7.608.645 4.231.451 2.008.671 880 12.816.725 10.041.173 4.227.770 1.429.636

CUSTOMER 3 Prod Fam 1 1.126 5.810.657 6.484.838 4.354.912 1.988.555 913 5.394.971 5.842.544 4.411.883 1.804.327

Prod Fam 2 11 158.283 156.664 0 0 9 118.124 116.810 0 0

Prod Fam 3 4 17.894 17.700 17.618 0

Prod Fam A 157 10.424 417.927 27.028 3.787 19 51.286 56.562 51.932 843

Prod Fam 4 2 5.020 0 0 0 4 30.980 0 0 0

Prod Fam 5 263 1.153.144 439.858 177.228 0 237 949.029 282.878 172.302 0

Prod Fam 6 6 4.247 4.216 14.914 0 2 3.104 3.085 3.085 0

Prod Fam 7 77 5.630.595 5.187.655 0 0 66 5.579.301 5.744.143 0 0

Prod Fam 8 25 9.019 10.149 0 0 22 8.644 8.508 0 0

CUSTOMER 3 TOTAL 1.671 12.799.283 12.719.007 4.591.700 1.992.342 1.272 12.135.439 12.054.530 4.639.202 1.805.170

OTHER INDUSTRIAL Prod Fam 1 20 120.273 117.773 107.986 0 62 388.552 389.717 179.877 0

Prod Fam 2 8 66.924 0 0 0 2 21.008 0 0 0

Prod Fam 3

Prod Fam A 8 0 3.233 17.562 0

Prod Fam 5 10 24.009 18.391 12.319 0

Prod Fam 7 2 47.000 46.356 0 0

Prod Fam 8

OTHER INDUSTRIAL TOTAL 30 234.197 164.129 107.986 0 82 433.569 411.341 209.758 0

2012 2013

N° Orders Filling

QTY

Packaging

QTY

Wrapping

QTY

Coding

QTY

N° Orders Filling

QTY

Packaging

QTY

Wrapping

QTY

Coding

QTY


Prod Fam 2 179 5.866.837 2.555.053 0 0 131 3.191.777 2.561.822 0 0

Prod Fam 3 53 1.060.758 1.052.234 0 0 40 555.221 552.361 63.660 0

Prod Fam A 22 0 52.412 0 0 77 0 289.112 25.092 18.964

Prod Fam 4 172 13.674.181 0 0 0 117 9.070.793 0 0 0

Prod Fam 5 258 4.628.844 913.021 502.879 0 177 1.939.011 763.733 514.315 0

Prod Fam 6 1 2.145 1.954 1.956 0

Prod Fam 7 24 5.859.630 760.032 0 0 59 12.706.319 36.047 0 0

Prod Fam 8 8 10.918 10.310 2.630 0 15 13.064 14.239 0 0

CUSTOMER 1 TOTAL 1.437 47.089.165 21.384.820 13.325.685 11.522.777 1.257 41.324.282 18.224.308 11.015.000 9.406.399


Prod Fam 2 75 1.636.809 1.693.681 0 0 99 1.456.931 1.466.011 0 0

Prod Fam 3

Prod Fam A 7 0 10.798 0 0 4 0 13.657 0 0

Prod Fam 4

Prod Fam 5 280 5.586.887 234.298 123.898 0 385 7.492.754 486.573 280.069 0

Prod Fam 7 42 2.580.312 2.257.377 0 0 27 3.460.309 436.353 0 0

Prod Fam 8 11 7.604 7.220 0 0 4 5.023 4.986 0 0

CUSTOMER 2 TOTAL 1.028 15.004.479 9.409.065 4.582.553 2.191.225 1.240 18.670.959 8.719.940 5.729.003 2.979.811


Prod Fam 2 6 14.638 30.168 0 0 8 111.922 111.864 0 0

Prod Fam 3

Prod Fam A 41 124.389 178.603 132.911 128.968 36 0 39.672 7.583 0

Prod Fam 4

Prod Fam 5 203 1.118.568 201.916 177.761 0 166 814.379 113.364 94.661 0

Prod Fam 6 2 10.601 10.546 1.014 0

Prod Fam 7 40 3.924.142 3.420.608 0 0 50 4.330.704 946.116 0 0

Prod Fam 8 11 5.245 5.179 0 0 14 3.964 3.963 0 0

CUSTOMER 3 TOTAL 858 8.723.737 7.590.194 2.959.533 1.731.816 964 9.049.923 5.282.749 3.300.303 2.153.815

OTHER INDUSTRIAL Prod Fam 1 52 312.755 308.424 138.476 0 88 535.827 527.830 339.022 0

Prod Fam 2 4 57.643 0 0 0 3 26.083 0 0 0

Prod Fam 3 15 244.588 244.080 0 0

Prod Fam A 6 0 0 13.229 0 39 0 31.052 0 0

Prod Fam 5 27 114.189 63.968 0 0 19 44.452 3.351 0 0

Prod Fam 7 1 10.142 0 0 0

Prod Fam 8 1 0 404 0 0

OTHER INDUSTRIAL TOTAL 90 494.729 372.392 151.705 0 165 850.950 806.717 339.022 0

Into the following table, (Table 4 Production lines used per customer and product family), some

useful data detailed by filling and packaging departments, our main production departments.

Some notes about the following (Table 4 Production lines used per customer and product family):

- 3 main customers with different numbers and very different trends. The main one with a

great increase between 2010 and 2011 but an important decrease in 2012 and 2013

— page 31 —

- the second customer with a positive trend which doubles the figures throughout the 4 years

and lets the customer pass from the third position, in customer mix, to the second one

- the third one with a declining step between 2011 and 2012 followed by a little regain in

2013. This customer, in reality, is managing several brands that are each one comparable to

the single or double brands managed by the other two main customers.

- the group “Other Industrials” concerns several different customers with one or more

brands which are more "handcraft" customers. The figures involved are completely different.

The average number of lines used to work the products into the filling and packaging departments

is based on every single reference: it is like saying that it is the average number of lines used for

each reference produced. Two could be the explanation of figures really limited to 1 or 2 lines for

most customers/product families considered:

- the quantity for each product reference is not important for the chosen production line and

the frequency of re-order is not relevant

- the single reference is somehow validated and linked only to 1 line or group of lines

Data aggregated at customer level show that the number of lines used for product families of each

brands are several. If we link the info of the previous section with this viewpoint, we can imagine

that the organization could prefer to split the references, product item, along the department lines

with a preference or a focus on a “dedicated line”. It is to say that the organization prefers to set up

the first production run on a single line making all the adjustments and regulations that it will use in

the future if, and only if, the reference is going to be re-produced.

Data shown in (Table 5 Production lines and product families served) confirms the explanation given

into the previous paragraph.

If (Table 4 Production lines used per customer and product family) considers and shows only the

average number of lines used for every single reference produced, (Table 5 Production lines and

product families served) considers how many lines worked effectively for all the reference of every

brands. The "Product Family Indicator" shows how many product families are manufactured all

along the lines of every department: if the same product family is manufactured in different lines is

counted as a different one. The ratio between the Product Family Indicator / the N° of Lines used

could be considered as an average indicator of technical flexibility: how many product family could

be served by each line.

As you can see the ratio between the brand figures and the total department figures is 3 at a

maximum.

— page 32 — Table 4 Production lines used per customer and product family

2010 2011

N° Orders Filling QTY Packagin

QTY

Avg N° of

lines

Filling per

Ref

Avg N° of

lines

Packaging

per Ref


QTY

Avg N° of

lines

Filling per

Ref

Avg N° of

lines

Packaging

per Ref

CUSTOMER 1 Prod Fam 1 894 14.477.471 14.442.988 2,00 3,00 962 17.090.040 16.994.421 2,00 3,00

Prod Fam 2 183 1.581.942 2.044.363 2,00 2,00 255 5.030.676 3.725.219 2,00 3,00

Prod Fam 3 79 215.437 447.828 2,00 2,00 80 788.709 805.119 2,00 2,00

Prod Fam A 61 9.688 139.110 1,00 2,00 93 504 341.051 1,00 1,00

Prod Fam 4 191 7.118.997 0 2,00 1,00 206 13.343.485 0 1,00 1,00

Prod Fam 5 361 4.676.177 702.599 2,00 2,00 316 6.459.856 1.418.817 2,00 2,00

Prod Fam 6 2 5.037 5.051 3,00 3,00

Prod Fam 7 14 3.722.869 0 1,00 1,00 28 6.982.937 0 1,00 1,00

Prod Fam 8 14 6.350 7.679 1,00 1,00 13 21.275 21.273 1,00 2,00

CUSTOMER 1 TOTAL 1.799 31.813.96

8

17.789.61

8

1,78 1,89 1.953 49.717.48

2

23.305.90

0

1,50 1,88


Prod Fam 2 24 557.513 545.108 2,00 2,00 37 811.331 765.279 4,00 4,00

Prod Fam 3 1 5.044 4.855 2,00 3,00

Prod Fam A 16 0 84.811 2,00 2,00 4 0 18.584 1,00 5,00

Prod Fam 4 2 13.603 8.418 1,00 1,00

Prod Fam 5 132 1.372.684 285.187 2,00 2,00 171 2.839.173 346.964 2,00 2,00

Prod Fam 7 37 1.970.163 1.945.593 2,00 2,00 73 4.465.647 4.305.555 4,00 5,00

Prod Fam 8 4 950 981 1,00 1,00 6 4.048 3.946 2,00 2,00

CUSTOMER 2 TOTAL 776 8.590.884 7.608.645 1,60 1,80 880 12.816.72

5

10.041.17

3

1,64 2,27

CUSTOMER 3 Prod Fam 1 1.126 5.810.657 6.484.838 15,00 20,00 913 5.394.971 5.842.544 16,00 19,00

Prod Fam 2 11 158.283 156.664 6,00 7,00 9 118.124 116.810 4,00 4,00

Prod Fam 3 4 17.894 17.700 2,00 2,00

Prod Fam A 157 10.424 417.927 6,00 7,00 19 51.286 56.562 8,00 9,00

Prod Fam 4 2 5.020 0 1,00 1,00 4 30.980 0 2,00 2,00

Prod Fam 5 263 1.153.144 439.858 6,00 6,00 237 949.029 282.878 6,00 6,00

Prod Fam 6 6 4.247 4.216 1,00 2,00 2 3.104 3.085 2,00 2,00

Prod Fam 7 77 5.630.595 5.187.655 9,00 9,00 66 5.579.301 5.744.143 9,00 11,00

Prod Fam 8 25 9.019 10.149 8,00 11,00 22 8.644 8.508 10,00 10,00


3

12.719.00

7

1,59 1,91 1.272 12.135.43

9

12.054.53

0

1,58 1,75

OTHER INDUSTRIAL Prod Fam 1 20 120.273 117.773 6,00 8,00 62 388.552 389.717 6,00 6,00

Prod Fam 2 8 66.924 0 2,00 1,00 2 21.008 0 1,00 1,00

Prod Fam 3

Prod Fam A 8 0 3.233 1,00 1,00

Prod Fam 5 10 24.009 18.391 3,00 3,00

Prod Fam 7 2 47.000 46.356 2,00 2,00

Prod Fam 8

OTHER INDUSTRIAL TOTAL 30 234.197 164.129 2,00 2,20 82 433.569 411.341 1,57 1,57 2012 2013


QTY

Avg N° of

lines

Filling per

Ref

Avg N° of

lines

Packaging

per Ref


QTY

Avg N° of

lines

Filling per

Ref

Avg N° of

lines

Packaging

per Ref


Prod Fam 2 179 5.866.837 2.555.053 2,00 2,00 131 3.191.777 2.561.822 2,00 3,00

Prod Fam 3 53 1.060.758 1.052.234 2,00 2,00 40 555.221 552.361 2,00 2,00

Prod Fam A 22 0 52.412 1,00 1,00 77 0 289.112 1,00 1,00

Prod Fam 4 172 13.674.181 0 1,00 1,00 117 9.070.793 0 1,00 1,00

Prod Fam 5 258 4.628.844 913.021 1,00 2,00 177 1.939.011 763.733 1,00 2,00

Prod Fam 6 1 2.145 1.954 2,00 2,00

Prod Fam 7 24 5.859.630 760.032 1,00 1,00 59 12.706.319 36.047 1,00 1,00

Prod Fam 8 8 10.918 10.310 2,00 2,00 15 13.064 14.239 1,00 1,00


5

21.384.82

0

1,56 1,78 1.257 41.324.28

2

18.224.30

8

1,38 1,63


Prod Fam 2 75 1.636.809 1.693.681 3,00 3,00 99 1.456.931 1.466.011 2,00 3,00

Prod Fam 3

Prod Fam A 7 0 10.798 1,00 1,00 4 0 13.657 1,00 1,00

Prod Fam 4

Prod Fam 5 280 5.586.887 234.298 2,00 2,00 385 7.492.754 486.573 2,00 2,00

Prod Fam 7 42 2.580.312 2.257.377 3,00 3,00 27 3.460.309 436.353 2,00 2,00

Prod Fam 8 11 7.604 7.220 1,00 1,00 4 5.023 4.986 2,00 2,00


9

9.409.065 1,40 1,60 1.240 18.670.95

9

8.719.940 1,56 1,89


Prod Fam 2 6 14.638 30.168 2,00 2,00 8 111.922 111.864 2,00 3,00

Prod Fam 3

Prod Fam A 41 124.389 178.603 6,00 8,00 36 0 39.672 5,00 6,00

Prod Fam 4

Prod Fam 5 203 1.118.568 201.916 8,00 8,00 166 814.379 113.364 6,00 6,00

Prod Fam 6 2 10.601 10.546 4,00 4,00

Prod Fam 7 40 3.924.142 3.420.608 6,00 6,00 50 4.330.704 946.116 5,00 5,00

Prod Fam 8 11 5.245 5.179 8,00 11,00 14 3.964 3.963 5,00 5,00

CUSTOMER 3 TOTAL 858 8.723.737 7.590.194 1,44 1,63 964 9.049.923 5.282.749 1,52 1,62

OTHER INDUSTRIAL Prod Fam 1 52 312.755 308.424 11,00 11,00 88 535.827 527.830 7,00 7,00

Prod Fam 2 4 57.643 0 1,00 1,00 3 26.083 0 2,00 1,00

Prod Fam 3 15 244.588 244.080 2,00 2,00

Prod Fam A 6 0 0 1,00 1,00 39 0 31.052 1,00 1,00

Prod Fam 5 27 114.189 63.968 5,00 5,00 19 44.452 3.351 2,00 2,00

Prod Fam 7 1 10.142 0 1,00 1,00

Prod Fam 8 1 0 404 1,00 1,00

OTHER INDUSTRIAL TOTAL 90 494.729 372.392 1,58 1,58 165 850.950 806.717 1,67 1,56

— page 33 — Table 5 Production lines and product families served

2010 2011 2012 2013

Product

Families

Indicator

N° of working

lines

Prod Fam

served per

line Average

Product

Families

Indicator

N° of working

lines

Prod Fam

served per

line Average

Product

Families

Indicator

N° of working

lines

Prod Fam

served per

line Average

Product

Families

Indicator

N° of working

lines

Prod Fam

served per

line Average

A - Filling Dept 54 32 2 45 29 2 44 28 2 48 34 1

B - Packaging Dept 105 32 3 87 29 3 96 31 3 95 32 3

C - Wrapping Dept 39 15 3 31 13 2 32 14 2 30 14 2

D - Coding Dept 16 12 1 13 12 1 16 12 1 14 13 1

Z - Other Ops Dept 1 1 1 NaN NaN NaN

To complete the analysis, a summary table shows the average working rate in the filling department

for each product family.

Table 6 Filling average working rate per product family

Alcool -

Bottles

Alcool -

Miniature

Alcool -

Vaposac

Coffret - Kit

- Bundle

Cream,

Hotel Line

Cream,

Shampoo

Deo, Deo

Stick

Vials Vari

WR Recal WR Recal WR Recal WR Recal WR Recal WR Recal WR Recal WR Recal WR Recal

CUSTOMER 1 2010 1.215 692 560 584 2.285 1.100 351 5.601 244

2011 1.158 1.247 1.260 144 4.251 1.236 NaN 6.079 202

2012 1.186 1.322 1.361 NaN 4.465 1.110 236 4.506 211

2013 1.210 1.251 1.306 NaN 4.062 954 NaN 4.946 153

CUSTOMER 1 TOTAL 1.190 1.177 1.183 507 3.741 1.129 306 5.151 192

CUSTOMER 2 2010 1.064 1.090 273 NaN NaN 1.149 NaN 3.885 119

2011 1.036 1.144 NaN NaN 1.297 1.327 NaN 5.717 155

2012 1.018 1.125 NaN NaN NaN 1.372 NaN 5.465 184

2013 1.114 1.181 NaN NaN NaN 1.452 NaN 4.774 198

CUSTOMER 2 TOTAL 1.060 1.141 273 NaN 1.297 1.376 NaN 5.020 175

CUSTOMER 3 2010 992 1.043 620 709 1.151 996 181 4.453 156

2011 1.048 875 NaN 878 1.476 983 146 5.411 155

2012 976 1.067 NaN 963 NaN 934 NaN 5.347 260

2013 1.023 1.142 NaN NaN NaN 1.004 766 4.372 174

CUSTOMER 3 TOTAL 1.011 1.011 620 920 1.420 977 306 4.842 171

OTHER INDUSTRIAL 2010 916 1.346 NaN NaN NaN NaN NaN 1.730 NaN

2011 807 978 NaN NaN NaN 640 NaN NaN NaN

2012 785 1.164 NaN NaN NaN 667 NaN 1.086 NaN

2013 956 1.151 1.236 NaN NaN 779 NaN NaN NaN

OTHER INDUSTRIAL TOTAL 864 1.197 1.236 NaN NaN 687 NaN 1.566 NaN

Grand Total 1.120 1.166 1.174 883 3.734 1.201 306 5.012 183

This table should be reviewed considering also the average team requested by the lines for each

product family: the “vials” and “hotel line” products are generally produced by more automatic lines

where the filling is completed with the packaging, wrapping and coding operations all in sequence;

the working rate is higher than in a standard line and the team requested is reduced (but the skills

requested are not the same as the normal lines).

Table 7 Packaging average working rate per product family

Alcool -

Flaconi

Alcool -

Miniature

Alcool -

Vaposac

Coffret - Kit

- Bundle

Creme,

Linee Hotel

Creme,

Shampoo

Deo, Deo

Stick

Fiale Vari

WR Recal WR Recal WR Recal WR Recal WR Recal WR Recal WR Recal WR Recal WR Recal

CUSTOMER 1 2011 2.122 1.848 1.800 754 NaN 2.577 NaN NaN 859

2012 1.296 1.522 1.306 419 NaN 1.485 888 2.619 520

2013 1.289 1.440 1.100 686 NaN 1.422 NaN 577 508

CUSTOMER 1 TOTAL 1.505 1.616 1.373 683 NaN 1.818 888 2.257 631

CUSTOMER 2 2011 2.303 2.096 NaN 2.215 1.545 2.265 NaN 2.615 530

2012 1.306 2.505 NaN 800 NaN 1.243 NaN 2.142 578

2013 1.361 1.905 NaN 444 NaN 1.316 NaN 2.136 612

CUSTOMER 2 TOTAL 1.517 2.168 NaN 817 1.545 1.501 NaN 2.409 575

CUSTOMER 3 2011 1.853 1.636 NaN 798 NaN 2.590 2.683 2.535 381

2012 1.078 1.335 NaN 945 NaN 1.081 NaN 2.210 333

2013 1.117 1.821 NaN 548 NaN 1.133 6.804 2.453 1.254

CUSTOMER 3 TOTAL 1.329 1.665 NaN 827 NaN 1.511 5.049 2.407 430

OTHER INDUSTRIAL 2011 1.438 NaN NaN NaN 2.874 NaN NaN NaN

2012 1.028 NaN NaN NaN NaN 513 NaN NaN NaN

2013 1.200 NaN 673 NaN 446 NaN NaN

OTHER INDUSTRIAL TOTAL 1.213 NaN 743 NaN 618 NaN NaN

Grand Total 1.468 1.751 1.512 724 1.545 1.644 3.181 2.401 564

(Table 7 Packaging average working rate per product family) shows the previous information

recorded during the second production step: this step could be considered important for two

reasons: firstly, the packaging department drives the following two steps, wrapping and coding,

giving the “rhythm”; secondly, the packaging department as well as the following two were

“outsourced” internally to the coop actors. In the general scheme, (Figure 12 Industrial process

description), this second line represented all the activities made all along the packaging to coding

steps: only during a very low level of customers’ orders some of these activities were taken back by

the internal department.

— page 34 —

4.1.5 A different Viewpoint: Time Series from Launch Time

The external context, described in the previous paragraph “Data Context Analysis: Market data”,

pushed the company to search for a new system to forecast the demand : the objective was to target

the sales volume per reference item just in time to produce and to ship the items to make them

available on the shelf at the desired launch time (push approach) or at the second demand coming

from the sales point (pull approach). In the past the precision requested to the forecast process was

not really truthful because the high “value added” products induced all the agents to always

consider a very high demand containing itself a high “security” level (often not recorded at the sell-

out moment). Everybody wanted to have product availability and the excess of product was

considered as collateral effect of the subystem.

The price pressure recorded all along the years which made it more appealing to have a more

accurate forecast planning: the target was to reduce the excessive production at the end of its life-

cycle always to ensure the fullfillment of the demand. The company organized a planning system

based on the sales information and “helped” the sales work with a statistical proposal and a

commercial product proportion.

It's really important to define the limits of the analysis concerning the forecast process put in place

by the group. Considering all the data treated by the "production" viewpoint, 861 million recorded

pieces, only 117 million pieces belong to the "commercial" series: the difference is how much data

was treated only as production data.

Nevertheless we also have to consider that only 650 million pieces are forecasted against 51 million

real data series: it is to say that only 51 million are series evaluated inside the forecast processing.

These 51 million have to be considered against the commercial data series in the first place as well

as against the total series afterwards. It's simply a 44% coverage of commercial series involved into

the forecasting process: the ratio is linked to the weight that these series represented for the

commercial company. From the commercial viewpoint the other 66 million pieces were sold or

shipped without an important effect on the commercial turnover. It could be completely different

from the production viewpoint, considering that only for the commercial data series 56% of pieces

were not forecasted. If you consider the incidence on the total production the percentage decreases

to 6%.

Below a Summary Table of Correlation Rates among the REAL data series and the 12 Rolling Forecast

series estimated by the commercial departments during the twelve months before is shown.

The measure tries to evaluate how the forecasted quantities for the real month move in accordance

with the real final data.

— page 35 — Table 8 Correlation Rates among Real and Forecast Series

Correlation REAL RF -1 RF -2 RF -3 RF -4 RF -5 RF -6 RF -7 RF -8 RF -9 RF -10 RF -11 RF -12

REAL 1,00 0,22 0,18 0,10 0,32 0,26 0,30 0,41 0,40 0,15 0,43 0,08 0,25

RF -1 1,00 0,23 0,42 0,28 0,07 0,05 0,02 0,04 -0,03 -0,03 0,02 -0,21

RF -2 1,00 0,07 0,14 -0,07 -0,08 0,08 0,00 -0,24 -0,01 -0,25 -0,01

RF -3 1,00 0,35 0,56 0,38 0,10 0,08 -0,15 -0,01 -0,12 -0,17

RF -4 1,00 0,21 0,11 0,30 0,26 -0,03 0,16 -0,17 0,05

RF -5 1,00 0,82 0,57 0,48 -0,03 0,28 -0,12 0,00

RF -6 1,00 0,80 0,68 0,08 0,42 -0,05 0,04

RF -7 1,00 0,89 0,16 0,68 0,02 0,17

RF -8 1,00 0,21 0,75 0,01 0,22

RF -9 1,00 0,32 0,33 0,20

RF -10 1,00 0,16 0,56

RF -11 1,00 0,03

RF -12 1,00

The rates recorded suggest a complete de-correlation between the real series and all the

« forecasted » series: even the «nearest» series (RF-1, RF-2 and RF-2 where the RF is meaning

« rolling forecast » and the « -n » is the number of months in advance of the estimate compared to

the real data : RF -2 is the rolling forecasted series estimated two months before the real data

targeted) which is the closest in time and is completely de-correlated.

The data suggest also a decorrelation among the forecast series.

It's interesting to analyze the errors between the REAL data series and each prediction made X

months before

Table 9 Error Statistics versus real data

Row ID RF -1 RF -2 RF -3 RF -4 RF -5 RF -6

R^2 -10,90 -7,71 -6,98 -2,48 -2,27 -4,43

mean absolute error 896.582,49 787.692,69 648.493,08 475.242,08 434.135,40 515.994,20

mean squared error 1.652.393.805.492,20 1.208.922.107.632,55 1.108.760.231.249,43 483.926.719.736,84 453.439.134.181,89 754.018.394.528,96

root mean squared deviation 1.285.454,71 1.099.509,94 1.052.976,84 695.648,42 673.378,89 868.342,33

mean signed difference 853.004,75 178.348,72 484.609,25 33.984,82 124.503,74 25.472,97 Row ID RF -1 RF -2 RF -3 RF -4 RF -5 RF -6 mean absolute error 896.582,49 787.692,69 648.493,08 475.242,08 434.135,40 515.994,20 root mean squared deviation 1.285.454,71 1.099.509,94 1.052.976,84 695.648,42 673.378,89 868.342,33

Row ID RF -7 RF -8 RF -9 RF -10 RF -11 RF -12

R^2 -9,16 -9,62 -4,21 -2,62 -3,59 -3,76

mean absolute error 617.901,58 680.155,76 671.684,53 577.084,85 715.373,48 725.833,73

mean squared error 1.411.062.421.842,61 1.474.881.215.829,82 722.862.775.109,12 502.336.320.011,86 637.381.350.514,20 660.639.537.862,78

root mean squared deviation 1.187.881,48 1.214.446,88 850.213,37 708.756,88 798.361,67 812.797,35

mean signed difference 46.578,75 -69.686,66 -364.321,53 -230.016,08 -626.745,31 -713.728,83 R^2 -9,16 -9,62 -4,21 -2,62 -3,59 -3,76 mean squared error 1.411.062.421.842,61 1.474.881.215.829,82 722.862.775.109,12 502.336.320.011,86 637.381.350.514,20 660.639.537.862,78 mean signed difference 46.578,75 -69.686,66 -364.321,53 -230.016,08 -626.745,31 -713.728,83

The table shown above, it contains certain statistics between the numeric column's values (REAL

with « ri » elements) and predicted (RF with « pi » elements) values. It records

R²=1-SSres/SStot=1-Σ(pi-ri)²/Σ(ri-1/n*Σri)² (can be negative!),

mean absolute error (1/n*Σ|pi-ri|),

mean squared error (1/n*Σ(pi-ri)²),

root mean squared error (sqrt(1/n*Σ(pi-ri)²)), and

mean signed difference (1/n*Σ(pi-ri)).

No expected trend (higher correlation as long as the decreasing interval with the real data or higher

correlation rate among nearest forecast series) : it could be an effect due to the attention required

by the sales operators in estimating the data (probably not exactly the ideal work for a salesforce)

or for the complexity of causes that have or could have effect on the forecast results.

As most of the « social » system, or at least part of a « social » system, the results are frequently the

sum of the two previous « approaches ». They probably have cross-side effects : not managed

complexity could cause stress on salesforce obliged to perfom a duty which was not really

understood, with poor results in terms of forecast accuracy ; this poor accuracy could itself make

the perceived complexity by the « forecast » operator.

— page 36 —

Analyzing the results of each series and considering that each one should have a reverse « S » shape

like a « Z » stretched in horizontal (high volumes at the launch time, a decreasing period after the

launch and finally a stable or « end » level like an asinthothic target), the idea was destructuring the

time series referring the data to this first launch time.

The series are transformed to find the maximum sales point reached for each one and this point is

set up as the "starting" point of the relative "time": in this way a new line is identified and this one

is analyzed with an exponential regression estimate. This analysis wants to identify if there exists a

"decreasing" pattern.

Re-aggregating all the time series data referring each one to the first Band/Line launch time, the

decreasing path showed by the aggregated data suggested to analyze the same data applying an

exponential regression estimate.

Figure 13 Production Real versus Estimate

The series analyzed in (Figure 8) represents a sum of pieces referred to different lines and inside

each line to a different product family. When a new line of product is launched on the market this

one is split into several kinds of products (the product families) and the marketing mix is generally

predefined. If we consider a new "relative" time axis where for each line we identify as time 0/1 the

first month of production/sale, we could build a new aggregation of every line considered to see if

the trend explains the life cycle of the products.

Please note that with some exceptions the blue line, (real pieces), reaches the maximum level

immediately after the launch, within the first 6 months.

The correlation ratio between the two lines, the real production (blue) and the estimate (green), is

relatively high (0.79) even if the volatility showed by the real series is well described by the mean

error of 1.4 million pieces.

— page 37 —

If we consider that the orders coming from the customers have a “validity” horizon of 3 months,

some analysis that uses a smoothed over series referring to a quarter period, as well as a moving

average always on a quarter period, could manifest some different information.

The following (Figure 14 Production real versus estimate - moving average 3 months) presents such

graphical analysis.

Figure 14 Production real versus estimate - moving average 3 months

Please note that the smoothing is applied with the 3 month moving average, (Figure 14), it

underlines the same period trends we could see in the previous graph (Figure 13):

- a first time section that goes up to the sixth period after the first production with growing

number of pieces (this could be due to the fact that the products aren’t launched all over the

world at the same time and in the same way or, more probably, to the fact that initially some

production is made for market presentations and distributors availability with a real starting

point slightly postponed)

- a second period with a fast decrease (between the 6th and the 36th month): this is a "post

launch” effect when the customers who bought the products motivated by the advertising

and launch effect define their purchase preference versus the product/line (please consider

that the same customers are in this period stressed by competitors’ new launches and

advertising)

- a third period (over the 36th month): the customer decision is taken and the customer

basis for the line/products is defined and stable. Throughout the month the consumption is

slowly decreasing. This is the end of the line life cycle. Generally a line is completely renewed

after 3 years and only if it was a real success the production could have important level

during the "final" cycle.

Please note that the decrease factor during the second section is more or less 80% over the

maximum reached and only the remaining 20% is lost after the 36th month.

— page 38 —

The following graph, (Figure 15 Production real versus estimate in 50ml equivalent), considers the

quantity trend in 50 ML equivalent compared with the best fit estimate.

Figure 15 Production real versus estimate in 50ml equivalent

In this configuration the correlation rate reaches 0.83

The scale of the analysis is more “compressed” having a “reduction” effect on promotional materials

that is really important during the first period when launching the new product. The decreasing

slope is “flatter” but an important volatility appears throughout the curve.

The main trend lines described in Figure 9 and Figure 10 could be seen as sum of customers’ or

product families’ line.

The following graph, (Figure 16 Production versus estimate two mail customers), describes the real

trend and the estimate trend only for our main two customers.

— page 39 — Figure 16 Production versus estimate two mail customers

Please note that even if we are observing the first main customer data, the correlation ratio between

the real and the estimated series is lower than the overall same ratio: 0.70

The error in terms of absolute number of pieces is more or less 1.0 million pieces while the error

calculated as root of mean squared error is 1.6 million pieces.

Please note that observing the second main customer data, the correlation ratio between the real

and the estimated series is the best of our dataset: 0.81



Please note that observing the third main customer data, the correlation ratio between the real

and the estimate series is lower than the overall ratio: 0.74



Concerning the customers’ base lines we could conclude that the sum of all customers’ datasets is

more statistically reliable than each single customers’ datasets. The single volatilities are not

interdependent.

— page 40 — Figure 17 Production versus estimate and exponential regression on estimate

The estimated data follows the real one throughout the decreasing slope after the launch time and

they also cover the volatility between each month and the previous one.

Really interesting to note that the estimated data could be interpolated by an exponential curve

with positive intercept and negative exponential coefficient.

The random trend line presented in previous graphs, Figure 4 to 7, denotes an “internal” path if we

change the viewpoint from the absolute time to “launch” relative time.

This new approach lets us hope to find some other “secret” path lines useful to estimate the future

trend line. A different approach could be investigated making the point “0” not relative to the set

Brand/Line/Product Family (each set presents its point “zero”) but relative to the same Brand/Line

where every Line is not stand alone if the same Line has completed or substituted the another

previous one.

The following graph, (Figure 15), tries to review the series of pieces grouped sub a new group named

“Woman” lines relative to the Customer2.

— page 41 — Figure 18 Series Analysis per Customer 2 Woman Lines

It is a group which covers some of the important feminine lines studied by our well known Customer

2. The main ideas that we can obtain from the graphical analysis are:

the decreasing slope of each set Brand/Line/ Product family is lost

no recurrent pattern is immediately recognizable

a general feeling of “expansion” generated by the “peaks” balanced by a “stable” impression

due to an overall “horizontal” interpolating line (this one broken in two: one from time 0 to

time 37/38 and a second one from 39 to the end. The first one more or less around 75000

pcs and the second one at 400 or 500 thousand pieces)

a first confirmation of a life cycle can be found at around 3 years (the first section was

described into the previous point), with a renewal (probably a launch of a main substituted)

at 37, not confirmed into the second section (no real scale uplift at around 72 or 108). The

low level at the end of the graph corresponds also with a 3 year life cycle end.

A growing volatility due to the 2 time section (before and after 37) levels but also expanding

during the second section in terms of frequency and max/min levels.

This analysis could only partially confirm some “expectations” in terms of new useful paths to

preview the trend lines.

— page 42 — Figure 19 Exponential Coefficients analysis

Another different approach consists in reviewing every series considering its intercept and

exponential regression coefficients. Figure 16 covers this analysis.

With some exceptions, which should be gone into depth (positive exponential coefficient) probably

due to some exceptional charachteristics (length of the series, stable products became some kind

of “evergreen” on the market, accessories lines probably with strange paths, …), most of the series

have positive intercepts and negative exponential coefficients: this distribution confirms the

decreasing slope of the model studied.

The distribution of the series shows an area of concentration with the shape of a “pointer” indicating

the point 0,0.

Future analysis has to go in-depth into the high intercept/lower exponential coefficient points to

find the causes, if they exist, of these series.

4.1.6 Series characteristics as Customers’ requests and their effects on the

industrial system

The series analysed show a random trend over the years probably linked to some characteristics:

The brand awareness and the existence of some other luxury or fashion product segments

have a positive elasticity on the fragrance market: this strong link could have a positive effect

on perfume sales but it could also have a real negative effect on the products analysed when

the brand is in decline.

Every launch could generally present a “bell” shape distribution over the time with the

increasing side very short and the declining side sloping down with various rates: this rate is

surely caused by the brand awareness here-above quoted as well as the brand presence into

the fragrance market or the life-cycle status of the brand fragrance products

— page 43 —

Over the year there are some periods when the products are generally launched: St.

Valentine’s Day, for example, or New Year’s Eve. These launches forecast to have the

products on the shelves just before these periods

The fragrance market requires, generally, that the brand covers the three main sectors

(woman, man and young) with a sequence of launches that builds up the brand awareness

and position. These series of launches have to consider the “strong” periods of the year and

the brand position along its life-cycle. Once the series of launches is completed every line

could be supported with limited edition to “feed” the main line/product or replaced by a

new version launch. This last option, at least initially, does not cancel the old line but rapidly

“erodes” almost completely the “old” one. Only “classical” products, very special ones,

Chanel n° 5, for example, resist over time with a growing or a stable trend when the

“substitute” is launched.

The characteristics expressed strongly suggest the “random” path that every brand/line could

present. This “random” path is also altered by the “internationalization” that the brand has or

decides to have. The geographical launches try to copy the general idea already expressed but

sometimes need to be scheduled in different periods: in an absolute time scale this effect could

accentuate the “random” aspect of the brand/line/product series. As per previous sections, all the

analysis tries to discover some “recurring” paths to suggest a better forecast system and a better

response from all the supply chain involved.

Figure 20 Average Lot Size all along the time from launch

— page 44 — The (Figure 20) shows the recorded average lot size requested by each main customer all along the

time scale that starts with the “launch” period.

If the idea of “great” size for the beginning periods could have some logical expectancy, the (Figure

20) does not confirm this hypothesis: even if the different customers present different levels and

paths, after a first period the size of the average lot reaches more or less the “start” level presenting

a high level of volatility.

Figure 21 Average Lot Size, 50ml equivalent, all along the time from launch

The (Figure 21) shows the same data in the 50ml equivalent.

The suggestion of an average lot size not decreasing over time is strengthened: the lot size at the

end of the period is strongly higher than at the outset one. Moreover the volatility along the periods

becomes really important over the 100th month since the launch.

With this analysis is also clear that the differences in terms of quantities due to the different

customers, see (Table 3 Production data 2010 2013), are not reflected into the average lot size:

while at the beginning the “greatness” is reflected into the data, moving on the right side and

especially after the 100th month the lines lie on the same area and overlap.

— page 45 — Table 10 Product Family Correlation

Correlation Rate Alcool -

Flaconi

Real

Alcool -

Flaconi

Estimate

Alcool -

Miniature

Real

Alcool -

Miniature

Estimate

Alcool -

Vaposac

Real

Alcool -

Vaposac

Estimate

Coffret -

Kit -

Bundle

Real

Coffret -

Kit -

Bundle

Estimate

Creme,

Linee

Hotel

Real

Creme,

Linee

Hotel

Estimate

Creme,

Shampo

o Real

Creme,

Shampo

o

Estimate

Deo, Deo

Stick

Real

Deo, Deo

Stick

Estimate

Fiale Real Fiale

Estimate

Vari

Real

Vari

Estimate

Alcool - Flaconi Real 1,00 0,97 0,85 0,87 0,68 0,71 0,64 0,75 -0,42 -0,49 0,92 0,93 0,86 0,87 0,89 0,90 0,53 0,54

Alcool - Flaconi

Estimate

0,97 1,00 0,85 0,88 0,70 0,74 0,65 0,79 -0,46 -0,53 0,90 0,93 0,80 0,83 0,83 0,85 0,49 0,51

Alcool - Miniature

Real

0,85 0,85 1,00 0,97 0,76 0,81 0,63 0,72 -0,46 -0,53 0,83 0,86 0,64 0,65 0,64 0,65 0,36 0,37

Alcool - Miniature

Estimate

0,87 0,88 0,97 1,00 0,77 0,81 0,64 0,76 -0,48 -0,54 0,84 0,88 0,67 0,69 0,65 0,66 0,39 0,39

Alcool - Vaposac Real 0,68 0,70 0,76 0,77 1,00 0,94 0,53 0,63 -0,36 -0,42 0,63 0,65 0,49 0,53 0,44 0,45 0,17 0,14

Alcool - Vaposac

Estimate

0,71 0,74 0,81 0,81 0,94 1,00 0,57 0,69 -0,39 -0,44 0,66 0,69 0,47 0,51 0,45 0,45 0,19 0,18

Coffret - Kit - Bundle

Real

0,64 0,65 0,63 0,64 0,53 0,57 1,00 0,82 -0,39 -0,45 0,59 0,61 0,41 0,43 0,39 0,40 0,20 0,20

Coffret - Kit - Bundle

Estimate

0,75 0,79 0,72 0,76 0,63 0,69 0,82 1,00 -0,45 -0,53 0,69 0,73 0,53 0,54 0,47 0,49 0,18 0,18

Creme, Linee Hotel

Real

-0,42 -0,46 -0,46 -0,48 -0,36 -0,39 -0,39 -0,45 1,00 0,90 -0,48 -0,49 -0,25 -0,28 -0,27 -0,27 -0,25 -0,22

Creme, Linee Hotel

Estimate

-0,49 -0,53 -0,53 -0,54 -0,42 -0,44 -0,45 -0,53 0,90 1,00 -0,55 -0,56 -0,26 -0,30 -0,33 -0,33 -0,25 -0,21

Creme, Shampoo

Real

0,92 0,90 0,83 0,84 0,63 0,66 0,59 0,69 -0,48 -0,55 1,00 0,97 0,73 0,74 0,75 0,76 0,39 0,42

Creme, Shampoo

Estimate

0,93 0,93 0,86 0,88 0,65 0,69 0,61 0,73 -0,49 -0,56 0,97 1,00 0,75 0,77 0,76 0,78 0,43 0,45

Deo, Deo Stick Real 0,86 0,80 0,64 0,67 0,49 0,47 0,41 0,53 -0,25 -0,26 0,73 0,75 1,00 0,97 0,87 0,88 0,45 0,41

Deo, Deo Stick

Estimate

0,87 0,83 0,65 0,69 0,53 0,51 0,43 0,54 -0,28 -0,30 0,74 0,77 0,97 1,00 0,89 0,89 0,47 0,44

Fiale Real 0,89 0,83 0,64 0,65 0,44 0,45 0,39 0,47 -0,27 -0,33 0,75 0,76 0,87 0,89 1,00 0,99 0,54 0,54

Fiale Estimate 0,90 0,85 0,65 0,66 0,45 0,45 0,40 0,49 -0,27 -0,33 0,76 0,78 0,88 0,89 0,99 1,00 0,56 0,56

Vari Real 0,53 0,49 0,36 0,39 0,17 0,19 0,20 0,18 -0,25 -0,25 0,39 0,43 0,45 0,47 0,54 0,56 1,00 0,98

Vari Estimate 0,54 0,51 0,37 0,39 0,14 0,18 0,20 0,18 -0,22 -0,21 0,42 0,45 0,41 0,44 0,54 0,56 0,98 1,00

The (Table 10 Product Family Correlation) shows as some product families could be clustered. Real

data or the estimated ones, with the exponential model, both based on the launch time, show a first

group of products moving together with the alcohol and cream products and a second one, the hotel

line products, completely de-correlated.

We should consider that the customers’ demand seeks:

Growing quantities of products apparently randomly distributed

Grouping of products clustered and with opposite trends along their life-cycle

An uncertainty moving far from the launch time that the customers translate into a higher

and higher volatility of the order lot size

4.1.7 Some Industrial trend lines

After having explored the market and its characteristics, or at least the section of market covered

by the company, underlined some base trend lines translated into the demand for the production,

we want in this section to pinpoint the “pressure” received through the customers’ requests.

We need to separate the “big” customers from the others.

Over the years, the main customers, named one to three in our analysis, showed a growing level in

terms of quantities. The quantities, and their translation into costs for the customer, were forcing

the customer itself to a keener attention on the price: a double attention.

First of all, a growing cost is always seen as an opportunity by the purchase dept. because more

money is involved more is the negotiation margin available. This margin could be a poor

“negotiation” field (discounts or target bonus) or a “partnership” lever (pushing the partner to new

production solutions with an agreed margin distribution).

According to a second viewpoint, a growing quantity and its absolute cost let the purchase dept. to

negotiate with multiple suppliers putting immediately pressure on price. When some absolute levels

of cost are reached and the activity becomes relevant for the customer profit or loss, every risk

— page 46 — analysis obliges to have different supply chains: the concurrence level is due to become higher and

higher.

The here-above double effect of a growing customer on the production partner is to increasingly

search a higher level of efficiency maintaining the “efficacy” to fulfil the “partnership” requirements.

If a luxury or fashion small customer asks for a “handmade” quality service, the same customer,

once grown up, becomes more “industrial” and careful to price and service “efficiency”.

This transformation pushes the organization versus more “industrial” process with a growing trade-

off between flexibility (generally seen by customer as ability to change the production flow even

once the order is activated) and efficiency (translated to a lower price): the company structure,

initially, tries to resist forcing the customer to accept the “handmade” flow with complete separate

production phases and, only when forced to change process, agrees to a whole new rigid system.

Referring to (Figure 3 ICR people "Change" attitude) and the related attitude discussed in the next

section (CHAPTER 3

3 Research Limitations), when the initial “equilibrium” between people and equipment has to be

changed, the management structure pushes strongly to a “rigid” equipment structure. This choice

is probably made to avoid problems of people training or “soft” skills implementation: from a “total

free” system, composed by simple single steps or phases where simple manual activities are made

by “inexpert” workers, to a “continuous” line with production phases hard-linked with fewer but

specialized workers. This last option drives the flow with only one path without many alternative

options and it is simple to direct “inexpert” people.

Between the two extreme philosophies here-above described, a mix of other solutions could be

imagined but the passage from “inexpert” to “skillful” workers is necessary. Difficult to estimate the

cost of this transition and more difficult to cover all the hidden costs and problems that such

transition could produce to the planning-scheduling-production system, offices and people. This

system “resilience” due to the low professional experience is difficult to be broken because it is

statistically difficult to evaluate the possible gain or “win” to face the possible “training” costs.

These external forces to change versus a more industrial system with focus on price and cost have

to be considered as a part of a supply chain characterized by high value added products and,

perhaps, a “weaker” attention to the overall supply-chain efficiency. A strong upside sales trend

turns the customer attention to planning more than price and cost question: when the positive

cycle, even always expanding, slows down the attention comes back to efficiency and price issues.

Last but not least, even for “big” customers, when the line or product life-cycle turns to the end, the

customer behaviour is more erratic and similar to the “handcraft” product opportunity

management: previous (Figure 20 Average Lot Size all along the time from launch) and (Figure 21

Average Lot Size, 50ml equivalent, all along the time from launch) suggest as over the 100th month

the order size from the three main customers overlap. This effect is important to understand why

the production system has to maintain a different set of options even for the same customers’ size:

the customer needs change according to the line and/or product considered and the customer

behaviour

It could be simple to reduce the “small” customers’ needs to the opposite of the “large, big”

customers’ ones.

If it is true that the product quality and the service level are more important for “small” customers,

it is also important to note that it is not merely “small” the customer who orders few pieces.

— page 47 — Sometimes new industrial customers are part of large corporations and they adopt procedures

covering all the aspects: price, efficiency, quality and service. Sometimes the new customer does

not know the industrial process but sometimes he operates in similar market sectors (cosmetics, for

example) or even has an internal production capacity and for some reason wants to outsource the

line/products: in this case the demand is overviewing all the aspects already described.

The “partnership” could also dispatch the improvements made for “big” customers to the “small”

ones: when the investment is made the efficiency target tends to charge the new lines with every

order which covers the fixed cost and fulfil the available capacity of the line installed. In this way, if

the technical conditions are respected, “small” customers’ orders inherit the efficiency and cost

conditions applied to “big” customers.

On the other hand, it is clear that small customers, when possible, absorb the conditions granted to

different size of orders and, consequently, are pushed to reason with the same boundaries and logic.

The “small” customers’ demand tend to reach the conditions, positives (price, efficiency …) and

negatives (minimum batch size, preparation time …) acknowledged to “large” demand.

Finally we could say that the system is “tensed” by opposite forces:

Efficiency and price conditions due to industrial automation, often considered as “hard”

equipment instalments

Flexibility and quality or service level targeted on “handcraft” operations

These two forces are more and more overlapping and the customers’ requests tend to push the

organization to search both ones at the same time.

— page 48 —

CHAPTER 5

5 Literature Review

The “Industrial revolution” started a continuous process of improving the manufacturing

techniques. Managers and researchers developed a series of new approaches to the industrial

operations.

The industrial operations were focused on maximizing the performances trying to optimize the

production cost. The Taylorism and the Ford production process were probably the first steps in

organizing the operations searching this objective.

As well recorded by Roberto Panizzolo in his paper “Applying the lessons learned from 27 lean

manufacturers. The relevance of relationships management” (1998, International Journal of

Production Economics), “For many years, the operations management discipline was dominated by

mathematical and statistical studies”.

Only in years ‘60s and ‘70s the manufacturing was reviewed as a strategic issue investigating the

trade-off among the different performance measures (cost, quality, production time …) but during

the ‘80s the Japanese success launched a new paradigm or philosophy: today we call this way of

thinking “Lean Production”. Just in Time, Total Quality Control,… many systems were studied but

most of the researchers (K. Ferdows, A. De Meyer, Lasting improvement in manufacturing

performance, Journal of Operations Management 9 (1990), E.J. Flynn, B.B. Flynn, Achieving

simultaneous cost and differentiation competitive advantages through continuous improvement:

world class manufacturing as a competitive strategy, Journal of Managerial Issues 8 (3) (1996))

stated that: “world-class manufacturers make a big point of doing everything well, and argue that

automated, flexible technologies coupled with management innovations have made this possible.”

Today the debate is much more open to the extent in which the Lean Approach and the Flexibility

could be applied:

inside any individual firm with a continuous improving effort,

among different firms all along the supply chain from suppliers to final customers

and, finally, at a national level considering all the network connections ruled by the social

environment (innovative industrials relationships, creations of logistic and communications

networks, new educational and professional training schemes, …)

Many papers since Womack et al. (1990), who are credited as the first to coin the name “lean

production” (LP) (Sawhney and Chason, 2005), focused on the successful conditions which the Lean

Approach requires.

As stated by Jagdish R. Jadhav et al, “Exploring barriers in lean implementation”, International

Journal of Lean Six Sigma, 2014, “The lean deployment faces many challenges or barriers. There are

many factors that can hinder or enable the lean implementation process (Aurelio et al., 2011).

Benton and Shin (1998) mentioned that the major implementation problems center on cultural,

human and geographical factors. Only 10 per cent or less of companies succeed at implementing

TPM and lean manufacturing (LM) practices (Bhasin and Burcher, 2006; Mora, 1999). Only 10 per

— page 49 — cent have the philosophy properly instituted (Bhasin and Burcher, 2006; Sohal and Eggleston,

1994).”.

All the studies in the beginning were set to discover the “internal” conditions requested by a

successful lean production project: the analysis were principally centred on the set of tools used by

Japanese companies. Most of the surveys were focused on the internal conditions needed inside

the company to complete the lean implementation: even in surveys covering internal and external

risk factors the weight due to internal conditions is extremely high (Barriers to Implement Lean

Manufacturing in Malaysian Automotive Industry, Mohd Azhar Sahwana, Mohd Nizam Ab Rahmanb,

Baba Md Derosb, 2012, Journal Tecknologi).

When the surveys, mostly in automotive sectors, broadened the viewpoint covering the supply

chain actors, the approach was in effect statistically oriented to define a correlation between the

successful rate in Lean implementing and the characteristics of suppliers or customers. With this

viewpoint, we can consider a type of “positive” risk for the adoption of a lean approach is the

function of some events (constrains by suppliers or customers, this needs to be strictly linked to

previous or following actors all along the supply chain, level of “service” requested by final

customers, …). From this viewpoint, all the surveys made for automotive sector (Barriers to

Implement Lean Manufacturing in Malaysian Automotive Industry, Mohd Azhar Sahwana, Mohd

Nizam Ab Rahmanb, Baba Md Derosb, 2012, Journal Tecknologi, - Implementation of Lean

Manufacturing Principles in Auto Industry*, R. P. Mohanty, O. P. Yadav & R. Jain, 2006, Vilakshan,

XIMB Journal of Management - Cooperation in the supply chain and lean production adoption.

Evidence from the Spanish automotive industry, José Moyano-Fuentes, Macarena Sacristán-Díaz,

Pedro José Martínez-Jurado, 2012, International Journal of Operations & Production Management,

Vol. 32) stated “[…]Nonetheless, the impact that building close relationships with supply chain

agents has on the development or progress of LP at the operational level has not been studied to

date.”. These surveys, in different geographical markets, tried to measure the effect of “developing

relationships with chain agents might have on the intensity of LP adoption”.

In the paper of José Moyano-Fuentes, Macarena Sacristán-Díaz, Pedro José Martínez-Jurado, 2012,

the research questions could be summarized by the following figure

where the two main research questions are linked to the aim of the survey. Some other hypothesis

is stated inside the paper for extra research questions but the main issue from our viewpoint is well

defined in the previous scheme.

As previously stated the Lean Implementation was originally studied searching the difference

between Japanese and Western actors involved in similar implementation processes. The

automotive sector was the main area of study because the Japanese way of thinking was born inside

Toyota. Nevertheless, as stated in some research, not only the “inside” successful factors were

studied: some research was carried out on the supply chain influence on the aim (product/service

provided to final customers).

— page 50 — Richard B. Chase, (1991),"The Service Factory: A Future Vision", International Journal of Service

Industry Management, Vol. 2, described a new situation where the focus was not only on efficiency

but also on operations flexibility: “A well performing factory was one that accomplished these

transformations at low cost, and with high quality. In recent years, attention has shifted to flexibility:

the range of transformations or products that the factory can accommodate, and the speed with

which changes can be made. A well-performing factory is now judged not only by the efficiency of

its operations but also by their scope and ease of change”. He also states: “The service factory,

however, presents a fifth possible focus: service, defined as some combination of information,

problem solving, sales and support”.

In “Relationship building, lean strategy and firm performance: an exploratory study in the

automotive supplier industry”, J. Jayaram , S. Vickery & C. Droge, 2008, International Journal of

Production Research, the authors affirm:” Porter contended that the identification and strategic

use of linkages both within a firm’s value chain (i.e. linkages in and across internal functions) and

linkages between value chains (i.e. company to company linkages or vertical linkages) can improve

competitive performance.”. These two examples underline how the attention to a broader scene

other than inside company performance indicators were already identified all along the studies.

We didn’t find any paper with a risk management approach to the broad supply chain factors in

implementing a lean project, either lean production or lean management project. Most of the

studies analysed the automotive sector in different geographical areas from an historical/statistical

viewpoint.

But the focus of this paper is not only on the classic Lean approach applied to operations but also

on the flexibility of operations, the measurement of this flexibility linked to the lean approach, the

effect of soft skill and flexibility through specialization training and, last but not least, the simulation

method applied to all the matters quoted.

Concerning this last point we have already quoted Prof. Jay W. Forrester founder of “system

dynamics” at MIT School of Management who deals with simulations on dynamics systems,

especially social or complex systems. Several are his studies and books considered today as this

discipline foundation. But he also worked on the field with GE managers: in 1950s he was able to

show that the internal GE employment instability was not explained by the three year cycle theory

but by the internal structure of the company and its decision making structure for hiring and layoffs.

During 1950s and 1960s Prof Forrester built a team of collaborators who translated the theory into

computer science and models. Richard Bennet (SIMPLE programming language) and Alexander Pugh

(DYNAMO program, acronym of DYNAmic Model) among the others. From these years on, Prof

Forrester was involved in several collaborations, business collaborations, and research works.

“Urban Dynamics” (Forrester, Urban Dynamics, 1969) was the book born from the collaboration

with Prof John F. Collins, visiting professor at MIT. From the collaboration with the Club of Rome,

Prof Forrester produced a model of the World socioeconomic system: the resulting book was

the World Dynamics (Forrester, World Dynamics, 1971).

The MIT School is a landmark in system dynamics research and Prof John D. Sterman is Prof

Forrester’s successor of in all the research centres at MIT with a focus on models and flight

simulators applied to social and business dynamics environment. His book “Business Dynamics:

System Thinking and Modelling for a complex World” is a summary of the wisdom and knowledge

gathered over more than twenty years of research dedicated to the study of practical methods for

system thinking and the dynamic modelling of complex systems.

— page 51 — Rethinking the literature review from the already quoted (Chase, 1991) "The Service Factory: A

Future Vision", the case study requested an investigation on the terms “agility” and “flexibility”.

The term “agility”, in a “service factory” as it is defined by Chase and which perfectly matches with

ICR case, is mandatory and the literature provides different levels of focus regarding this topic,

which will be presented in the following paragraphs.

A first research area on agility and flexibility is naturally focused on defining these terms. Without a

clear definition of each term, many arguments and situations studied overlap themselves and the

conclusions could be misleading just because the study areas and their boundaries are not

commonly accepted. Ednilson Santos Bernardes and mark D. Hanna in their paper “A theoretical

review of flexibility, agility and responsiveness in operations management literature: Toward a

conceptual definition of customer responsiveness”, International Journal of Operations &

Production Management, Vol. 29 Iss:1, 2009, tried to define different terms used in literature to

describe similar arguments or viewpoints (flexibility versus agility and versus responsiveness).

Flexibility is most commonly associated with the inherent property of systems which allows them

to change within pre-established parameters; agility is predominantly used to describe an

organization approach which allows a rapid system reconfiguration in front of unforeseeable

changes; and responsiveness commonly refers to a systematic behavior which involves timely

purposeful change when modulating stimuli occur.

A second viewpoint, as in H. Sharifi and Z.Zhang in “A methodology for achieving agility in

manufacturing organizations: An introduction”, Int.J.Production Economics 62, 1999, analyzed the

macro-economic environment. The paper covered a general evaluation process from market forces

to company attributes to define a list of factors relevant for “agility” and their level of importance.

They defined the “agility” as the ability to cope with unexpected changes but most of the work is

dedicated to the effect of market turbulence.

Another “high level” series of research covered the “agility” concept as an attribute requested by

the supply chain configuration. Chun-Yean Chiang, Canan Kocabasoglu-Hillmer and Nallah Suresh

“An empirical investigation of the impact of strategic sourcing and flexibility on firm’s supply chain

agility”, International Journal of Operations & Production Management Vol. 32 Iss:1, 2012, stated

that both strategic sourcing and firm’s strategic flexibility were significantly related to the firm’s

supply chain agility. The whole supply chain, complex or simple, adapts itself to the market or final

customer demand (considered either for the product side or the service connected to the product):

all the supply chain components adjust themselves all along the time and in function of the

concurrence applied to each component or to the whole supply chain. This last consideration is the

foundation of this research. Strategic sourcing and strategic flexibility are requested by the whole

SC and they operate directly on each SC component.

From this eminent viewpoint, other academic papers have tried to explain at a business level why

and how a system has to go towards more “agility”: A. Gunasekaran “Agile manufacturing: A

framework for research and development”, International Journal Production Economics 62, 1999,

is a review of paper-works summarizing the forces that could push a manufacturing system versus

agility and concludes defining 4 areas of investigation:

competitive basis and effects on flexibility-productivity-responsiveness in other terms

agility,

knowledge management and its effects on virtual enterprise,

— page 52 —

operations models and effects on agility manufacturing,

workforce characteristics on agility manufacturing.

Realizing the importance of agile manufacturing in the 21st century manufacturing competitiveness,

an attempt has been made in this paper to review the literature available on AM (Agile

Manufacturing) with the target to:

(i) identify key strategies and techniques of AM,

(ii) suggest some future research directions and

(iii) develop a framework for the development of agile manufacturing systems (AMSs) along

four key dimensions which include strategies, technologies, systems and people.

Another attempt in the analysis of the agility at a business level is the paper of C. Tsinopoulos and

I.P. McCarthy, “Achieving agility using cladistics: an evolutionary analysis”, Journal of Materials

Processing Technology 107, 2000, which describes the evolution of a business unit from the “rigid”

classical operations to increasing agility in the organizations. The research is extremely focused on

a classification method which aims to define some paths and modelling techniques able to manage

and understand the emergence of new manufacturing forms moving from “non-agility” versus

“agility” through groups of similar manufacturing organizations. It’s always a paper which considers

a panel of case studies grouped according to some cluster attributes with similar characteristics in

their history.

The literature also tried to consider the arguments of interrelationships among different but

connected topics: lean approach versus agility, agility and flexibility together and their differences

or contact points, lean approach inside agility companies or structures…

An interesting paper on these interrelationships, investigated by our case study centered on the

ability of a “service factory” to respond in a different way to the market, customer and internal

structure needs, is Layeck Abdel-Malek, Sanchoy K. Das and Carl Wolf “Design and implementation

of flexible manufacturing solutions in agile enterprises”, International Journal of Agile Management

Systems, Vol 2 Iss 3, 2000. It’s an interesting literature review on flexibility types (underlying the Lim

and Slack results in 5 types flexibility: Machine, Routing, Process, Product and Volume), on flexibility

measurements (with Das multiple levels of flexibility) and on innovation effects on product

manufacturing. Based on previous literature review but also on a technical survey, the authors

provide three steps to approach a factory analysis in order to propose a flexibility optimum level

and a solutions comparison approach.

In the same research field the paper A.Gunasekaran, E.Tirtiroglu and V. Wolstencroft “An

investigation into the application of agile manufacturing in an aerospace company”, Technovation

22, 2002, applied the here-above Gunasekaran framework to the GECMAe case study to find a direct

relation between short product lifecycle and agility required. Nevertheless some interesting effects

on change mentality are linked to BPR tools used by the company “change team” to improve the

business competitiveness. This paper analyzes more the effects of implementing the “agility” idea

or concept to real structure already working. The business process reengineering (BPR) tools and

the way to drive the change process in a context that has to move towards service, agility and

flexibility ensuring efficiency and cost optimization is exactly the step which is not covered by the

ICR case study but to be implemented once simulated and decided on the opportunity to proceed.

It’s the future step on how to implement the solutions chosen on the simulation results.

The ICR case study, a real case study, concerning a company operating since 1975 on the market,

recalls many literature concepts (lean applied to the operations and lean historical development on

operations, agility and flexibility as attributes of the “service factory” development over the years,

— page 53 — …) but also a focus on the market and sector engaged by the company: the “luxury” industry. If we

have to specify the sector, we could define the portion of the market according to different

viewpoints:

from the “product” side it could be the “luxury perfumes or fragrances” even if the first term

is more appropriate due to the fact that fragrance is more related to raw material while

perfume concerns the finished product sold on the market;

from the “price” viewpoint we could say that the we are considering an “accessible luxury

market” because the products are associated to the most famous international luxury

brands but they are accessories with price levels that could be faced by a large consumer

market and they are not inaccessible or exclusively reserved to richest people;

from the “kind or type” of product we could say that they are batch products made in limited

series even if the quantities are really significant: the product family philosophy is

production per series; each one covering some market target concept in a limited life-cycle.

All these considerations are object of research studies in the “Luxury” industry and specifically

“luxury” industry and supply chain issues. As per the case study, the effects of the supply chain (SC)

on the single organization operating inside, in the middle of, the supply chain are extremely

relevant because the operator must absorb the information, requests and restrains from the

boundaries actors and calibrate the internal structure and system to maximize its efficacy and

efficiency given the outside limits received.

Brun, A., Caniato, F., Caridi, M., Castelli, C., Miragliotta, G., Ronchi, S., … Spina, G., International

Journal of Production Economics, 114(2), 2008, in their “Logistics and supply chain management in

luxury fashion retail: Empirical investigation of Italian firms.” presents the results of the exploratory

stage of a research project ongoing at Politecnico di Milano and dealing with supply chain

management in the luxury fashion industry. In total, 12 Italian luxury fashion retailers have been

studied to describe the main features of operations and supply chain strategies in the luxury fashion

segment and to identify their role with respect to the relevant critical success factors.

Brun A., Castelli C. , Int. Journal Production Economics 116, 2008, in their “Supply chain strategy in

the fashion industry: Developing a portfolio model depending on product, retail channel and brand”

tried to assess the relevance of Supply Chain Management (SCM) approach in the fashion context,

where operations and manufacturing seem to be considered as ancillary to marketing and

communications activities. They propose a model called the ‘‘segmentation tree’’ model, to adopt

a focused approach to SCM resulting in a SC strategy segmentation based on three drivers, namely

product, brand and retail channel. Furthermore, the authors identified the following gap:

contributions are available for implementing a portfolio of SC strategies in the fashion industry.

This last paper well describes a sector where the weight of some functions or departments

(marketing, communication and sales) are much over weighted compared to the industrial or

logistic ones. The core industrial and logistic structures inherit all the inefficiencies and inefficacies

generated by a marketing approach not found on the production and logistic restrains.

As per previous arguments, this research field, covering the luxury sector “stream”, is investigating

a top down approach with statistical analysis of the different strategies put in place by the different

actors of the market. Caniato and others, “Supply chain management in the luxury industry: A first

classification of companies and their strategies”, International Journal of Production Economics,

133(2), 2011, clustered manufacturing companies from the luxury industry and among others tried

to identify which supply chain strategy is currently applied within each cluster. On the basis of five

classification variables (company size, selling volume, product complexity, product fashion appeal

and brand reputation) four clusters are identified. S.C strategy addresses issues such as

manufacturing, sourcing and distribution process.

— page 54 —

Last but not least it was necessary to review in the literature the studies and papers concerning the

“Performance Measurement” and “Operational Performance Measurement”.

Academic literature in operations management and supply chain management tends to segregate

main arguments concerning performance measurement into three broader categories: financial

performance, marketing performance, and operational performance. In many of the frameworks

and mathematical models that conceptualized manufacturing firm related performance

measurement, they, in one way or another, used the aforementioned classification (Inman, Sale,

Green, & Whitten, 2011; Samson & Terziovski, 1999; Shah & Ward, 2003).

We do not review the financial and marketing performances in this paper because the first ones

are always relevant in all company choices but the way to measure these performances is covered

by a large economic or financial literature and does not really clarify the case study target. This

target is in fact operative because we seek to measure the differential costs or revenues useful to

evaluate different operational scenarios. We do not want to discuss how to actualize the results

born in different time or the theoretical foundation of some indicators (pay-back period versus NPV

“net present value” or which interest rate has to be used to actualize the series value, …). At the

same time it is not interesting in this context to evaluate the marketing or strategic indicators that

some choices could have on these important levels: strategy or marketing position of the company

in the market. Even if these levels could be more important for future company survival, we are

concerned here by the measurement of the operational key indicators. We want to try to measure

the company gain to train or develop new soft skills or flexibility through specialization. From our

viewpoint this type of flexibility is really close to a more general concept of “agility” than a technical

“flexibility” as defined in here above references and sections concerning the flexibility term.

We will review the “Operational Performance” issue in literature.

The choice of type of performance metric for a specific management level depends on the type of

supply chain activity or process (i.e. plan, source, make/assemble, deliver) (Gunasekaran, Patel, &

Mcgaughey, 2004).

This way, the measurement system helps to coordinate aggregation/disaggregation vertically and

integration among processes horizontally (Arzu Akyuz & Erman Erkan, 2010; Gunasekaran et al.,

2004).

Operational performance measurement is about how a firm, typically a production firm in the

manufacturing sector, consisting of one or more plants, needs to measure and evaluate its results.

Scholars in the performance measurement have proposed several elements to be considered as

metrics or measures of operational performance of manufacturing firms as early as 1970’s.

Primary contributors include Wheelwright, Hayes, Schmenner and Hill. For example, Wheelwright

(1978) discusses the implementation of proper measures for manufacturing decisions that reflect

corporate strategies. He suggests the use of four performance measures: cost (efficiency), quality,

dependability and flexibility by supporting validation of proposed framework by manufacturing

decision makers. Named and arranged in different ways, these authors have proposed quality

conformance, manufacturing unit cost, delivery time, and flexibility to volume change as important

measures of operational performance. After performing a factor analysis and arguments of

clustering, they state four broader manufacturing priorities: delivery (consisting of volume

flexibility, delivery speed, and delivery dependability), value (low cost, product reliability and

quality), flexibility (product and process), and innovation (new product introduction, design

quality/innovation).

— page 55 — In another instance, (Bowersox, Closs, D., & T., & Keller, 2000) used 13 performance metrics to

measure operational performance in an attempt to demonstrate how competence in supply chain

operations may help to achieve business success. The 13 measures have been categorized under

cost management, quality, productivity, customer service, and asset management. Comparing this

to the other scholars mentioned above, we can observe that measures of quality and cost are

common even though they have further sub-measures in the latter case. Another way the literature

looked at operational performance is by viewing aspects to be measured from competitive priority

perspective (Peng, Schroeder, & Shah, 2011). In some sense the aspects of operational

performance compete for resources and capabilities that firms usually tend to set priorities for

shaping their offers. Essentially decisions related to operations strategies and practice

implementations are bound to trade-offs (Vickery, Dröge, & Markland, 1997; Wheelwright, 1978).

Literature shows that depending on what the priority is, it is possible to identify mediation effects

of some measures over others even though, all may belong to operational performance (Peng et

al., 2011).

The four performance measures of a manufacturing firm: product quality, [production] flexibility,

delivery, and cost have been captured under the theory of performance frontiers, which includes

the law of trade-offs and law of cumulative capabilities (e.g. Schmenner & Swink, 1998). An

additional dimension included by other scholars is rate of new product introduction. These

measures have been used later to conceptualize operational performance as multidimensional

construct by several different authors.

In practically measuring operational performance, two alternative approaches could be adopted.

The first approach is to treat each individual performance priority separately. This provides a more

detailed view of performance aspects because it is simpler to trace the causal relationship of factors

and influences on performance. Such approach is practically simpler to foster commination among

managers but could be cumbersome in having several variables and data to interpret.

The second approach is forming a composite measure out of the estimated performance priorities

by using weighted average values. Ahmad & Schroeder (2003) have performed uni-dimensionality

and factor loading analysis on previously identified performance measures (cost, quality, flexibility,

rate of new product introduction, and delivery) so as to use their sum as a justifiable performance

index. This means that if the cost, quality, flexibility, new product introduction speed, and delivery

are measured with some consistent scale, such combination is essentially an indicator of how well

a firm performes operationally (Wu, Melnyk, & Swink, 2012). This can also be used to compare

performance level against other firms after having controlled the factors which contribute to

industry, environment, scale or related variations.

A notable work in composite performance measurement is that of Bozarth & Edwards (1997). They

have considered five broad performance priorities: cost, quality, flexibility, dependability, and

speed as they are typical performance measures in firms with manufacturing operations. They form

a single performance measure by combining the five performance priorities. This is done by

multiplying performance level of each priority criterion by its priority weight, and dividing by the

sum of priority weights to reach at a weighted average performance as given in equation (1):

— page 56 —

Y=∑ PERF𝑖*IMPORTi

5i=1

∑ IMPORTi5i=1

Where

IMPORTi represents the priority weight of criteria i (cost, speed, dependability, flexibility

and quality).

PERFi denotes the actual performance level of criteria i, and

Y denotes the weighted average of performance from all criteria at the end of the recovery

process.

This approach is analytically sound as it provides a unit dependent variable that also provides a

telescopic view on operational performance. On the other hand, such an approach is disputed for

being less practical.

These last two approaches will be applied all along the following case study and all the simulation

scenarios presented.

The “single” approach is used all along the simulation and sensitivity analysis where the chosen

indicators were initially calibrated on the existing setup operators and the indicators presented as

the different target levels are always considered stand alone

In the conclusion section, we will discuss how the single indicators could be summarized into a

“final” target formula where the different components could be differently weighed to find the best

structure to optimize this composed formula which retraces exactly the Bozarth & Edwards (1997)

quoted composite approach.

Another paper (Berk & Kaše, 2010) tries to solve the valuation problem of applying a flexible system

with trained workers: the approach starts from the mathematical formula created by Black-Scholes

to evaluate the option contracts. The idea is to have some “real” options to evaluate and these real

options are considered as a financial project split in two segments current NPV (net present value)

plus the NPV created or estimated by the new project or option. Considering two samples or

projects, the authors suggests to evaluate the differences between the two environments applying

all the corrections naturally considered by the option formulas that try to put inside the “price” all

the uncertainty, volatility and time discount of financial investments. The idea to compare two

parallel projects to evaluate the “RO(TECH)” and the “RO(HR)” where the first recall the results due

to differences in technology while the second one in Human Resources and training to flexibility in

this case. We will describe in our following sections considers some idea coming from a similar

approach: to compare two simulations on the same system with different degree of flexibility will

give us an incremental or detrimental value for the company applying or non flexible strategy with

its manpower. All the financial aspects described by (Berk & Kaše, 2010) are not developed but the

base idea concerning the differential value could be compared.

A different approach, which is more limited to measure the limits to the extent of flexibility search,

is well explained by (Hemant V. Kher, 1999) in their paper where they study “A numerical analysis

is performed on the LFL model within a DRC system to gain insights into the nature of relationships

between the extent of worker flexibility, forgetting rates, attrition rates, and flexibility acquisition

policies. Results suggest that in the presence of higher attrition and forgetting rates, a worker may

not be able to achieve full efficiency in as little as two different departments. Thus acquiring even

incremental worker flexibility under such conditions may be infeasible”. This area is not covered by

this project but surely it is an interesting area of investigation that could limit the theoretical

advantages we want to measure in a perfect simulation environment: as well expressed in several

— page 57 — “system dynamics” models, most of the “S” shape curves hide non-linear increasing factors at the

base of the growing up curve section as well as decreasing nonlinear factors operating during the

second curve phase. The attrition or forgetting rate, that identify some of the decreasing factors

operating contra flexibility maximization, could limit or completely swipe out the gains that the

simulations presented in the following sections could suggest.

In the same area of investigation we note also the work of (Hottenstein & Bowman, 1998) that

studied sixteen simulations of dual-resources constrained (DRC) searching the use and results

linked to cross-training, worker flexibility, centralization of control, worker assignment rules queue

discipline and transferring costs. Into the “worker’s flexibility” section, they stated “[…] Those

studies where worker flexibility was an experimental variable (Park & Bobrowski, 1989; Fryer, 1973,

1974, 1976; Nelson, 1967), tended to show that worker flexibility had a major impact on the

performance of the system. However, (Park and Bobrowski (1989)) found that when workers had

more than two skills, performance did not significantly improve”. The review made on different

authors finds really interesting results: “In their most recent study, Bobrowski and Park ( 1993)

investigated the case where workers were not perfectly interchangeable. While workers have

multiple skills, they cannot perform each skill equally well. The research generally showed that

worker assignment rules which considered how efficient the worker was, dominated all other rules.

A study by (Malhotra, Fry, Kher, and Donohue (1993)) also challenged the traditional assumption

that flexible workers are equally efficient. The study investigates the impact of worker learning to

acquire normal efficiency in new skills. While workers might have several skills, they need to receive

training and work their way down a learning curve to develop their full potential. Once the full

potential has been developed, a worker never loses it. However, the researchers experimentally

created conditions of attrition where new workers would need to periodically come into the

workforce and be exposed to cross-training.[…]

Based on the research findings, the following propositions are offered:

1.1 Cross-training workers improve the performance of job shop type systems; however, cross-

training beyond two or three skills per worker does not effectively improve the performance of the

system. The simulated systems seldom used the cross-trained workers total array of skills, but,

rather, focused on only two or three of them. Do high tech firms more fully exploit the skill arrays

of cross-trained workers? Ad hoc evidence leads us to believe that they don't.

1.2 Costs associated with worker cross-training, transfer, and information delays effectively reduce

the value of worker flexibility. To fully exploit the value of cross-trained workers, managers of high

tech firms must seek ways to reduce the cost associated with training, transfer, and information

delays.

1.3 Workers need not be perfectly interchangeable for a system to benefit from workforce

flexibility; however, the system will tend to exploit the worker's most efficient skill at the expense

of lesser skills. This proposition raises an interesting issue of whether real systems in high tech firms

behave in a similar fashion and tend to negate the value of cross-training by assigning workers to

jobs requiring their most efficient task and, thus, using them as specialists rather than as flexible

resources”.

These results do not affect our research target but they give us an overview on the limits we could

find pushing the simulation on flexibility over the boundaries described in the following section

imagining a “total flexibility” through the environment studied.

Before the next section which states specifically the objective of this research project, it is needed

to highlight that all this literature review is surely partial and missing most of the study that these

widespread research fields produced in the past and are also producing today.

— page 58 — Memory of the works studied but, even more important, the knowledge of the theoretical and

professional development is today one among the most important issues: to study the large

number of papers and books existing is a full time job. Reading is probably the main task, a never

ending task, of a researcher but also a manager must learn to become a researcher, otherwise his

work could completely misjudge his approach to problems. He could even imagine or simply dream

to be the first one to create or to apply something new. In effect, he could be led by the

environment to taking some decisions, in thinking to be “innovative”, while the same choices are

driven by an already known theoretical or practical context: unknown to or forgotten by the

“sincere and ignorant” manager, this context leads to some “obliged” or “rational” choices. And

the author could be a witness.

— page 59 —

CHAPTER 6

6 Research Approach

6.1 Theoretical Approach

The initial idea was to put in place an Action Research with a continuous loop between the academic

world and the company field.

Starting from the environment restraints and requests, illustrated in the previous section, to define

the trends and customers’ characteristics, the study of the social environment with a solid academic

background was to come down to the field to be tested and evaluated. This second part should have

requested an active participation of the company management (as per the studies of John Heron in

1971 and later Peter Reason and Demi Brown the main idea is a “research with” and not a “research

on” people). This participation was the real difficulty in this project.

Working on the company field needs to obtain a collaborative “mood” and attitude to change from

the main actors: managers and directors. As explained in the previous section, (CHAPTER 3

3 Research Limitations), the environment was contrary to any changes and the real risk for this

project was to obtain some mistaken results and measures by enforcing a tested changing program.

Last but not least a negative environment could have delayed the research in several ways (Unions

discussion, technical restrains, negative customers advice… for example).

With all these restraints and limitations, the “simulation” approach was preferred.

6.2 Methodological Approach

The simulation environment and system has to define

the system boundaries and

its internal rules

To build a simulation environment capable of giving us:

the estimate results in some scenarios

the differences on some target variable due to changes on some operational levers

The last passage, after the theoretical validation, is or will be the search of a field confirmation of

the results. The feed-back could generate either the confirmation of the simulation environment or

the parameters modification (in an extreme ratio even the model re-drawing). This last passage

could come back to the “Action Research” plan discussed into the previous section: but this part is

supported by some measure of the expected targeted results.

— page 60 —

6.2.1 Simulation

In this context the “simulation” tool is considered as a mix between the simple application and a

more general model of investigation. It is to say that the interest is a simple participating of results

and model drawing itself.

Applied to human or social environment that we couldn’t merely consider “target” matters studied

as a “technical”, “scientific” or simply “rigid” defined system, the simulation runs let us evaluate the

effect on some variables due to the variations in other inputs: it also lets us to modify the variables

considered or the system boundaries, if the simulation results suggest some model “lack” of

“completeness”.

This last sentence could be reviewed as an Action Research program based on a simulation

environment, which moves in depth according to the satisfaction originated by the results. This is

also a limit of the system: based on the “positive” proposition of the variables and cause-effect links

among them, it is difficult to affirm that all the links are considered. Sometimes, especially in a social

system, links always are not significant in the past and could become important or significant due

to system conditions that have never been verified before. It’s the Karl Popper falsification or

problem of induction: only the negative “falsification” is sure, the positive affirmation based on the

past conditions could simply have never known all the relationships of the system and have

deducted the results on partial causes-effects pictures (Popper, 1959 - 1992).

The “thinking” on the system, with the possible mapping of the relationships, relevant or not, is

important knowledge as well as the simulation sensitivity results or measurements.

The approach herewith outlined is in line with the Jay Forrester studies in System Dynamics,

especially all the parts covering the industrial and economics dynamics. These studies were

developed at MIT by the group led initially by Prof Jay Forrester (Forrester, Industrial dynamic, 1961)

(Forrester, Principles of Systems, 2nd edition, 1968) and at a second time by Prof John Sterman

(2000. Business Dynamics: Systems thinking and modelling for a complex world. McGraw Hill) and

others (Prof Nelson Reppening (Rudolph & Repenning, 2002), Prof Peter Senge (Senge, 1990 - 2006),

…).

6.2.1.1 Why?

In this section we consider some additional reasons that encourage us to use a simulation approach

further than the inability to “test” the hypothesis in the real world without tough feed-backs (and

sometimes the impossibility to check the action-results loop because the “action” itself changes the

test environment). This limit, as already stated, is often present in social environment where the

“student” has some effect on the “studied matters”. This situation could change the environment:

the “student” could be part of the environment itself and this participation could have distortion

effects in the field. A simulation approach tries to stay more “outside” or, at least, “to the side” of

the field, even if this attempt could also have a direct or, more frequently, indirect effect.

Quoting Herbert A. Simon we could say that we cannot rewind time and try an alternative strategy;

but a simulation can!

First of all, the modelling phase of the work, according to the way of working suggested by Prof

Forrester and Prof Sterman, quoted in previous section, permits to consider all the effects that could

influence the analysed system, or, at least, all the effects we could imagine or identify. The model

used for the simulation runs is surely a “limited draw” of the real environment, nevertheless, the

— page 61 — “brainstorming” phase pushes to make some choices among many variables: these are choices not

simply an elimination of variables. We know that these choices could limit the model if the existing

conditions change but we consider them important for the actual relationship picture. Some

variables, not relevant today, could be more important tomorrow but we have also to know that

some variables could have a “long term” effect, difficult to measure today among many other cause-

effect relationships characterized by shorter term effects inherent and relevant.

Even if the “simplification” during the modelling brainstorm pressures to question ourselves on the

existing cause-effect relationships and their “weight” inside the model, some relationships excluded

could maintain and show in a second time feed-backs and loop effects on the model variables. These

loops could be difficult to define in a mathematical system while they could be easier to be drawn

in a simplified simulation environment, where the conditions at the outset could be changed.

Some of the variables could not be measured: in a static system, this could be a problem because

the system or the model would not work. In a simulation environment, and we used this

opportunity, the variables could be estimated on the basis of other already known or estimated

indicators. The searched coefficients could be projected adapting the same coefficient to align some

model results to the existing and measured or real KPI (that are not those under search, naturally).

A simulation environment, working with different viewpoints, could explore several levers, alone or

together. The sensitivity, complex sensitivity with different variation ranges, let us appreciate the

model complexity due to simple single relationships which are very difficult to estimate when each

“force” operates independently and concurrently.

When the “time”, and its flow, is important because, for example, it is impossible to re-build the

initial conditions, a suitable simulation environment let us weigh the effects of some decisions or

levers, setting them far from the “initial” conditions, trying to assess the effects over the simulation

time schedule. Otherwise, some “wave” or “exponential” effects could be difficult to understand.

6.2.1.2 Context

In the following (Figure 22), we could review the simple value chain useful to describe the luxury

fragrance market which ICR operates in.

The “light blue” arrows describe the value chain steps directly taken in charge by the ICR partners:

in this market they are active such as suppliers as well as customers of the “service” operator, ICR.

In the past, some of the “light blue” steps were also “covered” by ICR, either as additional services

provided to the partners or directly made internally as “owner” of the brand name only for the

fragrance sector.

— page 62 — Figure 22 Supply Chain and Simulation Environment

The “light green” steps, Research and Logistic, are not covered by the simulation environment.

The “green” step, Production in a general way, is the simulated context: please note that, in a real

environment, inside the production department the first “stage” is the “bulk preparation”, which is

not deeply described by the simulation.

The external suppliers could provide the same services as ICR but they are only considered in some

scenarios (the “actual” ones), where the quantities are split according to the real situation recorded.

No other consideration is made: the external quantities simply reduce the input data and they are

not considered as a basis for calibration, simulation or sensitivity analysis.

The model had inside some parts covering also the bulk preparation step as well as a series of

“indirect” production services (quality control, handlers, dept. assistants and managers, G&A …) but

these were not used inside the analysis described.

6.2.1.3 Structure

Environment Description

Every “green” box of the (Figure 22 Supply Chain and Simulation Environment) has to be modelled

and simplified as shown in the scheme (Figure 23 ICR industrial phase scheme)

Every production phase analysed has been considered in the same way:

The quantities of products (per product families and customer) are the input

The resources available are the hours of each time batch (function of the working days

available, working shifts possible and hours per shift)

The control levers are:

o The working rate and the working team of the typical line of each phase (based on

historical data per product family and customer)

o The setup rate/time and setup team of the typical line of each phase (based on

historical data per product family and customer)

— page 63 —

o Planning Accuracy is a “dummy” model variable used to evaluate the “correction”

effect applied on the “concurrent rate” and “time batch” reduction coefficients

o Concurrent Rate is a measure of the concentration of work inside the time batch

which is qualified in operations requested at the same time or inputs not equally

transmitted inside the batch time

The output measured is:

o The number and cost of the production lines required

o The number and the cost of the “full time equivalent”, people, required either in

terms of workforce or setup operators

o The technical indicators useful to measure the level of service provided:

Technical process flow time

Process frozen time, which considers also the preparation operations

Total frozen time, which considers also the bulk preparation time (not

covered by the simulation model but considered as a fix input)

Figure 23 ICR industrial phase scheme

Model entities description

The model considers different “types” of data: the “real” one charges inside the model all the data

recorded by the company system. Not all the data were available in the past and some of them

were not (or are not, even today) accurate but, combining some of the real data sets, it is possible

to find a “calibration” of the main parameters.

Two other sets of data are considered: “Theo” and “Simulation”. The first one is used for all the

scenarios described; the last one was due to combine real data with a simulated one in a mix

scenario initially imagined as the right way to combine the past (with outsourced activities) with the

future (with all the activities made internally).

Given the final quantities per product family and customer as well as the production phase

quantities, it was possible, for example, to calculate the best fitting % of quantity worked in each

phase calculated on the final quantity sold: this parameter is the base to forecast the industrial

activity on future periods, given some “base” information on sales budget received by every single

customer.

— page 64 — In fact, the model, covering the past years, tries to project the data into the future: as a basis for

future evaluation, the trends recorded in the past are considered useful and reliable, a working

“base”. The random series described in the previous section (Market and Industrial Data) are the

basis for future estimation.

As per the input data (quantities), already described, most of the “control levers” were confronted

with real data: the availability of data concerning the working time per line, customer and product

as well as the team or the setup time and team… let us validate the average base values used as

control levers. Only the “planning accuracy” is an internal, dummy, variable used to simulate and

evaluate the effect that a “proactive” planning dept. could have on the other variables (concurrent

rate, % reduction in time batch …)

Concerning the “concurrent rate” a different approach was used: once confirmed the technical data

and the quantities in input, we used the model to calibrate this parameter to obtain an estimate of

the people involved in the setup operations equal to real people recorded by the company over the

months. This is an indirect method which does not consider the inefficiency inherent to the real

system. It could evaluate the “concurrent rate” as function of the setup resources available; the

hypothesis is: “if we had these people, we need them … so the concurrent operations exist and they

require more setup operators: which is the concurrent rate that implies an equivalence between

real people recorded and model results?”

This approach was also used to evaluate the % of reduction in days (and consequently in hours

available) concerning the resources modelled for each production phase: the “Batch Time Hours

available” considers initially the theoretical (working days) of every batch time (month) times the

(shift hours); the model calibrates also the reduction % of the time availability in terms of working

days to obtain an equivalence between the people recorded and people modelled. In some way the

reduction of the batch time and the concurrent rate were considered as similar in their effects and

mutually exclusive

The “Flexibility rate” showed among the “control levers” is set up as a series of parameters that let

the model simulate:

- the number of production lines if the same lines could or could not serve different

customers, different product families or different production phases

- the number of FTE if the same FTE could or could not work or make setup operations for

different customers, different product families or different production phases

- the number of FTE if the same FTE could or could not move across different specialization

(setup operation and working duties)

Finally, the outputs searched; they are a set of indicators:

the production line theoretical number and cost,

the worker and setup theoretical operator “equivalent” number and cost,

the process flow time per customer or product family, the process frozen time per customer

or product family and the total frozen time per customer or product family

Briefly the model has to simulate the number of production lines requested by the theoretical

parameters applied to the input data received and this number is linked also to the time

organization applicable (one, two or three shifts). This number could be compared to the real one.

Concerning the full time equivalents, workers or setup operators, the model recalculates the needs

— page 65 — considering the simulated lines, the requested hours by the different operations (setups and

working) and the teams applied to the operations. As per the production lines, the simulation results

are to be compared to the people in fact available. Finally, the technical indicators are calculated by

the system considering all data described and the input mix provided.

Model validation

Some sets of hypothesis grouped in some scenarios were used to validate the model. We will

describe the choices made in the following paragraph but we could say that all the “base” scenarios

were used to calibrate the model parameters to obtain “key” results comparable with the real data

as time went by.

Given the set of quantities, total and per production phase, customer and product family, actuallly

produced as an input to the model, given the average working and setup rate as well as the average

teams for each operation (detailed per production phase, customer and product family) as control

levers, given the real batch time over the months passed, the model provides a number of detailed

lines per phase, customer, product family and each batch time. The number of calculated lines, in

detail and/or aggregation, per phase with or without customer and/or product family, was used to

check the model results in comparison with the real number of production lines recorded by the

company. For the lines, the result was a proxy because all along the time the production flow was

organized with a mix of one, two or three shifts as a function of the quantities demanded by

customers. Graphically the line representing the installed real lines was between the one to three

shifts channel of requested lines calculated by the model.

Concerning the manpower requested by the simulations, the model calculates a number of full time

equivalent (“FTE”), considering the average teams and the hours (working and setup hours)

simulated applying the technical process rates but also considering some reduction in the time batch

due to time reduction per shift (shifts have a reduced real working time compared to daily normal

working time) and productivity reduction rate per shift (the night shift does not have the same

working rate as the daily ones). The FTE were compared to people hired, aware that positive

differences were acceptable if covered or partially covered by temporary workers while negative

ones could have different explanations: real inefficiency compared to the model parameters (the

model calibration could adjust the parameter level on the trend line or average level but not to

every single batch time value), vacation or absence concerning the real people employed. This

approach was used on workers and separately on setup operators.

Due to the differences among the model simulated FTE and the real people recorded by the

company, two different scenarios were put in place: in the first scenario, the cause of the differences

was considered to be the “concurrent” rate among the single production runs. When the model

simulates the best fitted hours, it considers that they are perfectly distributed inside every single

batch time (people or line works for one order and the second order is in queue waiting) but real

situations could request that the orders have to be worked in parallel: it is just like saying that the

time batch is reduced and we need a second line and a second team to fulfil the target. The input

data didn’t provide such information that was difficult to record, because it implies, at least, the

analysis of every single production order in terms of start and stop time but, probably, it needs also

the analysis of customers restrains in terms of timing. The scenario wants to deduct this

“concurrent” ratio adjusting the FTE simulated to the real people recorded using a parameter that

considers the possible order overlapping at different levels (customer, product family, production

phase).

— page 66 —

The second scenario considers a reduction of the available time inside every single batch time: the

ratio is due to the inflow timing of the orders coming from the customers’ purchase department. If

the orders are not equally distributed inside the batch time (the available working hours of each

month) the real situation is equivalent to a reduction of the available time inside the batch. If the

customers send the orders mostly during the second part of the month, the hours lost during the

first part of the same month are not recoverable, while the concurrent rate pushes the system to

consider a parallel working and, consequently, a double resources availability: the reduction of

hours in the batch time is due to the customer external transmission planning.

The results of the two previous scenarios are apparently similar but the first one implies that a better

scheduling could mitigate or erase the distortion effect while the second one, due to external

factors, is not possible to be reduced without tough work through the supply chain.

As always, probably, the real situation is a mix of the two scenarios but the real data do not let us

evaluate the precise measure of each one: we could estimate two extreme scenarios, mutually self-

excluding, in a separate simulation calibrating the results related to FTE versus the real people

employed. We obtain in this manner two sets of parameters, self-excluding, which represent the

two extreme limits: they could be combined, always considering these extreme values and using a

mix of value link together in an inversely proportional relationship.

Scenarios Hypothesis

The set of studied scenarios could be divided into two main groups: “base”, as already described in

previous paragraph, is the set of hypothesis trying to replicate the real conditions registered over

the last years; “all intern” is a set of hypothesis which tries to consider all the quantities as if they

were made and will be made only internally.

— page 67 — Table 11 Scenarios Setup Hypothesis

Scenario Name-> Base ActualBase

Optimum

Base Actual

reduction

days

Base

Optimum

reduction

days

Base Actual

Concurrent

All Intern

Base

All Intern

Base

Optimum

All Intern

Reduction

Days

Optimum

All Intern

Reduction

Days

All Intern

Reduction

Days,

concurrent,

flex

Activity (quantity) distribution among INT, Ext and Coop REAL REAL REAL REAL REAL SIM all int SIM all int SIM all int SIM all int SIM all int

Flex Manpower between specialization (set-ups vs

operating activities)NO YES NO YES NO NO YES YES NO NO

Flex Coop Manpower between specialization (set-ups vs

operating activities)NO YES NO YES NO NO YES YES NO NO

Planning Accuracy "Perfect" "Perfect" "Perfect" "Perfect" NO - 0 "Perfect" "Perfect" "Perfect" "Perfect" 0,50

Ops Flex per:

- Customer YES - Total - 1 YES - Total - 1

- Product familyYES - On real

families - 1

YES - On real

families - 1

- MacrophaseNO - 0 on RIE,

YES - 1 Other

NO - 0 on RIE,

YES - 1 Other

Set-up Flex per:



families - 1

YES - On real

families - 1

- Macrophase YES - Total - 1 YES - Total - 1

Line Flex per:



families - 1

YES - On real

families - 1

- Macrophase NO - 0 NO - 0

Coop Ops Flex per:



families - 1

YES - On real

families - 1


Coop Set-up Flex per:



families - 1

YES - On real

families - 1


Coop Line Flex per:



families - 1

YES - On real

families - 1

- Macrophase NO - 0 NO - 0

N.A. N.A. N.A. N.A. N.A.N.A. N.A. N.A.

The “base” set considers a production process distributed as per (Figure 12 Industrial process

description) or (Figure 22 Supply Chain and Simulation Environment): the ICR history well represents

an ideal entrepreneurial Italian company with a structure constantly searching to mix efficiency and

flexibility through the “internal-outsourcing-external” calibration. The mix “internal-outsourcing-

external” tried to compose a supply-chain configuration useful for the customer: it maintains all

core activity under the direct control of the company, with an outsourced activity covering most of

the “handcraft” operations, absorbing all or most of the “labour intensive” industrial steps; external

suppliers were used to recover extraordinary “peaks” of orders, committed by customers in some

periods (typically quarterly ends, fiscal year closure, …) as well as productions totally assigned

externally to “specialist” companies or to industrial partners able to support and cover the high

demand volatility. This was the typical configuration adopted by Italian medium or small companies

since the end of WWII; it let the typical private entrepreneurial company operate inside the “rigid”

Italian manpower context with the desired flexibility level: the level necessary to compete and to

survive in internal and export markets.

The “base” set considers the historical splitting of quantities among the three different activity lines:

internal, outsourced and external. This “partition” is maintained fixed until February, 2015 when ICR

decided to “internalize” the previous “outsourced” activity. This hypothesis is necessary to compare

the model results, in terms of installed lines, working FTE and setup FTE, with the number of lines

and workers recorded by the official company data.

The “base actual” scenario is trying to replicate the historical situation: quantities committed as per

historical records among internal, outsourced and external activities, lines and people (workers and

setup operators) with no manpower flexibility across the two specializations (setup and “normal”

— page 68 — working operations) either for internal or for outsourced activities. Concerning the flexibility among

customers or product family or industrial macro-phases (always split between internal and external

flows and always maintaining the parting between setup and working operations), the scenario

considers that the hypothesis of a “perfect” planning activity could eliminate all the effects due to

a real partial or no flexibility inside each technical partition (always considering the equipment,

setup operators and workers separately): the idea is due to the possibility that a “perfect” planning

could provide a time table which lets the people and equipment to be engaged or utilized even in

part time positions as per planning requests. No inefficiency, on employees or lines utilization, is

considered if the planning department is working perfectly.

In this scenario the planning dept. is able to correct also the distortion due to customer orders

transmission concentration: the reduction of time inside the time batch is completely eliminated by

the planners’ ability to re-arrange the scheduling inside each single time batch.

The “base actual” scenario calculates the number of lines and FTE required as the integer of the final

sum of every partial request at customer, product family and phase level. The “discrete” rounding

is really important.

The “base optimum” scenario starts on the basis of the “base actual” one, but it wants to review

the best solution that the model could obtain if the employees were able to operate across the

different operations: setup and general work total flexibility.

Reviewing (Table 11 Scenarios Setup Hypothesis) and (Table 12 Scenarios Setup Hypothesis 2nd

part), it is clear that the input quantities reflect the historical data as all the other technical

parameters like average line speed, setup time, teams… All the control levers applied for the “base

actual” scenario are valid, apart from the two “flexibility … between specialization” parameters

(either for Outsourced/Coop or Internal).

This scenario will consider the FTE all together across the different operations (setup and general

work) with the idea that the preparation and cleaning ops on the production lines could be

completed by everybody.

The difference between the “base actual” and the “base optimum”, in terms of FTE, could provide

a measure of the “lost” gain that the activity could have obtained if the employees as well as the

external operators had more skills.

The “base actual reduction days” is the scenario were the % of reduction inside every batch time is

recalculated to obtain a number of FTE requested by setup operations in line with the real recorded

numbers.

All the other parameters are the same of the “base actual” scenario, with exception of the “perfect”

effect of the “planning accuracy” on the concentration of customers’ orders inside the batch time.

The real number of setup operators was considered the target to measure because it reflects the

real sources needed by lines preparation, while the higher number of general operators are

functions of more different characteristics (setup operations number is generally function of

quantities and runs size, while the general operations are also dependent of the production phase

considered, the team requested by product, …)

As per (Table 12 Scenarios Setup Hypothesis 2nd part), the “effect of planning” on the parameter

“reduction days” is forced to NO just to underline that this scenario considers the request by

company customers as “not modifiable”.

This is a “calibration” scenario were the % of reduction, always the same, to be applied at every

batch time, is recalculated to obtain the average best fit between setup operators simulated and

officially recorded.

— page 69 —

The “base optimum reduction days” is the scenario where the previous one accepts the hypothesis

of the two “flexibility … between specializations” parameters maximized (either for

Outsourced/Coop or Internal).

Fixing either the “base actual reduction days” scenario parameters or the % of reduction to be

applied at every batch time found in this previous scenario, the model can calculate the effect of

the perfect flexibility through the specializations.

The last scenario considered in the “base” set is the “base actual concurrent”: this last one wants to

test the model configuring a new set of philosophical hypothesis much more real than the previous

scenarios ones.

The input quantities are always the real ones as per all the “base” scenarios; the two “flexibility …

between specialization” parameters (either for outsourced/Coop or internal) are set up to zero in

order to reflect the reality, where the setup operators do not operate as unspecialized workers.

The control lever “planning accuracy” is set to zero just to eliminate the effect that the planning

ability could mitigate or eliminate the “concurrent” simulated effect.

Concerning the flexibility among customers, product family or industrial macro-phases (always split

between internal, outsourced and external flows; always maintaining also the difference between

setup and working operations), this scenario considers these parameters trying to reflect the reality:

a total flexibility through customers, some flexibility concerning the product families and some

flexibility through the industrial macro-phases (several different considerations and corresponding

values as function of the lines, setup operators or, again, general workers flexibility).

As per (Table 12 Scenarios Setup Hypothesis 2nd part), the “time concentration rate” found into the

scenario “base actual reduction days” is not considered: the batch time is always the theoretical

one.

The scenario tries to find the best fit parameters concerning the % of concurrent activities, analysed

per customers, product families and macro-phase to adjust the simulated setup operators to the

real recorded ones. This target parameter is composed of two parts: the first one is internal to the

model, the second one is an adjustment we need to calibrate, to search the best fit value for the

setup operators. The first one is linked to the working time available inside the batch time. When

the equipment set up time requested for the level analysed (customer, product family or macro-

phase) divided by the max time available for an average line is one or a multiple of one the

concurrent rate is at least equal to the integer part of this “ratio-1”. The second part of the

parameter is a “dummy” value searched to obtain the best fit simulation.

Even if the first part is technically calculated by the model and implicitly inserted into the FTEs or

LTEs (“line technical equivalent”), the idea is “more naturally concurrent operation, more confusion

is possible, more the effect on the system in terms of available equipment or people’s request.

The second part takes into account everything else, even a high level of inefficiency, to reach the

real number targeted (setup operators enrolled).

— page 70 — Table 12 Scenarios Setup Hypothesis 2nd part


Optimum

Base Actual

reduction

days

Base

Optimum

reduction

days

Base Actual

Concurrent

All Intern

Base

All Intern

Base

Optimum

All Intern

Reduction

Days

Optimum

All Intern

Reduction

Days

All Intern

Reduction

Days,

concurrent,

flex

Ops "Concurrent" rate per:

- Customer NO - 0 NO - 0


families - 1

YES - On real

families - 1

- Macrophase

YES - On real

macrophases -

1

YES - On real

macrophases -

1

Line "Concurrent" rate per:



families - 1

YES - On real

families - 1

- Macrophase

YES - On real

macrophases -

1

YES - On real

macrophases -

1

Set-up "Concurrent" rate per:



families - 1

YES - On real

families - 1

- Macrophase

YES - On real

macrophases -

1

YES - On real

macrophases -

1

Coop Ops "Concurrent" rate per:



families - 1

YES - On real

families - 1

- Macrophase

YES - On real

macrophases -

1

YES - On real

macrophases -

1

Coop Line "Concurrent" rate per:



families - 1

YES - On real

families - 1

- Macrophase

YES - On real

macrophases -

1

YES - On real

macrophases -

1

Coop Set-up "Concurrent" rate per:



families - 1

YES - On real

families - 1

- Macrophase

YES - On real

macrophases -

1

YES - On real

macrophases -

1

Time "concentration" rate NO NO YES YES NO NO NO YES YES YES

effect of Planning Accuracy on Time "concentration" rate NO NO NO NO NO NO NO NO NO YES

N.A.N.A. N.A. N.A. N.A.N.A. N.A. N.A.

The second set of scenarios, the “All Intern”, is useful for many reasons.

Above all, while the “base” set let us validate the model, proving the model results with the real

recorded data, when and if they are available, the set “all intern” is more up-to-date: since February

2015 the outsourced activity was reduced to zero, internalizing it and enrolling 212 workers; all the

simulations have to consider the input series since then as completely produced internally. A series

of data re-simulating a complete internal activity since the beginning, let us appreciate the trend of

all the variables all along the entire time horizon. We lose the reality checking, that let us validate

the parameters, but it is likely we obtain a better view of the system.

A second point to be considered is due to the differences between the internal activities and the

outsourced ones: similar in technical description, they were finalized in a completely different social

and organizational environment. Working rate for handcraft operations was really different: higher

in outsourced than internal process. Nevertheless the people could not be mixed. Even in a perfect

simulation environment, where the flexibility through the specializations could be imagined, some

inefficiency rate in resources allocation, it is logically present: the model considers that the final

calculation, once it has weighted all the control levers according to the scenario chosen, has to

provide an “integer” number of FTE. This “integer” condition is linked to the real labour market rules

and it allows to imagine a use of a “fractional” FTE only in very special situations (call centres and

similar).

— page 71 — A third consideration, opposite to the second one here above mentioned, but in some way strictly

linked to this one, is: internal production process could be set up or at least imagined as a

“continuous” flow, while the outsourced one, at least for one phase, the first one, “filling”, (see

(Figure 12 Industrial process description) or (Figure 23 ICR industrial phase scheme)) which is always

completely either internal or external in our model, has to consider some kind of “lag” time and,

consequently, extra operations in terms of managing the semi-finished products between the

internal and the outsourced phases. In a “perfect” environment it could be possible to imagine a

process flow impeccably “timed”, just to obtain an outsourced start time for the activity/step/phase

right in line with the first semi-finished product filled by the internal phase, but real tests made on

the field confirmed the difficulties to operate this synchronism. The two different teams, internal

and outsourced, could not be mixed and the instructions have to be handed in an “official” way

which does not help to quickly adjust the two linked phases. Moreover, the different working rate

registered by the two, internal and outsourced, environments obliges either to build some extra

semi-finished product inter-phase inventory, to eliminate frequent stops for lack of input materials

into the second phase, the outsourced and the fastest in terms of working rate, or to reduce the

working rate of the “speediest” phase in order to obtain a balanced process flow. This last option

was tested on the field by the company over the last year, just before the decision to internalize the

outsourced activities (February, 2015): it was very difficult to manage the “balance” of the two

phases even in a technical environment where the two linked phases, internal filling and outsourced

packaging, were physically connected (to avoid extra people delivering the products between the

two phases). The target of the outsourced supplier is, often, to use all the available workers,

maximizing the output, which is the basis of his revenue: even if the workers could be allocated to

other productions, the request to align teams and working rates to the new “logical” internal-

outsourced integrated line, is seen as a “lost” revenue by the management of the outsourced

supplier. Our model considers different working rates between the two different environments

(internal and outsourced) as well as the possibility to create a set of “linked” phases (with fewer

workers but slower working rates) ideally only in the internal environment.

If the previous consideration is valid for the outsourced activities, which are, in our model,

committed to external suppliers operating inside the company facilities, even more, the same

consideration is valid for the model “external suppliers”, which accept the outsourced committed

activities and operate far from the company facilities: in this case, it is impossible to consider a

“technical” linking between the equipment (internal-external) but also a “soft” linking, because the

distance between the facilities implies a time lag and some kind of other operations and/or

bureaucracy (the delivery of products need some shipment preparation and official documents for

transport). According to (Figure 22 Supply Chain and Simulation Environment), the external activity

covers all the phases, starting from the bulk preparation and ending with the coding activity, but

the model, like the real life, has to consider this “production process” like a “black box”: the external

supplier fulfils the request for a fee but he does not really share all the information or the efficiency

gains with his customer; only some kind of “heavy” pressure on the entire supply chain could push

to a real “integration” between internal and external/outsourced actors and this is not the case of

the environment described.

Setting some scenarios exploiting a new “philosophy” – soft, planning linking, or “technical”, hard

or equipment linked or continuous line -, it is valuable only in an “all internal” activity.

— page 72 — Among the “all intern” set of scenarios, it is possible to find a “perfect” match with the “base” set

already described.

The “all intern base” considers the same hypothesis and parameters put in place for the “base

actual” scenario and modifies only the quantities, considered as made completely internally.

The “all intern optimum” recovers the “base optimum”, always with all quantities made internally.

The “all intern reduction days” and the “all intern reduction day optimum” copy the parameters sets

from the scenarios “base actual reduction days” and “base optimum reduction days”, even the best

fit parameters estimated inside these last scenarios (% of reduction inside every time batch to fit

the setup operators calculated by the model on the real recorded number), to simulate how the

target measures moves with a complete internal production.

Finally, the “all intern, reduction days, concurrent and flex” scenario tries to simulate a “real”

situation, always with the idea that all the quantities were made internally.

This scenario is not a strict transformation of the previous one “base actual concurrent”, with only

a difference in the input data, which instead of the real historical quantities considers all the

quantities as made completely internally, because it tries to consider a different mix useful to reflect

the real situation.

Applying an effect of “planning accuracy” parameter equal to 50%, we could appreciate a new mix

of the parameters, independently measured into the previous “base” scenarios: “time

concentration reduction”, operations – setup – equipment “concurrent” and “flex” (only inside each

specialization: setup and normal production or general working), while the “flexibility” through the

specialization is set up to zero.

The “time concentration reduction rate” as well as the “concurrent rate” (measured as already

described at customer, product family or macro-phase level) and the “process flexibility rate”

(measured itself at the same level of the concurrent rate through customers, product families or

macro-phases), were put in the model on the basis of the real data (process flexibility) or the

recalculated values of other scenarios (% reduction day into the “base actual reduction days” and %

concurrent into the “base actual concurrent” scenario).

In this set of hypothesis, we try to find an effect on Lines, FTE and technical indicators (flow and

frozen time) due to a set of parameters which try to reflect the normal or real working situation,

always considering that all the quantities were, are and will be committed internally.

The choice at the base of this set of scenarios, in terms of parameters, could be very wide and the

hypothesis that could be tested is broad as well. But our primary goal is to measure the effect of the

flexibility through the specialization and the ability to use soft skills, as the planning accuracy,

choosing the main target variables. Both of these measures could be seen as primary indicators of

efficacy, in markets scaling up versus new “service” levels from the original simple products driven

ones: most of the Italian well known products are moving to an upper level of service integrated

inside the product. Tomorrow, probably, the service level provided inside the product will be greater

than the physical or material value of the same item.

In the next section we will present the results of the scenarios described. We could also imagine a

set of interesting sensitivity analysis based on the scenarios provided.

— page 73 —

Scenarios Results

In this section, we would provide the results of the simulations. These results could be discussed in

the following section because they could have a different conceptual value depending on the cause-

effect considerations that they could motivate.

They could be divided into two main groups: economic and technical targets.

Table 13 through Table 16 present the economics for the different scenarios, considering that the

activity could be organized in 1 to 3 shifts per day.

Why we have to consider this extra category? Because the organization of the production process

has to consider different working limits and or parameters, if the process flow needs from one to

three shifts per day or if the management decides to apply a different organizational scheme.

One shift per day, for example, according to the Italian National Labour Contract, Chemical sector

(integrated at company level), contemplates 8 working hours per day while 2 or 3 shifts per day

provide a reduction in time per shift to 7,30 hours. Even more, the reality checked applied a three

shift per day in some extreme or special situations (special customer requests for high priority

products, in some special new launch time with problems concerning the materials provided directly

by customers themselves) provided a clear indication of efficiency reduction during the third shift,

the night one, especially if not the whole plant is working with the same organization. The model

considers a 10% of efficiency loss in working with the third shift.

Table 13 Scenarios Result per one shift


Optimum

Base Actual

reduction

days

Base

Optimum

reduction

days

Base Actual

Concurrent

All Intern

Base

All Intern

Base

Optimum

All Intern

Reduction

Days

Optimum

All Intern

Reduction

Days

All Intern

Reduction

Days,

concurrent,

flex

One Shift per Day

Full Time Equivalent (Sum of Monthly Data) 24.530 23.790 41.516 40.793 35.243 29.414 28.842 44.080 44.657 44.684

Internal Full Time Equivalent (Sum of Monthly Data) 14.206 13.627 21.397 20.827 20.361 29.414 28.842 44.080 44.657 44.684

Cost of Full Time Equivalent (Sum of Monthly Data) 61,32 59,46 103,79 101,97 88,10 73,53 72,10 110,20 111,64 111,71

Cost of Internal Full Time Equivalent (Sum of Monthly Data) Mln EUR 35,51 34,06 53,49 52,06 50,90 73,53 72,10 110,20 111,64 111,71

Full Time Line Equivalent Installed (estimate):

- Filling 37 37 55 55 39 43 43 62 62 50

- Packaging 20 20 29 29 21 28 28 42 42 35

- Wrapping 13 13 20 20 13 18 18 27 27 22

- Code Writing 9 9 13 13 9 10 10 15 15 12

Space requested by Full Time Line Equivalent Installed (estimate) m2 3.945 3.945 5.861 5.861 4.114 5.015 5.015 7.382 7.382 6.030

Cost of Full Time Line Equivalent Installed (estimate) Mln EUR 32,05 32,05 47,95 47,95 33,25 40,20 40,20 59,05 59,05 48,00

The Table 13 here-above presents the economics of every scenarios with the “one shift”

organization put in place.

First of all, according to the outputs description provided in the previous section “Model entities

description”, we find the estimation of the FTE requested by each simulation run. In order to obtain

a set of information useful to compare the two main simulations groups described in the previous

paragraph, “base” and “all intern”, the FTE calculated are presented from two different viewpoints:

the internal FTE, only the FTE simulated for “internal” process, and the more general FTE, that are

grouping internal and outsourced FTE calculated by the model. Even if the trends of the two

indicators could be analysed and presented over the 120 months of the simulation period (this

detailed review underlines the effect on the production line due to customer and product family

mix), we need a “significant” summary indicator for each scenario: the sum of the monthly FTE,

either internally or total (sum of the internal plus the outsourced), is considered valid.

— page 74 —

The “base” set scenarios present a significant difference between internal and total FTE:

using the historical distribution charge between internal and outsourced activities, most of

the packaging, wrapping and coding production steps are committed to outsourced

suppliers. The “base actual” scenario presents 14.2 versus 24.5 thousand “monthly FTE” as

internal and total FTE required by the simulation: 58% of the FTE simulated are “internal”;

this measure reflects the magnitude that the “outsourced” activity represented over the past

years (please note that today the model considers all the activities made internally: at least

three years over the total of ten are forced to 100% internally, with an average of 58% on

the 10 years; consequently, the outsourced activity up to the beginning of 2015 was higher

than the 58% calculated as average).

The cost of the FTE, either internal or total, is calculated applying an average monthly salary

recorded by the company: to compare the values, all the FTE are evaluated at the internal

cost rate. With this hypothesis we could appreciate the differences with the other scenarios

in terms of efficiency (not caused by the “price” of the resources). In past years, the hourly

cost rate of the outsourced workers was at maximum 15% less than the internal cost: over

the years this difference was reduced almost to zero.

The “base optimum” scenario with the perfect flexibility through the two specializations

presents internal monthly FTE at 13.6 thousand versus the 23.8 thousand total FTE. The % of

internal on the total is in line with the “base actual” scenario but more interesting it is to

note the difference either total or internal FTE between the two scenarios (“base actual” and

“base optimum”): 0.58 thousand monthly FTE concerning the internal figures and 0.74

thousand for total FTE. In terms of cost, always calculated with the internal cost rate, we

note a 1.9 million of difference between the total FTE of the “base actual” versus the same

FTE of the “base optimum”. This difference is the “gain” that using the same people for both

setup and normal working activities could provide over the 10 years’ simulation. If we

consider only the internal FTE the “gain” is reduced to 1.45 million euro. These economic

values represent a 4% of internal cost reduction and a 3% of gain on the total cost.

The other three “base” scenarios, as described into the previous paragraph, (Scenarios

Hypothesis), consider the target “indicator”, searching the best fit in the setup people

changing the “control levers” relating to the reduction of time (inside the batch time) or the

concurrent rate to be applied at the activity level. Once found the best fit on the targeted

parameter, the FTE provided by the “base actual reduction day” scenario is 21.4 for internal

KFTE (where KFTE is thousands of FTE) and 41.5 , while the base optimum reduction days” is

40.8 KFTE and 20.8 KFT and , finally, the “base actual concurrent” is 35.2 KFTE and 20.4 KFTE.

It is interesting to note that the model considers always as a “gain” between actual and

optimum scenarios the part of the FTE in setup operations transformed in “integer” people,

according to the rules described: 0.72 in total FTE and 0.57 in internal FTE. These values are

in absolute value similar to the estimate made here-above and relating to the difference

between “base actual” and “base optimum” scenarios. The KFTE, internal or total, are

completely different: the best fit calibration finds a % reduction inside the single batch time

that is important. This situation tries to reach a reality with the average reduction in time to

adjust real setup operators to the model estimation.

The “base actual concurrent” utilizes the same approach here-above described for the “base

actual reduction days” but the parameters touched by the calibration are more and the

— page 75 —

estimate is surely more realistic. While the “reduction days” lever is generally applicable to

all the production over the simulation periods and indifferently applied to all the customers,

product families, macro-phases, internal or outsourced flow, the concurrent rate are

calibrated on different levers that consider the customer, product families and macro-

phases.

As already described into the previous sections about the (Model validation) and the

(Scenarios Hypothesis), the “real” situation is probably in the middle, “in medio stat virtus”,

somewhere, between the results concerning the “base actual reduction days” and the “base

actual concurrent” scenarios. The “base optimum reduction days” is presented only to

underline again the relation between the gain of efficiency, either in terms of KFTE or in cost

of the KTFE, and the setup operators’ analysis.

Regarding the “all intern” scenarios, we note that most of the information found into the

“base” scenarios analysis are confirmed. “All intern base” scenario versus the optimum one

presents a “gain” of 0.57 KFTE and a reduction of cost equal to 1.43 million euro; “all intern

reduction days” shows 44.7 KFTE versus an optimum scenario at 44.1 KFTE with a difference

0.577 while in terms of cost 111.64 million euro versus 110.20 million euro is a difference of

1.44 million euro.

The last scenario, “all intern reduction days, concurrent, flex”, as described into previous

hypothesis, tries to mix most of the conditions separately analysed into the “base” set of

scenarios: this scenario does notreally cover a similar one of the previous “base” set, but it

tries to imagine a real situation mixing and smoothing the time reduction with the

concurrent rate, both as a result of calibration made in other scenarios. This scenario shows

the higher values in terms of KFTE (44.7) and cost (111.71 million euro).

The second part of the (Table 13 Scenarios Result per one shift) shows some different information:

the theoretical number of lines requested per production department in each scenario at the end

of the simulation as well as the space covered by the lines and their estimated cost (investment

cost).

This information, the number of lines requested, which for example varies from 37 to 62 in filling

dept., has to be compared with the real situation recorded by the company to be appreciated. Most

of the comments already expressed here-above for the FTE analysis are also valid for the

“production line” analysis.

The effect of the “reduction” in batch time or “concurrent” rate directly affects also the number of

lines requested (and consequently the space covered and the investment request)

Table 14 Real lines installed

Scenario Name-> Real

Full Time Line Equivalent Installed real:

- Filling 29

- Packaging 24

- Wrapping 12

- Code Writing 12

Space requested by Full Time Line Equivalent Installed (estimate) m2 3.663

Cost of Full Time Line Equivalent Installed (estimate) Mln EUR 28,30

The data presented for all the scenarios have to be checked with the real data recorded by the

company, (Table 14 Real lines installed): the real data refer to the end of September, 2015 the last

real month considered by the program.

— page 76 — The simulation does not move over the different months, choosing every time the organization,

shifts, to be applied, but it presents the results with the idea of a constant organization, one, two or

three shifts, all along the entire simulation. The real check has to be made considering the shifts

simulated as boundaries or channels with the real data moving inside the min/max of the simulation.

The simulation could be used in a type of mode “game”, changing the input/control levers every

time batches, but the philosophy used as a base to provide the output do notlet the shift to be

operated as a changing choice.

The real numbers, provided with the Table 14, suggest that the company operates in a double shift

organization (see the Table 15 Scenarios Results per two shifts): the “all intern” scenarios, especially

the “all intern reduction days”, “all intern reduction days optimum” and “all intern reduction days,

concurrent, flex”, confirm that the filling real lines are well estimated by the model. Only the

packaging lines are overestimated by the model for the “all intern” and “base” scenarios with the

“reduction days” lever applied, while the “base actual”, “base optimum” and “base concurrent” are

working well.

The overestimated data should be reviewed considering that the packaging activity in the past could

be modified sensibly in its working rate, applying more workers, and this flexibility could reduce the

number of lines requested. Historically, the organization applied for packaging activities was a mix

of one shift plus “something” of or entirely a double shift, depending on the period of time and the

orders sent by customers.

Table 15 Scenarios Results per two shifts


Optimum

Base Actual

reduction

days

Base

Optimum

reduction

days

Base Actual

Concurrent

All Intern

Base

All Intern

Base

Optimum

All Intern

Reduction

Days

Optimum

All Intern

Reduction

Days

All Intern

Reduction

Days,

concurrent,

flex

Two Shifts per Day






- Filling 22 22 29 29 23 24 24 33 33 27

- Packaging 11 11 15 15 11 15 15 22 22 19

- Wrapping 8 8 11 11 8 10 10 14 14 12

- Code Writing 5 5 7 7 5 6 6 8 8 7



(Table 15 Scenarios Results per two shifts) shows the same results for the two shift organization,

detailed for all the same scenarios already described.

It is interesting to note that all the FTE expressed are higher that the corresponding values of one

shift scenarios: the working time per shift available using a two shift organization is lower than the

daily one for external restrictions, law and national contract applied. But the gain measured by the

delta of FTE between base and optimum scenarios or reduction and concurrent versus base or

optimum scenarios still remains in line with the one shift results, already noted.

In fact, as already commented, the difference between the actual/base and the optimum scenarios

is calculated on the “partial” FTEs in setup operations possibly offset with “partial” workers in

normal operations.

The difference in KFTE between “base actual” and “base optimum” is equal to 0.75 while it was 0.74,

for one shift and Total FTE (internal plus outsourced), and 0.59 while it was 0.57 for internal FTE;

between the base and optimum scenarios, with a % of reduction day based on real situation, the

difference in KFTE for total FTE is 0.722 while it was 0.723 for one shift and, respectively, 0.569 while

it was 0.570 for internal FTE. In terms of cost the differences are similar: 1.88 versus 1.85 for total

FTE and 1.48 versus 1.45 for internal FTE in base vs optimum scenarios; concerning the base

reduction days versus its optimum scenarios, the difference is 1.81 versus 1.82 for total FTE and

1.43 versus 1.43 for internal FTE.

— page 77 — These figures confirm the explanation made.

A completely different situation is simulated for the number of lines requested and their cost in

terms of space and euro: already partially described in notes and comments concerning the real

data and one shift analysis, the number of lines in two shifts organization is more or less half the

daily estimation. The gain in terms of cost and space is relevant, but independent by the

optimization in terms of flexibility through the specialization. The equipment and its space are

sensibly reduced comparing the theoretical perfect scenarios (“base”, actual or optimum, and “all

intern” base or optimum) with the “real concentration and concurrent rate” ones, because in these

set of scenarios the planning efficiency is considered “optimum” and the technical lines are already

adjusted to the theoretical orders and quantities.

Table 16 Scenarios results per three shifts


Optimum

Base Actual

reduction

days

Base

Optimum

reduction

days

Base Actual

Concurrent

All Intern

Base

All Intern

Base

Optimum

All Intern

Reduction

Days

Optimum

All Intern

Reduction

Days

All Intern

Reduction

Days,

concurrent,

flex

Three Shifts per Day






- Filling 16 16 22 22 17 19 19 24 24 21

- Packaging 9 9 12 12 9 12 12 17 17 14

- Wrapping 6 6 8 8 6 8 8 10 10 9

- Code Writing 4 4 6 6 4 5 5 6 6 6



Concerning (Table 16 Scenarios results per three shifts), we have to note that the “gains” are in line

with the one or two shifts differences already observed. The difference in KFTE between “base

actual” and “base optimum” is equal to 0.74 for total FTE and 0.58 for internal FTE; between “base

actual reduction” and “base actual reduction optimum” is .74 and .58 for total and internal FTE ...

While the differences are more or less in line with the ones described for one and two shifts, the

absolute number of FTE, total or internal, is generally higher than the corresponding ones of the

two other shifts: this higher value is caused by the inefficiency rate linked to the night shift that the

model considers. Historically a third shift didn’t record the same productivity as the daily or evening

shifts. Probably, a 10% loss in terms of efficiency is not in line with the real inefficiency recorded,

but imagining a “regular” three shift organization it is not possible to calibrate a model only on data

exceptionally recorded during the last ten years.

The following tables show for the same set of scenarios the technical indicators presented into the

(CHAPTER 2

2 Research Objectives) section: technical operation flow time, technical operation frozen time and

total frozen time. These technical indicators are presented either per customer or per product

family.

— page 78 — Table 17 Technical operation flow time per customer

TechOps Flow Time per Customer (1000 pieces, hours) Count Min Max Mean Median StDev (Norm)

Customer 1

: All Intern Reduction Days, concurrent, flex 120 0,67 0,91 0,82 0,82 0,05 0,06

: All Intern Reduction Days 120 0,67 0,91 0,82 0,82 0,05 0,06

: All Intern Reduction Days Optimum 120 0,67 0,91 0,82 0,82 0,05 0,06

: All Intern Base Optimum 120 0,67 0,91 0,82 0,82 0,05 0,06

: All Intern Base 120 0,67 0,91 0,82 0,82 0,05 0,06

: Base Actual Concurrent 120 0,65 0,89 0,80 0,80 0,05 0,06

: Base Actual Optimum reduction Days 120 0,65 0,89 0,80 0,80 0,05 0,06

: Base Actual Reduction Days 120 0,65 0,89 0,80 0,80 0,05 0,06

: Base Actual Optimum 120 0,65 0,89 0,80 0,80 0,05 0,06

: Base Actual 120 0,65 0,89 0,80 0,80 0,05 0,06

Customer 2





: All Intern Base 120 0,82 1,26 0,99 0,98 0,08 0,08





: Base Actual 120 0,74 1,01 0,91 0,91 0,06 0,06

Customer 3





: All Intern Base 120 0,54 1,13 0,77 0,74 0,12 0,16





: Base Actual 120 0,48 0,97 0,72 0,68 0,12 0,17

Table 17 here-above and the following tables, Table 18 and Table 19, present the technical indicator

with the customer viewpoint.

Please note that these indicators are not linked to the control levers used during the simulations

runs; and only these control levers determine the previous results (FTE and economics).

They remain fixed through all the scenarios and they depend only on, inside the model, by the

technical hypothesis made: production batch or order size, working and setup rate, order and

production preparation time and, finally, bulk preparation time(due to the maceration time

requested by the fragrances suppliers).

Table 17 and Table 18 show all the same data throughout the three main customers: all the

statistical data are due to the calculation made all along every batch time. These values are an

average based on the product family and production phases mix that every customer request, in

different quantities, all along the time horizon simulated.

Please note that the indicators change only between “base” and “all intern” scenarios: the

hypothesis that the quantities are all made internally and not committed to external suppliers’ will

change the mix of products in each batch time and, consequently, changes the indicators in all the

components (minimum, maximum, average…)

— page 79 — Table 18 Technical operation frozen time per customer

TechOps Frozen Time per Customer (1000 pieces, hours) Count Min Max Mean Median StDev (Norm)

Customer 1





: All Intern Base 120 0,68 0,95 0,83 0,84 0,06 0,07





: Base Actual 120 0,66 0,92 0,82 0,82 0,06 0,07

Customer 2





: All Intern Base 120 0,85 1,32 1,03 1,02 0,08 0,08





: Base Actual 120 0,77 1,09 0,95 0,95 0,07 0,07

Customer 3





: All Intern Base 120 0,55 1,19 0,80 0,77 0,14 0,17





: Base Actual 120 0,50 1,04 0,75 0,71 0,14 0,18

Table 19, on the contrary, shows an higher level of volatility among the data: even if a careful review

could see immediately that the values are grouped by customer, “base” or “all intern” scenario

groups (and this last split is the same already been seen for the two previous technical operation

indicators) and shift.

This last parameter pushes the “total frozen time” to change, because the maceration time, the bulk

mixing and “rest” period, is prescribed by the customer and it is recommended in days. If the

working day is measured in different working hours according to the number of shifts considered

and the indicator itself is measured in hours, the result is that 1 day of maceration correspond to 8

hours per one shift/day, 15 hours per two shifts/day and 22.5 hours per three shifts/day. The

indicator measured in hours “frozen” for each customer grows up with the shift organization

applied.

— page 80 — Table 19 Total frozen time per customer

Variable (1000 pieces, hours)

Total Frozen Time per Customer Count Min Max Mean Median StDev (Norm) Min Max Mean Median StDev (Norm) Min Max Mean Median StDev (Norm)

Customer 1

: All Intern Reduction Days, concurrent, flex 120 88,40 114,20 104,97 106,82 5,52 0,05 164,64 213,11 195,76 199,25 10,36 0,05 246,48 319,29 293,22 298,48 15,55 0,05

: All Intern Reduction Days 120 88,40 114,20 104,97 106,82 5,52 0,05 164,64 213,11 195,76 199,25 10,36 0,05 246,48 319,29 293,22 298,48 15,55 0,05

: All Intern Reduction Days Optimum 120 88,40 114,20 104,97 106,82 5,52 0,05 164,64 213,11 195,76 199,25 10,36 0,05 246,48 319,29 293,22 298,48 15,55 0,05

: All Intern Base Optimum 120 88,40 114,20 104,97 106,82 5,52 0,05 164,64 213,11 195,76 199,25 10,36 0,05 246,48 319,29 293,22 298,48 15,55 0,05

: All Intern Base 120 88,40 114,20 104,97 106,82 5,52 0,05 164,64 213,11 195,76 199,25 10,36 0,05 246,48 319,29 293,22 298,48 15,55 0,05

: Base Actual Concurrent 120 95,63 116,06 109,63 110,88 5,02 0,05 178,24 216,65 204,49 206,79 9,42 0,05 266,92 324,63 306,33 309,76 14,14 0,05

: Base Actual Optimum reduction Days 120 95,63 116,06 109,63 110,88 5,02 0,05 178,24 216,65 204,49 206,79 9,42 0,05 266,92 324,63 306,33 309,76 14,14 0,05

: Base Actual Reduction Days 120 95,63 116,06 109,63 110,88 5,02 0,05 178,24 216,65 204,49 206,79 9,42 0,05 266,92 324,63 306,33 309,76 14,14 0,05

: Base Actual Optimum 120 95,63 116,06 109,63 110,88 5,02 0,05 178,24 216,65 204,49 206,79 9,42 0,05 266,92 324,63 306,33 309,76 14,14 0,05

: Base Actual 120 95,63 116,06 109,63 110,88 5,02 0,05 178,24 216,65 204,49 206,79 9,42 0,05 266,92 324,63 306,33 309,76 14,14 0,05

Customer 2





: All Intern Base 120 78,47 114,28 95,09 94,84 7,74 0,08 145,99 213,09 177,08 176,68 14,51 0,08 218,48 319,17 265,10 264,55 21,77 0,08





: Base Actual 120 82,52 116,88 99,51 98,53 7,83 0,08 153,63 218,00 185,43 183,63 14,69 0,08 229,97 326,56 277,67 274,98 22,05 0,08

Customer 3





: All Intern Base 120 95,53 117,33 113,42 114,66 3,62 0,03 177,78 218,90 211,60 213,96 6,80 0,03 266,08 327,94 317,00 320,43 10,22 0,03





: Base Actual 120 107,13 117,88 115,08 115,75 2,21 0,02 199,84 219,81 214,75 216,06 4,16 0,02 299,37 329,22 321,75 323,78 6,25 0,02

One shift per day Two shifts per day Three shifts per day

— page 81 — As per the customer viewpoint analysis, the same technical indicators could be reviewed per product family. Table 20 to Table 25 show the product family viewpoint. Table 20 Technical operation flow time per product family 1st part

TechOps Flow Time per ProdFam (1000 pieces, hours) Count Min Max Mean Median StDev (Norm)

Alcohol Bottles





: All Intern Base 120 0,87 0,93 0,90 0,90 0,01 0,01





: Base Actual 120 0,85 0,93 0,88 0,88 0,02 0,02

Alcohol Miniatures





: All Intern Base 120 0,95 1,03 0,96 0,96 0,01 0,01





: Base Actual 120 0,92 1,01 0,94 0,94 0,02 0,02

Alcohol Vaposac

: All Intern Reduction Days, concurrent, flex 120 - 1,09 0,87 0,90 0,20 0,23

: All Intern Reduction Days 120 - 1,09 0,87 0,90 0,20 0,23

: All Intern Reduction Days Optimum 120 - 1,09 0,87 0,90 0,20 0,23

: All Intern Base Optimum 120 - 1,09 0,87 0,90 0,20 0,23

: All Intern Base 120 - 1,09 0,87 0,90 0,20 0,23

: Base Actual Concurrent 120 - 0,98 0,84 0,87 0,19 0,23

: Base Actual Optimum reduction Days 120 - 0,98 0,84 0,87 0,19 0,23

: Base Actual Reduction Days 120 - 0,98 0,84 0,87 0,19 0,23

: Base Actual Optimum 120 - 0,98 0,84 0,87 0,19 0,23

: Base Actual 120 - 0,98 0,84 0,87 0,19 0,23

Creams





: All Intern Base 120 1,02 1,08 1,04 1,04 0,02 0,02





: Base Actual 120 1,02 1,07 1,04 1,04 0,01 0,01

Various





: All Intern Base 120 - 9,55 8,18 9,45 1,98 0,24





: Base Actual 120 - 9,65 6,56 7,54 2,78 0,42

The technical indicators are always the same for all the scenarios considered: no control levers,

useful to touch or modify the technical “time”, have been changed.

Only the difference between the “base” and “all intern” scenarios implies a change: we are

reviewing some indicators that take into account the mix of quantities per customer worked;

— page 82 — considering all the quantities internally made it could change the proportion mix even in every time

batch.

The values are relatively short and the variance, as already discussed, is due to the different mix of

customer quantities inside every time batch.

Table 21 Technical operation flow time per product family 2nd part

TechOps Flow Time per ProdFam Count Min Max Mean Median StDev (Norm)

Hotel Line





: All Intern Base 120 - 2,05 1,55 2,05 0,71 0,46





: Base Actual 120 - 2,05 1,55 2,05 0,71 0,46

New Hotel Line





: All Intern Base 120 - 0,33 0,26 0,33 0,13 0,51





: Base Actual 120 - 0,33 0,26 0,33 0,13 0,51

Giftsets - Coffrets





: All Intern Base 120 0,99 1,54 1,16 1,14 0,11 0,09





: Base Actual 120 1,14 1,45 1,26 1,25 0,07 0,06

Vials





: All Intern Base 120 0,24 0,38 0,31 0,32 0,03 0,11





: Base Actual 120 0,24 0,38 0,31 0,32 0,03 0,10

Deo





: All Intern Base 120 - 1,93 1,62 1,92 0,70 0,43





: Base Actual 120 - 1,95 1,57 1,85 0,68 0,43

The following Table 22 and Table 23 shows the technical time, flow time, considering also the tech

preparation ops. As per previous comments, no levers used in these scenarios could change this

data: only the mix among the customer quantities and the base and all intern split could affect the

“ratios”.

— page 83 — Table 22 Technical operation frozen time per product family 1st part

TechOps Frozen Time per ProdFam (1000 pieces, hours) Count Min Max Mean Median StDev (Norm)

Alcohol Bottles





: All Intern Base 120 0,91 0,98 0,94 0,94 0,02 0,02





: Base Actual 120 0,88 0,97 0,92 0,92 0,02 0,02

Alcohol Miniatures





: All Intern Base 120 1,00 1,06 1,01 1,01 0,01 0,01





: Base Actual 120 0,98 1,04 1,00 0,99 0,01 0,01

Alcohol Vaposac





: All Intern Base 120 - 1,19 0,94 0,97 0,22 0,23





: Base Actual 120 - 1,06 0,91 0,95 0,21 0,23

Creams





: All Intern Base 120 1,12 1,13 1,12 1,12 0,00 0,00





: Base Actual 120 1,11 1,13 1,12 1,12 0,00 0,00

Various





: All Intern Base 120 - 12,3 10,3 12,07 2,68 0,26





: Base Actual 120 - 12,37 8,38 9,18 3,62 0,43

Some of the product families analysed present a minimum value at zero: not all the products present

a frequent or constant presence in the past. Some month at zero push the boundaries to the nil

limit.

— page 84 — Table 23 Technical operation frozen time per product family 2nd part

TechOps Frozen Time per ProdFam (1000 pieces, hours) Count Min Max Mean Median StDev (Norm)

Hotel Line





: All Intern Base 120 - 2,07 1,57 2,07 0,72 0,46





: Base Actual 120 - 2,07 1,57 2,07 0,72 0,46

New Hotel Line





: All Intern Base 120 - 0,34 0,27 0,34 0,14 0,51





: Base Actual 120 - 0,34 0,27 0,34 0,14 0,51

Giftsets - Coffrets





: All Intern Base 120 1,11 1,68 1,28 1,26 0,11 0,08





: Base Actual 120 1,26 1,59 1,39 1,38 0,08 0,05

Vials





: All Intern Base 120 0,24 0,39 0,32 0,32 0,04 0,11





: Base Actual 120 0,24 0,39 0,32 0,32 0,04 0,11

Deo





: All Intern Base 120 - 2,13 1,79 2,12 0,77 0,43





: Base Actual 120 - 2,15 1,74 2,05 0,75 0,43

As per the “customer” viewpoint analysis, the “product families” total frozen time is directly

affected by the mix of different customers’ quantities, the split between the base and all intern

scenarios hypothesis but, also, by the “maceration” time, externally defined in days, that the shifts

available hours translate in different measures.

The comments already expressed for Table 19 are valid also for (Table 24 Total frozen time per product family 1st part) and (Table 25 Total frozen time per product family 2nd part).

— page 85 — Table 24 Total frozen time per product family 1st part


Total Frozen Time per ProdFam Count Min Max Mean Median StDev (Norm) Min Max Mean Median StDev (Norm) Min Max Mean Median StDev (Norm)

Alcohol Bottles





: All Intern Base 120 117,91 117,98 117,94 117,94 0,02 0,00 219,91 219,98 219,94 219,94 0,02 0,00 329,41 329,48 329,44 329,44 0,02 0,00





: Base Actual 120 117,88 117,97 117,92 117,92 0,02 0,00 219,88 219,97 219,92 219,92 0,02 0,00 329,38 329,47 329,42 329,42 0,02 0,00

Alcohol Miniatures





: All Intern Base 120 118,00 118,06 118,01 118,01 0,01 0,00 220,00 220,06 220,01 220,01 0,01 0,00 329,50 329,56 329,51 329,51 0,01 0,00





: Base Actual 120 117,98 118,04 118,00 117,99 0,01 0,00 219,98 220,04 220,00 219,99 0,01 0,00 329,48 329,54 329,50 329,49 0,01 0,00

Alcohol Vaposac

: All Intern Reduction Days, concurrent, flex 120 - 118,19 112,09 117,97 25,72 0,23 - 220,19 208,99 219,97 47,95 0,23 - 329,69 313,02 329,47 71,81 0,23

: All Intern Reduction Days 120 - 118,19 112,09 117,97 25,72 0,23 - 220,19 208,99 219,97 47,95 0,23 - 329,69 313,02 329,47 71,81 0,23

: All Intern Reduction Days Optimum 120 - 118,19 112,09 117,97 25,72 0,23 - 220,19 208,99 219,97 47,95 0,23 - 329,69 313,02 329,47 71,81 0,23

: All Intern Base Optimum 120 - 118,19 112,09 117,97 25,72 0,23 - 220,19 208,99 219,97 47,95 0,23 - 329,69 313,02 329,47 71,81 0,23

: All Intern Base 120 - 118,19 112,09 117,97 25,72 0,23 - 220,19 208,99 219,97 47,95 0,23 - 329,69 313,02 329,47 71,81 0,23

: Base Actual Concurrent 120 - 118,06 112,06 117,95 25,71 0,23 - 220,06 208,96 219,95 47,94 0,23 - 329,56 312,99 329,45 71,80 0,23

: Base Actual Optimum reduction Days 120 - 118,06 112,06 117,95 25,71 0,23 - 220,06 208,96 219,95 47,94 0,23 - 329,56 312,99 329,45 71,80 0,23

: Base Actual Reduction Days 120 - 118,06 112,06 117,95 25,71 0,23 - 220,06 208,96 219,95 47,94 0,23 - 329,56 312,99 329,45 71,80 0,23

: Base Actual Optimum 120 - 118,06 112,06 117,95 25,71 0,23 - 220,06 208,96 219,95 47,94 0,23 - 329,56 312,99 329,45 71,80 0,23

: Base Actual 120 - 118,06 112,06 117,95 25,71 0,23 - 220,06 208,96 219,95 47,94 0,23 - 329,56 312,99 329,45 71,80 0,23

Creams





: All Intern Base 120 16,72 16,73 16,72 16,72 0,00 0,00 30,32 30,33 30,32 30,32 0,00 0,00 44,92 44,93 44,92 44,92 0,00 0,00





: Base Actual 120 16,71 16,73 16,72 16,72 0,00 0,00 30,31 30,33 30,32 30,32 0,00 0,00 44,91 44,93 44,92 44,92 0,00 0,00

Various

: All Intern Reduction Days, concurrent, flex 120 - 12,28 10,31 12,07 2,68 0,26 - 12,28 10,31 12,07 2,68 0,26 12,28 10,31 12,07 2,68 0,26 -

: All Intern Reduction Days 120 - 12,28 10,31 12,07 2,68 0,26 - 12,28 10,31 12,07 2,68 0,26 12,28 10,31 12,07 2,68 0,26 -

: All Intern Reduction Days Optimum 120 - 12,28 10,31 12,07 2,68 0,26 - 12,28 10,31 12,07 2,68 0,26 12,28 10,31 12,07 2,68 0,26 -

: All Intern Base Optimum 120 - 12,28 10,31 12,07 2,68 0,26 - 12,28 10,31 12,07 2,68 0,26 12,28 10,31 12,07 2,68 0,26 -

: All Intern Base 120 - 12,28 10,31 12,07 2,68 0,26 - 12,28 10,31 12,07 2,68 0,26 12,28 10,31 12,07 2,68 0,26 -

: Base Actual Concurrent 120 - 12,37 8,38 9,18 3,62 0,43 - 12,37 8,38 9,18 3,62 0,43 12,37 8,38 9,18 3,62 0,43 -

: Base Actual Optimum reduction Days 120 - 12,37 8,38 9,18 3,62 0,43 - 12,37 8,38 9,18 3,62 0,43 12,37 8,38 9,18 3,62 0,43 -

: Base Actual Reduction Days 120 - 12,37 8,38 9,18 3,62 0,43 - 12,37 8,38 9,18 3,62 0,43 12,37 8,38 9,18 3,62 0,43 -

: Base Actual Optimum 120 - 12,37 8,38 9,18 3,62 0,43 - 12,37 8,38 9,18 3,62 0,43 12,37 8,38 9,18 3,62 0,43 -

: Base Actual 120 - 12,37 8,38 9,18 3,62 0,43 - 12,37 8,38 9,18 3,62 0,43 12,37 8,38 9,18 3,62 0,43 -


— page 86 — Table 25 Total frozen time per product family 2nd part


Total Frozen Time per ProdFam Count Min Max Mean Median StDev (Norm) Min Max Mean Median StDev (Norm) Min Max Mean Median StDev (Norm)

Hotel Line





: All Intern Base 120 - 17,67 15,09 17,67 5,93 0,39 - 31,27 26,88 31,27 10,55 0,39 - 45,87 39,53 45,87 15,51 0,39





: Base Actual 120 - 17,67 15,09 17,67 5,93 0,39 - 31,27 26,88 31,27 10,55 0,39 - 45,87 39,53 45,87 15,51 0,39

New Hotel Line





: All Intern Base 120 - 15,94 12,62 15,94 6,47 0,51 - 29,54 23,38 29,54 12,00 0,51 - 44,14 34,94 44,14 17,93 0,51





: Base Actual 120 - 15,94 12,62 15,94 6,47 0,51 - 29,54 23,38 29,54 12,00 0,51 - 44,14 34,94 44,14 17,93 0,51

Giftsets - Coffrets





: All Intern Base 120 1,11 1,68 1,28 1,26 0,11 0,08 1,11 1,68 1,28 1,26 0,11 0,08 1,11 1,68 1,28 1,26 0,11 0,08





: Base Actual 120 1,26 1,59 1,39 1,38 0,08 0,05 1,26 1,59 1,39 1,38 0,08 0,05 1,26 1,59 1,39 1,38 0,08 0,05

Vials





: All Intern Base 120 117,24 117,39 117,32 117,32 0,04 0,00 219,24 219,39 219,32 219,32 0,04 0,00 328,74 328,89 328,82 328,82 0,04 0,00





: Base Actual 120 117,24 117,39 117,32 117,32 0,04 0,00 219,24 219,39 219,32 219,32 0,04 0,00 328,74 328,89 328,82 328,82 0,04 0,00

Deo





: All Intern Base 120 - 17,73 14,92 17,72 6,47 0,43 - 31,33 26,36 31,32 11,43 0,43 - 45,93 38,65 45,92 16,76 0,43





: Base Actual 120 - 17,75 14,87 17,65 6,45 0,43 - 31,35 26,31 31,25 11,41 0,43 - 45,95 38,60 45,85 16,74 0,43


— page 87 — These sets of technical indicators could also be declined with different approaches: measured per

the customer “standard” order, or alternatively per product family “standard” order and/or in a

cumulative or progressive way. This last one could smooth the errors or special situations that every

single time batch could present. Time and quantity measures are summarized all along the time

batches and the ratios are calculated on these “up-to-date” sums.

These alternative ratios do not have a different meaning inside the scenarios presented and they

will be used into the following sections.

Sensitivity

In order to better evaluate the differences that the customers’ trends and company “tactical”

strategies could have on the industrial organization, the simulation environment was tested with a

series of sensitivity analysis.

Sensitivity Scenarios

The scenarios chosen to be tested were all in the “all intern” group. We considered that this group

loses the “economic” positive difference between the internal and outsourced manpower cost and

efficiency (in terms of handcraft ability or speed), but provides a better continuity between the past

(when the outsourcing was used, but this set of scenarios re-builds the conditions imagining to

transfer the outsourced ops inside), the present and the future, with all the activities already inside

(only a little part is nowadays externalized, no outsourcing activity is present inside the plant).

Among the “all intern” scenarios, only three of them are tested with the sensitivity analysis:

the all intern base,

the all intern base optimum and

the all intern reduction days, concurrent

Concerning the differences among the three chosen scenarios, we considered that:

the first and the second one reflect a “perfect” planning ability to reduce or completely wipe

out the “concentration” available time on some “reduced” days inside the time batch

requested by customers as well as the concurrent rate among the different operations

(especially the setup operations),

while the first scenario considers a splitting between specializations (setup and normal

working activities) and the real specialization recorded per each product family and

production macro-phase, the second one imagines a perfect osmosis through the

specializations (the full time equivalent is “transversally” able to operate all along the macro-

phases and product family lines as well as moving through the setup and the working

activities)

the third scenarios, finally, tries to consider the real day’s reduction requested by customers

in the past as well as the real actual specialization in setup/working activities, product

families and macro-phase activities without a “perfect” planning activity able to annihilate

these “real/actual” conditions

— page 88 — Concerning the last scenario, as described in the (Scenarios Hypothesis) section, the ratios used into

the model as reduction days and concurrent rate were not in fact registered by the case study but

they were “inferred” by the model calibration process, to adjust the setup operators calculated with

the real registered ones.

Sensitivity levers

The sensitivity analysis was made on two main levers that directly influence all the model structure:

the average order batch size that every customer sent or will send per each product family

the average working rate that each production line applies to each customer and product

family order

The sensitivity was made forcing a variability of the two here-above levers considering for the first

one a range of -80% - +100% while for the second one a range of -90% - +100%.

The system considered three sub scenarios:

a first one with only the varied order batch size

a second one with only the varied production working rate line

and a third one with both the varied order batch size and the varied production line working

rate

Every time step was simulated with 200 different values affected to the sensitivity levers and these

values were randomly chosen considering the variation range as normally distributed.

The following Table 26 shows, only for the production phase RIE (Filling), and inside for each customer

and product family, a summary of the sensitivity lever: the average, the minimum, the maximum and

the standard deviation regarding the order size registered during the simulation.

As per the previous phase RIE, a similar summary is available for each other phase.

A different viewpoint, always based on the same sensitivity data, is shown by the Table 27 where the

data are grouped by product family and customer.

This different grouping list allows us appreciate the differences among the three main customers:

considering the key product family, Alcohol Bottles, for example, Customer 1 shows data that are

largely the double of customer 2 or 3. In Vials the situation is more or less the same, while in other

product families this “hierarchical” approach is not confirmed; Miniature Alcohol, for example,

presents a reversed situation, where the third customer shows data higher than the second and first

customer; the sequence is exactly inverted.

The variation simulated, according to the hypothesis described, is really important and it could be

really appreciated in absolute values. The reduction or increase percentage rate do not let us

appreciate the batch order size considered as well as the average production working rate. The

absolute value, on the other hand, compares the order size with the hourly estimated production.

This last comment is evidently due to the case study conditions: a mix or equilibrium between the

order size and the hourly working rate. A real “batch” process with many changes and setups.

— page 89 — Table 26 Batch order sensitivity variation

— page 90 —

Table 27 Batch order sensitivity variation per product family

In the following Table 28, the sensitivity hypothesis is applied to the second lever considered: the

production line average working rate per phase. The rate is expressed as pieces per hour.

— page 91 — Table 28 Production Line Average Working rate sensitivity variation

The table shows the data only for the RIE (filling) phase, organized per product family and customer.

— page 92 — The data merges differently through the groups. Coffrets, or Gift-Sets, for example, are recorded as

zero working rate because they are not “filled”; they are only packed or wrapped but not filled. They

are a second level packaging: bottles are assembled together or with other products in a combined

set.

Sensitivity Results

As per the scenarios results (see Scenarios Results section), the sensitivity analysis made on the three

chosen scenarios (see Sensitivity Scenarios), applying the three levers combination already described

(see Sensitivity levers), produce the double set of indicators already detailed into the Environment

Description section:

o The number and cost of the production lines required and

The number and the cost of the full time equivalent, people, required either in terms

of working people or setup operators

o The technical indicators useful to measure the service level provided:

Technical process flow time

Process frozen time which considers also the preparation operations

Total frozen time which considers also the bulk preparation time (not covered

by the simulation model)

In this section every set of results is presented with the range of variation registered; the set is

grouped by scenario.

— page 93 —

Table 29 Scenarios base runs for sensitivity

The (Table 29) shows the data runs results for the three scenarios considered each one described

through the three sensitivity analysis: we have to note that every scenario presents the same initial

simulation result.

“All Intern Base” Sensitivity

This scenario considers all the quantities made internally and a “perfect” planning ability able to

annihilate the time concentration requested by customer orders transmission and the concurrent

activities rate. It does not consider a flexibility through the specialization, setup and general working

operations, while it considers the real specialization throughout customers, product families and

macro-phases.

FTE

The first set of indicators is related to the total number of FTE requested by the scenarios and its

variation as function of modified levers.

The three sensitivity analysis start from the same “run”.

Table 30 presents the range of FTE if only the order batch size is modified.

— page 94 —

Table 30 All Intern Base Batch Size variation - FTE Results

(Table 31) presents the FTE results, changing the working rate of the lines.

For this sensitivity levers we have to consider that, while the order batch size has effect only on the

number of orders to be considered as necessary to complete the quantity required, the working rate

has several effects:

The first one, the direct one, is an “inverse” change on the time required to complete the

customer order: considering the average team required by the production line standard, the

effect is directly reflected in the FTE required. It’s an inverse proportion, naturally, because

higher is the working rate less is the line time and the FTE required,

The second effect is due to the production lines installed: if the working rate varies between

90% and 110% of the existing standard, the team required is the standard one; if the working

rate drop down to 90% the team is increased of 10%, to 80% is increased of 20% and if the

working rate decreases to roughly zero, the team tends to be increased up to 175%. The idea

is that a relevant decrease in working rate suggests some product difficulties, with the

necessity to put in place more “handcraft” operators to maintain the productivity required.

This framework presents a double inefficiency all along the working rate drop down: the

“production line” time required increases and the order is completed in more time with more

FTE but, also, all along the working rate drop down the team is increased more than

proportionally, trying to compensate the product difficulties, and this effect itself enhances

the FTE required.

On the other hand. a working rate increase is managed inside the standard boundaries if it is

at maximum +10%, while if it is higher than +10% but less than +20%, it suggests a different

working structure with less people into the team (-5%). Over the +20%, it is like to say that

the production line has been completely changed and this new line could reduce the team of

an extra -20%, in addition to the -5% of previous increase.

— page 95 —

Table 31 All Intern Base Velocity Line variation - FTE Results

A third consequence is due to the effect that the working rate presents on the setup

operations: the standard setup time, one hour, is confirmed for lines with working rate up to

a maximum of +10% on standard working rate; the setup time is increased linearly up to

+25%, if the working rate grows up from +10% to +20%. With a working rate over the +20%

and up to +100%, the setup time is increased up to +50%.

The fourth effect is on the setup team: the team grows up with the same proportion of the

setup time. While the setup time of the production line is affected by the complexity of the

faster line, the team increase reflects the complexity due to synchronising the phases

The second, third and fourth effects are due to the reality check inside the company structure and

the analysis of new investments studied on customers’ requests.

Table 32 All Intern Base Order batch Size & Velocity Line variation - FTE Results

(Table 32) presents the results combining the order batch size and the working rate per standard line

variations.

The combined effect of the two levers is considerably amplified in terms of average absolute values,

minimum-maximum range and standard deviation.

— page 96 — COST

The results presented for the FTE find a direct replication on the people cost indicators.

Table 33 All Intern Base Batch Size variation - COST Results

(Table 33) shows the people cost split between workers and setup operators, changing only the order

size.

— page 97 — Table 34 All Intern Base Velocity Line variation - COST Results

Table 34 shows the results using the working rate as sensitivity lever.

— page 98 — Table 35 All Intern Base Order batch Size & Velocity Line variation - COST Results

Finally, Table 35 combines the two levers and presents the cost effects related.

Lines

As described for every scenario simulation, not only the people or manpower requested by the

operations is the only one useful indicator: the number of lines requested is equally important. It’s

important either for the “space” required inside the plant or the necessary investment amount to

put in place the “fair” equipment estimated to cover the customer demand.

Table 36 shows the number of lines simulated with the sensitivity analysis: please note that some

“aggregate” results are less meaningful than the “macro-phase” ones.

— page 99 — Table 36 All Intern Base Batch Size variation - Lines number Results

As per previous Table 36, the following Table 37 shows the number of lines requested by the line

average working rate sensitivity simulations applied on the “All Intern Base” scenario.

The results of the two “base” sensitivity analysis, Table 36 and Table 37, are more or less equivalent.

A completely different scenario appears when the two sensitivity levers are combined.

— page 100 — Table 37 All Intern Base Velocity Line variation – Lines number Results

Finally, (Table 38) shows the effects of moving the two levers together, order batch size and working

rate per standard line.

Please note that the normal distribution applied to the two sensitivity rates varying from 10% to 200%

has an exponential effect on the combined results: the average number of lines required is really

higher in this last analysis than into the two base sensitivities.

The maximum effect, a combination of the two worst rates, is completely out of the reality

boundaries but it is a "strong” message of risk: if customers push the target to “craftsman” products

with very limited orders and working rate really low, due to, for example, the intrinsic complexity in

the product, the needs, in terms of lines and FTEs, to comply with the historical quantity could be

really astonishing, almost impossible to be fulfilled.

This result suggests that the sensitivity is either unreal or impossible in its related hypothesis

concerning the quantity: “handcraft” product could not be considered with related “industrial”

quantity and, probably, “industrial” costs or prices. Nevertheless, this “impossible” message needs to

be understood: exceptional events are generally uncommon but disastrous if they happen. They have

to be considered, weighted, simulated and never forgiven or removed from the reasoning.

— page 101 — Table 38 All Intern Base Order batch Size & Velocity Line variation - Lines number Results

Technical Indicators

- Order Size Sensitivity Analysis

The following tables and graphs present the effect of the All Intern Base scenario sensitivity analysis

on the chosen technical indicators.

The following Figure 24 shows the Technical Operation Flow Time as an average of customers’ value

all along the time scale, while the Table 39 is a summary considering all the batch time and all the

customers.

Table 39 Technical Flow Time per pcs - order size variation

Maximum Average Minimum

2,13 0,93 0,53

For the internal purposes analysis, it is probably more interesting the technical flow time per

customer order, which measures the time in hours that the average order spends to pass through

the industrial process.

— page 102 —

Table 40 Technical Flow Time per order - order size variation


36,24 22,11 8,33

(Table 40) is a summary of the flow time in hours per order, considering all the three main customers (always with the order size as lever used into the sensitivity analysis).

Figure 24 All Intern Base Flow Time per Customer (average of customers)

0,5

0,7

0,9

1,1

1,3

1,5

1,7

2008/01 2008/07 2009/01 2009/07 2010/01 2010/07 2011/01 2011/07 2012/01 2012/07 2013/01 2013/07 2014/01 2014/07 2015/01 2015/07 2016/01 2016/07 2017/01 2017/07

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Process Flow Time per Customer (average on customers as function of order size variation)

Max Average Min

The following (Figure 25) considers the trends over the time schedule of the average Flow Time spent by the average order size. Figure 25 All Intern Flow Time per average order (all the customers)

0,5

10,5

20,5

30,5

40,5

50,5

60,5

2008/01 2008/07 2009/01 2009/07 2010/01 2010/07 2011/01 2011/07 2012/01 2012/07 2013/01 2013/07 2014/01 2014/07 2015/01 2015/07 2016/01 2016/07 2017/01 2017/07

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Process Flow Time per Order (average on customers' orders as function of order size variation)

Max Average Min

— page 103 — If we consider the Frozen Technical Flow Time inside this scenario and the order size sensitivity, the

data reported in the following (Table 41) are not far from the values simulated for the Technical

Flow Time in (Table 39). The reason is that the preparation time is equally spread on all the

quantities of the order.

Table 41 Frozen Technical Flow Time per pcs - order size variation


1,21 0,96 0,86

Even for the customer order, and not per piece, the values are not really different, if we compare

Table 42 with Table 40.

Table 42 Frozen Technical Flow Time per order - order size variation


103,48 22,71 4,90

Finally, we have to consider the Total Frozen Time indicators:

Table 43 Total Frozen Time per pcs - order size variation


one shift over 5 days 117,76 104,56 78,41

two shifts over 5 days 219,27 194,88 145,93

three shifts over 5 days 328,31 291,85 218,42

Totale complessivo 328,31 197,10 78,41

Table 43 shows the Total Frozen Time indicator, which considers also the “maceration” time. This

last one is a fixed time measured in days: this fixed time alters all the measures and it is so important

that the indicators become “flat” without any new major significance.

All these previous indicators could be examined also from the “product family” viewpoint.

Table 44 All Intern Base Flow Time per Product Family piece (average of customers)


Alcohol Bottles 1,14 0,96 0,87

Alcohol Miniature 1,72 1,01 0,90

Alcohol Vaposac 2,24 0,94 -

Coffrets /Giftsets 2,70 1,28 0,91

Cream 2,53 1,14 0,93

Deo 3,33 1,74 -

Vials 0,48 0,32 0,23

HotelLine 2,49 1,57 -

NewHotelLine 0,49 0,27 -

Grand Total 3,33 1,03 -

(Table 44) and (Table 45) present the data from the Product Family viewpoint: technical Flow Time

per piece and per order.

— page 104 —

Table 45 All Intern Base Flow Time per Product Family order (average of customers)






Cream 21,97 9,68 3,11

Deo 10,98 5,79 -

Vials 92,66 35,85 5,49

HotelLine 101,80 39,33 -


Grand Total 101,80 17,02 -

Not all the possible tables are showed because the already commented information covers most of

the situations. The notes explained about the customers’ viewpoint, even for the Frozen Technical

Time and the Total Frozen Time, are valid for the product Family view also.

- Working Rate Sensitivity Analysis

This section seeks to consider the effects on technical indicators due to the working rate sensitivity

analysis.

Table 46 Technical Flow Time per pcs - working rate variation


1,40 0,63 0,27

Table 46 and Figure 26 show the data for the Process Flow Time, customer viewpoint, with the

working rate sensitivity based on the All Intern Base scenario.

Figure 26 All Intern Base Flow Time per Customer (average of customers) working rate sensitivity

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

2008/01 2008/07 2009/01 2009/07 2010/01 2010/07 2011/01 2011/07 2012/01 2012/07 2013/01 2013/07 2014/01 2014/07 2015/01 2015/07 2016/01 2016/07 2017/01 2017/07

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Process Flow Time per Customer (average on customers as function of working ratevariation)

Max Average Min

— page 105 — As per Order size variation analysis, the data per average order could be more interesting even if

this data combine the flow time per pcs with the average size of the order transmitted by customer

(and this info is clearly external without any direct influence by the company).

Table 47 Technical Flow Time per order - working rate variation


52,11 14,52 4,61

The following graph shows the data all along the time schedule.

Figure 27 All Intern Base Flow Time per Customer Order (average of customers) working rate sensitivity

0

10

20

30

40

50

60

2008/01 2008/07 2009/01 2009/07 2010/01 2010/07 2011/01 2011/07 2012/01 2012/07 2013/01 2013/07 2014/01 2014/07 2015/01 2015/07 2016/01 2016/07 2017/01 2017/07

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Process Flow Time per Customer Order (average on customers as function of working ratevariation)

Max Average Min

The following two tables show the Frozen Flow Time per Customer piece and Customer order:

Table 48 Frozen Technical Flow Time per pcs -working rate variation


1,47 0,67 0,29

Table 49 Frozen Technical Flow Time per order -working rate variation


52,71 15,12 5,21

In conclusion, the Total Frozen Time:

— page 106 —

Table 50 Total Frozen Time per pcs - working rate variation






And an example of the Product Family viewpoint:

Table 51 All Intern Base Flow Time per Product Family piece (average of customers)





Coffrets/Giftsets 2,05 0,92 0,55

Cream 1,59 0,81 0,51

Deo 2,71 1,25 -

Vials 0,52 0,22 0,09




The same table could be viewed per average product family order:

Table 52 All Intern Base Flow Time per Product Family order (average of customers)





Coffrets/Giftsets 9,00 4,40 2,75

Cream 17,24 6,44 3,66

Deo 8,14 3,74 -

Vials 75,71 23,51 7,43

HotelLine 84,38 24,52 -



- Working Rate and Order Size Sensitivity Analysis

This section considers the combined effect of the two sensitivity levers together (order size and

working rate) on technical indicators.

Table 53 Technical Flow Time per pcs - order size & working rate variation


5,23 1,40 0,46

— page 107 — Table 53 shows the simple data per piece and, as per previous section, the following Table 54 shows

the same data per average customer order

Table 54 Technical Flow Time per order - order size & working rate variation


256,76 33,30 5,18

It’s also interesting to review the previous data all along the time schedule:

Figure 28 All Intern Base Flow Time per Customer (average of customers) working rate & order size sensitivity

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The effect of the two levers combination is clearly visible in the previous graph, Figure 28, where

the average is shifted towards the best option, with flow time less than 1 hour per piece. The

maximum, worst case, of the sensitivity is around the 4 hours per piece.

This extreme situation is due to an order size and a working rate typical of a “craftsman” activity:

the model considers always a quantity level typically “industrial”. The mix industrial quantity with

craftsman size and working rate causes this effect on lines and FTE which we have already explained

in previous sections.

The following Figure 29 translates data per piece in data per average customer order.

The comments already expressed for Figure 28 are more clearly shown: the average frozen flow

time per customer order is around 33 hours with an important difference in the best situation, which

is around 5 hours, but very far from the worst case which is up to 250 hours per order.

Table 55 Technical Frozen Flow Time per pcs - order size & working rate variation


5,30 1,43 0,47

— page 108 —

Here below is the frozen flow time per piece which is in line with the flow time.

Table 56 Technical Frozen Flow Time per order - order size & working rate variation


257,36 33,90 5,78

The Table 56 sets out the same data for the average order size.

Figure 29 All Intern Base Flow Time per Customer order (average of customers) working rate & order size sensitivity

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The following Table 57 reports the data taking into consideration the Total Frozen Time, including

the “maceration” time, function of the hours per shift (the maceration time is expressed in days).

Table 57 Total Frozen Time per pcs - working rate & order size variation






In sum, the data from the Product family viewpoint. Per piece:

— page 109 —

Table 58 All Intern Base Flow Time per Product Family piece (average of customers) working rate & order size

variation






Cream 8,05 1,64 0,60

Deo 15,90 2,63 -

Vials 2,18 0,52 0,13




And per average order:

Table 59 All Intern Base Flow Time per Product Family order (average of customers) working rate & order size

variation






Cream 131,13 14,28 3,14

Deo 57,12 8,52 -

Vials 478,24 58,11 5,56

HotelLine 572,87 58,44 -


Grand Total 572,87 25,73 -

“All Intern Base Optimum” Sensitivity

These scenarios considers all the quantities made internally as per previous “All intern base” as well

as most of the hypothesis made for the previous scenario but it also considers that the manpower

is “perfectly” flexible through the specializations, setup and general working activities.

FTE

The three sensitivity analysis start from the same “run”:

— page 110 —

Table 60 All Intern Base Optimum Batch Size variation - FTE Results

The above (Table 60) shows the FTEs required in this optimum scenario, when the order batch size

changes.

This (Table 60) has to be compared to the (Table 30) and the difference between the two tables

represent the gain that flexibility through specialization could carry on.

Table 61 All Intern Base Optimum Velocity Line variation - FTE Results

Table 61 considers the FTE results when the working rate per standard line is changed. These results

have to be compared with those of Table 31.

Table 62 All Intern Base Optimum Order batch Size & Velocity Line variation - FTE Results

Table 62 combines the two sensitivity levers: the results for this combination, as per previous set

“all Intern Base”, demonstrate a real exceptional value for Maximum number of FTEs simulated.

This hypothesis combines a very little order size with a very slow working rate. This typical

— page 111 — “handcraft” situation is faced by “industrial” quantities. The results are out of the normal

boundaries.

COST

As per the “All Intern Base” scenario and sensitivity sections, it could be useful to consider the cost

that the number of people requested causes.

Table 63 shows the cost for working people and setup operators. It has to be compared with Table

33.

The difference between the two tables suggests the possible gain as a consequence to the flexibility

through specialization. Table 63 All Intern Base Optimum Batch Size variation - COST Results

While Table 63 considers the order size variation, the following Table 64 considers only the working

rate variation.

— page 112 — Table 64 All Intern Base Optimum Velocity Line variation - COST Results

Finally, Table 65, as per scenario “All Intern Base” Table 35, considers the two sensitivity levers

together.

Table 65 All Intern Base Optimum Order batch Size & Velocity Line variation - COST Results

Maximum value, as has already been noted in the other previous sections, is probably linked to

extraordinary situation with “handcraft” order size and working rate but “industrial” quantities. But

also the average value shows how the combined levers push higher the environment costs.

— page 113 — Lines

Table 66 All Intern Base Optimum Batch Size variation - Lines number Results

It is essential to note the number of lines that the sensitivity applied to the “All Intern Base

Optimum” scenario could simulate.

Table 66 shows the results as function of the order size variation.

— page 114 — Table 67 All Intern Base Optimum Velocity Line variation – Lines number Results

Table 67 moves the working rate per standard line and shows the results in terms of lines required.

Finally, Table 68 reports the number of lines required by a sensitivity analysis on the “all Intern base

Optimum” scenario using both the levers, order size and working rate.

— page 115 — Table 68 All Intern Base Optimum Order batch Size & Velocity Line variation - Lines number Results

As per previous section, please note the extraordinary effect on the maximum value, due to the

combination of both levers: “handcraft” order size and working rate activity with “industrial”

quantities.



The following Table 69 shows the flow time per piece when the order size is varied inside the All

Intern Base Optimum scenario Table 69 Technical Flow Time per pcs - order size variation


2,13 0,93 0,53

As per the previous section, we can state that the same date simply presented per average order is

more significant. Following Table 70 presents this data:



102,88 22,11 4,30

The graph setting out the data along the time schedule could also give a global impression of the

variability of the series. It points up the difference between maximum, minimum and average

values.

— page 116 — Figure 30 All Intern Base Optimum Flow Time per Customer (average of customers)

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The following graph shows the same indicator per average order size

Figure 31 All Intern Flow Time per average order (all the customers)

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Table 71 illustrates the technical frozen time, including order and component preparation time



2,21 0,96 0,54

— page 117 —

The same data, but per order and not per piece, are shown into the following Table 72



103,48 22,71 4,90

As per previous sections, we report into the following Table 73 the data considering also the

“maceration” time fixed in days by external suppliers.







The fixed days for maceration obliges to consider the “total frozen” indicators as per shift: the

working day is different according to the working time applied.

A different viewpoint, is always that of the family product.

Table 74 All Intern Base Optimum Flow Time per Product Family piece (average of customers)






Cream 2,53 1,14 0,93

Deo 3,33 1,74 -

Vials 0,48 0,32 0,23




And even for the product family viewpoint, the average order scale size could be more interesting:

— page 118 —

Table 75 All Intern Base Optimum Flow Time per Product Family order (average of customers)






Cream 21,97 9,68 3,11

Deo 10,98 5,79 -

Vials 92,66 35,85 5,49

HotelLine 101,80 39,33 -


Grand Total 101,80 17,02 -


This section considers the effects inside the All Intern Base Optimum scenario of a working rate

sensitivity.



1,40 0,66 0,27

Table 76 considers the technical flow time per piece while the following Table 77 translates the

same data into the flow time per average order.



24,69 11,17 4,61

The following graph Figure 32 shows the “channels” maximum, minimum and average inside of the

flow time per piece.

— page 119 — Figure 32 All Intern Base Optimum Flow Time per Customer (average of customers) working rate sensitivity

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As per preceding sections, the following graph, Figure 33, translates the previous one in time, hours,

per average customer order size.

Figure 33 26 All Intern Base Optimum Flow Time per Customer Order (average of customers) working rate sensitivity

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The “frozen technical time” connects the process flow time with the order and components of

preparation time. Table 78 presents the indicator per single piece:

— page 120 —



1,47 0,70 0,29

While the following Table 79 translates the data into hours per average order.



25,29 11,77 5,21

Finally, the total frozen time takes also in consideration the maceration time expressed in days by

the external suppliers. The Table 80 is presented per shift considering that the hours per days are

function of the organization applied (shifts per day):







As always the following tables present data from a different viewpoint: the product families.

Table 81 All Intern Base Optimum Flow Time per Product Family piece (average of customers)






Cream 1,59 0,81 0,51

Deo 2,71 1,25 -

Vials 0,52 0,22 0,09




Table 81 presents the flow time per product family piece, while the following Table 82 translates

the data into hours concerning the product family flow time per average order.

— page 121 —

Table 82 All Intern Base Optimum Flow Time per Product Family order (average of customers)






Cream 17,84 7,04 4,26

Deo 8,74 4,25 -

Vials 76,31 24,11 8,03

HotelLine 84,98 25,04 -




This section considers the combined effect of the two sensitivity levers together (order size and

working rate) on technical indicators



5,23 1,40 0,46

Table 83 presents the flow time per piece, while the following Table 84 translates the time per

average customer order.



256,76 33,30 5,18

The same data is seen in the following graph showing the time per piece along the time schedule,

Figure 34, and the time per order, Figure 35.

— page 122 — Figure 34 All Intern Base Optimum Flow Time per Customer (average of customers) working rate & order size

sensitivity

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As per scenario All Intern base, please note that the combined effect of the two sensitivity levers

pushes towards a worst case far from the average data.

Figure 35 All Intern Base Optimum Flow Time per Customer order (average of customers) working rate & order size

sensitivity

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The following two tables present the technical frozen time (including the order and component

preparation time).

Table 85 shows the frozen time per piece

— page 123 —



5,30 1,43 0,47

While the following Table 86 translates the time per average order.



257,36 33,90 5,78

Table 87 considers the “maceration” time and sets out the data per shift.







Finally the product family viewpoint.

Table 88 All Intern Base Optimum Flow Time per Product Family piece (average of customers) working rate & order

size variation






Cream 8,05 1,64 0,60

Deo 15,90 2,63 -

Vials 2,18 0,52 0,13




Table 88 shows the operation flow time, hours, per piece.

Table 89 translates this time in hours per single average order.

— page 124 — Table 89 All Intern Base Optimum Flow Time per Product Family order (average of customers) working rate & order

size variation






Cream 17,84 7,04 4,26

Deo 8,74 4,25 -

Vials 76,31 24,11 8,03

HotelLine 84,98 25,04 -



“All Intern Reduction Days, Concurrent” Sensitivity

These scenarios, as already suggested, try to combine the actual situation in terms of customers’

order concentration over the days, flexibility through the customers, product family and macro-

phase activities and “concurrent” operations rate.

On this estimation of the real situation, the sensitivity analysis varies the order size and the working

rate.

FTE

The three sensitivity analysis start from the same simulation “run”:

Table 90 All Intern reduction days concurrent Batch Size variation - FTE Results

Previous Table 90 shows the FTEs required in this “realistic” scenario when the order batch size

changes.

This Table 90 has to be compared to the Table 30 and the difference between the two tables

represents the gain that soft skill or planning accuracy training could carry out.

— page 125 —

Table 91 All Intern reduction days concurrent Velocity Line variation - FTE Results

Table 91 considers the FTEs results when the working rate per standard line has changed. These

results have to be compared with those of Table 31.

Table 92 All Intern reduction days concurrent Order batch Size & Velocity Line variation - FTE Results

Table 92 merges the two sensitivity levers: the results for this combination, as per previous set “all

Intern Base”, show a real exceptional value for the simulated Maximum number of FTE. This

hypothesis combines a very little order size with a very slow working rate. This typical “handcraft”

situation is faced by “industrial” quantities and in this sets an inefficiency higher than in previous

“optimum” environments. The results are outside of the normal acceptable boundaries.

COST

As per the “All Intern Base” scenario and sensitivity it could be useful to consider the cost that the

number of people requested causes.

Table 93 shows the cost for working people and setup operators. It has to be compared with Table

33.

The difference between the two tables suggests the possible gain due to the optimization of the

planning activity, in a general way the soft skills training.

— page 126 — Table 93 All Intern reduction days concurrent Batch Size variation - COST Results

While Table 93 considers the order size variation, the following Table 94 considers only the working

rate variation.

Table 94 All Intern reduction days concurrent Velocity Line variation - COST Results

In conclusion, Table 95, as per scenario “All Intern Base”, Table 35, considers the two sensitivity

levers together. The average value, higher than the previous sets, underlines the shift that the two

levers together could put into the system.

— page 127 — Table 95 All Intern reduction days concurrent Order batch Size & Velocity Line variation - COST Results

Maximum value, as already noted in the other previous sections, are probably linked to

extraordinary situation with “handcraft” order size and working rate but in “industrial” quantities.

Lines

Table 96 All Intern reduction days concurrent Order batch Size & Velocity Line variation - Lines number Results

— page 128 — As per previous sections, it is important to note the number of lines that the sensitivity applied to

the “All Intern Base Optimum” scenario could simulate.

Table 96 shows the results as function of the order size variation.

Table 97 All Intern reduction days concurrent Velocity Line variation – Lines number Results

Table 97 moves the working rate per standard line and shows the results in terms of lines required.

Finally, Table 98 reports the number of lines required by a sensitivity analysis on the “all Intern

reduction day concurrent” scenario using both the levers, order size and working rate.

— page 129 — Table 98 All Intern reduction days concurrent Order batch Size & Velocity Line variation - Lines number Results

As per previous section, please note the extraordinary effect on maximum value due to the

combination of both levers: “handcraft” order size and working rate activity with “industrial”

quantities.



This section considers the effects on the technical indicators of the order size variation (inside the

approximation to the real situation scenario: “All Intern Reduction days and Concurrent”)

Table 99 Technical Flow Time per pcs - order size variation


2,13 0,93 0,53

Table 99 reports the operation flow time per piece, while the following Table 100 translates the

time per single average order.



102,88 22,11 4,30

The following graph shows the technical flow time per piece along the time schedule.

— page 130 — Figure 36 All Intern reduction day concurrent Flow Time per Customer (average of customers)

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Figure 37 translates always along the time schedule the single average order flow time.

Figure 37 All Intern reduction day concurrent Flow Time per Customer order (average of customers)

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The following Table 101 shows the time for the preparation activities (order and component

preparation time) and the “frozen” technical time expressed per single unit.



2,21 0,96 0,54

— page 131 —

The following Table 102 translates the data per single average order.



103,48 22,71 4,90

As always the “total frozen time” considers the “maceration” time given by external suppliers,

fragrance suppliers, and expressed in days: this indicator considers the working organization and it

is reported for the three different shifts







Finally the product family viewpoint with only the main flow time indicator.

Table 104 All Intern reduction days concurrent Flow Time per Product Family piece (average of customers)






Cream 2,53 1,14 0,93

Deo 3,33 1,74 -

Vials 0,48 0,32 0,23




Table 104 expresses the results in terms of time per single unit, while the following Table 105

translates the data per single average order.

— page 132 —

Table 105 All Intern reduction days concurrent Flow Time per Product Family order (average of customers)






Cream 21,97 9,68 3,11

Deo 10,98 5,79 -

Vials 92,66 35,85 5,49

HotelLine 101,80 39,33 -


Grand Total 101,80 17,02 -


This section considers the working rate lever applied to the “All Intern Reduction Days Concurrent”

scenario.



2,13 0,93 0,53

Table 106 shows the data, flow time, per single piece, while the following Table 107 sets out the

same data per single average order.



102,88 22,11 4,30

The following graph, Figure 38, shows the flow time per piece all along the time schedule

— page 133 — Figure 38 All Intern reduction days concurrent Flow Time per Customer (average of customers) working rate

sensitivity

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The Figure 39 sets out the previous graph in flow time per single average order and shows the

“channel” maximum-minimum, with the average value inside, for all the time schedule.

Figure 39 All Intern reduction days concurrent Flow Time per Customer Order (average of customers) working rate

sensitivity

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The “frozen technical time” considers the process flow time and the order and components

preparation time

— page 134 — Table 108 shows this time per single unit.



2,21 0,96 0,54

While the following Table 109 sets out the results for the average order.



103,48 22,71 4,90

Finally, the total frozen time considers also the maceration time expressed in days by the external

suppliers. The Table 110 is presented per shift considering that the hours per days are function of

the organization applied (shifts per day):







Finally, the product family point of view only for the main operational flow time.

Table 111 All Intern reduction days concurrent Flow Time per Product Family piece (average of customers)






Cream 2,53 1,14 0,93

Deo 3,33 1,74 -

Vials 0,48 0,32 0,23




Table 111 presents data per single piece, while the following Table 112 shows the flow time per

product family order, average single order.

— page 135 —

Table 112 All Intern reduction days concurrent Flow Time per Product Family order (average of customers)






Cream 21,97 9,68 3,11

Deo 10,98 5,79 -

Vials 92,66 35,85 5,49

HotelLine 101,80 39,33 -


Grand Total 101,80 17,02 -


This section presents the combined effect of the two sensitivity levers together (order size and

working rate) on technical indicators



5,23 1,40 0,46

Table 113 presents as always the flow time per piece while the following Table 114 sets out the time

per average customer order.



256,76 33,30 5,18

This previous data is shown along the time schedule in the following two graph.

— page 136 — Figure 40 All Intern reduction days concurrent Flow Time per Customer (average of customers) working rate & order

size sensitivity

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Figure 40 presents the flow time per single unit.

Figure 41 All Intern reduction days concurrent Flow Time per Customer order (average of customers) working rate &

order size sensitivity

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Figure 41 shows the flow time per average order all along the time schedule.

The following two tables present the technical frozen time (including the order and component

preparation time).

— page 137 — Table 115 shows the frozen time per piece



5,30 1,43 0,47

While the following Table 116 sets out the time per average order.



257,36 33,90 5,78

Table 117 considers the “maceration” time and shows the data per shift.







Finally the product family viewpoint.

Table 118 All Intern reduction days concurrent Flow Time per Product Family piece (average of customers) working

rate & order size variation






Cream 8,05 1,64 0,60

Deo 15,90 2,63 -

Vials 2,18 0,52 0,13




The previous table shows data per single unit while the following table sets out the same flow time

per average product family order.

— page 138 — Table 119 All Intern reduction days concurrent Flow Time per Product Family order (average of customers) working

rate & order size variation






Cream 131,13 14,28 3,14

Deo 57,12 8,52 -

Vials 478,24 58,11 5,56

HotelLine 572,87 58,44 -


Grand Total 572,87 25,73 -

— page 139 —

CHAPTER 7

7 Discussion

After all the presentations made for the background and the case study, we have to set out the aim

of the research project: measuring and evaluating the organizational levers to cope with extreme

plan, volume and mix flexibility requirements.

This statement summarises also the introduction to ICR spa environment and mission: “Today well-

known in the market for the high quality served, ICR defines the quality offered not only in terms of

product characteristics but also in terms of services complementary to the “physical” product

attributes. A mix of “old” and “new” economy.” This was the last statement before exploring ICR

Spa in the previous sections.

What is the mix between the old and new economy that we refer to?

An operator, like ICR Spa, completely in the middle of the supply chain, which has to provide a

“physical” product/line (the “old” part declined in a mix of products, and its attributes, inside every

product line) enriched by “services” (the “new”: from the purchase to the delivery service, coming

through the planning and scheduling - not only “industrial” but also “commercial” or at least dealing

with the customer commercial requirements -, the quality, the regulatory, …).

Often and even more frequently, the customer asks for a new active role played by ICR, searching

and imagining new services or, at least, anticipating and solving problems, accidents and customer

future challenging necessities: proposition of planning alternatives or different production process

path… These are more and more frequently “wishes” expressed by customers during the

coordination meetings.

This “new service” viewpoint has to be combined with the efficiency search forced by customers

with growing quantities, who reach a relevant industrial demand level (the economic value, in

absolute terms as well as per single items or in %, of the customer activity entices the customer to

search more and more better possible prices or conditions), especially if the growing curve of his

demand shows a deceleration, even only for a more “mature” product line life cycle reached status.

If the service content provided is increasingly important in all the products that move along their

life-cycle (and ICR activity is an example), a second important point to be considered is linked to the

specific project analysed by this paper. As already described, the “negative” environment and the

author’s “driving” role inside the company obliged to change the focus from a real Action Research

project to a more “simulated” approach. This “change” let’s review the paper sections from a

slightly different viewpoint:

The literature review as a continuous search of interesting arguments useful to investigate

the economic “panorama”, especially the Italian one, with a focus in small/medium

companies developing choices: from a topic review to a an exploration of arguments that

need to be investigate in order to draw a future developing path in a continuous review of

new theoretical contributions

— page 140 —

The Action Research from a pro-active plan and positive loop action-test-feedback to a new

research project less action “inside” and more “external” evaluation to define the desirable

future “démarche”

The simulation project from an evaluation tool to “thinking” moment on the system and its

future developing path

With these previous context boundaries, we could review the data presented into previous sections

making some notes and remarks. First of all, some reflections on the model building itself

7.1 Some notes and remarks about the model

Firstly, of all we have to consider that all the set of “base” scenarios were built to measure the model

with the real company world and validate the relationships and their estimated parameters.

This passage was necessary because not all the data were available to confirm the model validity.

Checked the answer of the model, it was possible to recalculate some coefficients, used as levers in

different other scenarios and sensitivity analysis, with a calibration approach: given some results,

for example, which combination of unknown and not measured levers, in real context, could explain

the setup operators hired levels? This was the case concerning the “Reduction Days” or

“Concurrent” rates.

Another point to be considered is directly link to the “use” of the model: if the time to set-up the

model itself is relevant – and this was the real case – different conditions of the environment appear,

while the study time is spent, and these ones could or should have to be entered into the model.

The model has to adapt itself to reality but always maintaining a structure useful either to check the

“past” or to come into the near future: it is not an easy task. The “base” set of scenarios was helpful

to draw the system in the past while the “all intern” one was convenient to measure some key

indicators in the past (the setup operators who covered internal and outsourced activities) but it is

surely instrumental to deeply measure the future, given the important change recorded with the

“internalization” of the outsourced activities, made at the beginning of 2015.

It’s not the target of this work but the role of the simulation environment in driving the thinking of

the actors involved in a change process or project, it is an important argument to be considered:

imagining the system with an internal viewpoint and trying to shift this viewpoint externally, even

in a simulated way, with all the limits of a simplified model, lets researchers to forecast the rules

and the answers of an initially “black” box. Maybe the system is not really a black box, but surely a

social system is difficult to describe entirely or at least it is difficult to be sure that our system is

complete and exhaustive. This is the main boost of using a simulation model: continuous

improvement in thinking.

7.2 Some notes and remarks about the environment

All the data presented for the ICR Spa case study in the previous sections about the market trends,

the customer characteristics and the industrial structure are not only the “difficult” part of the

environment, they are the “external” forces of the model.

— page 141 — Thinking, about these external boundaries and the “pressure” that forces external to these system

“walls” could apply on the variables of our model, is important for several reasons.

Surely to understand which forces could have a higher probability to show and which effects we

could simulate: when (Figure 20 Average Lot Size all along the time from launch) and (Figure 21

Average Lot Size, 50ml equivalent, all along the time from launch) point up the lot variability and

the coincidence, or at least the approximation, of the lot size and variability among different

customers, even at the end of the life cycle, a sensitivity analysis on the lot size becomes more

interesting and it is not only a simple curiosity. Modelling and simulating on the main variables is

important, otherwise any variable has to be tested without an initial logic hypothesis. In this way

we chose the “lot size/order size” and the “working rate per average production line” as sensitivity

levers to be tested inside the three meaningful scenarios (“All Intern Base”, “All Intern Base

Optimum” and “All Intern Reduction Days Concurrent and Flexibility”).

These two levers are “external” to the model, even if the second one, “working rate”, could be seen

as an element of the system (and consequently of the resulting model). The “working rate” is an

attribute of the existing production line apparatus but if the sensitivity analysis tests values out of

the “normal” technological tolerance, the model builds up some new hypothesis, concerning the

main characteristics of production lines not available (working rate variability but also effect on the

working team, setup team, efficiency on working time, efficiency on setup time, ….). Another

question useful to be considered is “At which moment we can consider that the horizon considered

shifts from short to long?”. If theory, economic theory, describes long term horizon as the time

where all the restrains are not fixed, a simulation on the field could find some difficulties to clearly

identify this time. Moreover, short term decisions could often have relevant effects on long term

available choices.

The comprehension or at least the attempt to comprehend the external boundaries and the forces

operating outside the model scope, in our case all the analysis on the commercial side, the series

concerning customers or products presented in previous sections (“4.1.2 Some data to understand

the trends”, “4.1.3 Data Context Analysis: Market data”, “4.1.5 A different Viewpoint: Time Series

from Launch Time” and “4.1.6 Series characteristics as Customers’ requests and their effects on the

industrial system”) is useful to understand if this study fields could be reviewed as new forces model

in a different way the system. From this viewpoint the “detection” of rules or pattern in “external”

environment and/or forces could immediately push to draw a different model, able to answer

effectively and efficiently to “outer shock” but this “drawing and testing” phase has to be translated

into a system change, once confirmed and evaluated. It is similar to imagine a continuous modelling-

testing-changing-remodelling loop.

7.3 Some notes and remarks about the simulation results

To describe, comment and think about the model simulation scenarios results and sensitivity

analysis, we have to recall the Figure 2 Framework and objectives and just to focus on this scheme

we re-present the scheme in the following Figure 42 Framework and objectives - Figure 19 recall

— page 142 — Figure 42 Framework and objectives - Figure 19 recall

As per previous presentations and discussions, the “AS IS” situation is identified with the scenario

“All Intern reduction days, Concurrent Flexibility” where we applied the coefficients calibrated with

the “Base Reduction Days Concurrent Flexibility”. This last one found the right values for some

coefficients (concentration or reduction days requested by customers, concurrent operations rate

during the batch time, flexibility rate among customers’, production family, macro-phase

operations) to be inserted into the model to obtain some indicator levels (mainly the workers and

the setup operators).

This scenario could be seen as the starting point which the system, analysed by the model, wants to

move from.

7.3.1 Scenarios comparison

We will review in detail the results following a theoretical path that from the actual situation tries

to reach an “optimum” target.

But we think that a brief summary could also help the perception just before analysing the result

detailed.

In the following (Figure 43), we could find a summary of the scenarios results.

If the first one it could be viewed as the AS-IS situation, the last one is the possible target once

obtained an optimum planning activity.

The positive difference between the two last scenarios shows the effect of the flexibility through

the workers’ specialization while the second and the third scenarios are a technical passage from

the AS-IS situation to the two last scenario targets.

— page 143 — Figure 43 - Total Monthly FTE over 10 year horizon

The number of Monthly FTEs requested from ta AS-IS and the optimum targets scenarios is

significant.

Figure 44 - Total Manpower Cost

If Figure 43 could be considered as a technical summary, the following Figure 44 is more

“professional” and less “theoretical”: it is an economic summary of the gain, cost reduction,

attainable over ten years if the company could move from the AS-IS to the optimum situation.

From a theoretical point of view we could say that this results depends by the initial situation and

the company ability to pass through the different status, but in a more general way we could also

review into the differences the attainable efficiency.

The question at this moment could be: “is it only an efficiency issue?” The answer needs also to view

the following figure.

— page 144 — Figure 45 - Filling Full Time Equivalent Lines over 10 year horizon

If the FTEs and their cost could be viewed as a profit and loss argument, the number of production

lines requested to fulfil the customers’ demand has a double viewpoint:

A cost issue, linked to the equipment requested to produce, and its amortization aspects

An investment meaning, which recalls the multi-year contribution to the production capacity

but also to the fixed costs that the activity needs to cover with its margin

The last point remembers not only the variable-fix leverage analysis that every industrial situation

requests but also the risk, negative risk, which a fixed structure could represent in a “turbulent”

market. If the downside of the market could really question the “survival” of a fixed structure, this

Figure 46 - Full Time Equivalent Line space requested

risk value needs to be carefully analysed and the flexibility issue becomes and efficiency one in

medium/long term horizon.

The production Line number could find a more important indicator in the squared meters requested (see

— page 145 — Figure 46) or in the Investment amount, if we want to review the economic side of the argument

(see Figure 47).

Figure 47 - Full time Equivalent line Investment

7.3.1.1 “Starting point” scenario: “All Intern Reduction Days Concurrent

Flexibility”

Concerning the simulation model, the scenario “All Intern Reduction Days, Concurrent and

Flexibility” shows the main data:

- FTE necessary, registered as progressive sum of single month full time equivalent, workers

plus setup operators, equal to 44,7 KFTE in one shift per day configuration, 44,2 KFTE in two

shifts configuration and 49,7 KFTE in three shifts configuration (see Table 13 Scenarios Result

per one shift, Table 15 Scenarios Results per two shifts and Table 16 Scenarios results per

three shifts)

- FTE Necessary Cost, in line with base FTE numbers, is 111,71 million euro in one shift per day

configuration, 110,43 million euro in two shifts configuration and 124,14 million euro in

three shifts configuration (see Table 13 Scenarios Result per one shift, Table 15 Scenarios

Results per two shifts and Table 16 Scenarios results per three shifts)

- The production line equivalents, wished to fulfil the quantity requested by customers and

equal to historical quantities, are recorded into the following summary table:

Table 120 Summary lines All Intern reduction Days Concurrent Flexibility

N° of lines equivalent per one shift two shifts three shifts

Filling 50 27 21

Packaging 35 19 14

Wrapping 22 12 9

Code Writing 12 7 6

Cost of Lines in million euro 48,00 26,05 19,90

Space requested in squared meters 6.030 3.268 2.479

N° Lines equivalent

— page 146 —

(see Table 13 Scenarios Result per one shift, Table 15 Scenarios Results per two shifts and

Table 16 Scenarios results per three shifts).

- The technical indicators concerning the “Technical Flow Time, the Technical Frozen Time and

the Total Frozen Time”, measured in hours per piece and customer point of view, are

recorded in (Table 17 Technical operation flow time per customer), (Table 18 Technical

operation frozen time per customer) and (Table 19 Total frozen time per customer); the

same indicators, in hours per piece but per product family and not per customer point of

view, are recorded in (Table 20 Technical operation flow time per product family 1st part),

(Table 21 Technical operation flow time per product family 2nd part), (Table 22 Technical

operation frozen time per product family 1st part), (Table 23 Technical operation frozen time

per product family 2nd part), (Table 24 Total frozen time per product family 1st part) and

(Table 25 Total frozen time per product family 2nd part). These indicators are not detailed

and discussed here, because according to the model they are function of some coefficients

and variables not involved by the different hypothesis of the “All Intern “scenarios. These

data are fix through the different scenarios of the same set: some difference between the

“Base” and “All Intern” sets exist, because the quantities assigned to outsourced or external

suppliers are not proportionally in line with the internal ones, either in product family or

macro-phase and customer split.

Some general comments are appropriate.

The values per pieces show technical times more or less around 1 hour per piece and customer

viewpoint: more variable per product family viewpoint (not all the product family are requested by

all the customers and some of these one are not really representatives). Due to the real situation

the process time is very slender.

More we move along the technical indicators versus the total frozen time, per customer or per

product family, more we realize that the “technical” (process time plus technical preparation) or

“process” (only process time) time are potentially reactive, due to their short incidence on the total

frozen time. This last one is mostly due to the “external” maceration time, expressed by the “fine

fragrance” supplier. This is an external restraint, which could be hardly modified only in a “long”

time (and only with a strong commitment by the customers, owners of the raw materials and the

real contact of the fragrance suppliers). These characteristics recall the limits due to the role

assigned to a company occupying a middle position inside the supply chain: all the chain has to work

together to lower some restraints, otherwise they become external and not modifiable by every

single supply chain actor.

These indicators, expressed per customer average order or per product family average order, show

the same information, but, sometimes, they could be more significant. Even if it is important to have

the perception of the process time per piece compared to every other time necessary to complete

the operations and make the product available for the customer, it is probably more interesting to

have the same information for an average order and not for single piece: the customer asks for his

order in total and not the single piece; when are all the pieces available for shipment, generally

speaking, when is the order complete? From this viewpoint these indicators are a mix of internal,

mainly the process and technical preparation time, and external info, order size and maceration

time for example.

— page 147 —

7.3.1.2 “Final target” scenario: “All Intern base Optimum”

If we consider the “target” situation, “TO BE”, of the (Figure 2 Framework and objectives) and

(Figure 42 Framework and objectives - Figure 19 recall), we could consider this objective situation

as “pictured” by “All Intern Base Optimum” scenario.

As already mentioned, this scenario considers

all the quantities as worked “inside”,

the “perfect” planning and scheduling activity able to optimize the production plan and the

available resources and

a “perfect” flexibility through the “workers’ specialization”.

In other words this is the best scenario: everything is optimized.

Considering our (Figure 42 Framework and objectives - Figure 19 recall), with this scenario we are

in the upper right corner of the Figure with the “Scheduling” and “Workers” flex completely reached.

If we want to follow the scheme of the previous scenario, the “All Intern Reduction Days, Concurrent

and Flexibility”, we have to note that:

- FTE necessary, registered as progressive sum of single month full time equivalent, workers




three shifts)


configuration, 77,04 million euro in two shifts configuration and 84,55 million euro in three

shifts configuration (see Table 13 Scenarios Result per one shift, Table 15 Scenarios Results

per two shifts and Table 16 Scenarios results per three shifts)



Table 121 Summary lines All Intern Base Optimum


Filling 43 24 19

Packaging 28 15 12

Wrapping 18 10 8

Code Writing 10 6 5






- The technical indicators concerning the “Technical Flow Time, the Technical Frozen Time and

the Total Frozen Time”, measured in hours per piece and customer viewpoint, are recorded

in (Table 17) through (Table 25), are fixed. As already mentioned, these indicators do not

pertain to the changed levers through the two scenarios considered: the actual-real situation

approximated by the “All Intern Reduction Days Concurrent Flexibility” and the optimum-

result approximated by the “All Intern Base optimum”.

Even for this scenario, some overall comments are important.

— page 148 —

- The difference of FTE and cost between the two scenarios is

o Relevant in absolute values:

FTE necessary, registered as progressive sum of single month full time

equivalent, workers plus setup operators, equal to -15,9 KFTE in one shift per

day configuration, -13,4 KFTE in two shifts configuration and -15,9 KFTE in

three shifts configuration

FTE Necessary Cost, in line with base FTE numbers, is -39,10 million euro in

one shift per day configuration, -33,39 million euro in two shifts configuration

and -39,59 million euro in three shifts configuration

o Relevant in a different scale if considered on the monthly time batch:


equivalent, workers plus setup operators, equal to -133 FTE in one shift per

day configuration, -112 FTE in two shifts configuration and -132 FTE in three

shifts configuration

FTE Necessary Cost, in line with base FTE numbers, is -0,33 million euro in one

shift per day configuration, -0,28 million euro in two shifts configuration and

-0,33 million euro in three shifts configuration

o Significantly relevant if considered in %:


equivalent, workers plus setup operators, equal to -36% in one shift per day

configuration, -30% in two shifts configuration and -32% in three shifts

configuration

FTE Necessary Cost, in line with base FTE numbers, is -14% in one shift per

day configuration, -12% in two shifts configuration and -13% in three shifts

configuration

- Considering the technical configuration requested, the following table points out the

differences between the two scenarios:

Table 122 Summary lines differences between Optimum and Actual scenarios


Filling -7 -3 -2

Packaging -7 -4 -2

Wrapping -4 -2 -1

Code Writing -2 -1 -1

Cost of Lines in million euro 7,80- 3,75- 2,15-

Space requested in squared meters 1.015- 507- 281-


This table suggests a series of thinking:

The values and their differences have a “meaning” if compared in a “vertical” way:

data related to “one shift” configuration, for example. From this viewpoint, the

differences confirm the “big” gap that could be covered with people trained to be

flexible through the specializations and “optimum” soft skills implemented in the

best planning

But the values open also some different considerations, reviewing them moving the

analysis focus from “vertical” to “horizontal”: simply checking (Table 14 Real lines

— page 149 —

installed) this is obvious that the company organization mixed in the past the “one

shift” and “two shifts” organization. The number of lines installed and used confirm

this idea placing themselves between the boundaries calculated by the model.

Changing the organization could improve the positive gap searching a new optimum

target (from one shift to three shifts, for example; for some macro-phase or not all?)

The space and number of lines, actually, is probably considered only when they

become a restraint: when the space is not available or the cost of new equipment is

relevant and has to be discussed for authorization

- And finally concerning the technical indicators like process or frozen time no differences

appear due to the fact that the levers influencing these indicators have not changed inside

the “All Intern” family scenarios.

7.3.1.3 “Mid-Point” scenario: “All Intern Base”

Before reviewing the data of this scenario, compared to the actual situation, again, we have to come

back to (Figure 42 Framework and objectives - Figure 19 recall).

If the title “Mid-Point” itself suggests that we are discussing a “passage” between actual, starting

point situation, and the final target, which path is this scenario following, according to the double

way (Figure 42) suggested as possible?

The two paths described had a simple idea as foundation: “workers flexibility” and

“planning/scheduling flexibility” are independent. We could reach the target modifying the

“workers” skills with training and education before changing the “soft” skills of the system like

“planning and scheduling” ability or vice versa.

Inside the simulation model, it was easier to consider a single path where the “mid-point” scenario

considers to have obtained all the soft skills, like planning and scheduling, eliminating the orders

concentration rate or the activity concurrent rate but maintaining the specializations existing. The

difference between the actual situation, partly calibrated in some “important” coefficients/levers

from the actual data, and the “planning/scheduling” optimum is the first passage of the simulation,

because the final/target scenario simplifies all the rules forcing everything to the best option,

leaving little space to how this situation is reached: it is a “all-in” or “all-out” scenario not declined

in sub-elements.

With this approach, it is normal to consider a single simulation path where “mid-point” versus

“actual” suggests the first change, while the second step “optimum” versus “mid-point” is seen as

simple difference between the total change “optimum” versus “actual” minus the first change here-

above, “mid-point” versus “actual”. The second passage is simply a difference of two main values:

total minus first one; it is not measured as stand alone, from the mid-point to final.

If we want to follow the scheme of previous scenarios, the “All Intern Reduction Days, Concurrent

and Flexibility” and “All Intern Base Optimum”, we have to note that:

- FTEs necessary, registered as progressive sum of single month full time equivalent, workers




three shifts)

— page 150 —


configuration, 78,44 million euro in two shifts configuration and 86,03 million euro in three

shifts configuration (see Table 13 Scenarios Result per one shift, Table 15 Scenarios Results

per two shifts and Table 16 Scenarios results per three shifts)



Table 123 Summary lines All Intern Base


Filling 43 24 19

Packaging 28 15 12

Wrapping 18 10 8

Code Writing 10 6 5






These figures are exactly the same showed for previous “optimum” scenario: in fact, the

hypothesis made for this scenario is technically the same for the best solution with only one

difference; the workers flexibility through the specializations. This restraint has effect only

on the setup operator optimization due to the fact that the model didn’t have any input

concerning the inefficiency on “general workers” due to the lack on assignment during the

setup operation. This aspect, not measured in reality, was overridden by the perfect planning

hypothesis made for the two “All Intern Base” and “All Intern base Optimum” scenarios. The

model does not measure this “inefficiency” which could only be considered as drowned into

the main difference between these two scenarios and the actual “All Intern Reduction Days

Concurrent Flexibility”.

- And finally concerning the technical indicators like process or frozen time no differences

appear due to the fact that the levers influencing these indicators have not changed inside

the “All Intern” family scenarios.

According to the approach herewith above described, the differences presented are between both

the “extreme” scenarios of the “All Intern Base” set.

- The difference of FTE and cost is

o Versus the “All Intern Reduction Days Concurrent Flexibility”:

FTEs necessary, registered as progressive sum of single month full time

equivalent, workers plus setup operators, equal to -15,3 KFTE in one shift per

day configuration, -12,8 KFTE in two shifts configuration and -15,2 KFTE in

three shifts configuration

FTE Necessary Cost, in line with base FTE numbers, is -38,18 million euro in

one shift per day configuration, -31,95 million euro in two shifts configuration

and -38,08 million euro in three shifts configuration

o Versus the “All Intern Base Optimum”:

— page 151 —


equivalent, workers plus setup operators, equal to +0,6 KFTE in one shift per

day configuration, +0,6 KFTE in two shifts configuration and +0,6 KFTE in three

shifts configuration

(Even if the figures seem always the same, they are different at the second

decimal)

FTE Necessary Cost, in line with base FTE numbers, is +1,43 million euro in

one shift per day configuration, +1,44 million euro in two shifts configuration

and +1,48 million euro in three shifts configuration

- The difference of lines installed is zero versus the “All Intern Base Optimum” and equal to

the difference recorded by the difference between “All Intern Base Optimum” and the actual

situation for the difference versus “All Intern Reduction Days Concurrent Flexibility”: as

already explained this scenario coincides with the best one for the equipment installed,

having all the differences focused on the setup operators.

- As per previous scenarios, the technical indicators are fixed for all the “all Intern” family

scenarios.

As already mentioned most of the differences between best and worst scenarios is explained by the

“soft skills” optimization: the passage from worst scenario to the mid-point one explains most of

the possible increase. The last passage, between “mid-point” and the best case scenario, could only

record the optimization in setup operators and this “basket”, in our model and system, is limited to

about 10 FTE.

7.3.2 Scenarios Sensitivity

Based on the three scenarios already described, we want to review how the system, the model,

reacts to important changes on some basic levers: the two levers chosen, customer order size and

working rate per production line, are the main variables which is considered as external with a great

impact on the internal structure and organization.

We want to put a special emphasis on the two levers because they assume a significance as key

indicator of the passage from “handcraft” to an “industrial” activity. The Italian well-known quality

in some markets or activity (and the luxury, fashion market with the fine fragrance or perfume sector

is an example) is often linked to the small/medium company, working little orders in a very

“handcraft” way with extreme attention to quality issues, using simple equipment or working line

qualified by “slow”, very “slow” production rates; on the other hand “old economy plant”, partly

described in previous Context section, big continuous production plant, is characterized by large

order size, typically standardized products, and equipment line “hardly” connected with several

phases integrated and high production rate, low level team, important setup time.

Using the two levers quoted is like to say we want to test the answer of the system to a broad range

of activity: “handcraft” versus “industrial”, or vice versa. The only limit of our sensitivity is due to

the quantity used as input of the system: it is always the same at company level and not function of

the activity “type”; low quantities for “handcraft” and high quantities for “industrial”.

The Table 26 through Table 28 show the two levers, Customer Order Size and Working Rate per

production line, and the range values used for the sensitivity runs.

— page 152 — Three are the sets considered: two with each lever alone, one with the two levers combined

together.

For a detailed analysis of the sensitivity runs we have to refer to previous sections, but in the

following paragraphs we present some summary tables and the related notes

7.3.2.1 FTE

Following (Table 124) to (Table 126) show, for every scenario, a comparison of FTE and FTE costs in

all the sensitivity subsets.

(Table 127) shows only the sensitivity set which combines the order size and working rate variations

together for the three scenarios analysed. We chose this set because it covers a large “spectrum”

and we could really imagine extreme situations with little orders made with low working rate, typical

of handmade work, with, at the other hand, large order size made with fast working rate

equipment, typical of process industry.

First of all, considering the “order size variation” alone set, reproduced into the first three tables,

(Table 124) to (Table 126Table 126), with the title “order Size”. We note that the effect of the

reduction in “order size”, affecting directly the number of production changes, is really strong on

the setup operators because not only the number of change increases but also the concurrent rate,

due to the high frequency, has an effect that the system and the model tries to estimate applying

an extra coefficient.

As already mentioned, no effect on general workers was modelled. But it could be realistic to

imagine an effect on the confusion due to frequent team movements, through different lines,

requested to people working on little production batch. If we imagine the actual environment,

where the lack of flexibility through specialization obliges to maintain extra production lines free

where teams ending their work orders have to be transferred (otherwise they should have to wait

without working while the setups are made by specialized colleagues), little orders imply frequent

team change from one line to another. An extreme picture presents a plant with continuous people

moving through the lines: confusion is an understatement.

Extra investigations should be necessary and they would only find an increase in inefficiency.

The (Figure 48) and (Figure 49) show some significant results obtained by the sensitivity analysis on

the three chosen scenarios, each one in the three shift configurations.

Either FTEs or FTE Cost are drawn in a typical “financial” configuration: maximum-average-

minimum values with the quantity substituted by the standard deviation recorded by each

sensitivity.

These two graphs let us appreciate the AS-IS versus target values (scenario All Intern Reduction Days

versus the two All Intern Base ones) but also the variation ranges (maximum and minimum versus

the average). The standard deviation gives us an extra information useful to appreciate the range

maximum vs minimum.

The average shifting versus minimum values suggests another consideration: even if the sensitivity

levers, working rate and order size variations, are normally “distributed”, are the results also

normally distributed?

— page 153 — For this last sentence is useful to consider the standard deviation that the graph shows in the bottom

section.

The “working rate” alone sensitivity shows different points that request our attention:

The effect on FTE required of this working rate sensitivity is much more relevant than the

order size one and this effect is direct consequence of the working time applied. Faster

working rate implies a lower workers time equivalent requested, even if the model applies

some corrections when the working rate exceeds the “normal” variation (-10% to +10%). On

the other hand lower working rate increases sensibly the workers time requested: due to

team coefficient to be applied to the technical working time, the FTE requested increase.

The effect on setup operations is more reduced because the working rate does not have any

influence on the setup operation and team, if it is inside the “normal” boundaries (-10% /

+10%), already mentioned, while the model considers that extra these limits the equipment

is different and the setup time and team must change. Only this last situation change the

FTE requested.

Considering both the order size and the working rate sensitivity (from 10% to 200% of the order size

and working rate simulated), the combined effects are really relevant. As per previous sensitivity,

we have to note some points:

The combined levers push the absolute data very far from the two single sensitivity values:

the average of the three shift organization in this sensitivity is three times of the working

rate sensitivity and more or less the double of the order size sensitivity (see Table 90 to Table

92 for FTE and Table 93 to Table 95 for related costs). This combined effect put together the

main effect of each single sensitivity (order size on setup operators and working rate on

general workers) amplifying their effect.

The effect is more important for the worst side of the sensitivity analysis: considering that

the values with negative percentage are in some way a “gain” in efficiency or at least in

resources absorption; the positive side, exactly the opposite, is much greater than the first

one. Industrially speaking this side is the more “risky”, considering order size and working

rate of some “handcraft” activities but quantity in line with the industrial levels simulated.

This couple of hypotheses or set of hypotheses, considering that the sensitivity calculates

many levers combinations fixing the model quantities, has an “exploding” effect on all the

target variables (FTE, FTE costs but also Lines and technical indicators)

— page 154 — Figure 48 Combined Working Rate & Oder Size Sensitivity Analysis - FTE

— page 155 — Figure 49 - Combined Working Rate & Order Size Sensitivity Analysis FTE Cost

— page 156 — Table 124 Sensitivity Result - FTE - All Intern Reduction Days Concurrent Flexibility

Lines labels Average Minimum MaximumStandard

deviationAverage Minimum Maximum

Standard


Standard

deviation

Progressive FTE TOT Necessary 94.971 40.431 337.917 34.725 31.065 19.638 53.023 5.845 46.072 40.275 54.055 1.577

Theo 94.971 40.431 337.917 34.725 31.065 19.638 53.023 5.845 46.072 40.275 54.055 1.577

one shift over 5 days 89.829 40.431 256.620 32.551 30.107 19.638 47.056 5.637 43.847 40.275 47.004 1.498

two shifts over 5 days 93.042 41.278 288.988 33.964 30.417 20.467 48.842 5.639 45.096 41.466 50.156 1.631

three shifts over 5 days 102.043 43.099 337.917 37.661 32.671 21.877 53.023 6.260 49.273 45.605 54.055 1.602

Grand Total 94.971 40.431 337.917 34.725 31.065 19.638 53.023 5.845 46.072 40.275 54.055 1.577



Standard


Standard

deviation

Cumul Estimated Operator Cost as per Model 237.428.292 101.077.000 844.793.000 86.812.812 77.662.523 49.095.300 132.557.000 14.613.430 115.179.927 100.687.000 135.137.000 3.941.786

Theo 237.428.292 101.077.000 844.793.000 86.812.812 77.662.523 49.095.300 132.557.000 14.613.430 115.179.927 100.687.000 135.137.000 3.941.786

one shift over 5 days 224.571.985 101.077.000 641.550.000 81.376.714 75.266.313 49.095.300 117.639.000 14.092.410 109.617.725 100.687.000 117.509.000 3.744.736

two shifts over 5 days 232.604.750 103.194.000 722.470.000 84.910.346 76.043.670 51.166.800 122.105.000 14.098.042 112.738.890 103.665.000 125.391.000 4.076.223

three shifts over 5 days 255.108.140 107.748.000 844.793.000 94.151.375 81.677.585 54.691.100 132.557.000 15.649.839 123.183.165 114.014.000 135.137.000 4.004.399

Grand total 237.428.292 101.077.000 844.793.000 86.812.812 77.662.523 49.095.300 132.557.000 14.613.430 115.179.927 100.687.000 135.137.000 3.941.786

Data in Euro



Standard


Standard

deviation

Cumul Set Up Operator Cost as per Model 10.090.039 6.739.180 19.762.500 1.856.593 10.015.943 8.176.620 11.920.300 710.823 7.828.053 5.144.590 14.340.900 1.225.618

Theo 10.090.039 6.739.180 19.762.500 1.856.593 10.015.943 8.176.620 11.920.300 710.823 7.828.053 5.144.590 14.340.900 1.225.618

one shift over 5 days 9.923.932 6.739.180 17.494.400 1.785.558 10.024.399 8.363.810 11.713.200 678.333 7.781.965 5.231.360 12.962.900 1.190.218

two shifts over 5 days 10.255.168 7.032.500 19.762.500 1.932.788 10.104.755 8.301.020 11.920.300 739.706 7.959.197 5.358.550 14.340.900 1.262.968

three shifts over 5 days 10.091.015 6.810.090 18.564.600 1.851.432 9.918.675 8.176.620 11.608.200 714.430 7.742.997 5.144.590 13.210.100 1.223.668

Grand Total 10.090.039 6.739.180 19.762.500 1.856.593 10.015.943 8.176.620 11.920.300 710.823 7.828.053 5.144.590 14.340.900 1.225.618



Standard


Standard

deviation

Progressive FTE TOT Necessary

Theo

one shift over 5 days 0% -55% 186% 0% -35% 56% 0% -8% 7%

two shifts over 5 days 0% -56% 211% 0% -33% 61% 0% -8% 11%

three shifts over 5 days 0% -58% 231% 0% -33% 62% 0% -7% 10%



Standard


Standard

deviation

Cumul Estimated Operator Cost as per Model

Theo






Standard


Standard

deviation

Cumul Set Up Operator Cost as per Model

Theo




Combined Working rate & Order Size Working rate Order Size


— page 157 —

(Table 124) shows the results of the three sensitivities applied to the scenario considered as the best

approximation of the actual situation. All the notes for the three sensitivity sets are valid but we

want also to note the values recorded.

The % of reduction or increase versus the average value of the subset confirms the here-above

notes: range of (–8% / +11%) for the order size variation on FTE or FTE cost are less important than

the (-33% /+62%) concerning the working rate variation or the (-58% / +231%) for the combined set.

The cost variation in % are more or less in line with the FTE variation.

Same valid consideration for the reduction/increase of the setup operators cost that are more

important in the order size variation set (-34% / +80%) then working rate set (-18& / +18%) while

the combined set is always the more variable (-33% / +93%) set.

But all these % variation have to be considered viewing the absolute value related to each set

average value: they are really different. 46 KFTE in order size set with 115 million of euro versus a

working rate set showing 31 KFTE and 77 millions of euro and finally a combined set with 95 KFTE

and 237 million of euro.

— page 158 — Table 125 Sensitivity Result - FTE - All Intern Base



Standard


Standard

deviation

Progressive FTE TOT Necessary 66.400 27.673 224.717 24.099 21.520 13.486 37.132 3.997 32.177 29.356 36.050 246

Theo 66.400 27.673 224.717 24.099 21.520 13.486 37.132 3.997 32.177 29.356 36.050 246

one shift over 5 days 61.505 27.673 191.418 22.286 19.998 13.486 31.800 3.693 29.831 29.356 30.944 235

two shifts over 5 days 65.677 29.530 204.498 23.813 21.325 14.352 33.939 3.947 31.839 31.332 33.029 251

three shifts over 5 days 72.019 32.287 224.717 26.199 23.236 15.541 37.132 4.351 34.860 34.353 36.050 251

Grand Total 66.400 27.673 224.717 24.099 21.520 13.486 37.132 3.997 32.177 29.356 36.050 246



Standard


Standard

deviation

Cumul Estimated Operator Cost as per Model 166.000.835 69.183.300 561.792.000 60.248.472 53.799.618 33.714.700 92.828.700 9.992.522 80.441.471 73.390.000 90.125.700 614.863

Theo 166.000.835 69.183.300 561.792.000 60.248.472 53.799.618 33.714.700 92.828.700 9.992.522 80.441.471 73.390.000 90.125.700 614.863

one shift over 5 days 153.763.348 69.183.300 478.545.000 55.715.295 49.995.873 33.714.700 79.500.300 9.232.236 74.576.535 73.390.000 77.360.800 587.994

two shifts over 5 days 164.192.103 73.823.800 511.245.000 59.532.792 53.313.035 35.879.400 84.848.100 9.867.135 79.597.020 78.329.100 82.571.900 628.301

three shifts over 5 days 180.047.053 80.716.600 561.792.000 65.497.329 58.089.946 38.852.000 92.828.700 10.878.193 87.150.860 85.882.900 90.125.700 628.293

Grand total 166.000.835 69.183.300 561.792.000 60.248.472 53.799.618 33.714.700 92.828.700 9.992.522 80.441.471 73.390.000 90.125.700 614.863

Data in Euro



Standard


Standard

deviation

Cumul Set Up Operator Cost as per Model 6.941.022 4.547.870 13.368.300 1.331.909 6.764.906 5.383.080 8.098.080 491.635 5.328.425 3.453.940 9.664.340 876.702

Theo 6.941.022 4.547.870 13.368.300 1.331.909 6.764.906 5.383.080 8.098.080 491.635 5.328.425 3.453.940 9.664.340 876.702

one shift over 5 days 6.689.161 4.547.870 12.587.300 1.273.710 6.521.214 5.383.080 7.654.890 470.167 5.147.334 3.453.940 9.120.750 838.396

two shifts over 5 days 7.066.953 4.778.250 13.368.300 1.361.009 6.886.753 5.670.670 8.098.080 502.369 5.418.971 3.609.390 9.664.340 895.854

three shifts over 5 days 7.066.953 4.778.250 13.368.300 1.361.009 6.886.753 5.670.670 8.098.080 502.369 5.418.971 3.609.390 9.664.340 895.854

Grand Total 6.941.022 4.547.870 13.368.300 1.331.909 6.764.906 5.383.080 8.098.080 491.635 5.328.425 3.453.940 9.664.340 876.702



Standard


Standard

deviation


Theo






Standard


Standard

deviation


Theo






Standard


Standard

deviation


Theo






— page 159 — (Table 125) shows the values but also the % reduction/increase referred to each sensitivity subset

for the scenario approximating the “perfect” situation concerning the soft skill or planning ability.

The % are in line with previous scenario, more “actual” (with exception of the order set variation

sensitivity, where the FTE and total cost show a range more restrained -2% / +4%), but the absolute

values are really lower than the previous ones:

order size variation with 32 KFTE, 80 million euro for total FTE and 5.3 million for setup

operator,

working rate variation with 22 KFTE, 54 million euro for total FTE and 6.8 million for setup

operator,

combined set variation with 66 KFTE, 166 million euro and 6.9 million for setup operator

Finally, (Table 126) considers the situation “target” with the best scenario: people flexibility through

the specialization,

The reduction or increase versus the average values replicates, more or less, the % commented for

the first and second scenarios here-above.

The setup cost values and % variation is not relevant, because the model didn’t modify this value

between the two “base” and “base optimum” scenarios: the flexibility effect is recorded into total

FTE and Total FTE cost.

As per previous scenario notes, even if the % values are similar, the starting absolute values are

really different:

order size variation with 31 KFTE, 79 million euro for total FTE and 5.3 million for setup

operator,

working rate variation with 21 KFTE, 52 million euro for total FTE and 6.8 million for setup

operator,

combined set variation with 66 KFTE, 164 million euro for total FTE and 6.9 million for setup

operator.

Please note that the setup operator cost should be reduced by the same difference that total

operator cost discloses between the “base” and this “base optimum” scenario.

(Table 127) let us compare the three combined sensitivity sets of each scenario. Even the variation

% are calculated through the three scenarios using the “actual: All Intern Reduction Days Concurrent

Flexibility” as primary reference.

If we are able to change from the actual situation to the optimum planning situation (the “base”

scenario) the FTE could be reduced by 30%, 30% and 32% in one, two or three shifts per day

organization.

As you can immediately see, most of the “gain” is obtained with the first passage from actual to best

planning situation, while the second optimization is showing a residual margin. This is due to the

model assumptions, with the efficiency gain linked only to the optimization of setup operators,

while the general workers inefficiency allocation is totally measured by the difference between the

other two previous scenarios.

Probably the general workers inefficiency due to real conditions that should have to be estimated

or at least studied for future model implementation: the effect could be higher than the actual

versus base scenarios difference. And probably this inefficiency should also be studied in all the

indirect functions and related people.

— page 160 — Table 126 Sensitivity Result - FTE - All Intern Base Optimum



Standard


Standard

deviation

Progressive FTE TOT Necessary 65.845 27.114 224.146 24.097 20.957 12.904 36.568 3.997 31.607 28.798 35.497 246

Theo 65.845 27.114 224.146 24.097 20.957 12.904 36.568 3.997 31.607 28.798 35.497 246

one shift over 5 days 60.947 27.114 190.855 22.285 19.432 12.904 31.238 3.694 29.269 28.798 30.408 235

two shifts over 5 days 65.122 28.971 203.928 23.811 20.763 13.788 33.378 3.947 31.273 30.770 32.491 252

three shifts over 5 days 71.465 31.723 224.146 26.196 22.675 14.983 36.568 4.351 34.279 33.776 35.497 252

Grand Total 65.845 27.114 224.146 24.097 20.957 12.904 36.568 3.997 31.607 28.798 35.497 246



Standard


Standard

deviation

Cumul Estimated Operator Cost as per Model 164.611.294 67.785.600 560.365.000 60.243.131 52.392.495 32.259.700 91.420.800 9.992.748 79.017.360 71.994.700 88.741.200 615.620

Theo 164.611.294 67.785.600 560.365.000 60.243.131 52.392.495 32.259.700 91.420.800 9.992.748 79.017.360 71.994.700 88.741.200 615.620

one shift over 5 days 152.368.034 67.785.600 477.139.000 55.711.330 48.580.661 32.259.700 78.094.900 9.233.816 73.171.591 71.994.700 76.020.500 588.736

two shifts over 5 days 162.804.199 72.428.500 509.819.000 59.527.168 51.908.102 34.469.200 83.443.900 9.866.268 78.183.344 76.925.900 81.227.400 629.062

three shifts over 5 days 178.661.650 79.306.400 560.365.000 65.490.894 56.688.723 37.457.300 91.420.800 10.878.161 85.697.144 84.439.700 88.741.200 629.062

Grand total 164.611.294 67.785.600 560.365.000 60.243.131 52.392.495 32.259.700 91.420.800 9.992.748 79.017.360 71.994.700 88.741.200 615.620

Data in Euro



Standard


Standard

deviation

Cumul Set Up Operator Cost as per Model 6.941.022 4.547.870 13.368.300 1.331.909 6.764.906 5.383.080 8.098.080 491.635 5.328.425 3.453.940 9.664.340 876.702

Theo 6.941.022 4.547.870 13.368.300 1.331.909 6.764.906 5.383.080 8.098.080 491.635 5.328.425 3.453.940 9.664.340 876.702

one shift over 5 days 6.689.161 4.547.870 12.587.300 1.273.710 6.521.214 5.383.080 7.654.890 470.167 5.147.334 3.453.940 9.120.750 838.396

two shifts over 5 days 7.066.953 4.778.250 13.368.300 1.361.009 6.886.753 5.670.670 8.098.080 502.369 5.418.971 3.609.390 9.664.340 895.854

three shifts over 5 days 7.066.953 4.778.250 13.368.300 1.361.009 6.886.753 5.670.670 8.098.080 502.369 5.418.971 3.609.390 9.664.340 895.854

Grand Total 6.941.022 4.547.870 13.368.300 1.331.909 6.764.906 5.383.080 8.098.080 491.635 5.328.425 3.453.940 9.664.340 876.702



Standard


Standard

deviation


Theo






Standard


Standard

deviation


Theo






Standard


Standard

deviation


Theo






— page 161 — Table 127 Order Size & Working Rate Sensitivity - FTE - All Intern Scenarios



Standard


Standard

deviation

Progressive FTE TOT Necessary 94.971 40.431 337.917 34.725 66.400 27.673 224.717 24.099 65.845 27.114 224.146 24.097

Theo 94.971 40.431 337.917 34.725 66.400 27.673 224.717 24.099 65.845 27.114 224.146 24.097

one shift over 5 days 89.829 40.431 256.620 32.551 61.505 27.673 191.418 22.286 60.947 27.114 190.855 22.285

two shifts over 5 days 93.042 41.278 288.988 33.964 65.677 29.530 204.498 23.813 65.122 28.971 203.928 23.811

three shifts over 5 days 102.043 43.099 337.917 37.661 72.019 32.287 224.717 26.199 71.465 31.723 224.146 26.196

Grand Total 94.971 40.431 337.917 34.725 66.400 27.673 224.717 24.099 65.845 27.114 224.146 24.097



Standard


Standard

deviation

Cumul Estimated Operator Cost as per Model 237.428.292 101.077.000 844.793.000 86.812.812 166.000.835 69.183.300 561.792.000 60.248.472 164.611.294 67.785.600 560.365.000 60.243.131

Theo 237.428.292 101.077.000 844.793.000 86.812.812 166.000.835 69.183.300 561.792.000 60.248.472 164.611.294 67.785.600 560.365.000 60.243.131




Grand total 237.428.292 101.077.000 844.793.000 86.812.812 166.000.835 69.183.300 561.792.000 60.248.472 164.611.294 67.785.600 560.365.000 60.243.131

Data in Euro



Standard


Standard

deviation

Cumul Set Up Operator Cost as per Model 10.090.039 6.739.180 19.762.500 1.856.593 6.941.022 4.547.870 13.368.300 1.331.909 6.941.022 4.547.870 13.368.300 1.331.909

Theo 10.090.039 6.739.180 19.762.500 1.856.593 6.941.022 4.547.870 13.368.300 1.331.909 6.941.022 4.547.870 13.368.300 1.331.909




Grand Total 10.090.039 6.739.180 19.762.500 1.856.593 6.941.022 4.547.870 13.368.300 1.331.909 6.941.022 4.547.870 13.368.300 1.331.909

Average Minimum MaximumStandard


Standard

deviation


Theo

one shift over 5 days -30% -32% -33% -31% -33% -34%

two shifts over 5 days -30% -32% -33% -31% -33% -34%

three shifts over 5 days -32% -32% -25% -32% -33% -26%



Standard


Standard

deviation


Theo






Standard


Standard

deviation


Theo




All Intern Reduction Days Concurrent Flex All Intern Base All Intern Base Optimum

Difference with All Intern Reduction Days Concurrent Flex Difference with All Intern Reduction Days Concurrent Flex

Combined Working rate & Order Size Combined Working rate & Order Size Combined Working rate & Order Size

— page 162 —

7.3.2.2 Lines

As already mentioned, a second area fundamentally important to be considered is the “investment”

or “capital” absorption.

The number of lines installed or, in a different way, requested to fulfil the demand could be seen

from different viewpoint:

as a simple “technical” indicator which measures the number of lines requested by the

organization structure chosen (one to three shifts per day), given the technical hypothesis

(quantity to be produced, order size, working rate, setup team, workers team, …)

as an investment to be made, due to some cost hypothesis for each production line or

section of line

as an investment to be made in space occupied, because every line needs technical space

but also “general” space all around for indirect services

as a measure of “technical absorption” or “technical efficiency”: more the average line is

used with an organization covering more shifts per day or per week, higher is the efficiency

or absorption rate, even if while this rate is expanding, also the related maintenance costs

are increasing

as a measure of “flexibility”, which is opposite to the previous indicator. Larger is the

difference between the maximum possible working hours per line and the real hours

recorded by the same line, larger is the flexibility available to the system to cover inefficiency

(concurrent operations, concurrent setup, reduction days, as already mentioned, which

measures the order concentration due to the customer inefficiency to transmit the same

order equally distributed inside the time batch,…)

Defined these different viewpoints, we could review the sensitivity results.

(Table 128) to (Table 130), as per previous FTE analysis, show the three sensitivity sets (order size,

working rate or combined order size and working rate variations) for each scenario, considering the

“All Intern Base reduction days Concurrent Flexibility” like the starting or actual situation, which all

the two other scenarios have to be compared with.

Some of the total and grand total showed are not relevant, but the detail inside every macro-phase

has to be carefully regarded.

The absolute line number has to be compared with real line numbers (see Table 14 Real lines

installed).

As per FTE analysis, the different sensitivity sets have different relevance on the line number

variance: the more limited effect are related to the order size variation while the greater effect is

linked to the combine effect of order size and working rate variations. In the middle the working

rate alone variation.

— page 163 — The % of variations are clustered in three blocks:

-16% / + 35% for order size

-35% / +83% for working rate

-58% / +238% for combined levers

These three “groups” of results, expressed for the three sensitivity sets, are in % reproduced in the

same way for each one of three scenarios.

In the following (Figure 50) , the graph shows the squared meters requested for the production lines

simulated in each scenario and shift configuration.

— page 164 — Figure 50- Combined Working rate & order Size Sensitivity Analysis - Space requested

Actual Plant

Future Plant

— page 165 — Table 128 Sensitivity Result - Lines - All Intern Reduction Days Concurrent Flexibility



Standard


Standard

deviation

LTE Theoretically to be installed at MacroPhase Level 32 4 188 11 15 3 54 2 21 5 66 1

Filling 53 18 188 16 26 13 54 3 35 20 66 2

one shift over 5 days 81 40 188 25 38 29 54 5 53 49 66 3

two shifts over 5 days 44 23 99 13 22 17 29 3 29 27 36 2

three shifts over 5 days 33 18 72 9 17 13 23 2 22 20 27 1

Packaging 38 12 133 13 18 9 36 2 24 13 43 1




Wrapping 24 7 108 9 11 6 25 2 15 9 30 1




CodeWriting 13 4 64 7 6 3 17 1 9 5 17 1




Grand Total 32 4 188 11 15 3 54 2 21 5 66 1



Standard


Standard

deviation

LTE Theoretically to be installed at MacroPhase Level

Filling




Packaging




Wrapping




CodeWriting






Standard


Standard

deviation

Space in squared meters

one shift over 5 days 9.931 4.846 24.175 4.616 3.437 6.480 6.344 5.861 7.833

two shifts over 5 days 5.355 2.705 12.623 2.638 1.972 3.550 3.485 3.156 4.339

three shifts over 5 days 3.999 2.085 9.354 2.011 1.578 2.705 2.618 2.367 3.156

Cost in million euro

one shift over 5 days 78,79 38,60 199,40 36,70 27,20 52,55 50,59 46,60 63,45

two shifts over 5 days 42,59 21,75 104,20 21,26 15,80 28,75 27,74 25,15 35,20

three shifts over 5 days 31,80 16,50 77,05 16,04 12,65 21,85 21,01 19,15 25,65




— page 166 — Table 129 Sensitivity Result - Lines - All Intern Base



Standard


Standard

deviation


Filling 44 15 156 13 22 12 45 3 30 19 56 2




Packaging 32 10 109 11 15 8 30 2 20 11 35 1




Wrapping 20 6 90 8 9 5 21 1 13 7 25 1




CodeWriting 11 3 53 6 6 3 14 1 7 5 15 1




Grand Total 27 3 156 9 13 3 45 2 17 5 56 1



Standard


Standard

deviation


Filling




Packaging




Wrapping




CodeWriting






Standard


Standard

deviation












— page 167 — Table 130 Sensitivity Result - Lines - All Intern Base Optimum



Standard


Standard

deviation


Filling 44 15 156 13 22 12 45 3 30 19 56 2




Packaging 32 10 109 11 15 8 30 2 20 11 35 1




Wrapping 20 6 90 8 9 5 21 1 13 7 25 1




CodeWriting 11 3 53 6 6 3 14 1 7 5 15 1




Grand Total 27 3 156 9 13 3 45 2 17 5 56 1



Standard


Standard

deviation


Filling




Packaging




Wrapping




CodeWriting






Standard


Standard

deviation












— page 168 — Table 131 Order Size & Working Rate Sensitivity - Lines - All Intern Scenarios



Standard


Standard

deviation


Filling 53 18 188 16 44 15 156 13 44 15 156 13




Packaging 38 12 133 13 32 10 109 11 32 10 109 11




Wrapping 24 7 108 9 20 6 90 8 20 6 90 8




CodeWriting 13 4 64 7 11 3 53 6 11 3 53 6




Grand Total 32 4 188 11 27 3 156 9 27 3 156 9



Standard

deviation

LTE Theoretically to be installed at MacroPhase Level -16% -25% -17% -16% -25% -17%

Filling -16% -17% -17% -16% -17% -17%




Packaging -17% -17% -18% -17% -17% -18%




Wrapping -16% -14% -17% -16% -14% -17%




CodeWriting -15% -25% -17% -15% -25% -17%






Standard


Standard

deviation















Difference with All Intern Reduction Days

Concurrent Flex

Difference with All Intern Reduction Days

Concurrent Flex





— page 169 — Finally, (Table 131) shows the reduction that we could obtain passing from the actual situation to

the best planning scenario or the optimum situation

The two base and optimum scenarios present the same requested lines, because the difference

between the two situations is only due to the flexibility through the workers’ specialization. This

“gain” is recorded by the model in terms of total FTE and its related cost: the difference is only in

these two values and it is not recorded in the setup operators FTE or cost.

Another point to be considered is the difference in terms of space and investment cost, measured

as % reduction between the actual situation and the two best scenarios: this is expressed for the

average, minimum and maximum values of the “combined levers” sensitivity set in each scenario

considered. It’s always important to note the absolute values other than the % reduction: the

maximum abs value is more than the double compared to the average situation with at least 20

thousand squared meters in one shift per day organization.

The “variation” range in terms of space and investment costs requested is crucial. In actual scenario

minimum point is at 2 thousand squared meters while the maximum is more than 24 thousand. The

same for the investments cost with minimum at 17 million euro and the maximum at 200 million

euro.

The difference between actual and the two “best” scenarios, equals in terms of space and

investment costs for the lines (only the setup operators change between the two optimized

scenarios), is in the range (-15%/-19%). Significantly relevant.

7.3.2.3 Technical Indicators

Last but not least we have to review the effects on the technical indicators chosen.

For a detailed review of the values we have to come back to the previous sections, Scenarios Results

and Sensitivity Results, because the process flow time, the technical frozen time and the total frozen

time could be described in many ways (per customer piece, product family piece, per customer

order, product family order, …. and combinations of these parameters).

As per the lines installed, we would like to note that these indicators could be discussed in different

ways or at least they could be considered from different viewpoints:

Simply a technical answer of the system due to the input values (quantity per product family

and customer) worked by an existing set of equipment and procedures (working rate, setup

rate, working team, setup team, …)

As a measure of the system efficiency to fulfil the demand: the faster, the best. From this

viewpoint it is a simple target indicator to calibrate the system with the best fit solution in

terms of equipment and organization. From this viewpoint the indicator composed with

external input (the average order size) is more representative even if it loses the “pure”

technical aspect.

As a measure of the system efficiency analysed in its components: while the technical

process flow time per piece accepts only internal technical “levers”, the more we combined

this indicator with other components (order size, already mentioned, but also order

preparation time and component preparation time to obtain the technical frozen time,

expressed per customer and/or per product family and/or per other aggregation level of

— page 170 —

these “attributes”, fragrance maceration time for total frozen time, …), the more we have to

weigh the different parts and especially the less “technical” ones. The sensitivity analysis

made covers only the “technical” part but the results on the more general “total frozen time”

are really weak. All the tables showing the results for the Total Frozen time considered by all

the sensitivity set in each scenario have important numbers of hours that change as a

function of the hours per day, different according to the shift organization considered, not

moved by the technical variation.

As a measure to match the customer demand in terms of flexibility, change requests on a

planning or scheduling plan, and the possible answer of the system. These indicators have

the extreme limits; shorter than the time calculated, it is impossible unless the “restraints”

are modified by a different configuration: the working rate sensitivity, and consequently the

combined set of order size and working rate variation, tries to simulate scenarios with

different technical equipment to test the answer ,system. (Table 46), for example, shows this

effect for the “All Intern Base” scenario, “perfect planning” scenario, with a range of

variation (1,40 -0,27) hours per piece concerning the Technical Flow Time. (Figure 26) shows

the same data in a graphic form.

As a measure of “risk/opportunity”: given the actual situation as per fix values of the

technical indicators listed in the previous section Scenarios Results, which are not “touched”

by the change hypothesis made in the scenarios simulation, all the sensitivity sets in each

scenario give the risk of an unsuccessful condition combination measured in terms of

maximum technical indicator or, graphically review, as area between the average indicators

and maximum indicator. See, for example the quoted Figure 26 where the difference

between the maximum line referred to the technical flow time and its average one is an area

much more larger than the “opportunity” side (measured by the difference between the

lower line, the minimum, and the average. The graphical analysis or perception is a “more

working” approach compared to the statistical analysis on the standard deviation and

confidence intervals that could be possible with all the model data. This part is not presented

in previous sections.

All these viewpoints are presented in a general way without summary tables which could be only a

double presentation of all the data already noted in previous scenarios results or sensitivity results

sections.

— page 171 —

CHAPTER 8

8 Conclusion

This conclusion has to generalize the results presented, which are mainly based on the case study.

Nevertheless, the industrial structure presented, with four macro-phase in line, without special

characteristics, could be generalized to most of the Italian private companies.

The simple industrial process described into the (Figure 12), detailed into the (Figure 22), and the

simple, in the same way, structure of each process phase modelled, lets us generalize the results

that could be applicable to several other medium size companies.

The simulation – Model approach is a first point to underline, because company organizations do

not use frequently this concept/tool to face new problems that the world economy creates every

day. Flight Simulator, quoting Prof Jay Forrester, Prof John Sterman and Prof Nelson Repenning, had

used in many environments but not frequently in economics and company strategy.

Not only the brain-storming on the model, in reality the system not the model, is useful, but also

the idea that the system is never completely understood and we have to constantly re-draw a more

complete model. The social environment changes so the model has to do so as well.

As frequently reported, the author is also the general manager of ICR, the company case study: with

this “special” viewpoint, all the “negative” forces, that limited the Action Research Project, pushed

the project itself to a simulation approach, just trying to analyse the system without “internal”

pressure.

Nevertheless, the model continuous study was useful during the Unions negotiation: identifying the

benefits, risks and costs due to the system working way gave a clear “picture” of the possible future

configuration. On this possible configuration the negotiation was based: the result was a 5 year

agreement concerning the internalization of 250 workers previously employed by outsourced

suppliers. This agreement, by the way, knew an exceptional acceleration with the “Job Act” law in

2015.

Moreover, this system study and the simulation results pushed the management to a new

“continuous line” configuration. This approach change was more important than the analysis result.

It is important to set up how the management approach changed as long as the negotiation was

been completed, but it was based on “general” conditions more than data analysis.

If we consider the technical lines requested by the analysis, it is immediately clear that the phases

are not equilibrated: the decreasing number of lines passing through the sequence “filling-code

writing” should create some doubts about a “hard continuous” configuration. If this approach was

the beginning of a change process, useful to discuss with the Unions and to reflect on the company

future, today the focus has to be placed on a new production line configuration, with a decreasing

number of lines passing through the phases and new “connection” equipment among the

production phases but with new people skills. Flexibility, “many to many”, considering the lines of

two consequential phases (one filling line could serve different or many concurrently packaging lines

as well as one packaging line could be served by different or many concurrently filling lines), is

— page 172 — strictly connected to lean approach and efficiency viewpoint. The space and investment requested

offset the operating low cost of a “hard” continuous line. The analysis has to consider not only the

operating costs but also a higher level: space and its complexity in terms of lines and plants.

Moreover, we have also to consider that space and investment could present a “step” profile, where

every “step” could be relevant.

The (Figure 50) shows a sensitivity analysis concerning the space requested for the production lines

calculated with several different hypothesis, but the red and green lines indicate the actual and

future plant sizes: these measures are not simulated, they are real. The company is investing more

than ten million Euro only in building construction. From the operating viewpoint, is this investment

necessary? The answer, “typical” in Italian style, is: “It depends!”. Today, probably, it is not possible

to proceed in a different way, due to the actual conditions (people skills, management approach …)

but, using different levers, the situation could have been completely different. Imagine to push on

soft skills optimization discussing with customer new optimum order size or new investment in new

equipment: the sensitivity could show the targets obtainable. Probably a new plant was unuseful.

And every time a decision is taken, the path is narrowed and the past is made: the time is always an

independent variable which has to be computed. Everything could be changed; an investment is

evaluated considering the future options but a decision taken influences directly the present.

The simulation environment has to change with the decision taken hoping that the analysis

completed inside the model could give a better view on the consequences due to every choice. The

simulation could help to take the decisions.

This argument suggests another interesting matter for future investigations: decision-making. It

could be interesting to compare theory (H.A. Simon’s “Administrative Behaviour”, for example) with

a real company field like ICR Spa.

Next step will be to consider new technical configurations in place of standardized continuous lines:

the company will have to be efficient and flexible with a production which is not equally transformed

by each phase. ICR Spa has to decide the right mix of lines (detailed in each phase) and workers with

a quantitative measuring approach. Once chosen the soft skill knowledge and the flexibility through

specializations the company wants for its employees. And we are not considering the new open

fields that some results let us imagine as interesting (indirect functions…). This last point shows us

how powerful could be to start a “continuous” reflection on a complex system.

Concerning the results of the simulation and sensitivity, it is important to point out that the model

itself could be generalized to many other case studies due to its simple structure and limited number

of relevant coefficients. Some important notes are related to:

- The relevance that “soft skills” and “workers flexibility” could take on in growing

organizations born as “handcraft” activities which are, over the time, more and more

industrialized. This relevance is well noted by the “gain” in people involved and costs for

people and equipment all along an interesting period of time (10 years)

- The fact that the two areas investigated, soft skill, such as planning, and workers flexibility

through specializations, even for the case study presented, could be transposed in other

similar areas: production indirect, QC activities, industrial handling… In all these areas could

be found benefits with a better organization, soft skill, or with a strong flexibility through the

different specializations, at least for some base activities

- The re-thinking of the equipment installation and, consequently, of the company strategy: if

it is simple to move from an “handcraft” organization, without structured logic, to an “hard”

— page 173 —

industrial approach, with technological equipment, made by single production line covering

all the phases in an integrated way, is this philosophy the right one if the input from customer

demand shows real different quantities per phase and/or different lot size? How much more

complexity could another approach cause to the system searching a best fit quantity-lines?

- The results are based on a system where the “starting point” is really far from the best

solution: as most of the social analysis based on people, most of the results depend on the

“initial” status. Like in mathematics, we are speaking of “differential” equations, with a real

effect of the system level at time “0”, the starting time. The simulation model helps to

understand the cross-effects, without a system of mathematical expressions.

Concerning the professional viewpoint, we could say:

The model lets us measure the effects that pushing on some levers the system could attain:

the difference between the number of FTEs as well as the cost of personnel (general workers

or setup operators) is either a measure of the "gain” reachable or from the opposite point

of view the maximum cost to be faced to obtain the desired level of soft skills (perfect

planning, …) or workers flexibility (through specialization).

The same approach provides a measure of the restraints linked to some structural

organization: space, number of lines, number of FTE… are functions of the company

organization (shifts per day) and company “will” to invest on the soft skill and flexibility.

These restraints could be seen as a fixed limit (maximum of xxx squared meters available or

maximum investments of yyy euro, …) because “physically” unbeatable at least in the “short

term” or they could be seen as “target” limit, a boundary for the system

The sensitivity results give us an immediate perception of the risks that some conditions

could have a negative impact on the company structure and we can measure this impact: if

the order size in the actual scenario moves versus its lower simulation limits, the number of

FTE requested is 7% higher (this is like to say for the case study, 67 persons more than the

average situation) with an extra cost of 166 K Euro per month. The same information is

available for the number of lines requested and its investment cost or the extra space

requested to install the lines…

The sensitivity results, on the other side, give us a perception of the gains, measured in the

same way of the risky factors here above mentioned, that we could reach if we push the

customers to better organize their order in bigger lots or if we push our equipment

performance to their upper limit or, more again, if we change the equipment to new ones

with better performances. These simple examples based on the case study find a

mathematical formulation and an economic translation in terms of FTE required and costs

related: always referring to the actual scenario, if the order size is pushed to the high level

of the sensitivity, the total FTE required decrease from 46 KFTE to 40 KFTE (which is in

monthly basis a -48 FTE) and in total cost for workers a gain of 14.5 million euro overall and

121 K Euro monthly). Generalizing this results the -8% is more representative; it is not in fact

linked to the case study. As per previous “risky” discussion the investments cost, the space

required but also the technical indicators viewed as possible answers to customer requests,

they could be measured and evaluated in the same way.

— page 174 — Concerning the theoretical viewpoint, the model could open some discussions:

The formulas for the different FTEs, cost, space requested are based on “general”

coefficients applicable on wider industrial basis and not linked to specificities of the case

study

The model considers probably only a part of the areas that could be positively affected by

the formulas used to evaluate the “gain”, applying a new structure with “perfect” soft skills

and/or flexibility through the specializations: indirect people or functions are not drawn into

the model and they are considered fixed, even if, as per the setup operators, imagining a

flexibility through direct/indirect could improve the FTE efficiency

As already mentioned, it was not possible to measure, and consequently modelling the

system, considering the inefficiency caused on direct general workers due to a “faulty” or

“partially faulty” planning (in a more general way the soft skills). This part of the model is

absorbed by the difference between the actual and base scenarios but it could be useful to

understand and describe the effect of growing better soft skills on the inefficiency hidden by

“more people” than the necessary

An easy development could be represented by describing the target searched with a

weighted sum of the indicators noted all along this paper: this approach could transform all

the approach and the model from a “descriptive” to a “targeted” analysis. As per previous

paragraphs, all the hypothesis, model drawing, simulation levers, sensitivity levers, results,

… let us describe a series of different pictures, measuring some delta between indicators and

finding in these deltas a sort of indications as “to follow” or “to avoid” path.

Summarizing into a weighted formula the indicators the approach here above described:

𝐹𝑇𝐸𝑤𝑜𝑟𝑘𝑒𝑟𝑠 ∗ 𝑊1 + 𝐹𝑇𝐸𝑠𝑒𝑡𝑢𝑝 𝑜𝑝𝑒𝑟𝑎𝑡𝑜𝑟𝑠 ∗ 𝑊2 + 𝐶𝑜𝑠𝑡𝑤𝑜𝑟𝑘𝑒𝑟𝑠 ∗ 𝑊3 + 𝐶𝑜𝑠𝑡𝑠𝑒𝑡𝑢𝑝 𝑜𝑝𝑒𝑟𝑎𝑡𝑜𝑟𝑠 ∗ 𝑊4 + 𝑆𝑝𝑎𝑐𝑒𝑙𝑖𝑛𝑒 ∗ 𝑊5

+ 𝐶𝑜𝑠𝑡𝑙𝑖𝑛𝑒𝑠 ∗ 𝑊6 + 𝑇𝑒𝑐ℎ𝐼𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟𝑠𝑖 ∗ 𝑊𝑖

The coefficients W and the sign of the formula have to be considered positive or negative

according to the “versus” of the element: Cost is a negative element and if it is increased the

target formula has to receive a negative variation. This sign could be reflected into the

“weigh” factor “W”.

This formula is an example based on our case study and it reflects the coefficients and

variables described, but it could be generalized. If the model should be expanded to cover

indirect functions and people, the first part of the formula could be expanded to FTE and

Cost related to the new areas.

This kind of description opens the model to a different approach: the simulation process

could become a “calibration” process, to optimize the levers obtaining the maximum results

for the target value of the formula here-above.

In this way all the previous work lets us understand the system, at least in a simplified

scheme, trying to reduce the complexity due to too many subsystems inter-acting among

themselves with loops, while the same model, once used to defined the relationships and

indicators, becomes itself the optimization instrument to calibrate the possible choices to

maximize a formula

The theoretical discrepancy between the two concepts “flexibility” versus “lean or efficiency

maximization”, often viewed as an excluding relationship, has to be analysed coming down

to the “real” state of the system: once reached the “optimum” scenario, where all the “soft

— page 175 —

skills” are maximized, the search of maximum flexibility versus maximum specialization

depends on:

o the cost difference between specialized and “flexibility” resources, knowing that

specialization is always expensive in some limited resources while “flexibility” could

push higher the cost of the “generalized” resources, especially if the system grows

up from a specialized to “flexibility” one

o the productivity difference between specialized and general but trained to flexibility

resources

o the inefficiency cost due to the fact that in real world is not easy to have and organize

“fraction” of resources (think about the production line: more the machinery and the

equipment installed more it is possible, if technology permits it, to add part of the

machinery to strengthen some phase, less the machinery and equipment installed,

more the minimum equipment plays a role and has to be considered in an “integer”

way). Concerning the people, worker resources, this discussion has to consider also

the local law restrictions, that could really affect the number of people needed versus

the theoretical FTE requested by the model in optimum situation.

As mentioned in previous bullet points, the part of the model covering people resources,

either for the specialization versus flexibility workers in terms of “integer” people requested

or simply in terms of “integer” versus “fraction” FTE requested by “inefficiency” causes,

becomes really relevant if we are not in the “best planning” scenario but in an actual or real

situation as already mentioned. We have also to remember that the simulation and

sensitivity analysis covered also the “specialization” versus flexibility of workers but they

didn’t investigate the detail of the inefficiency in general workers who have to transit

through different lines in a concurrent change of productions.

Considering the Italian economic panorama, really rich in small or medium business realities but

increasingly poor of the old economy process industries, that in the past were not only important

for dimensions and employment but also for professional training and research (IRI, but most of the

public sector companies also, had in its statutory rules the obligation to reserve part of its results to

professional training and research: the internal management school was well known as well as the

research centres were among the most innovative; even today some of the celebrated private

sectors managers come from this world), this paper sought to focus the attention on the “gain” that

growing realities without a strong historical background, belonging to the actual Italian panorama,

could obtain, measuring the possible results attainable developing the soft skills and flexibility

abilities. Finally, small or medium companies should have to find in Universities and Academic World

what large institutions had in the past inside their structure: inspiration coming from Accademia

could find test fields in changing companies. Students could find their testing ground in companies

that do not have resources to invest in internal research. Companies should have to change their

approach accepting news coming in from Accademia, Professors and Students, without the idea to

use only “stable” internal people. In and Out concerning people and ideas has to be accepted and

managed. And Professors should have to coordinate theories with test on fields making the

communication between Accademia and Companies easier. Otherwise we risk that the theory will

leave most of the “old” economy, the small or medium size, alone.

This simple idea was perfectly described by Vannevar Bush, Director of the Office of Scientific

Research and Development, in his “Science The Endless Frontier” (Bush, 1945), a report to the

President:

— page 176 — “[…]

Industrial research

The simplest and most effective way in which the Government can strengthen industrial research is

to support basic research and to develop scientific talent.

The benefits of basic research do not reach all industries equally or at the same speed. Some small

enterprises never receive any of the benefits. It has been suggested that the benefits might be

better utilized if "research clinics" for such enterprises were to be established. Businessmen would

thus be able to make more use of research than they now do. This proposal is certainly worthy of

further study.[…]”

And this was the beginning of the information technology developing “golden” era.

— page 177 — ANNEX – Figure 51 Detailed Literature Review

Argument Titles Historica

l Context

Lean / Bas

e

lean / Internal Focus

Lean / Ouverture versus Supply Chain

System Dynamic

s

Flexibility

Agility - Market

Turbulence

Agility - Definition

and Investigatio

n areas

Supply

Chain

Operational performance

s - General

Operational performances - Trade Off

drivers

Operational performances / Uni-driver


s / Multi-drivers

Training and

Educational

Agility

Bernardes, E. H. (2009). A theoretical review of flexibility, agility and responsiveness in the operations management literature: Toward a conceptual definition of customer responsiveness. International Journal of Operations and Production Management, 29 (1), pp. 30-53.

X

Agility

Chiang, C.-Y. K.-H. (2012). An empirical investigation of the impact of strategic sourcing and flexibility on firm's supply chain agility. International Journal of Operations and Production Management, 32 (1), pp. 49-78.

X

Agility

Gunasekaran, A. (1999). Agile Manufacturing: A Framework for research and development . International Journal of Production Economics, Vol.62(1, pp.87-105.

X

Agility

Sharifi, H. Z. (1999). Methodology for achieving agility in manufacturing organisations: an introduction. Source of the DocumentInternational Journal of Production Economics, 62 (1), pp. 7-22.

X

Agility

Tsinopoulos, C., & Mccarthy, I. (2000). Achieving agility using cladistics: an evolutionary analysis. Journal of Materials Processing Tech., Vol.107(1), pp.338-346.

X

Base

Chase, R. B. (1991). The Service Factory: A Future Vision. International Journal of Service Industry management, Vol. 2.

X

Base

Flynn, E., & Flynn, B. (Fall 1996). Achieving Simultaneous Cost And Differentiation Competitive Advantages Through Continuous Improvement: World Class Manufacturing As A Competitive Strategy. Journal of Managerial Issues, Vol. 8, No. 3, pp. 360-379.

X

Base

Forrester, J. (1961). Industrial dynamic. Waltham MA: Pegasus Communications.

X

Base

Forrester, J. (1968). Principles of Systems, 2nd edition. Pegasus Communications.

X

Base

Forrester, J. (1969). Urban Dynamics. Pegasus Communications.

X

Base Forrester, J. (1971). World Dynamics. Wright-Allen Press.

X

— page 178 —


l Context

Lean / Bas

e



System Dynamic

s

Flexibility

Agility - Market

Turbulence


and Investigatio

n areas

Supply

Chain


s - General


drivers



s / Multi-drivers

Training and

Educational

Base Gleick, J. (1987). Chaos. New York: Viking Penguin Inc.

X

Base

Panizzolo, R. (1998). Applying the lessons learned from 27 lean manufacturers.: The relevance of relationships management. International Journal of Production Economics, Vol.55(3, pp.223-240.

X

Base

Rudolph, J., & Repenning, N. (2002). Disaster Dynamics: Understanding the Role of Stress and Interruptions in Organizational Collapse. Administrative Science Quarterly, 47, pp 1-30.

X

Base

Senge, P. (1990 - 2006). The Fifth Discipline Fieldbook: Strategies and Tools for Building a Learning Organization. USA: Doubleday a division of Random House, Inc.

X

Base

Sterman, J. D. (2000). Business Dynamics, System Thinking and Modeling for a Complex World. USA: The MacGrawn-Hill Companies Inc.

X

Flexibility

Abdel-Malek, L., Das, S. K., & Wolf, C. (2000). Design and implementation of flexible manufacturing solutions in agile enterprises. International Journal of Agile Management Systems, Vol.2(3), pp.187-195.

X

Flexibility

Gunasekaran, A., Tirtiroglu, E., & Wolstencroft, V. (2002). An investigation into the application of agile manufacturing in an aerospace company. Technovation, Vol.22(7), pp.405-415.

X

Historical Context

Amatori, F. (2013). Storia dell'IRI volumi 1 -6. Editore Laterza.

X

Historical Context

Avagliano, L. (1991). La mano visibile in Italia, le vicende della finanziaria IRI (1933 - 1985). Roma: Edizioni Studium .

X

Historical Context

Bagella, M. (1999). Efibanca e l'industria Italiana. Firenze: BNL Edizioni - Giunti Gruppo Editoriale.

X

Historical Context

Franzinelii, M., & Magnani, M. (2009). Beneduce, Il Finanziere di Mussolini. Arnoldo Mondadori Editore Spa.

X

Historical Context

Friedman, m. (1987). Capitalismo e Libertà. Pordenone: Edizioni Studio Tesi.

X

Historical Context

Galbraith, J. (2002). Il Grande Crollo - The Great Crash. Milano: RCS Libri S.p.A.

X

— page 179 —


l Context

Lean / Bas

e



System Dynamic

s

Flexibility

Agility - Market

Turbulence


and Investigatio

n areas

Supply

Chain


s - General


drivers



s / Multi-drivers

Training and

Educational

Historical Context

Galbraith, J. (2004). L'economia della Truffa - The Economics of Innocent Fraud. Milano: RCS Libri S.p.A.

X

Historical Context

Galbraith, J. K. (1988). Storia della Economia. Milano: RCS Rizzoli Libri S.p.A.

X

Historical Context

Heron, J. (1971). EXPERIENCE AND METHOD An Inquiry into the Concept of Experiential Research. Guildford Surrey GU2 5XH : Human Potential Research Project - Department of Educational Studies - University of Surrey.

X

Historical Context

Keynes, J. (1931). The Collected Writings Volume IX Essays in Persuasion: Economic Possibilities for Our Grandchildren. The Royal Economic Society - Palgrave Macmillian a division of MacMillian Publishers Limited.

X

Historical Context

La Bella, G. (1983). L'IRI nel dopoguerra. Roma: Edizioni Studium.

X

Historical Context

Modigliani, F., & Miller, M. (1958). The Cost of Capital, Corporation Finance and the Theory of Investment. American Economic Review. 48 (3), pp 261–297.

X

Historical Context

Modigliani, F., & Miller, M. (1963). Corporate income taxes and the cost of capital: a correction. American Economic Review. 53 (3), pp 433–443.

X

Historical Context

Nicholas Wapshott (Author), G. C. (2015). Keynes o Hayek. Lo scontro che ha definito l'economia moderna. Milano: Universale Economica Feltrinelli Storia.

X

Historical Context

Pini, M. (2000). I giorni dell'IRI, Storie e misfatti da beneduce a Prodi. Milano: Arnoldo Mondadori Editore S.p.A.

X

Historical Context

Popper, K. (1959 - 1992). The Logic of Scientific Discovery. New York: First edition Hutchinson & Co - Reprinted by Routledge.

X

Historical Context

Schumpeter, J. A. (1942 - 1994). Capitalism, Socialism and Democracy. London (GB): Riutledge.

X

Historical Context

Smith, A. (1776). An Inquiry into the Nature and Causes of the Wealth of Nations. London: Methuen & Co., Ltd.

X

Historical Context

Troillo, C. (2008). 1963-1982, I vent'anni che sconvolsero l'IRI. Milano-Roma: Bevivino Editore.

X

— page 180 —


l Context

Lean / Bas

e



System Dynamic

s

Flexibility

Agility - Market

Turbulence


and Investigatio

n areas

Supply

Chain


s - General


drivers



s / Multi-drivers

Training and

Educational

Historical Context

von Hayek, F. (2009). Tra Realismo e utopia Liberale - scritti 1949-1956 a cura di Mario Gregori. Milano-Udine: Mimesis Edizioni.

X

Historical Context

von Hayek, F. A. (1988). Conoscenza, mercato, pianificazione. Bologna: Scietà editrice il Mulino.

X

Historical Context

Wapshott, N. (2011). Keynes Hayek: the clash that defined modern economics. New York: W. W. Norton & Company, Inc.

X

Lean Approach

J. Jayaram, S. V. (2008). Relationship building, lean strategy and firm performance: An exploratory study in the automotive supplier industry. International Journal of Production Research, 40 (20), pp. 5633-5649.

X

Lean Approach

Jadhav, J. M. (January 2014). Exploring barriers in lean implementation. International Journal of Lean Six Sigma, Volume 5, Issue 2, Pages 122-148.

X

Lean Approach

Moyano-Fuentes, J. S.-D.-J. (2012). Cooperation in the supply chain and lean production adoption: Evidence from the Spanish automotive industry. International Journal of Operations and Production Management, 32 (9), pp. 1075-1096.

X

Lean Approach

R. P. Mohanty, O. P. (2006). Implementation of Lean Manufacturing Principles in Auto Industry*. XIMB Journal of Management.

X

Lean Approach

Sahwan, M. R. (2012). Barriers to implement lean manufacturing in malaysian automotive industry. Jurnal Teknologi (Sciences and Engineering) 59, (SUPPL. 2), pp. 107-110.

X

Lean Approach

Womack, J., Jones, D., & Roos, D. (1990). The Machine That Changed the World. Free Press.

X

Luxury

Brun, A., & Castelli, C. (2008). Supply chain strategy in the fashion industry: Developing a portfolio model depending on product, retail channel and brand. International Journal of Production Economics, Vol.116(2), pp.169-181.

X

Luxury

Brun, A., Caniato, F., Caridi, M., Castelli, C., Miragliotta, G., Ronchi, S., . . . Spina, G. (2008). Logistics and supply chain management in luxury fashion retail: Empirical investigation of Italian firms. International Journal of Production Economics, Vol.114(2, pp.554-570.

X

— page 181 —


l Context

Lean / Bas

e



System Dynamic

s

Flexibility

Agility - Market

Turbulence


and Investigatio

n areas

Supply

Chain


s - General


drivers



s / Multi-drivers

Training and

Educational

Luxury

Caniato, F., Caridi, M., Castelli, C., & Golini, R. (2011). Supply chain management in the luxury industry: A first classification of companies and their strategies. International Journal of Production Economics, Vol.133(2), pp.622-633.

X

Performance Measuremen

t

Arzu Akyuz, G., & Erman Erkan, T. (2010). Supply chain performance measurement: A literature review. International Journal of Production Research, Vol.48(17), pp.5137-5155.

X


t

Berk, A., & Kaše, R. (2010). Establishing the Value of Flexibility Created by Training: Applying Real Options Methodology to a Single HR Practice. Organization Science 21(3), pp 765-780.

X


t

Bozarth, C., & Edwards, S. (1997). The impact of market requirements focus and manufacturing characteristics focus on plant performance. Journal of Operations Management, Volume 15, Issue 3, pp 161-180.

X


t

Cecil Bozarth, S. E. (1997). The impact of market requirements focus and manufacturing characteristics focus on plant performance. Journal of Operations Management, Volume: 15, Issue: 3, Pages: 161-180.

X


t

David Xiaosong Peng, R. G. (2011). Competitive priorities, plant improvement and innovation capabilities, and operational performance: A test of two forms of fit. International Journal of Operations & Production Management, Volume: 31, Issue: 5, Pages: 484-510.

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t

Donald J Bowersox, D. J. (2000). How Supply Chain Competency Leads to Business Success. Supply Chain Management Review, Volume: 4, Issue: 4, Pages: 70.

X


t

Ferdows, K., & De meyer, A. (1990). Lasting Improvements in Manufacturing Performance: In Search of a New Theory. Journal of Operations Management, Vol. 9, Nà 2, 168-184.

X


t

Gunasekaran, A., Patel, C., & Mcgaughey, R. E. (2004). A framework for supply chain performance measurement. International Journal of Production Economics, Vol.87(3), pp.333-347.

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System Dynamic

s

Flexibility

Agility - Market

Turbulence


and Investigatio

n areas

Supply

Chain


s - General


drivers



s / Multi-drivers

Training and

Educational


t

Inman, R. A., Sale, R. S., Green, K. W., & Whitten, D. (2011). Agile Manufacturing: Relation to JIT, operational performance and firm performance. Journal of Operations Management, Vol.29(4), pp.343-355.

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t

Roger W Schmenner, M. L. (1998). On theory in operations management. Journal of Operations Management, Volume: 17, Issue: 1, Pages: 97-113.

X


t

Samson, D., & Terziovski, M. (1999). The relationship between total quality management practices and operational performance. Journal of Operations Management, Vol.17(4), pp.393-409.

X


t

Sarah Jinhui Wu, S. a. (2012). An empirical investigation of the combinatorial nature of operational practices and operational capabilities: Compensatory or additive? International Journal of Operations & Production Management, , Pages: 121-155.

X


t

Shah, R., & Ward, P. T. (2003). Lean manufacturing: context, practice bundles, and performance. Journal of Operations Management, Vol.21(2), pp.129-149.

X


t

Sohel Ahmad, R. G. (2003). The impact of human resource management practices on operational performence: recognising country and industry differences. Journal of Operations Management, Volume: 21, , Pages: 19-43.

X


t

Wheelwright, S. (1978). Reflecting Corporate Strategy in Manufacturing Decisions. Business Horizons, Volume: 21, Issue: February, Pages: 57-66.

X

Training & Educational

Fryer, J. (1973). Operating policies in mutiechelon dual-constraints job shops. Management Science, 9, pp 1001-1012.

X


Fryer, J. (1974). Labor flexibility in multiechelon dual-constraint job shops. Management Science, 7, pp 1073-1080.

X


Fryer, J. (1976). Organizational segmentettion and labor transfer policies in labor and machine limited production systems. Decision Science, 7, pp 725-738.

X

— page 183 —


l Context

Lean / Bas

e



System Dynamic

s

Flexibility

Agility - Market

Turbulence


and Investigatio

n areas

Supply

Chain


s - General


drivers



s / Multi-drivers

Training and

Educational


Hemant V. Kher, M. K. (1999). Modeling simultaneous worker learning and forgetting in dual resource constrained systems. European Journal of Operational Research 115, pp 158-172.

X


Hottenstein, M. P., & Bowman, S. A. (1998). Cross-Training and Worker Flexibility: a review of DRC System research. The Journal of High technology Management research, Volume 9, Number 2, pp 157-174.

X


Malhotra, M., Fry, T., Kher, H., & Donohue, J. (1993). The impact of learning and labor attrition on worker flexibility in dual resource job shops. decisions Science, 3, pp 641-663.

X


Park, P. &. (1989). Job release and labor flexibility in a dual resource constrained job shop. Journal ofOperations Management, 3,, pp 230--249.

X

71 23 4 1 3 8 2 1 4 4 8 1 2 2 8

— page 184 —

Aknowledgement

Thanks to Mr Roberto Martone, ICR President, and to ICR Spa where I'm working since 2003. They believed in me and in my research. A special thanks to Luigi Lambardi, Emanuele Wissiak , Matteo Pontecorvo and Laura-Belle Mugnaini for their help in reviewing my "Italian" English. To Matteo and Emanuele, I hope future may reserve great success as researchers: they deserve it and … I'm sure of it. To Luigi because he is probably the only one who could completely appreciate the work made in ICR: he is working in ICR. A very special thanks to Prof. Mariano Corso, without him I should never have begun this project; to Prof. Alberto Portioli for his project discussions over these last five years and to Mrs Gisella DiTavi and her colleagues for all the help in administrative matters: five years of really appreciated help. And what I could say to Prof. Giovanni Miragliotta: without him I would never have been here today. Many times I thought to give up but he was present to help me. It was a real honour to work with him. A real friend, even if I never told him. A really special thanks to my family: many years ago, my grandparents pushed me to study far from Genoa: my parents and sisters supported me every time I started a new project. Last but not least, to my wife, who let me follow any dream I had during these last 16 years, and to my children, who were with me during these last "difficult" 5 years. Finally, to myself: a difficult task … a real great difficult task but proud to be here today. A dream

comes true. And … what next?

— page 185 —

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