Final Year Honours Project

for the degree of

BSc. in Engineering with Management

Journal Paper

N. Al Helo

Method to determine the optimum degree of lean activity

applied to an industry using fuzzy logic

May 2016

Project Supervisor: Dr. Sibi Chacko

School of Engineering and Physical Sciences

Heriot-Watt University Dubai Campus

2

Contents 1. Abstract ....................................................................................................................................................... 4

2. Introduction ................................................................................................................................................. 4

3. Literature Review ........................................................................................................................................ 4

Lean manufacturing principles ........................................................................................................................ 4

Overall Equipment Effectiveness (OEE) ........................................................................................................... 5

Fuzzy logic ........................................................................................................................................................ 6

Applications of fuzzy logic coupled with lean tools ......................................................................................... 7

4. Overview of Methodology ........................................................................................................................... 8

5. Data collection and Analysis ........................................................................................................................ 8

Brief description of company .......................................................................................................................... 8

Analysis of Data ............................................................................................................................................... 9

Data Analysis of Individual OEE parameters ............................................................................................... 9

6. Development of conceptual models ......................................................................................................... 10

Procedure .................................................................................................................................................. 10

Model 1- Measuring availability .................................................................................................................... 10

Fuzzy system input and output variable(s) ................................................................................................ 10

Different MF shapes .................................................................................................................................. 11

Fuzzy system functionalities ...................................................................................................................... 11

Rule editor ................................................................................................................................................. 11

Rule viewer ................................................................................................................................................ 14

Fuzzy system validation ............................................................................................................................. 14

Model 2-Determine the required downtimes for a specified availability ..................................................... 15

Fuzzy system input and output variable(s) ................................................................................................ 15

Fuzzy system functionalities ...................................................................................................................... 15

Rule viewer ................................................................................................................................................ 15

Fuzzy system validation ............................................................................................................................. 16

Fuzzy system output .................................................................................................................................. 16

7. Results and Discussion ............................................................................................................................... 16

General recommendation topics ................................................................................................................... 17

Tackling downtime .................................................................................................................................... 17

TPM (Total productive maintenance) ........................................................................................................ 17

VSM (Value stream map) ........................................................................................................................... 17

Single minute exchange of dies (SMED) .................................................................................................... 17

Proposed improvement plans ....................................................................................................................... 18

8. Conclusions ................................................................................................................................................ 18

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9. Limitations and future research direction ................................................................................................. 19

10. References ............................................................................................................................................. 20

11. Appendix ................................................................................................................................................ 22

Appendix A-Brief descriptions of the various downtime categories ............................................................. 22

Appendix B-Tri, Trap and Gauss MFs [14] ................................................................................................ 22

Appendix C-Data used to define the MF of input and output variables ....................................................... 23

For input variables ..................................................................................................................................... 23

For output variables .................................................................................................................................. 23

Appendix D-How MFs were defined with an example .................................................................................. 24

‘Gaussmf’ ................................................................................................................................................... 24

‘Trapmf’ ..................................................................................................................................................... 24

Appendix E-Aggregated downtime values to depict the magnitude of effect availability can have ............ 25

Appendix F-Various graphical representations resulting from data analyses............................................... 26

Appendix G-Some factors that affect performance as noticed ..................................................................... 29

Effective content word count≈5600

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1. Abstract Globalisation and emerging technologies have

made the market even more competitive. Lean

manufacturing is a technique by which firms

eliminate waste to cut costs and gain market share

and profit. However, many lean manufacturing

decisions are faced with imprecise data that needs

to be analysed before an informed decision can be

made. Fuzzy logic assigns degrees of membership

to allow such data to be utilised. Within this paper

two conceptual fuzzy lean models are developed

that help highlight improvement areas. As a result

of the model output, development plans are

proposed to help increase productivity.

2. Introduction Manufacturing organisations have been witnessing

a paradigm shift towards lean manufacturing from

mass production [1]. Globalisation and emerging

technologies have resulted in a heightened market

competitiveness where firms are at war for market

share, and more importantly, profit. Lean

manufacturing helps firms cut costs of

manufacture by the elimination of waste, allowing

more room for profits and gains [2]. Lean has the

ability to realize improved outputs while using

fewer resources than in traditional manufacturing

systems [3]. It is reported that lean compared to

mass production uses half of human effort,

manufacturing space, investment in tools,

inventory and time to market; making the

enterprise more responsive to customer demand

[4]. This philosophy also has the effect of increasing

productivity, reducing lead times and improving

quality [5,6]. All the benefits pose to make it seem

as if this systematic theory is easy to implement.

However, companies in the manufacturing

environment are faced with the need to make

informed decisions but seldom work with accurate

and precise data. As a solution, the use of fuzzy

logic alongside lean principles may help decision

makers in their ordeal.

Fuzzy logic is a useful tool to deal with the

phenomenon where data is imprecise, vague and

prone to being utilized in a subjective and biased

manner [1,7].

Fuzzy set theory is deemed an appropriate

method because it uses approximate reasoning to

compile the limited data into linguistic sets [2]. This

paper presents conceptual fuzzy computer models

that are able to process imprecise lean

manufacturing data. The use of fuzzy membership

functions (MFs), with quantitative lean numbers,

has an effect of reducing this uncertainty and

creates a consensus to facilitate decision-making

[4].

The goal of this thesis is hence to make the use of

lean tools and techniques more effective by

integrating lean concepts with fuzzy logic to

formulate models that appropriately help reduce

waste systematically and continuously improve

operational efficiency. This project was completed

in collaboration with an industrial company (more

details in Section 5) and aims to increase the

manufacturing capacity and efficiency using

various lean and fuzzy logic tools and techniques.

After the development and testing of the

conceptual models, various lean improvement

recommendations were determined.

3. Literature Review

Lean manufacturing principles After World War II; Japanese manufacturers

(especially automotive) were faced with financial

and human resource shortages, driving Eiji Toyoda

and Taiichi Ohno at Toyota in Japan to pioneer the

Toyota Production System (TPS); otherwise termed

‘Lean Manufacturing’ by the West. Lean

manufacturing aims at eliminating waste, defined

by practitioners as anything that does not add

value to the end product from the perspective of

the customer. This innovative system quickly

spread on from Japan to the US and was reported

by Womack and Jones in the well-renown book,

“The machine that changed the world”, published

in 1994. Furthermore, this remarkable insight

utilises less input to create the same output as by

a traditional mass production system; in less time

and resources, at the highest quality and usually

the lowest costs [3].

5

Lean manufacturing is an integrated

manufacturing system that aims to maximise

capacity while minimising waste, input, buffer

inventory, system variability and time to market;

allowing for the production of quality products in a

more efficient and economical manner [7]. Lean

focuses on abolishing one or more categories of

waste, ‘muda’ in Japanese. From literature the

seven types of waste apparent in manufacturing

firms are:

o Overproduction-producing too much too

soon.

o Added inventory-excessive storage of

products, resulting in high holding and

preparation costs.

o Waiting-linked to the popular quote “time

is money”; waiting induces inactivity of

people, products, and information, leading

to longer lead times.

o Excessive transportation-unnecessary or

exaggerated movement of people or

commodities adding to wasted time and

hence, extra costs.

o Defects-inclusive, but not exclusive of,

errors in paperwork and final product

requiring rework or scrap.

o Ineffective motion-poor process design

resulting in the employee wasting more

time and energy than required to perform

a task.

o Inappropriate processing-performing a

task using the wrong set of tools or

procedures [3,4].

Dal et al. [6] conducted a case study in the ready

wear industry to increase the efficiency of a firm by

the useful implementation of lean manufacturing

techniques along the production line. The line was

rearranged to achieve a 29% increased efficiency in

the improved production rate of 2400 pieces per

day. In another case study, Dora and Gellynck [8]

implement the lean philosophy to a medium-sized

confectionary with issues in overfilling and defects.

The methodological implementation of lean

resulted in a reduced machine breakdown, an

increase in the employees’ morale, as well as

solutions to the problems faced.

Furthermore; perishability of the product and

climate change, although difficult to control, can

be worked around by adaptation of the firm using

lean.

These case studies prove the versatility of lean

techniques and the possibility of gaining benefits

from its implementation whatever the nature of

the firm.

Overall Equipment Effectiveness (OEE) Lean practices focus on pinpointing waste sources

by using various tools and techniques such as just-

in-time (JIT), Kanban, Kaizen, total productive

maintenance (TPM), single minute exchange of

dies (SMED), value stream mapping (VSM), 5S,

workforce involvement and OEE amongst others.

OEE quantifies the percentage of planned

production time that is truly productive. An OEE

score of 100% signifies a perfect production; where

only good parts are manufactured (100% Quality),

as quick as possible (100% Performance) and with

no down time (100% Availability).

From definition,

𝑂𝐸𝐸 = 𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑡𝑦 × 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 × 𝑄𝑢𝑎𝑙𝑖𝑡𝑦

Equation 1-General OEE equation

𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦

=𝑁𝑒𝑡 𝑟𝑢𝑛 𝑡𝑖𝑚𝑒 (minutes)

𝑇𝑜𝑡𝑎𝑙 𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑇𝑖𝑚𝑒 − 𝑇𝑜𝑡𝑎𝑙 𝑃𝑙𝑎𝑛𝑛𝑒𝑑 𝑡𝑖𝑚𝑒

Equation 2-General Availability

𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 =𝑃𝑟𝑜𝑑𝑢𝑐𝑒𝑑 𝐵𝑎𝑡𝑐ℎ𝑒𝑠

𝑇𝑎𝑟𝑔𝑒𝑡 𝐵𝑎𝑡𝑐ℎ𝑒𝑠

Equation 3-Performance equation

𝑄𝑢𝑎𝑙𝑖𝑡𝑦 =𝑃𝑟𝑜𝑑𝑢𝑐𝑒𝑑 𝐺𝑜𝑜𝑑 𝐵𝑎𝑡𝑐ℎ𝑒𝑠

𝑇𝑜𝑡𝑎𝑙 𝑃𝑟𝑜𝑑𝑢𝑐𝑒𝑑 𝐵𝑎𝑡𝑐ℎ𝑒𝑠

Equation 4-Quality equation

6

OEE factor Implication

Availability Signifies to what extent the process is running during the

planned production time.

Performance Implies to what extent the process is

running at the theoretical maximum

speed.

Quality Determines how many defective parts

there are in the produced batch.

Table 1-This table briefly explains the meaning of each OEE parameters.

From the seven deadly types of waste listed above,

the OEE is powerful at quantifying the proportion

of waste inherent in the production process.

Availability loss incorporates anything that halts

the planned production for an appreciable amount

of time. For example; equipment failure,

changeovers, unplanned breakdowns, and

maintenance. Performance loss can be described

as any factor such as machine wear and usage of

substandard raw material, causing the process to

operate at a lower speed than optimum. The

quality loss includes parts that do not pass quality

inspection standards and require rework or

disposal.

OEE is useful as:

A benchmark to compare operational

performances to industry standards or for

different shifts working on the same asset.

Figure 1-Shows the proportion of true productivity to waste for various benchmarks [9]

Within this project, a low to typical OEE is studied

in an attempt to reach towards the ultimatum, a

world class production.

A baseline to keep track of company

progress in waste elimination. [9]

Establishing a baseline for a process by calculating

OEE provides an objective measure towards

improving manufacturing productivity. However,

using OEE scores to compare divisions across a

company can be problematic since these

comparisons are only truly meaningful if between

the same processes under the same conditions.

OEE is extremely effective at fulfilling the objective

of lean tools in exposing transparent waste

sources, effectively “uncovering the hidden

factory” within a process [10].

Fuzzy logic Fuzzy theory originates from the human inference

process, taking advantage of knowledge without

boundaries [1]. Fuzzy incorporates an alternative

way of thinking in computer language and can be

expressed by linguistic variables mapped as

numerical ranges. Unlike classical logic, not

everything has to be True or not True but rather a

degree of trueness specified [11]. The fuzzy

method, formulated by Zadeh (1965), is a

mathematical theory that allows for ambiguity and

vagueness to be modelled using fuzzy numbers.

This concept was not recognized until Mamdani

applied it in a practical situation to control a steam

engine almost a decade after it was invented [12].

Concepts like fuzzy sets, linguistic variables and if-

then rules are included within fuzzy logic [1]. Since

the 1980s, fuzzy methods have been applied in

many areas such as economics, manufacturing

operations, health sciences, automatic control,

engineering and communication technologies

[12,13]. The use of fuzzy logic has also been seen

applied in customer and domestic products such as

washing machines, microwave ovens, and medical

instrumentation, amongst many others [14]. The

application of fuzzy logic is especially important in

terms of making decisions for lean manufacturing

due to the characteristics of this logic, allowing for

both qualitative and quantitative analysis of

provided data [13].

Fuzzy logic is depicted as a method to transform

input vectors, based on a set of rules, to an output

vector(s).

7

A fuzzy set is a set without a crisp boundary that

contains elements with partial degrees of

membership. A clear illustration can be given by

the example of what season of the year one is

experiencing based on astronomical definitions

and the current climate. According to astronomy,

summer starts when the earth’s orbit points the

North Pole directly towards the sun, hence defining

a crisp boundary of exactly when summer is. In the

northern hemisphere, summer occurs in July and

lasts for one month. However, as we experience

here in Dubai, the climate expresses the heat of

summer from March through September,

regardless of the earth’s orbitals. Therefore, to

define such controversial subjects, fuzzy logic can

be used to allow continuity rather than sharp

boundaries, and the possibility of a assigning a

degree of membership as opposed to either 1 or 0,

as depicted by the Boolean logic [14].

Figure 2-This figure depicts the differences between Boolean and fuzzy logic explained by the example of the four seasons [14]

In more technical terms, linguistic variables are

described by a membership function (MF) µ(x) that

assigns to each number, x, a degree to which the

number satisfies the property.

For example; the property being ‘small’, the

degree of membership describes to what extent x

is small [15].

µ(x) ∈ [0, 1]

Equation 5-Property of the MF in assigning a degree of membership

The value 0 means x has no membership with the

set, while 1 denotes complete membership. Any

degree in the range between 0 and 1 denote a

partial membership to the fuzzy set. A series of if-

then rules are formulated to make the fuzzy

inference system (FIS), which uses input values as

weighted factors to map the final fuzzy output sets.

Once all the rules are inferred, scaled and

combined, a crisp output is obtained by

defuzzification methods [12].

Figure 3-This figure shows a triangular MF and how fuzzy sets and degrees of memberships can be defined [13]

To summarize; fuzzy logic is flexible, tolerant of

imprecise data, can model nonlinear functions, is

based on natural language and can be blended with

other tools; as will be presented in this paper [14].

Applications of fuzzy logic coupled with lean

tools In a paper by Susilawati et al. [13], fuzzy logic was

used to determine the degree of lean activity on

the areas: supplier and customer issues,

manufacturing, R&D and investment. The

proposed fuzzy number based scoring is applied to

aggregate multiple evaluators’ scores and analyse

this vague and subjective data.

8

In a study by Mohanraj et al. [16] a framework for

VSM integrated with fuzzy logic was developed and

tested by an automotive components

manufacturer to prioritize improvement plans

according to the results of the VSM. This initiative

was promoted due to the fact that it is infeasible to

implement all proposals concurrently. VSM is an

approach that identifies and eliminates non-value

added (NVA) activities from the value chain,

effectively removing waste. Examples include

walking long routes to unpack inventory or

transporting goods, which does not add any real

value to the product from the customers’

perspective. Vinodh and Balaji [1] state that one of

the contemporary agendas of lean manufacturing

is the quantification of leanness within a firm. To

build on this, they developed a fuzzy based system

that presents leanness assessment for an

organization manufacturing modular switches.

The case studies and academic articles on these

topics together prove that fuzzy logic can be used

with multiple lean tools to record great benefits in

terms of improved operational productivity. Fuzzy

integrated with a lean tool should provide a

framework for an improved lean tool effectiveness

and elimination of inconsistencies with crisp

values, to enable continuous improvement.

However; to the extent of research carried out,

there is no application in which OEE is fuzzified and

processed by fuzzy logic. The fact that OEE, a highly

numerical lean tool, has not been used in

conjunction with fuzzy logic, a mathematical

model, is the motivation for the developed models.

A successful model incorporating both aspects

should without doubt be deemed extremely useful

for any enterprise longing for a powerful tool to

eliminate or reduce waste while suggesting

improvement areas. Using OEE and fuzzy should

provide sufficient justification and quantifiable

evidence, convincing of lean approaches.

4. Overview of Methodology The methodology followed for this thesis is shown

in Figure 4.

Figure 4-This is the sequence of the processes followed to complete this thesis

The project begins with a literature review of lean

manufacturing tools and techniques, fuzzy logic,

practical applications of both concepts and the

novelty of the conceptual models developed. Since

model development requires data, manufacturing

information was collected and analysed from a firm

in the United Arab Emirates in Section 5. The model

inputs, functionalities, and validation are then

explained in Section 6. Results, discussions, and

implications are studied in Section 7. Concluding

points and statements are listed in Section 8,

before the limitations and future research

directions are mentioned in the concluding Section

9.

5. Data collection and Analysis

Brief description of company This thesis aims to help implement and optimise

lean activity using fuzzy logic, in a private paints

manufacturer located in Dubai.

The objective of the study is to increase the

manufacturing capacity and efficiency using OEE

and fuzzy logic.

The following section contains both qualitative and

quantitative analyses, with a focus on the latter, for

the two main manufacturing processes at the firm:

paint processing and paint filling. Data comes from

the processing and filling of water-based paints,

which makes up 75% of produced product.

Due to an increased competition, the organization

is looking at improving productivity and reducing

costs.

Derivation of improvement proposals

Model Computations and results

Development of conceptual fuzzy lean models

Data collection and analyses

Literature review of lean manufacturing and fuzzy logic

9

OEE needs to be raised with the aim of reaching

world-class standards (OEE≈80%) in the near

future.

Analysis of Data Data was collected and analysed through company

visits, documentation analysis and interview

techniques with various inter-department

personnel. Since the whole manufacturing firm is

vast, analyses of data should direct the study;

firstly, whether to focus on processing or filling

lines; secondly, which OEE parameter is the

limiting; and finally, which machine meets the

above criteria and can be analysed to design the

models accordingly.

The OEE for 2014 and 2015 was analysed, and

some remarks comparing processing and filling are

added after the data is reported. The overall

processing and filling averages are as follows for

2014 and 2015 across all machines and lines:

Functional Line

OEE

2014 2015

Processing 78.68% 73.85%

Filling 47.89% 47.46%

Table 2-Shows a comparison of OEE between the processing and filling lines to help decide on which area to focus

Performance enhancement is to be

focused on filling lines instead of

processing as a result of very low-

efficiency percentages and a lot of room

for improvement.

Data Analysis of Individual OEE parameters Breaking down the OEE scores to the three

parameters gives insight into which area shows

maximum losses, thus highlighting where

improvement efforts need to be focused.

After the data was analysed it was determined that

the parameters ‘Availability’ and ‘Performance’

were affecting the OEE adversely due to their low

scores. However, availability is the easier score to

improve with limited resources, and the

performance is simultaneously improved as a

result. ‘Quality’ was very rarely a non-perfect value

of 100%.

As a result of multiple analyses; it was concluded

that out of the various filling machines, machine

151 had the lowest of Availability scores, and

hence is the machine requiring the most effort.

MACHINE-151

% VALUES

OEE Parameter

2014 2015

Availability 65.1 62.97

Performance 73.83 74.46

Quality 100 100

Table 3-Shows the individual OEE parameters of machine 151

Under Availability; Planned and Unplanned

downtimes are considered important to minimize,

if not eliminate, to improve the overall OEE score.

Unplanned downtimes show variations and hence

a possibility of not only reducing the amount of

downtime, but also minimising the fluctuations to

maximise the operational capacity [7]. By having

well-designed plans complete elimination may also

be sought for some categories of unplanned

downtime.

Conversely, planned downtime remains mostly

constant, shifting focus from the reduction of

variability to magnitude reduction.

Exercise/Communication time and Tea/Lunch

break fall under planned downtime. Line

preparation, changeover, size change,

slip/can/pallet waiting, breakdown and spillage

cleaning are the 6 types of unplanned downtime.

Multiple graphical representations in order to

better understand the magnitudes and

fluctuations of each of these downtimes are

attached in the Appendix.

10

The pie chart below shows the proportion each

category impacts the total downtime; and that a

large proportion, approximately 40%, is due to

changeover time while switching between batches.

Frequent changes in filled product results in a

measurable loss in production due to wasted

changeover times [17].

6. Development of conceptual

models The design and simulations are done using MATLAB

R2013a /Simulink software using the inbuilt fuzzy

logic toolbox.

It was deduced that availability is the constraint,

and machine 151 was the machine with the lowest

score on that measure. Moreover, downtime

parameters are known and can be fuzzified by a set

of MFs. The inference system incorporates a set of

if/then rules and was designed to mimic the

availability formulae aforementioned. Two

computational models were designed: the first

helps measure the availability given a set of

downtimes, and the second operates vice versa; by

inputting the desired availability and the required

amount each downtime category should be

reduced to, displayed. This helps in formulating

recommendation plans reducing the availability

losses and improving the OEE score. The objective

of the models is to eliminate time waste induced

by variability.

Procedure 1. We commence by starting up MATLAB and

typing ‘fuzzy’ into the command line to bring

up the FIS GUI.

2. For the first model, we add the 8 downtime

categories as input variables and a single

output as ‘Availability’.

3. Double clicking on any input variable brings up

the ‘variable editor’ in which we choose which

type and how many MFs define the model. The

analysed statistical data can then be input as

parameters to define these MFs.

4. After all the variables are defined, the series of

if-then rules are then formulated in the FIS

editor.

5. The ‘rule viewer’ shows the crisp defuzzified

value of availability given quantities of each

downtime category.

6. For the second model; a single input as

‘Availability’ is added, with 8 output variables

signifying the respective downtimes.

7. The same set of rules defined in step 4 is used

again, after slight modifications, since they

adequately model the problem in hand.

8. The rule viewer this time around shows the

required downtimes to realize a particular

availability.

Model 1- Measuring availability

Fuzzy system input and output variable(s) The first step in system development is the

identifying of input and output variables necessary

to define the envisaged fuzzy model. The output of

a computational model is just as good as the inputs

specified, with improper data guaranteeing

inaccuracies. Therefore, analysis of a considerable

volume of data and shop floor observation was

performed to confirm consistency of the data

before utilisation. The input and output MFs were

defined by the analysed data. Each of the inputs

uses 3 MFs named ‘Low’, ‘Average’ and ‘Long’.

Availability data for machine 151 over 2015 was

aggregated and statistically manipulated to obtain

certain parameters needed to define each MF. The

‘Low’ defines a range of values, from analysis, that

is accepted to show an appropriate value for the

downtime of the respected category. ‘Average’

defines the range of values that occur on average,

and ‘Long’ specifies the range which incorporates

quantities that are deemed longer than average.

Exercise & Communication

Time (min)5%

Tea/Lunch Break Time (min)

23%

Line Preparation Time (min)

11%

Changeover Time (min)45%

Size change Time (min)

2%

Slip/Can/Pallet Waiting Time

(min)4%

Breakdown Time (min)

6%

Spilage Cleaning Time (min)

4%

FILLING MACHINE 151-DOWN TIMES-2015

Figure 5-A pi chart showing the proportion of each downtime category to total downtime

11

Similarly; the output variable, availability, is

defined by the 3 membership functions ‘Poor’,

‘Average’ and ‘Excellent’ characterized in the same

manner as the inputs.

Different MF shapes The MF shapes of triangular, trapezoidal and

Gaussian were defined by the analysed data. These

3 specific MF types were found to be the most

accurate of the numerous shapes available within

the toolbox [12,15,18]. As will be analysed in the

validation section, changing the MFs of a variable

has a measurable impact on the functionality of a

common system. Gaussian MFs are used to

represent variables that are smooth, non-linear

and deal in probabilities and statistics [13,19].

Proper choice of MFs allow the user to: define

linguistic variables, and enable the system to

realise accurate results [2].

According to Ali et al. [12] triangular MF, a particular case of trapezoidal [15], shows the best performance.

This accuracy is due to its simplicity and being described by a set of linear regions [18].

Fuzzy system functionalities Illustrations and screenshots are used to how the model was designed and functions.

Figure 6-This is FIS editor, which is the heart of the lean toolbox, in which the input and output variables are specified and the rule editor can be accessed

Rule editor After all variables and MFs are defined, rules need

to be created to allow model simulation. This is

where the series of rules are defined using ‘and/or’

connections to tie the inputs to the output.

Numerous if-then rules were tested until 8 were

concluded as optimal. The series of 8 if-then rules

that make up the FIS are designed to obtain

accurate results and mimic human reasoning in

tackling the vagueness of data used.

Knowing that fuzzy logic is a superset of Boolean

logic, at special extremity conditions standard

logical operations can be extended to use with

fuzzy logic.

12

In order to capture the problem best, logical

operations in logic gates and truth tables are used

as a motivation to design and illustrate the rules

used.

In classical reasoning theory, the truth of any

statement is either True (1) or False (0).

Conversely; in fuzzy logic, a multivalued logic, a

degree of truth is described. Using truth tables, the

AND/OR operators of Boolean logic can be

conserved on an extension to fuzzy reasoning by

use of the MIN and MAX operators respectively.

Figure 7-This image shows how the Boolean operators are extended to fuzzy logic.

AND operates if both A and B conditions are satisfied, OR if any of A or B are present and NOT is a

complementary function.

It can hence be deduced that the operation A AND B is similar to min (A, B), A OR B to max (A, B) and NOT A

to 1-A [14,19].

Figure 8-(a) Figure 6-(b)

Figure 8-(c) the images show the different logic gates that were used and their

operations.

13

The 8 rules defined can be represented by the following illustrations which define the rule operations used

according to logic gates.

Legend for logic gate rules 1-8

Letter Name of variable

A Exercise and Communication Time

B Tea/Lunch Break Time

C Line Preparation Time

D Changeover Time

E Size Change Time

F Slip/Can/Pallet Waiting Time

G Breakdown Time

H Spillage Cleaning Time

X Availability

14

Rule viewer Defuzzification using the centroid technique after the operation of rules on the input sets results in a crisp

output value.

Figure 9-shows the resultant defuzzified crisp output obtained from the specified inputs

The inputs can be entered numerically or by sliding the red cursors. The rules work on the input variables to

produce the output, with a defuzzified crisp value seen in the above example as Availability=73.9%. The usage

of this model can hence calculate the availability from a set of downtimes entered.

Fuzzy system validation Verification is a necessary step in any

computational or simulation model to ensure that

the real system is adequately replicated by the

conceptual model [4].

In order to reach accurate validation, the same set

of variables and rules need to be used for each of

the models produced with the different MF types.

To do this, practical data from the company at 3

different dates were input to the model, and the

defuzzified output compared to that calculated

theoretically. The percentage error table below

shows that trapezoidal MF is highly inaccurate for

this application, and was hence dropped. Although

the Triangular MF yields the least inaccuracy,

Gaussian MFs are also highly accurate and better

represent some data types such as the changeover,

waiting and breakdown times due to the

characteristics of the Gaussian curve. Therefore, a

hybrid model consistent of both Triangular and

Gaussian MFs is used.

% Errors for the different MF shapes

Date 10th April

27th July

1st December Average

Gauss 12 5 4 7

Tri 10 4 3 6

Trap 40 36 56 44 Table 4-error table comparing the accuracy of

different MF shapes

To validate the hybrid model, the same procedure

as above is repeated using data from April 23rd,

2015 and 28th arbitrarily. The resultant % error is

only around 2% in comparison to the practical

setting, which is even more accurate than solely

using triangular MF.

15

Model 2-Determine the required downtimes for a specified availability

This particular model is possibly more useful than the primary model since it informs the user of the amount

each downtime needs to become from a current state to reach the desired availability. The results of this

computation can help direct top management towards improvement plans by focusing resources on specific

areas highlighted by the model. Some recommendations of these improvement plans are discussed in the next

section.

Fuzzy system input and output variable(s) Reverse to model 1; a single input, ‘Availability’, is created alongside the 8 downtime categories as the output

variables.

Figure 10-Overall structure of developed model 2

Fuzzy system functionalities All variables are described by the same set of information utilised in creating model 1. The logic and MFs

defined are also similar to the case of model 1, with some modifications to incorporate backward integration.

Rule viewer

Figure 11-shows the value of the downtimes required to obtain an availability of 70%

16

Fuzzy system validation Testing these particular output values with

formulae, the model is found to be accurate with

only a 2% error. The same holds true with the

outputs generated when an Availability of 80% is

input.

Fuzzy system output Aiming to raise the Availability to 70% from the

Average of 62% gives a system output of the

downtimes required as:

Downtime Category Value (Minutes)

Exercise & Communication

6.5

Tea/Lunch break 30

Line preparation 30

Changeover 65

Size change 5

Waiting time 10

Breakdown time 55

Spillage cleaning 8

Table 5-similar to Figure 13 in a cleaner tabular form

Calculating this using the formula:

The total available time is calculated to be 570

minutes from an effective 9.5-hour shift starting at

7.30AM and ending 5PM.

𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦

=𝑁𝑒𝑡 𝑟𝑢𝑛 𝑡𝑖𝑚𝑒 (minutes)

𝑇𝑜𝑡𝑎𝑙 𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑇𝑖𝑚𝑒 − 𝑇𝑜𝑡𝑎𝑙 𝑃𝑙𝑎𝑛𝑛𝑒𝑑 𝑡𝑖𝑚𝑒

Equation 6-Detailed availability equation

=

𝑇𝑜𝑡𝑎𝑙 𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑇𝑖𝑚𝑒 − 𝑇𝑜𝑡𝑎𝑙 𝑃𝑙𝑎𝑛𝑛𝑒𝑑 𝑡𝑖𝑚𝑒−𝑇𝑜𝑡𝑎𝑙 𝑢𝑛𝑝𝑙𝑎𝑛𝑛𝑒𝑑 𝑑𝑜𝑤𝑛𝑡𝑖𝑚𝑒

𝑇𝑜𝑡𝑎𝑙 𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑇𝑖𝑚𝑒 − 𝑇𝑜𝑡𝑎𝑙 𝑃𝑙𝑎𝑛𝑛𝑒𝑑 𝑡𝑖𝑚𝑒

=570 − (6.5 + 30) − (30 + 65 + 15 + 55 + 8)

570 − 36.5× 100 = 67.6%

This proves that the %error is 2%.

Figure 12-surafce graph showing the relationship between availability, changeover and breakdown in 3D

7. Results and Discussion Simulation of the model is a mean by which

uncertainty is reduced and consensus created to

explore alternative strategies which suit the

production line. One of the many positives about

the fuzzy model on MATLAB is that aside its

accuracy, it remains flexible to details of the

organization.

Some general and specific recommendations for

improvement plans were devised to allow machine

availability maximization to 70%, and these are

outlined below. Since lean is a continuous

improvement philosophy, ‘Kaizen’ in Japanese,

improvements should continuously be sought. For

example, instead of increasing the availability to

70% without an aim to further improve; one should

sustain the improvement and work towards

further eliminating losses. This project not only

suggests the area of improvement but also how to

achieve them. Intangible benefits may also be

achieved in employees’ motivation and a positive

attitude towards change, alongside the primary

measurable benefits of reduction in lead time and

increased productivity [2].

17

General recommendation topics

Tackling downtime Downtime is the largest source of productivity

decrease for manufacturing firms, and if solutions

to eliminate them are devised, gains can be

realised rather quickly. It is due to these reasons

that the sole focus of the conceptual models was

downtime and its various categories. It is

important to improve the constraint and focus

resources on ensuring a strong improvement

impact. The constraint was determined to be the

changeover in the filling lines as per data analyses.

Any downtime should be made visual by the

operators, and if the machine is down for an

extended period of time the supervisor should be

informed of the issue. This prevents small issues

from becoming larger ones. This is both a general

and specific recommendation as it was noticed

once during collection of data that the filling nozzle

was not operating optimally. The designated

operators stopped the machine and tried fixing the

problem. On resuming normal operation; the

problem recurred, and the issue was not even

reported to top management [17].

TPM (Total productive maintenance) Focus is shifted from fixing breakdowns to

preventing them. There is a difference between

being reactive (fixing issues as they occur) and

being proactive (planning long-term fixes). If a

company spends time reactively fixing issues then

progress will be limited. Most of the mechanical

equipment has parts that wear out on repetitive

usage (e.g. seals, bearings, and belts) that may

cause breakdowns towards the end of their

lifecycle. A proper TPM plan makes sure that all

these parts are operable, replacing parts in adverse

conditions. TPM motivates operators to take the

initiative to maintain their equipment. [6,17] TPM

also helps in reducing small stoppages, slow

running, and accidents on the shop floor [20].

VSM (Value stream map) Where there is a product being developed for a

customer there is a value stream. This lean tool

tags operations in the value stream as NVA, VA,

and NNVA; in an attempt to identify waste sources.

The VSM helps visualize cycle times, WIP (work in

progress), manpower deployment and information

flow [4]. Non-value adding activities (NVA) are not

useful for the company or the customer. This

category effectively entails the waste which needs

to be eradicated. This may include material

handling, waiting and transport. Necessary but

non-value adding activities (NNVA) are considered

as waste by the customer but are a company

requirement. Examples include operators

unpacking inventory prior to assembling. Value-

adding activities (VA) are those which convert the

input into a useful output and may include

machining materials and joining subassemblies.

These are activities which the customer associates

with value and is willing to pay for. Within this

project, materials move in batches using pallets at

distances of around 40m instead of by continuous

flow. Reinstating, excess transport and ineffective

motion are some of the 7 types of waste [21].

Single minute exchange of dies (SMED) Setup time reduction is a continuously sought

objective [6]. This lean tool helps reduce the time

it takes for changeovers, and due to the fact that

changeovers make up around 40% of the total

downtime; SMED is a vital tool to solve the

problem in hand. This concept was developed and

tested by Japanese industrial engineer Shigeo

Shingo. SMED involves performing as many steps

as possible before the stage that depends on them,

and to do so with a coordinated team performing

multiple steps in parallel. For example, it takes

many people 15 minutes to change one tire, while

it takes a NASCAR pit crew 15 seconds to change

four. Processes that can be performed while

another is running can be moved externally. This

concept is a lot like multitasking. Using the same

NASCAR example, the tools and materials are

prepared prior to the tire changing stage. The next

step is performing those tasks as quick as possible.

For example, using quick release mechanisms

instead of traditional bolts. Reducing downtime

and building a smooth start-up (both improving

OEE) are some of the short-term benefits of SMED.

18

Some points that a particular process has to

possess in order to be a good candidate for this

lean tool is that the changeover should be

relatively long, variable and performed many times

a week to test proposed improvements quickly; all

conditions of which are satisfied by changeovers in

the filling line. SMED can be achieved by either

human or technical improvements, with the first

being faster and less expensive. This can be

brought about by defining roles properly,

involvement in brainstorming sessions and the

creation and following of standardized work

instructions [22].

Proposed improvement plans Proposed improvements should be assessed

against cost and time to implementation. This

makes larger improvements such as remodelling

the shop floor layout less favourable than

identifying and focusing resources on required

areas.

I. Integrating a swipe card or barcode reader

to ensure employees return to the station

on time in the morning and after breaks.

This will help reduce variability in the

planned downtime.

II. Introducing a belt conveyor system

throughout the shop floor for raw

materials and finished products to ensure

continuous flow while cutting down on

changeover times, waiting times and

waste relevant to the ineffective motion of

around 40m by pallet operators.

III. Automatically updated online order

distribution system at each machine to cut

down on slip waiting times, share

important information and allow for the

authorization of processes at the point;

such as direct communication with the QC

lab or PLC operator.

IV. There are off the shelf systems for

automating OEE measurement [9].

V. SMED is useful in tackling waiting and

changeover times; especially for cans, lids,

and pallets by preparing them prior to the

authorization to start filling.

VI. Flashing lights to show when the line is

down and requires extra attention.

This could be automatically activated or

switched on by the operator.

VII. Other important factors to consider may

include training inter-changeable

operators, which is what is done in the

case of area managers that change areas

every couple of weeks.

VIII. Furthermore, transparent information

sharing is vital and operators should not be

hesitant to report occurring issues [7].

Lean manufacturing is a long-term philosophy that

takes time and cost to arrive at the desired results

[21]. On implementing a few of the plans, the

productivity might improve quickly, not as the

result of the plan’s success, but rather the

‘Hawthorne Effect’; where employees work harder

to impress top management personnel

overlooking the process [22]. These changes

should help ensure an optimised material flow,

especially the filling lines. Customer demands can

now be met with even shorter lead times, adding

to marketing benefits [5]. Employees require

adequate training prior to the introduction of

various lean tools. Improvements should be

implemented one at a time to evaluate the

effectiveness of each as a solution.

8. Conclusions Knowing the benefits lean techniques and fuzzy

logic pose on a manufacturing unit, two conceptual

fuzzy lean integrated models were designed and

developed. Data collected from a paints company

was analysed to conclude that the filling lines’ OEE

score was limited by availability, hence adversely

affecting the overall productivity of the shop floor.

The input and output variables were defined by a

hybrid tri/gauss MF, after deduced that trapezoidal

MFs were highly inaccurate. The model output

allowed for general and specific improvement

proposals to be formulated that would help raise

the availability from 60% to 70%, possibly further,

resulting in a more efficient filling line.

19

9. Limitations and future research

direction The reported results are initial, with additional

efforts being to reduce downtimes further in the

future. The improvement plans were merely noted

down in a list; therefore to help with the

prioritization of proposals, a computerized support

system could be deployed [16]. In the future, more

case studies could be carried out across different

departments (processing lines for example) to

assess and improve the practical validity of the

system. Similar to the availability improvement,

the performance parameter also requires

improvement. The model is found to be weak at

extreme values, and can be further enhanced in

the future to solve this. Future research could build

on these proposed models, comparing the

potential strengths and weaknesses.

ACKNOWLEDGEMENTS

I would like to show appreciation and thanks to my

academic supervisor, Dr. Sibi Chacko, who has

been extremely open, helpful and cooperative

throughout the entire project.

I would also like to thank my industrial supervisor,

Mr. Shukla, for the opportunity to experience

practical implementation of the theoretical

knowledge gained, and the support and

information passed on from him.

I would also like to extend my thanks to Mr. Titus,

of the industrial company’s management, for

making this whole industrial collaboration

possible.

20

10. References

[1] Vinodh, S., and Balaji, S. R., 2011, “Fuzzy logic based leanness assessment and its decision support system,” Int. J. Prod. Res., 49(13), pp. 4027–4041.

[2] Achanga, P., Shehab, E., Roy, R., and Nelder, G., 2012, “A fuzzy-logic advisory system for lean manufacturing within SMEs,” Int. J. Comput. Integr. Manuf., 25(9), pp. 839–852.

[3] Adullah, F., 2003, “Lean Manufacturing tools and techniques in the process industry with a focus on steel,” University of Pittsburgh.

[4] Xia, W., and Sun, J., 2013, “Simulation guided value stream mapping and lean improvement: A case study of a tubular machining facility,” J. Ind. Eng. Manag., 6(2), pp. 456–476.

[5] Anvari, A., Zulkifli, N., and Yusuff, R. M., 2013, “A dynamic modeling to measure lean performance within lean attributes,” Int. J. Adv. Manuf. Technol., 66(5-8), pp. 663–677.

[6] Dal, V., and Akçagün, E., 2013, “Using Lean Manufacturing Techniques to Improve Production Efficiency in the Ready Wear Industry and a Case Study,” FIBRES Text. East. Eur., 21(100), pp. 16–22.

[7] Vinodh, S., and Chintha, S. K., 2011, “Leanness assessment using multi-grade fuzzy approach.,” Int. J. Prod. Res., 49(2), pp. 431–445.

[8] Dora, M., and Gellynck, X., 2015, “Lean Six Sigma Implementation in a Food Processing SME: A Case Study,” Qual. Reliab. Eng. Int., 31(7), pp. 1151–1159.

[9] Industries, V., 2016, “OEE (Overall Equipment Effectiveness)” [Online]. Available: http://www.leanproduction.com/oee.html.

[10] Vorne Industries, 2013, “Overall Equipment Effectiveness.”

[11] Fuller, R., 1998, Fuzzy Reasoning and Fuzzy Optimization.

[12] Ali, O. A. M., Ali, A. Y., and Sumait, B. S., 2015, “Comparison between the Effects of Different Types of Membership Functions on Fuzzy Logic Controller Performance,” Int. J. Emerg. Eng. Res. Technol., 3(3), pp. 76–83.

[13] Susilawati, A., Tan, J., Bell, D., and Sarwar, M., 2015, “Fuzzy logic based method to measure degree of lean activity in manufacturing industry,” J. Manuf. Syst., 34(C), pp. 1–11.

[14] The MathWorks, I., 2016, “Getting Started with Fuzzy Logic Toolbox,” MathWorks [Online]. Available: http://www.mathworks.com/help/fuzzy/getting-started-with-fuzzy-logic-toolbox.html.

[15] Barua, A., Mudunuri, L. S., and Kosheleva, O., 2014, “Why trapezoidal and triangular membership functions work so well: Towards a theoretical explanation,” J. Uncertain Syst., 8(3), pp. 164–168.

[16] Vimal, K. E. K., Mohanraj, R., Sakthivel, M., and Vinodh, S., 2015, “A framework for VSM integrated with Fuzzy QFD,” TQM J., 27(5), pp. 616–632.

[17] Vorne Industries, 2013, “Down_Time.”

[18] Zhao, J., and Bose, B. K., 2002, “Evaluation of Membership Functions for Fuzzy Logic Controlled Induction Motor Drive,” IEEE 2002 28th Annu. Conf. Ind. Electron. Soc., pp. 229–234.

[19] TechTarget, “logic gate” [Online]. Available: http://whatis.techtarget.com/definition/logic-gate-AND-OR-XOR-NOT-NAND-NOR-and-XNOR.

21

[20] Shahid, S., Chacko, S., and Shukla, C. M., 2013, “Energy Conservation for a Paint Company Using Lean Manufacturing Technique,” Univers. J. Ind. Bus. Manag. 1(3), 1(3), pp. 83–89.

[21] Vamsi, N., Jasti, K., and Sharma, A., 2014, “Lean manufacturing implementation using value stream mapping as a tool,” Int. J. Lean Six Sigma, 5(1), pp. 89–116.

[22] Vorne Industries, 2013, “Single-Minute Exchange of Die,” Single-Minute Exch. Die.

[23] Bicheno, J., and Holweg, M., 2009, The Lean Toolbox, PICSIE Books.

22

11. Appendix

Appendix A-Brief descriptions of the various downtime categories

DOWNTIME CATEGORY DESCRIPTION

EXERCISE & COMMUNICATION Prior to the start of the shift, employees exercise and communicate that day’s quotas.

TEA/LUNCH BREAKS In between batches, there are break times for the operators.

LINE PREPARATION The operations are initiated before starting the work.

CHANGEOVER TIME Various setups and preparations are required between successive batches, as different

products require different settings. SIZE CHANGE Settings such as filling rate need to be altered

when filling products and cans of different sizes.

WAITING TIME Sometimes, the operators wait for empty cans, lids, and pallets before proceeding, which

should not be the case. BREAKDOWN Equipment failures occur that deem the

machine inoperable. SPILLAGE CLEANING Machines and equipment are cleaned from

spills so as not to contaminate products, affecting the quality.

Appendix B-Tri, Trap and Gauss MFs [14]

23

Appendix C-Data used to define the MF of input and output variables

For input variables

For Machine 151

Exerc

ise

Tim

e (

min

)

Te

a/L

un

ch

Bre

ak T

ime

(min

)

Lin

e

Pre

pa

rati

on

Tim

e (

min

)

Ch

an

geo

ver

Tim

e (

min

)

Siz

e c

ha

ng

e

Tim

e (

min

)

Wait

ing

Tim

e

(min

)

Bre

akd

ow

n

Tim

e (

min

)

Sp

illa

ge

Cle

an

ing

Tim

e (

min

)

Average Min 8 20 11 34 0 0 0 0

Max 15 57 40 152 16 65 80 39

Average 10 44 22 86 4 7 12 8

Standard deviation

Min 4.05 16.8 7.01 16.2 0 0 0 0

Max 9.07 6.07 18.9 47.0 19.79 64.66 65.01 18.33

Average 0.52 5.23 5.75 19.47 6.88 6.66 7.07 5.02

For output variables

For Machine 151

Perc

en

tag

e

Avail

ab

ilit

y

Averages Min 48.7

Max 79.91

Average 63

Standard deviation

Min 4.78

Max 7.942

Average 4.94

24

Appendix D-How MFs were defined with an example

‘Gaussmf’ [Standard deviation mean]

The SD and mean were obtained from OEE data using Excel functions of ‘AVERAGE’ and ‘STDEV.S’.

In this example, the standard deviation is ’10.29’ and average ’17.35’. Both these parameters define the ‘Short’

time range specific to Tea/Lunch break time, a planned downtime. Similarly, the triangular MF was defined.

‘Trapmf’

25

Appendix E-Aggregated downtime values to depict the magnitude of effect availability can have

E

xerc

ise &

C

om

mu

nic

ati

on

Tim

e

(min

)

Te

a/L

un

ch

Bre

ak T

ime

(min

)

Lin

e P

rep

ara

tio

n T

ime

(min

)

Ch

an

geo

ver

Tim

e (

min

)

Siz

e c

ha

ng

e T

ime (

min

)

Slip

/Can

/Pall

et

Wait

ing

T

ime (

min

)

Bre

akd

ow

n T

ime (

min

)

Sp

illa

ge C

lean

ing

Tim

e

(min

)

Oth

er

Mis

c. T

ime (

min

)

To

tal

(pe

r d

ay)

To

tal

(pe

r m

on

th)

To

tal

(pe

r year)

150 2014 10 42 13 50 1 33 11 2 15 177 3895 46741

2015 10 43 16 56 1 32 16 1 17 191 4206 50472

151 2014 10 43 16 64 2 12 12 5 36 201 4425 53105

2015 10 44 22 86 4 7 12 8 33 225 4957 59483

152 2014 10 25 13 44 0 12 11 2 18 134 2953 35435

2015 10 27 18 63 0 10 12 3 18 160 3517 42205

153 2014 10 42 30 72 0 3 6 4 31 198 4360 52319

2015 12 42 36 70 0 9 15 8 29 222 4883 58595

As per capacity calculations, around 4000 20L cans can be filled per day (570 minutes).

Therefore, taking machine 151, around 55,000 minutes are wasted annually due to availability losses. This

approximates to around 385,000 cans that could have been filled, but have not, per year. This is effectively

the sale of around 8 million Litres of product with the capacity to be sold wasted. This should be sufficient

motivation to improve the capacity and efficiency of the filling lines, and is a good reason this project is

initiated.

These calculations assume a 9.5-hour shift, 5 days working week, 22 working days per month and 12 months

per year.

26

Appendix F-Various graphical representations resulting from data analyses

55.46

48.04

58.12

31.32

46.45

54.32

46.89

55.88

33.76

46.45

0

10

20

30

40

50

60

70

80

90

100

150 151 152 153 158

OEE

ave

rage

Machine

Filling OEE average2014 vs 2015

2014

2015

27

28

29

Appendix G-Some factors that affect performance as noticed