an application of the glenday sieve to an fmcg product...
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[MBA Thesis]
AN APPLICATION OF THE GLENDAY SIEVE TO AN FMCG
PRODUCT LINE
A Thesis
presented to
The Graduate School of Business
University of Cape Town
in partial fulfilment
of the requirements for the
Masters of Business Administration Degree
by
Miriam Motha
11th
December 2009
Supervisor: Professor Norman Faull
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Declaration
This thesis is not confidential. It may be used freely by the Graduate School of Business.
I know that plagiarism is wrong. Plagiarism is to use another‟s work and pretend that it is
one‟s own.
I have used a recognized convention for citation and referencing. Each significant
contribution and quotation from the works of other people has been attributed, cited and
referenced.
I certify that this submission is all our own work.
I have not allowed and will not allow anyone to copy this essay with the intention of passing
it off as his or her own work.
Miriam Lefetogile, lftmir001
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ACKNOWLEDGEMENTS
This research report is not confidential and may be used freely by the UCT Graduate school of
Business.
Firstly I would like to thank Professor Norman Faull for his patience, guidance and invaluable
advice during this research process.
Secondly I would like to thank my classmate, Billal Jhavary for his assistance and guidance during
this research process.
Thirdly I would like to thank Company X management for allowing me to test the Glenday sieve on
their company.
Lastly, I would like to thank my husband Bandile, my daughter Khonziwe, my son Akwande my
mother and my father for their love and support over the last very hectic two years. I could have
never done this without you.
Signed: Miriam Motha
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ABSTRACT
Company X is a Fast Moving Consumer Goods (FMCG) company that manufactures 116 different products.
Production is generally based on a make to stock concept with buffer limits targeted at 2.5 weeks‟ cover and
a minimum and maximum of 2 and 4 weeks‟ covers respectively. However, these buffer levels are usually
difficult to maintain due to frequent plan changes resulting from sudden raw material shortage or stock
requests. Consequently, this result in loss in productivity, increased waste as well as increased inventory
levels to above target. The production planners have responded to these challenges by planning more
production in order to increase inventory levels to above target to ensure stock availability at all times.
However, this response was seen to be risky as it incurred an inventory holding cost, a risk of stock
obsolescence and also failed to display true responsiveness to customer demand, i.e. production is not Just-
In-Time (JIT). JIT briefly means producing what is needed, in the right quantities, within the shortest
possible lead time and is based on the principles of levelled production. Therefore, for production to be JIT,
levelled production needs to be first established. A tool developed to help with implementation of levelled
production is called the Glenday sieve.
The objectives of this research were to apply the Glenday sieve to company X‟s product line and explore the
opportunities that could result and make recommendations on how the Glenday sieve could be implemented
in order for the company to realise these opportunities. The sieve was applied to the company‟s weekly
historical sales data from the first 8 months of 2009. The research design method that was used was action
research that is simulation based.
Upon the application of the sieve to the company‟s product line, a fixed cycle (consisting of only four green
stream SKUs: 1542, 12754, 1502 and 1524) was established and was to run for 104.13 hours out of the
available 168 hours per week. However, the use of this cycle together with Glenday‟s recommended buffer
limits (aimed at absorbing demand fluctuations) proved to be unsuccessful as the data displayed stock out
most of the time. This was attributed to the high variability on the demand since a simulation on a normally
distributed demand showed the success of the sieve.
A conclusion drawn from this research was that the Glenday sieve is efficient in generating a fixed cycle,
hence levelled production; nevertheless, it does not work for demand that is not normally distributed. The
recommendation made was that, for the company to benefit from the Glenday sieve application, it needs to
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work with its customers and negotiate better buying patterns that would stabilise or normalise the high
demand variability that was observed.
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Table of Contents
ACKNOWLEDGEMENTS .................................................................................................................................... II
ABSTRACT .......................................................................................................................................................III
LIST OF FIGURES ............................................................................................................................................ VII
LIST OF TABLES ............................................................................................................................................ VIII
1 INTRODUCTION ...................................................................................................................................... 1
1.1 RESEARCH AREA AND PROBLEM ....................................................................................................................... 1
1.1.1 Background ..................................................................................................................................... 1
1.1.2 Problem Statement ......................................................................................................................... 1
1.2 RESEARCH QUESTIONS AND SCOPE ................................................................................................................... 3
1.2.1 Research Questions ......................................................................................................................... 3
1.2.2 Research Objectives ........................................................................................................................ 3
1.2.3 Research Hypothesis ....................................................................................................................... 4
1.2.4 Research Scope ............................................................................................................................... 4
1.3 RESEARCH ASSUMPTIONS ............................................................................................................................... 5
1.4 RESEARCH ETHICS .......................................................................................................................................... 6
2 LITERATURE REVIEW ............................................................................................................................... 7
2.1 DISCUSSION ................................................................................................................................................. 7
2.1.1 Lean manufacturing ........................................................................................................................ 7
2.1.2 Toyota Production System (TPS) ..................................................................................................... 9
2.1.3 Levelled production – Heijunka ..................................................................................................... 11
2.1.4 Economies of repetition ................................................................................................................ 13
2.1.5 The Glenday sieve ......................................................................................................................... 14
2.1.6 The application of the Glenday Sieve ............................................................................................ 15
2.1.7 Implementation of the Glenday sieve ........................................................................................... 17
2.1.8 Success stories............................................................................................................................... 18
2.2 CONCLUSION .............................................................................................................................................. 18
3 RESEARCH METHODOLOGY .................................................................................................................. 19
3.1 RESEARCH APPROACH AND STRATEGY .............................................................................................................. 19
3.2 RESEARCH DESIGN, DATA COLLECTION METHODS AND RESEARCH INSTRUMENTS ....................................................... 20
3.2.1 Research design ............................................................................................................................ 20
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3.2.2 Data collection methods ............................................................................................................... 26
3.2.3 Research instruments ................................................................................................................... 26
3.3 SAMPLING ................................................................................................................................................. 27
3.4 DATA ANALYSIS METHODS ............................................................................................................................. 27
4 HYPOTHESIS TESTING, FINDINGS, ANALYSIS AND DISCUSSION ............................................................. 29
4.1 HYPOTHESIS ONE TESTING ............................................................................................................................. 29
4.2 RESEARCH FINDINGS: HYPOTHESIS ONE ........................................................................................................... 35
4.3 RESEARCH ANALYSIS AND DISCUSSION: HYPOTHESIS ONE .................................................................................... 38
4.4 LIMITATIONS OF THE STUDY ........................................................................................................................... 43
5 RESEARCH CONCLUSIONS ..................................................................................................................... 43
6 FUTURE RESEARCH DIRECTIONS ........................................................................................................... 45
7 REFERENCES AND BIBLIOGRAPHY ......................................................................................................... 46
8 APENDICES ........................................................................................................................................... 49
8.1 APPENDIX 1: AN AR CRITERIA/METHODOLOGY CHECKLIST ................................................................................... 49
8.2 APPENDIX 2: THE SEVEN-PART STRUCTURE FOR AR ANALYSIS ............................................................................... 50
8.3 APPENDIX 3: SIEVE ANALYSIS RESULTS FOR BOTH THE SALES VOLUME AND SALES VALUE ............................................. 51
8.4 APPENDIX 4: SALES DATA USED FOR THE SIEVE ANALYSIS AND THE CATEGORIES THAT RESULTED .................................. 52
8.5 APPENDIX 5: CAPACITY CALCULATIONS ............................................................................................................ 55
8.6 APPENDIX 6: PRODUCT COMPATIBILITY MATRIX FOR THE PRODUCTS THAT COULD RUN ON BOTH LINE 14 AND 15 .......... 56
8.7 APPENDIX 7: THE DEMAND BEHAVIOUR OVER THE PERIOD BETWEEN JANUARY TO AUGUST 2009 ............................... 58
8.8 APPENDIX 8: SIMULATION 1 GRAPHS .............................................................................................................. 60
8.9 APPENDIX 9: SIMULATION 2 GRAPHS .............................................................................................................. 62
8.10 APPENDIX 10: SIMULATION 3 GRAPHS ........................................................................................................ 64
8.11 APPENDIX 11: SIMULATION 5 GRAPHS ........................................................................................................ 66
8.12 APPENDIX 12: LEARNING JOURNAL ............................................................................................................ 68
8.13 APPENDIX 13: GRAPHS THAT RESULTED FROM FILTERING OUT SPIKES ................................................................ 72
8.14 APPENDIX 14: DEMAND RANGE SEGMENTS FOR EACH GREEN STREAM SKU ACROSS THE 36 WEEK PERIOD ............... 74
8.15 APPENDIX 15: REQUIRED CAPACITY CALCULATED FROM MANIPULATED DATA ..................................................... 76
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LIST OF FIGURES
Figure 1: Toyota Lean Model (Source: Glenday, 2004) .......................................................... 11
Figure 2: Economies of repetition virtuous circle (Source: Glenday, 2005, p. 17) ................. 14
Figure 3: Action research repeating cycles (source: Coughlan& Coghlan, 2002) .................. 24
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LIST OF TABLES
Table 1: Typical sieve analysis results (Source: Glenday, 2005, p.21) ................................... 16
Table 2: Sieve Analysis results for the sales volumes (tons) ................................................... 35
Table 3: The weekly optimum cycle sequence and time for each production line .................. 36
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1 INTRODUCTION
1.1 Research Area and Problem
The information cited in both the background and the problem statement was gathered during a
meeting in July 2009 with the Supply Chain manager for Company X:
1.1.1 Background
Company X is a Fast Moving Consumer Goods (FMCG) company that manufactures 116 different
products that run in specific assigned production lines. Production planning starts with management
aspirations to increase product performance in the field. This information is passed on to the Demand
Planning Team that look at customer demand information (i.e. both history and periodic demand
patterns) and couple it with management aspirations to develop their twenty four months national
forecast. This forecast would then be broken down into monthly forecasts. The team also plans for
activities (e.g. promotional activities, adverts, etc) that would promote how to get the product in the
field to move, to meet management‟s requirements. This team meets monthly to review the plan.
The monthly demand planning information would then be passed on to the production planning team
who would break the forecast down into weekly requirements. These weekly requirements would
further be broken down into daily requirements that would then be allocated to each production line,
taking into account operating parameters like machine capacity (e.g. line speeds), product production
lead times, HR capacity, stock levels, minimum production batches and available hours in a day or
week. The planning system is linked to a Material Requirement Planning (MRP) system containing
each product‟s Bill Of Material (BOM). The BOM is used to determine the material type and quantity
needed to meet the plan which would then be ordered as needed.
1.1.2 Problem Statement
Production is generally based on a make to stock concept, i.e. produce to maintain the required buffer
levels and replenish as required. Company X targets stock or buffer limits to be maintained nationally
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at 2.5 weeks‟ cover; otherwise the target is to maintain a minimum of 2 weeks‟ and maximum of 4
weeks‟ covers. The production planning and the production personnel meet daily to review the
previous day‟s performance and evaluate if the week‟s production plan would be met. Raw material
and packaging availability, buffer levels, any sudden increase in demand e.g. marketing department
stock requests, and any other issues that could affect the production plan are discussed.
Normally at these daily meetings, the team is forced to adjust the plan due to sudden material shortage,
stock requests or buffer level being far from target. Problems associated with these plan changes
includes productivity loss due to increased change over time (change over times could be as long as up
to 3 hours depending on the product type), increased waste (frequent changeovers between the
products of different quality could result in material loss during change over cleaning as the leftover
material in the line is either downgraded to another product of lower quality or to the re-work line or
can be completely lost to waste), increased inventory levels to above target, etc. There have been
instances when the marketing department‟s sudden stock requests were rejected by the planning
department as it was thought that it would interfere with the production plan too much and these
definitely did not please the marketing personnel. The production planners have very often responded
to these challenges by increasing the inventory levels (i.e. overproduction to cover any anticipated
plan changes or stoppages) so that stock availability is ensured at all times while on the other hand, the
management team have frequently not been happy about the inventory levels as most of the time it is
above target.
According to Drew, McCallum & Roggenhofer (2004, p. 27), the production planner‟s response
strategy mentioned above is a very risky one as “it incurs an inventory holding cost and a consequent
risk of obsolescence; it also fails to display true responsiveness to customer demand” All the above
issues show that a flexible operation to cater for peak times in order to ensure a reduction in inventory
levels to target, as well as a reduction in generated waste due to product downgrades during change
over, is necessary for company X. To be truly responsive to customer demand and reduce inventory
levels, Just-In-Time (JIT) production seems to be an ideal solution as Drew et al (2004, p. 27) describe
it to be the production and the transportation of what is needed, just when it is needed, in just the
amount needed within the shortest possible lead time. This type of production (i.e. JIT), is founded on
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levelled production principles as indicated by Drew et al (2004, p. 27). The Glenday sieve is a tool
developed to help people start implementing levelled production (Glenday, 2005, p.19).
1.2 Research Questions and Scope
1.2.1 Research Questions
This research is aimed at answering the questions outlined below.
Core Research question:
What benefits can company X get from applying the Glenday Sieve to its product line?
Additional Research question:
Given the postulated question, how should company X adopt the Glenday Sieve?
1.2.2 Research Objectives
The objectives of this research are
to explore the opportunities that company X would get from applying the Glenday Sieve to its
product line
having identified these opportunities, it is intended to make recommendations on how company
X could adopt the Glenday Sieve
to learn from the different research activities as they unfold
to produce a document that would provide information that could be customised to
organisations similar to company X depending on their different situations. The document is
also aimed at informing any other parties interested in the Glenday sieve and its application.
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1.2.3 Research Hypothesis
The hypotheses for this research project are therefore:
Hypothesis 1: Glenday Sieve could be applied to company X‟s product line to create levelled
production that would result in the following opportunities:
reduced inventory levels to match the target
reduced changeover time hence increasing actual production capacity
increased process capability
actual cost reduction
reliable delivery to customers
an overall improved process stability
Hypothesis 2: The Glenday Sieve can be adopted better by using it together with a Value Stream Map
(VSM) of the process in order to identify areas of waste (non value adding activities) and applying
relevant lean improvement tools to make small step changes to the process so as to reduce non value
adding activities.
1.2.4 Research Scope
The research was started by applying the Glenday Sieve to the historical weekly sales data for
Company X and thereafter opportunities that could result if the company had adopted the Glenday
sieve during that period or another period with identical demand were identified. Recommendations
were made on how company X could adopt the Glenday sieve in order to benefit from the identified
opportunities. The research design method that was used was action research (more details on why this
design method was chosen may be found in section 3.2.1) that is simulation based. According to Perry
and Zuber-Skerritt (1991) cited in French (2009), “a Masters core AR project need only progress
through one planning, acting, observing, reflecting, cycle of management practice to demonstrate
mastery of the research methodology” For this research, the planning and the reflecting stages were
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done fully while the rest of the steps that includes the acting and observing stages were simulated.
Therefore the opportunities or benefits identified that company X could get were based on a simulation
exercise.
Due to the unavailability of data, the research was limited to the sales data for the period January to
August 2009 (i.e. 36 weeks). This data was seen to be highly variable and with this kind of behaviour,
there would have been even more learnings if the available data was over a longer period than 36
weeks, e.g. a year or two so that the sieve behaviour could be investigated under a more diverse
environment. The other limitation in the scope was that opportunity identification was only limited to
the green stream products as the time was not sufficient to do an implementation of all the streams to
identify even more opportunities. Added to this, the green stream opportunity identification was also
only based on experiments (again due to the fact that real life implementation could not be done due to
research time constraints), meaning that other opportunities that could have resulted from a real life
implementation could have been missed.
1.3 Research Assumptions
The major assumption that was made for this research exercise was that, because company X is a
multinational organisation, with world class systems in place, it would be easy to obtain sales data as
far back as 12 or even 24 months; but it turned out that it was extremely difficult to obtain this data and
only 8 months worth of data was obtained. The impact that this had on the research was that there was
not enough data to observe any recurring demand patterns to conclude if the variable demand
behaviour observed was seasonal. Also, even though there was a lot of learning during the research,
this was limited by the fact that a full demand behaviour pattern over a year, which could have had a
different response upon the sieve application, was not available for investigation.
The other assumption that was made was that data would be available for all the company products; but
it turned out that for the first 8 months of 2009, only 62 out of the 116 products were sold. Again this
limited the learning that could have resulted if the Glenday sieve was applied to the full range of the
company products. This implies that the results obtained, could be different if a whole year worth of
complete sales data was used.
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However, it was assumed that the demand pattern observed in the data used would be the same for the
same time in future years, such that recommendations made based on these data would still apply in
future years for the same period.
1.4 Research Ethics
Resnik (n.d.) defines ethics to be the “norms for conduct that distinguish between acceptable and
unacceptable behaviour”. Ethical principles that were taken into account during this research as
suggested by Shamoo & Resnik (2003) cited in Resnik (n.d.) include:
Honesty: a high level of honesty in reporting the results, methods and procedures was
exercised
Objectivity: where ever objectivity was expected or required, bias in data analysis and
interpretation was avoided at all cost
Openness: Data, results, ideas, tools etc were shared and the researcher was open to criticism
and new ideas.
Respect for Intellectual Property: other peoples‟ material was honoured and referenced at all
times
Confidentiality: the company‟s confidential information was protected and treated with care,
for example, the company name was kept anonymous as well as its products were only referred
to by their SKU numbers.
Respect for colleagues: Team members during research were respected at all times
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2 LITERATURE REVIEW
2.1 Discussion
The primary objective of this research, as indicated above, was to apply the Glenday sieve to a product
range of a manufacturing facility as well as to identify opportunities that could be brought about by
implementing the sieve results. Glenday sieve is one of the many lean manufacturing tools that could
be used to support lean transformation. To contextualise the Glenday sieve application, this literature
review is commenced by exploring the concept of lean manufacturing and all its aspects that are
relevant to the research.
2.1.1 Lean manufacturing
LearnSigma Handbook (2008, p.239) defines lean manufacturing, often simply known as "Lean", to
mean, a production practice that considers the expenditure of resources for any goal other than the
creation of value for the end customer to be wasteful, and thus a target for elimination. The handbook,
further highlights that the implementation of lean is focused on “getting the right things, to the right
place, at the right time, in the right quantity to achieve perfect work flow while minimizing waste,
being flexible and able to change. These concepts of flexibility and change are principally required to
allow production levelling” (LearnSigma Handbook, 2008, p.239). To augment the above lean
definitions, Drew, McCallum & Roggenhofer (2004, p.15), believe lean is a systematic approach aimed
at maximising both customer and shareholder value in an operation. In their work, Drew et al (2004,
p.15) described lean as an “integrated set of principles, practices, tools and techniques designed to
address the root causes of operational underperformance”.
As for how lean maximises both customer and shareholder value, Drew et al (2004, p.15) draw
attention to the fact that lean focuses on process management and improvement by way of eliminating
three key sources of loss from the operating system, which are a) waste b) variability and c)
inflexibility which would eventually help to reduce cost, improve quality and optimise delivery
respectively.
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a) Waste as a source of loss in an operating system
Drew et al (2004, p.15) & LearnSigma Handbook (2008, p.273), pointed out that waste, commonly
known by the Japanese as “muda”, is anything that does not add value or is unproductive, and instead
adds cost to a process. This implies, according to Drew et al (2004, p.15) & LearnSigma Handbook
(2008, p.273), that reducing muda effectively increases profitability.
Learn Sigma Handbook (2008, Pp. 245 – 246); Drew et al (2004, Appendix), identifies the following
as the original Toyota seven types of muda:
i. Transportation: unnecessary movement of materials. Symptoms: multiple or
excessive handling of material, long distances travelled by material between
processes, etc.
ii. Inventory: any parts or materials above the minimum required to deliver what
customers want when they want it. Symptoms: Obsolete stock, cash flow
problems, lack of space, etc.
iii. Motion: unnecessary movement of people, materials or equipment within a
process i.e. more than is required to perform the processing. Symptoms:
searching for tools or parts, double handling of parts, equipment running
empty, etc
iv. Waiting: Idle time (for people or machines) in which no value-adding
activities take place. Symptoms: operators waiting for material or information,
operators standing and watching machines run, etc.
v. Overproduction: production faster or in great quantities than needed by the
customer. Symptoms: Parts accumulate in uncontrolled inventories, parts are
produced too early, too many parts are produced, etc.
vi. Over Processing: effort that is not required by the customer and adds no
value. Symptoms: performing of processes that are not required by the
customer, redundant approval requirements, etc.
vii. Defects/Rework: the effort involved in inspecting for and fixing defects.
Symptoms: Dedicated re-work process, high defects rate, large quality or
inspection departments
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There are several lean "tools" that selectively used in the identification and steady elimination of waste.
According to Learn Sigma Handbook (2008, p.240), “examples of such "tools" are, Value Stream
Mapping, Five S, Kanban (pull systems) and poka-yoke (error-proofing)”.
b) Variability as a source of loss in an operating system
Variability “is any deviation from the standard that detracts from the quality of a service or product
delivered to the customer” Drew et al (2004, p.16). Another way of looking at variability as a source of
loss is as defined by LearnSigma Handbook (2008, p.278) to mean “mura” which the handbook
indicates to be a traditional general Japanese term for unevenness and inconsistency in the physical
matter.
c) Inflexibility as a source of loss in an operating system
Inflexibility, as viewed by Drew et al (2004, p.16), “is any barrier to meeting changing customer
requirements that can be overcome without incurring extraordinary cost.” As an example, the authors
illustrate this type of loss to an operating system by indicating that the actual making of goods could
take only a few hours while the lead time from the customer placing an order to them receiving the
goods could be a couple of weeks. This huge difference in time, as indicated by Drew and his team, is
a result of inflexibility caused by e.g. waiting of the parts from supplier and it could cost the company
some business as the customer could go elsewhere, where the lead time on delivery would be shorter.
Drew et al (appendix) indicated that the symptoms of inflexibility in an operating system are: being
unable to respond quickly to changes in customer demand, high levels of overtime, etc.
2.1.2 Toyota Production System (TPS)
Lean, according to Learn Sigma Handbook (2008, p.239), was derived mostly from the Toyota
Production System (TPS), which on p.40 was indicated to have “originated, and progressively
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developed the implementation of production levelling thereby steadily eliminating mura
("unevenness") through the system and not upon 'waste reduction' per se”. It is further indicated on
p.40 that the use of production levelling together with other lean production techniques massively
helped Toyota to reduce vehicle production times as well as inventory levels during the 1980s.
Drew et al (2004, pp.26 - 33) points out that TPS has three key elements, namely
a) Just-in-Time production (JIT) whose primary objective as implied, by Drew et al (2004,
p.26), is “to produce and transport just what is needed, just when it is needed, in just the amount
needed, within the shortest possible lead time”. Drew et al (2004, p.27) further indicates that
JIT entails responding to customer demand through manufacturing the required goods at the
required quality and quantities (not just delivered from stock) in the shortest possible time and
this, Drew et al (2004, p.27) say, would reduce inventory holding costs as well as the risk of
obsolescence. Drew et al (2004, p.27) suggests that in order to be JIT capable, a company
should implement JIT production building blocks, namely continuous flow processing,
production rate matched to customer demand by means of takt time as well as production
control through a pull system. However, “these building blocks depend on the foundation of
levelled production, which smoothes the workload over time” Drew et al (2004, p.27).
b) Autonomation, which is designed to allow operators to detect and resolve problems quickly
and decisively with the aim of improving equipment reliability, enhance product quality and
increase productivity.
c) Flexible staffing systems which are aimed at continuously optimising labour productivity to
whatever the level of demand at any point in time.
Figure 1 below, shows levelled production as a foundation for JIT and autonomation which all
contributes to cost reduction through the elimination of muda / waste.
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Figure 1: Toyota Lean Model (Source: Glenday, 2004)
2.1.3 Levelled production – Heijunka
Drew et al (2004, p.27) defines levelled production as a way of artificially smoothing true demand
within a production period in order to create a steady „pull‟ rate and product mix. Production levelling,
according to (LearnSigma hand book, 2008, p.297), “also known as production smoothing or by its
Japanese original term, heijunka, is a technique for reducing the mura waste and is vital to the
development of production efficiency in the Toyota Production System and Lean
Manufacturing. The general idea is to produce intermediate goods at a constant rate, to allow
further processing to be carried out at a constant and predictable rate”. According to Liker (2004, p.
116), heijunka “does not build products according to the actual flow of customer orders, which can
swing up and down wildly, but takes the total volume of orders in a period and levels them out so the
same amount and mix are being made each day”. Liker (2004, p. 116) also highlights that unlevelled
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production carries with it four bad situations:
Customers usually do not buy products predictably and could decide to buy more than they
usually do which would put a strain in the process. Normally in an unlevelled production,
producers respond to this situation by holding a lot of finished goods inventory (as is the case
with the company as indicated in the problem statement) which consequently leads to a high
cost of inventory, with all its related costs.
A risk of unsold goods whereby if the company does not sell all the products, it is forced to
keep them in inventory.
Unbalanced use of resources which would result in different resource capacity employed
during high and low demand periods.
Placing an uneven demand on upstream processes; for example there is a possibility of
putting strain on the suppliers as sudden demand increase would result in the company having
to suddenly request raw material unexpectedly. According to Liker (2004, p. 116), this would
be multiplied further backward through the supply chain by a phenomenon called the “bullwhip
effect” where a small change in the schedule will result in ever-increasing inventory banks at
each stage of the supply chain as you move backward from the end customer.
All the above is an indication that levelled production, if adopted by a company, could save the
company inventory holding cost, costs that could result from unsold obsolete stock, cost of overtime
during peak demand, etc.
Glenday & Brunt (2007,) asserted that the traditional way of scheduling is done on a batch or
„campaign‟ logic and these batches are treated as unique events that are planned, processed and
monitored differently. The problem with this way of processing, especially in high volume operations,
as highlighted by Glenday & Brunt (2007), is that if anything goes wrong with any batch, for example,
if the plan had to change for any particular reason, normally fire fighting results, with the aim to meet
customer demands; that is, the situation would be such that the best people focus their efforts on
dealing with crises and solving urgent problems. Mitchell (n.d.) also supports levelled scheduling by
commenting that “with the new system they can switch their attention from fire-fighting today's
mistakes to focusing on how to improve tomorrow”
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A fire fighting environment is not conducive as normally finger pointing and blaming among
employees emerge and these would consequently affect employee relationships and morale. Lean
Enterprise Australia (2006) claim that other problems associated with batch logic include a recurring
cycle of unpredictable demands, short term plan changes, high stocks yet low customer service as well
as reduced level of accuracy in demand forecasting. Recurring cycle of unpredictable demands, short
term plan changes, and high stocks is, as indicated in the problem statement, what the company is
currently facing.
Glenday (2005, p.15) indicates that ultimately the objective of levelled production is perfect flow
where EPEC (Every Product Every Cycle) is a false bridge to help a company get there. The false
bridge is achieved by fixed sequence and volume cycles which help create a phenomena that Glenday
calls “economies of repetition” (2005, p. 16). According to Glenday (2005, p. 16), economies of
repetition is “the magic that makes the impossible possible”.
2.1.4 Economies of repetition
As mentioned above, levelled scheduling, through EPEC, introduces economies of repetition.
According to Glenday (2005, p.16), economies of repetition is a phenomenon that emerges when every
product is produced every cycle which results in a virtuous circle giving rise to performance
improvement naturally. The three aspects that result from economies of repetition according to
Glenday (2005, p.16) are:
Learning curve: when people do the same task repeatedly, they get better at it; their confidence
and security improves and with time they would require less supervision. Consequently they
would feel more responsible and empowered and their morale would increase. According to
Bayer (n.d.) “Positive employee morale is the corporate version of good mental health. Good
mental health enhances performance for individuals and for organizations”.
Routine: people doing tasks in the same sequence results in them being more relaxed, less
stressed and more motivated since they would be informed on what is going on, at what times
and what‟s expected of them. Routines also results in process stability which would help in
quicker problem root cause identification and resolution. This is a foundation for continuous
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improvement and encourages work standardisation and would consequently contribute to
improved performance efficiency.
Creating an atmosphere for work standardisation: Standard work, according to
Leansixsigma (2009), “is centred on human movements, it outlines efficient, safe work methods
and helps eliminate muda/waste. Standardized Work in processing and assembly maintains
quality and provides safer and faster operations while ensuring proper use of equipment and
machinery”.
Figure 2 below shows the virtuous circle that results from economies of repetition.
Faster
EPEC
-Learning Curve
-Routines
-Standard work
Above
Expectation
results
Natural
Continuous
Improvement
Economies
of repetition
VIRTUOUS CIRCLE
Figure 2: Economies of repetition virtuous circle (Source: Glenday, 2005, p. 17)
As indicated at the beginning of this section, economies of repetition could be achieved through a fixed
sequence and volume production cycle and this kind of cycle, could be achieved through the
implementation of the Glenday sieve as alluded by Glenday (2005, p.19).
2.1.5 The Glenday sieve
The Glenday sieve, according to Glenday (2004, p.19), “was developed to implement every product
every cycle, flow and levelled production” through a fixed cycle production schedule. As indicated in
the earlier sections, the sieve ultimately helps “to progressively increase capability and responsiveness
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so that the supply chain can meet market pull through TAKT time”, Glenday (2005, p.35). Glenday
(2005, p.19) highlights other functions of the sieve to be:
targeting where to start value stream mapping
assessing where to apply capability improvements
identifying non value adding complexity (institutional waste)
helping to get all the organisation involved in breaking through to
flow
Lean Enterprise Australia (2006), describes the Glenday sieve as a management practice or a tool that:
helps to make the transition to a lean production system with current plant and equipment
limitations. This is done through the introduction of levelled production to produce the same
sequence of type and quantity of products over a fixed period
is achieved through making step changes to the process that would lead to step improvement in
the current levels of performance, margins and customer responsiveness
can be used in any industry sector i.e. including industries where levelled production may
currently seem impossible
2.1.6 The application of the Glenday Sieve
According to Glenday, (2005, p.22), the sales data is sorted from the highest to the lowest sales items;
and percentage cumulative sales are then used to categorise products into green, yellow, blue and red
streams and resulting in the typical results shown in table 1 below:
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Table 1: Typical sieve analysis results (Source: Glenday, 2005, p.21)
However, these figures are only a guide; “One needs to look at the products and see which ones would
fit into a sensible fixed sequence cycle” (Glenday, 2005, p.22). Glenday further describes the different
categories as:
Green Stream: high volume items that are probably already produced frequently, i.e. about 6%
of the products accounting for 50% of the sales. Glenday (2005, p.22) asserts that this is a group
of products to start a fixed sequence and volume cycle. Glenday & Brunt (2007) added that the
demand for these items is inherently predictable and the time to carry out the task is highly
predictable
Yellow stream: Together with the green stream, these products make up about 50% of the total
product line and accounts for about 95 % of the total sales. These are the products where there
are practical barriers to implementing every product every cycle hence more efforts need to be
concentrated on these for capability improvements. Glenday & Brunt (2007) argue that when
capacity improves, these products move into green stream.
Blue stream: Together with the green and yellow streams, these products make up about 70%
of the total product line and accounts for about 99 % of the total sales. These products contain
material that adds complexity to the process, yet not increasing customer value; for example
packaging material with slight differences that add no value to the customer or raw materials
with marginal grade differences. Reducing these complexities reduces the chances for mistakes,
and trying to harmonise these stream would need a lot of time and effort. The company
therefore needs to weigh their options.
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Red stream: These products constitute 30% of the total product line, but only account for 1%
of sales the volume. According to Glenday (2005, p.27), the practicalities of including these
products in the cycle would be ridiculous and, Glenday & Brunt (2007) supports this claim by
highlighting that these products have inherently high variability in demand. Glenday (2005,
p.27) suggests that a company needs to agree on the strategy to make these products both
valuable to the customer and profitable to the company.
2.1.7 Implementation of the Glenday sieve
Implementing the sieve starts with fixing the green stream production into equal volumes in the same
sequence for every cycle. However, with fixed production cycles, there would still be demand
fluctuations that would induce pressure on the company if the company were to avoid stock outs.
According to Glenday (2005, p.1), having a buffer is one of the ways used to absorb variability
between the demand and supply, hence protecting the fixed cycle from demand fluctuations; a buffer is
“part of the false bridge needed to create economies of repetition” Glenday (2005, p.46). Glenday
(2005, p.63) indicates that an upper and a lower buffer limit needs to be established and the actual
buffer level be monitored within these limits. These limits serve as a warning such that whenever the
buffer level goes outside any of these two limits, it would be an indication of an out of control buffer
level which could be a signal to a problem upstream.
Another tool that would help with the implementation of the Glenday sieve is the VSM. According to
Learn Sigma Handbook, (2008), VSM “is a lean technique used to analyze the flow of material and
information currently required to bring a product or service to a consumer...VSM is commonly used in
lean environments to identify opportunities for improvement in lead time”. Therefore with the use of a
VSM, more improvement opportunities that would make implementation of the sieve easier would
become more visible; for example, constraints to the levelled flow of the fixed cycle as identified by
the sieve would be easier to identify on a VSM and the necessary action taken to ensure that the cycle
flow is more efficient.
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2.1.8 Success stories
The following is a paragraph taken from Mitchell (n.d.) that indicates some of the success stories of
implementing levelled production:
One 3M factory has boosted output by 50%, without any extra investment in
machinery or people. Kimberly-Clark has seen throughput increases of 15% at no
extra cost, with much more predictable and stable production. For Wrigley, the
chewing gum company, an output jump of 10% at its Plymouth factory was just
the beginning. After levelled scheduling was introduced, huge amounts of space
were freed up (50% on the packing floor), and that space is being filled with new
machines to produce new products for new markets; in other words to deliver real
growth.
2.2 Conclusion
Glenday Sieve is a lean tool that can be used to introduce levelled production through categorising
products according to their contribution to the total sales. The sieve categorises products as green,
yellow, blue and red streams cumulatively believed to represent about 6%, 50%, 70 % of the
product line for the green, yellow and blue streams respectively and the last 30 % of the product
line being the red stream. These categories have been indicated by Glenday (2005, p. 21) to
cumulatively account for 50%, 95%, 99% of the total sales for the green, yellow and blue
respectively and 1% of the total sales for the red stream. Levelled production has been identified to
have many benefits including: reduced inventory levels to match the target, increased actual
production capacity, increased process capability, actual cost reduction, reliable delivery to
customers, an overall improved process stability, etc.
Once the products have been categorised, the starting point would be to fix the sequence and
volume cycle of the green stream. A well sized buffer would be needed to absorb variability
between the demand and supply, hence protecting the fixed cycle from demand fluctuations. A
value stream map of the fixed cycle could be done to identify areas of inefficiencies or waste that
would need to be eliminated or reduced to acceptable levels, in order to create flow of the cycle.
Besides, implementation of levelled scheduling requires a paradigm shift from the traditional batch
logic to flow processing.
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3 RESEARCH METHODOLOGY
3.1 Research approach and strategy
Research approach
The research approach that was followed for this research is a deductive approach. Bryman & Bell
(2007) explains a deductive approach as the approach whereby “the researcher, on the basis of what is
known about a particular domain and of theoretical considerations in relation to that domain, deduces a
hypothesis (or hypotheses) that must be subjected to empirical scrutiny”. This approach is particularly
relevant to this research because in theory it is claimed that an application of the Glenday sieve to a
company product line yields benefits as deduced in hypothesis 1. Another claim made in theory is that
the Glenday sieve can be applied or adopted in any type of business and hypothesis 2 deduces that by
applying a VSM to the process would serve as a starting point to the adoption of the sieve. The two
hypotheses were subjected to investigation during the research, i.e. first the theory behind the use of the
Glenday sieve was gathered and related to the company and the hypothesis were then established and
then scrutinised for applicability and relevance to the company. The assumption made was that the
experimental simulations done during this investigation would be similar to the ones that would be
done in real life implementation of the Glenday sieve such that the identified opportunities would be
the same as those that would be identified in a real life implementation.
Research strategy
A research strategy is a “general orientation to the conduct of business research” (Bryman & Bell,
2007, p.28). For this research project, the strategy that was used is quantitative. Bryman & Bell (2007)
describe quantitative research as “a research strategy that emphasizes quantification in the collection
and analysis of data”. (Bryman & Bell, 2007, p. 28) further more indicated that quantitative research:
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Entails a deductive approach to the relationships between theory and
research, in which the accent is placed on the testing of the theories
Has incorporated the practices and norms of the natural scientific
model and of positivism in particular
Embodies a view of social reality as an external, objective reality
The fact that data was collected and analysed and as mentioned in the research approach, a
deductive approach was used as well as the fact that positivism and objectivism were the
epistemological and ontological assumptions made justifies this research to be that of a quantitative
strategy. Also the fact that data was collected, analysed and conclusions drawn whether to accept or
reject the hypothesis justifies the positivism position of epistemology. Objectivism as an
ontological position for these research is justified by the fact that “there is external view point from
which it is possible to view the organisation, which is comprised of consequently processes and
structures” (Bryman & Bell, 2007, p.25).
3.2 Research design, data collection methods and research instruments
3.2.1 Research design
The research design that was used for this research is action research that is simulation based. GoldSim
(2009) defines simulation to be “the process of creating a model (i.e. an abstract representation or
facsimile) of an existing or proposed system (e.g. a project, a business, a mine, a watershed, a forest,
the organs in your body) in order to identify and understand those factors which control the system
and/or to predict (forecast) the future behaviour of the system”. GoldSim (2009) further indicates that
simulation is powerful and important in that it “provides a way in which alternative designs, plans
and/or policies can be evaluated without having to experiment on a real system, which may be
prohibitively costly, time-consuming, or simply impractical to do. That is, it allows you to ask "What
if?" questions about a system without having to experiment on the actual system itself (and hence incur
the costs of field tests)”.
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On the other hand, “Action research is an approach to research that aims both at taking action and
creating knowledge or theory about that action” (Coughlan & Coghlan, 2002, p.220). Coughlan&
Coghlan (2002, p. 224 - 226) cited Gummesson (2001) discusses the following ten characteristics of
action research:
1. Action researchers take action: Action researchers are actively involved in the actions that are
being taken rather than being observers of action only
2. AR always involves two goals: There are two goals involved in action research that requires
that the researcher be involved in the actual action of problem solving and consequently reflect
on the action in order to contribute theory to the existing body of knowledge
3. AR is interactive: Collaboration between the researcher and the client personnel as well as
being able to adjust to new information. New events are required during the process of
unfolding of the unpredictable events so that a contribution is made to the body of knowledge.
4. AR aims at developing holistic understanding during a project and recognizing complexity: An
action researcher needs to have a broader view of how the system works and be able to work
with the dynamic complexity involved that results from the multiple causes and effects over
time.
5. AR is fundamentally about change: It is applicable to the understanding, planning and
implementation of change in businesses, firms and other organisations
6. AR requires an understanding of the ethical framework, values and norms within which it is
used in a particular context. That is, authentic relationship between the researcher and the
members of the client system as to how they understand the process and taking significant
action is involved
7. AR can include all types of data gathering methods: Both quantitative and qualitative tools such
as interviews and surveys are commonly used. What is important is that the planning and use
of these tools should be well thought out with members of the organisation and be clearly
integrated into the AR process.
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8. AR requires a breadth of the pre-understanding of the corporate environment, the conditions of
the business, the structure and the dynamics of the operating systems and the theoretical
underpinnings of such systems
9. AR should be conducted in real time, i.e. it is a “live” case being written as it unfolds. Though
retrospective AR is also acceptable i.e. it can also take the form of a traditional case study
written in retrospect
10. The AR paradigm requires its own quality criteria; this involves:
How well the AR reflect on the co-operation between the action researcher and the
members of the organisation
AR to be guided by a reflective concern for practical outcomes, .i.e. constant and
iterative reflection of the project should drive the change process
AR should ensure that the methods used are appropriate, the concepts have theoretical
integrity and extends knowledge of the participants
AR should engage significant work
The project should result in new and sustainable changes
As described by Coghlan & Brannick (2001) cited in Coughlan& Coghlan (2002, p. 227), it is
appropriate to use action research in general “when the research question relates to describing an
unfolding series of actions over time in a given group, community or organisation; understanding as a
member of a group how and why their action can change or improve the working of some aspects of a
system; and understanding the process of change or improvement in order to learn from it” These
perfectly fits with the research done at company X as the research questions that were asked required
different experiments to be simulated, reflected upon and eventually establishing an “optimal” fixed
production cycle and identifying opportunities. These experiments were carried out in conjunction with
consulting the planning team.
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The other reason why action research was seen to be the most appropriate design method is that the
intention of this research exercise was to apply the Glenday sieve by introducing small step changes, on
the data and evaluate the change; which by definition is both action and research. From the literature,
action research methodology came across as a method that provides the flexibility and responsiveness
needed for effective change.
The action research process
As highlighted in its definition above, AR is an emergent process that emerges as events unfold during
the project. Coughlan& Coghlan (2002, p. 229), asserts that “the philosophy underlying AR is that the
stated aims of the project lead to planning the first action, which is then evaluated. So, the second
action cannot be planned until evaluation of the first action has taken place”. In other words, the whole
process cannot be designed and planned in detail in advance.
Coughlan& Coghlan (2002, p. 230) argued that AR is comprised of the following three steps:
1. a pre-step to understand
the context of the project i.e. why the project is desirable as well as any economic,
political, social and technical forces driving the need to do this project
the purpose of these project i.e. this involves asking why the project is worth studying,
what contribution it is expected to make to knowledge and how AR is the appropriate
method to adopt
Both these points have been discussed in the problem statement section (1.1.2) and the appropriateness
of AR in this project is discussed above (in the same section).
2. six main steps: data gathering, data feedback, data analysis, action planning, implementation
and action evaluation
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3. a meta-step to monitor , i.e. continually monitoring each of the six main steps, inquiring in what
is taking place, how these steps are being conducted and what underlying assumptions are
operative.
Figure 3 on the next page illustrates the above three steps as well as how these steps repeat in each
research cycle. However, as mentioned above, the implementation for these steps was not done reality,
but in a simulation.
Context and Purpose
Monitoring
Data Gathering
Data Feedback
Data Analysis
Action Planning
Implementation
Evaluation
Monitoring
Data Gathering
Data Feedback
Data Analysis
Action Planning
Implementation
Evaluation
Cycle 1 Cycle 2
Figure 3: Action research repeating cycles (source: Coughlan& Coghlan, 2002)
Action research tools
The action research tools that were employed during the research as O‟Brien (1998) indicates them
were:
keeping a research journal
document collection and analysis
structured and unstructured interviews
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Theory and knowledge generation in action research
Coughlan& Coghlan (2002, p. 236) indicated that “AR projects are situation specific and do not aim to
create universal knowledge”. However, they further suggest that outcomes should be beyond
knowledge within the project, but should be extrapolated to other situations and needs to identify how
the AR project could inform like organisations, similar issues, etc. According to French (2009) action
research, “like any other small-scale research, can draw on existing theories, apply and test research
propositions, use suitable methods, and offer evaluation of existing knowledge”. This assertion by
French was followed as the outcomes of this research contribute to the existing knowledge.
Eden and Huxham (1996) cited in Coughlan& Coghlan (2002, p. 236) presented guides on how AR
contributes to theory to be:
The theory generated in AR is emergent in that it develops from the outcomes that emerge from
both the data and the practical application of the theory that informed the intervention and
research intention
Theory generated from AR is incremental, moving from the particular to the general in small
steps
As required by AR, the theory generated from the conceptualisation of the particular experience
in ways that are intended to be meaningful to others
The basis for the design tools, techniques and models used in AR must be explicit and be
related to the theory. As a result, drawing the generality of AR through these design tools,
techniques and models is not enough.
French (2009) further justifies theory developed through action research as by saying that, “through the
application of AR processes, practitioners are able to justify their work. The evidence that is gathered
during the process and the critical reflection, which constitutes data analysis, creates a developed,
tested, and critically examined rationale for the practitioner‟s practical change of practice”.
Developing the AR protocol
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To ensure that there is consistency with the intended outcomes of both action and research aims in this
research exercise, an AR criteria or methodology checklist provided in appendix 1 was followed and as
well as the seven-part structure for AR analysis documented in appendix 2 was also followed to ensure
that the final write up is consistent with the AR methodology.
3.2.2 Data collection methods
French (2009) highlighted that “in traditional research methodologies there is often a specific accepted
method of data collection that is symbiotic with the data analysis methodology. However, in AR this is
not generally the case”. Holter & Schwartz-Barcott, 1993 cited in French (2009) indicated that “There
also appears to be an understanding among action researchers that action research does not require any
special method of data collection”. Therefore, for this research exercise, various data collection
methods that were used (as identified by Coughlan & Coghlan (2002, p. 231) to be applicable to AR)
includes:
Operational statistics, planning reports and sales reports that were used for “hard” data
collection
Observation and interpretation of the simulation results, interviews, formal and informal
discussions with the planning and factory personnel were the forms used to collect the “soft”
data. However, the one problem is that the data is largely perceptual hence its valid
interpretation can always be challenged.
3.2.3 Research instruments
The research instruments used were:
the “hard” data that was obtained from the company database and reports
the “soft data”, i.e. observations / interpretations, was obtained from both face to face and
telephonic interviews and discussions as well as simulations results analysis.
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3.3 Sampling
Mugo (n.d.) defines a population and sampling as:
A population is a group of individual persons, objects, or items from which samples
are taken for measurement, for example a population of presidents or professors,
books or students.
Sampling is the act, process, or technique of selecting a suitable sample, or a
representative part of a population for the purpose of determining parameters or
characteristics of the whole population.
For this research, 100% of the sales data that was available, i.e. an entire population for 36 weeks was
used. Therefore, 100% sampling method was employed in this particular case. In addition, other
statistical parameters related to sampling, e.g. sampling error, would not be explored for this research
exercise. Mugo (n.d.) indicates that “there would be no need for statistical theory if a census rather than
a sample was always used to obtain information about populations”. A census according to Bryman &
Bell (2007, p.182), means “the enumeration of an entire population”.
3.4 Data analysis methods
Glenday (2005, p. 41) maintains that the data analysis used in the Glenday sieve covers “an analysis of
the numbers (i.e. a quantitative analysis) followed by a more subjective or qualitative assessment.”
According to Glenday (2005, p. 43), the qualitative analysis is mainly the assessment of opportunities
in each colour coded category. Therefore, for this research, the qualitative analysis involved the
researcher‟s reflections on the results obtained, formal and informal discussions around the quantitative
results by the team (i.e. the researcher, the company planner, supply chain manager and process
engineers) in order to identify the opportunities.
The quantitative data that was used for this research was:
Weekly sales data per SKU for the first 36 weeks of 2009
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Process steps, timings and capacities
Changeover times
Product compatibility matrix
The sales data analysis followed the sieve quantity analysis steps suggested by Glenday (2005, p. 41)
which are:
Listing the products from the biggest to the smallest sales
Calculation of the cumulative and percentage cumulative sales
Finding the nearest sales to 50%, 95% and 99% of total sales
Calculating percent of the product range for each percentage of sales
Following the above steps for the value and volume sales data, a comparison was made between
these two sets of results obtained to see if there are any differences and a decision on which
data set to use for the initial fixed cycle production was made.
The Glenday Sieve approach according to NHS Scotland (2007) “has its origins in the Pareto principle,
but has a stronger operational focus‟. Therefore, a Pareto principle would form the basis of the sales
data analysis. The rest of the data listed above, i.e. process steps, timings and capacities, changeover
time‟s as well as product compatibility matrix was mostly used for opportunity identification. An excel
spreadsheet was used to do simulations and observation and interpretation were used to analyse the
results.
Bryman & Bell (2007, p.368), defines a test of statistical significance to mean a test that “allows the
analyst to estimate how confident he or she can be that the results deriving from a study based on a
randomly selected sample are generalizable to the population from which the sample was drawn”.
Therefore for this research, the test of the statistical significance of the data was not necessary since
there was no sampling process involved, i.e. the entire population was used. Inference was made during
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simulations and the general conclusions on the application of the Glenday sieve to different demand
patterns.
4 HYPOTHESIS TESTING, FINDINGS, ANALYSIS AND
DISCUSSION
4.1 Hypothesis one testing
Ho: Glenday Sieve could be applied to company X’s product line to create levelled production that
would result in
i. reduced inventory levels to match the target
ii. reduced changeover time hence increasing actual production capacity
iii. increased process capability
iv. actual cost reduction
v. reliable delivery to customers
vi. an overall improved process stability
a) Application of the Glenday sieve to the product line
To test this first hypothesis, the sieve was applied to both the sales volume and the sales value obtained
from the company sales department in order to categorise the products into the green, yellow, blue and
the red streams. The data obtained was for the period between Jan – August 2009 (36 weeks). For this
period, the company sold only 62 of their 116 products.
b) Creation of levelled production
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As mentioned in the literature section, the Glenday sieve could be applied to a company‟s product
range to create levelled production through a fixed cycle production schedule. To test if this was
possible, the first step was to create a fixed cycle production schedule i.e. the type products that formed
the cycle, their quantities per cycle as well as their sequence during a production run. The steps used in
fixing the initial cycle were as follows:
i. The green stream product that would make the initial fixed production cycle were decided upon
ii. The average weekly production volume for each green stream product was calculated
iii. the length of the cycle was then decided upon
iv. the available capacity was calculated
v. the required capacity to run the green stream was calculated
vi. the production lines on which green stream products would run were finalised
vii. the product sequencing within the fixed cycle was done based on the type of cleaning (i.e. the
time it takes) required before each product run
viii. an optimum weekly cycle was fixed
The second step in the creation of levelled production was the sizing of the buffer for each SKU as
Glenday alluded in the previous sections that this is needed to absorb demand variability. To test the
usage of a buffer together with a fixed production cycle to assist in the creation of levelled production,
various simulations were performed to size the buffer according to Glenday‟s suggested procedure
(equations 1 and 2 below) and thereafter simulating the behaviour of the buffer within the calculated
limits as the demand varied. The aim was to see if it would absorb this variability. The equations used
to calculate buffer limits for each simulation were as follows:
..........................................................Equation 1
..........................................................Equation 2
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Where,
σ = standard deviation of the demand data
= the buffer upper limit
= the buffer lower limit
(Glenday, 2005, p.63)
The simulations performed included:
Simulation 1: The buffer for each of the green stream product that made up the fixed cycle was sized
using the 36 weeks sales volume averages. To test if this buffer would absorb the demand fluctuations,
it was decided to test the buffer behaviour with the demand data for the same 36 week period next year.
For this, it was assumed that the demand would be exactly the same in the first 36 weeks of next year,
with weekly production fixed at this year's levels. The resultant weekly buffer levels for each SKU
were then plotted on the same graph as Glenday‟s proposed buffer limits. The results obtained were
plotted in appendix 8.
Simulation 2: Another average weekly demand for each green stream SKU was calculated using the
demand data for the first 18 weeks of the available 36 weeks demand data. Weekly production volumes
for each SKU were fixed at these averages and the buffers were sized using these averages. The ability
of these buffer sizes to absorb the demand fluctuations were then tested using the remaining 18 weeks
of the 36 week demand data that was available. Again the resultant weekly buffer levels for these 18
weeks were plotted on the same graph as the calculated Glenday‟s buffer limits and the results are as
shown in appendix 9.
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Simulation 3: This simulation involved testing the ability of Glenday‟s buffer limits to absorb demand
fluctuations if each green stream SKU was started off with a big enough buffer to avoid stock outs
during the process. Simulation 2 data was used for this purpose the only difference being the buffer
starting level. This starting buffer level was obtained by determining the most negative stock out level
from simulation 2 (i.e. minimum buffer level) and adding its absolute value to simulation 2 starting
buffer level. As in the above two simulations, appendix 10 shows the buffer level behaviour that was
obtained.
Simulation 4: Simulations 1, 2 and 3 buffer levels were analysed against the company‟s current buffer
level targets. As indicated in the problem statement section, the company‟s set buffer limits are
targeted at 2.5 weeks‟ cover; otherwise a minimum of 2 weeks‟ and maximum of 4 weeks‟ covers
should be maintained. Therefore, weekly production volumes were multiplied with 2.5, 2 and 4 to
calculate the target, minimum and maximum company buffer limits.
Simulation 5: From the above simulations, it was suspected that the buffer behaviour could be
attributed to that fact that the data is not normally distributed. To test the data for normality, a chi
square goodness-of-fit test was applied to the 36 weeks demand data. For this test, equation 3 below
was used to compute the chi square for the data. For this computation, the following assumptions were
made:
that each week‟s demand data was an average for that week and was used as the observed mean
in the chi square (refer to equation 3 below)
the expected mean used in equation 3 was equal population mean
.....................................................................................Equation 3
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Where,
= chi squared
Observed = observed mean (= average weekly production)
Expected = Expected mean (= overall average over the 36 weeks)
According to Utts & Heckard (2007, p.652), the chi square goodness-of-fit is a chi squared statistic test
of significance “used to test hypothesis about a probability distribution of a single categorical
variable”. In other words, a distribution hypothesis is formulated and chi square goodness-of-fit is done
to see if the data (i.e. observed on equation 3) comes from the population with the claimed distribution.
In this case the claimed distribution is that, the weekly average production (i.e. observed on equation
3), is the same as the expected average weekly production (i.e. overall average for the 36 weeks).
The steps to test for significance as outlined in Utts & Heckard (2007, p.653) are:
a null hypothesis is formulated. In this case the null hypothesis was;
Ho: the weekly average demand data is very close to (i.e. almost the same as) the
average demand for the 36 weeks
a chi squared is calculated for each weekly average demand using equation 3 above
all the chi squared are added together to calculate as shown in equation 3.
the degrees of freedom are calculated as df = k – 1 where k = number of categories for the
variable of interest, in these case the variable of interest is the demand which is categorised into
36 weeks; hence k = 36
a standard significance level is chosen and 0.05 is normally used as a standard
a p-value is then calculated using an Excel command “CHIDIST( , df)” as indicated in Utts
& Heckard (2007, p.655)
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if the p-value obtained is less than or equal to 0.05, i.e. the standard significance, the null
hypothesis can be rejected, (Utts & Heckard, 2007, p.651); in other words, the result is
statistically significant, hence the null hypothesis cannot be accepted.
Consequently, this simulation approximated normality on the available 36 week data demand data and
tests the ability of the buffer to absorb demand fluctuations. Both the average and the standard
deviation for these data were used to calculate normalised data on an Excel spreadsheet using the
following equation:
=NORMINV(RAND(), mean_value, standard_deviation)
..........................................................Equation 4
Where,
mean_value = the average of the 36 week demand data
standard_deviation = the standard deviation of the 36 week demand data
In the above equation,
NORMINV ( ) generates a normal cumulative distribution for the specified man and the
standard deviation
RAND ( ) generates a random number greater than or equal to 0, and less than 1, evenly
distributed (changes on recalculation); the function can therefore be used to generate a random
percentage figure between 0 and 100%.
The way equation 4 works in an Excel is such that the equation generates a number (that has a
probability represented by RAND ( )) that belongs to a normal distribution curve with an average and a
standard deviation equal to the mean_value and standard_deviation in the above formula respectively.
By using both the RAND ( ) and the NORMINV ( ) functions together, a set of numbers normally
distributed over the entire range (0 to 100%) of a normal distribution curve with the given mean and
standard deviation can be created for each SKU.
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4.2 Research Findings: Hypothesis one
a) Findings on the application of the Glenday sieve to the product line
The sieve results performed on sales volume is as shown in the table below:
Table 2: Sieve Analysis results for the sales volumes (tons)
Glenday's
Proposed
Cumulative % of
sales (Vol)
Actual
Cumulative % of
sales (Vol)
Number of
Product range
(Vol)
Glenday's
Proposed
Cumulative % of
product range
(Vol)
Actual
Cumulative %
of product
range (Vol) Colour code
50% 51.57% 7 6% 11.29% Green95% 95.41% 32 50% 62.90% Yellow99% 99.11% 13 70% 83.87% Blue
Last 1% 0.89% 10 30% 16.13% Red
In addition to the above table, the following findings were also made upon the application of the sieve
to the company‟s product line:
It was found that the sieve could be successfully applied to the company‟s sales volume and
sales value to categorize the product range according to the green, yellow, blue and the red
streams
There was a lot of consistency in terms of product composition for each stream for both the
sales volume and the sales value sieves, that is,
o the cumulative % sales volume sieve were found to be 51.57%, 95.41%, 99.11% and
0.89% which was not far from to the cumulative % sales value sieve that were found to
be 51.30%, 95.01%, 99.11% and 0.99% for the green, yellow, blue and the red streams
respectively
o the cumulative % of the product range making the green, yellow, blue and the red
streams were found to be (in the above order of streams), 11.29%, 62.90%, 83.87% and
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16.13% for the sales volume sieve which was again not far from 12.90%, 64.52%,
85.48% and 14.52% for the sales value sieve.
The sieve analysis on both the sales volume and value confirmed that only a small percentage
of the total product range account for about 50% of the total sales and a significant amount of
the product range accounts for only about 1% of total sales
b) Findings on the creation of levelled production
Determining the initial fixed cycle production schedule
Table 3 below indicates the optimum fixed cycle that would run on each of the two chosen green
stream production lines. The table shows the four of the seven green SKUs that would form part of the
initial fixed cycle, the required cycle run time, the available capacity, product sequencing within the
fixed cycle and the type and the duration of the cleaning that needs to happen between each change
over. It should be noted that during change over, the only time consumed by the changeover is the
cleaning time. It should also be noted that “Push Push” reflected in table 13 is a cleaning type that
involves pushing the product that was running previously with the new one (refer to appendix 6 for
more information).
Table 3: The weekly optimum cycle sequence and time for each production line
Line 14
SKU #
Product / Change
Over
Average Weekly
demand (cases) /
Change over Cleaning
Type
Time Taken
(hrs)
Cumulative
Time Taken
1542 Product 1 9685.38 43.19 43.19Change Over Push Push 0.33 43.52
12754 Product 2 6160.07 27.47 70.99Change Over Push Push 0.33 71.33
1502 Product 3 4923.23 21.95 93.28Change Over Push Push 0.33 93.61
1524 Product 4 2283.71 10.18 103.80Change Over Push Push 0.33 104.13
Total time required for cycle weekly: (hrs) 104.13
Total available time weekly (hrs): 168
Line idle time weekly (hrs) 63.87
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Line 15
SKU #
Product / Change
Over
Average Weekly
demand (cases) /
Change over Cleaning
Type
Time Taken
(hrs)
Cumulative
Time Taken
1542 Product 1 10394.56 43.19 43.19Change Over Push Push 0.33 43.52
12754 Product 2 6611.12 27.47 70.99Change Over Push Push 0.33 71.33
1502 Product 3 5283.72 21.95 93.28Change Over Push Push 0.33 93.61
1524 Product 4 2450.93 10.18 103.80Change Over Push Push 0.33 104.13
Total time required for cycle weekly: (hrs) 104.13
Total available time weekly (hrs): 168
Line idle time weekly (hrs) 63.87
Sizing of the buffer for each SKU
The Glenday‟s lower buffer limit was found to be negative for almost all SKUs in all
simulations
Stock outs were observed for some weeks across all SKUs for both simulations 1 and 2
The company‟s buffer target levels were found to range from just about 1times (for the lower
limit) to 2 times (for the upper limit) relative to Glenday‟s upper buffer limit (refer to appendix
8). The company buffer limits absorbed better the higher value buffer levels that were outside
of the Glenday‟s buffer limits.
Most of the calculated weekly buffer levels for all the SKUs in simulations 1, 2 and 3 were
found to lie outside the Glenday‟s calculated buffer limits most of the time; however,
simulation 2 and 3 were found to be the worst as almost the entire curve lay outside the
Glenday buffer limits.
Starting off with a higher buffer (simulation 3) did avoid stock outs, however, the resultant
levels were most of the time running within the Glenday buffer limits (but not all the time, refer
to appendix 10). This initial buffer (to avoid stock outs) ranged from about 2.7 to 4.5 weeks‟
cover across all SKUs.
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The test for normality on the data revealed that the null hypothesis could not be accepted; i.e.
the weekly average demand data cannot be claimed to be very close (and almost the same as)
the average demand for the 36 weeks
Overall the normalised demand weekly buffer levels fitted perfectly within the Glenday‟s buffer
limits without stock outs, i.e. the buffer levels were all positive; however, the Glenday‟s buffer
limits were all again negative (refer to appendix 11).
It was also observed that, the lower the demand standard deviation (i.e. the less variability), the
better the buffer levels fitted within the Glenday‟s buffer limits.
4.3 Research Analysis and Discussion: Hypothesis one
a) An analysis and a discussion on the application of the Glenday sieve to the product line
Applying the sieve to both the sales volume and value data revealed that there was a lot of consistency
in terms of the composition of each stream (refer to Appendix 3 and 4) i.e. the actual SKUs that made
each stream for both the sales volume and value sieves were very similar. Both the sales volume and
sales value sieves revealed that the composition of each stream was consistent with Glenday‟s claims.
For example,
the cumulative percentage sales volume for the green, yellow, blue and the red streams were
found to be 51.57%, 95.41%, 99.11% and 0.89% respectively which is consistent with
Glenday‟s claimed 50%, 95%, 99% and 1% for these streams respectively . Moreover, the
cumulative percentage of the product range that make-up these streams were found to be
11.29%, 62.90%, 83.87% and 16.13% for the green, yellow, blue and the red streams
respectively . This is slightly off Glenday‟s claimed 6%, 50%, 70% and 30% respectively for
these streams; however, it still confirms Glenday‟s claim that only a small percentage of the
product range normally account for about 50% of the total sales volume and a significant
amount of the product range, 16.13% in this case, accounts for only about 1% of total sales
(0.89% in this case)
the cumulative percentage sales values for the green, yellow, blue and the red streams were
found to be 51.30%, 95.01%, 99.11% and 0.99% respectively which is also consistent with
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Glenday‟s claimed 50%, 95%, 99% and 1% for these streams respectively . The cumulative
percentage of the product range that make-up these streams were found to be 12.90%, 64.52%,
85.48% and 14.52% for the green, yellow, blue and the red streams respectively which is also
slightly off Glenday‟s claimed 6%, 50%, 70% and 30% respectively for these streams.
Based on the fact that there is not much difference between the two sieves, a conclusion was drawn that
any of them could be used for further analysis, i.e. for the rest of the research. It was therefore decided
to proceed with the sales volume sieve analysis results, more so that it is easier to work with tons and
cases than millions of Rands especially at a later stage in the research when buffers need to be sized; it
would be easier to monitor the buffer in cases and not its value.
The consistency observed above confirms the validity of the results, the reliability and replicability of
the procedure followed to perform the Glenday sieve on the sales data obtained. This consistency also
confirms Glenday‟s claims that in high volume operations, a significant amount of the sales value
comes from only a few products while a significant amount of the product range accounts for only
about 1% of total sales. The fact that the sieve could be applied to the company‟s sales data is an
indication that there is an opportunity to fix a production cycle in order to ultimately have levelled
production.
b) An analysis and a discussion on the creation of levelled production
Determining the initial fixed cycle production schedule
Out of the seven green stream products, only four SKUs (1542, 12754, 1502 and 1524) were made part
of the initial fixed cycle mainly because these SKUs can all be formed on the same production lines
(i.e. lines 14 or 15 and sometimes line 5) while technically these lines were not designed to run either
SKU 1522 or 2007539 as indicated by the Factory Process engineer & Company Planner (Personal
Interview, 2009). SKU 20060142 was excluded because even though it made it to be part of the green
stream, the demand (as well as the forecast) only started in week 29 and making it part of the initial
cycle starting in week 1 would make unnecessary inventory in the earlier weeks.
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Initially as Glenday (2005, p. 50) recommended, it was decided to run the cycle in a week. This choice
of the cycle length was also based on the fact that the current cycle length followed at company X is a
week as indicated by the Company Planner (2009, Personal interview) hence it was seen fit to maintain
this consistency. In terms of selecting the production line through which the initial cycle would run, at
first it was decided to fix this cycle on only one line and line 15 was considered for this purpose (as it
has a higher throughput i.e. 2888 cases per 12 hour shift). However, upon calculating the available
capacity (168 hours) versus the required capacity (198.87 hours), it was realised that more capacity was
needed to run the fixed cycle. Line 14 (with a throughput of 2697 cases per 12 hour shift) was therefore
introduced to increase the available capacity. These two lines were chosen mainly because, as
mentioned above, these products are normally run on these lines (i.e. line 14 and 15) and only
occasionally they do run on line 5 (i.e. only when the factory is under tremendous pressure). Also, line
5 was not chosen to run the fixed cycle because the line has been assigned to run other products which
could not run anywhere else as indicated by the Process engineer & Company Planner (Personal
Interview, 2009). These capacity calculations can be found in appendix 5.
Production was allocated to each line as shown in appendix 5. To sequence the cycle with the lowest
overall cleaning time, the optimal sequence was found to be 1524, 1542, 12754 and 1502. This
sequence requires only one product being pushed by another after each run and there is a 20 minutes
only cleaning and at the same time the product waste that results from this type of cleaning is only a
downgrade to another lower quality product instead of being a complete waste by washing off the
product from the previous run. Appendix 6 was used to do this sequencing and other cleaning types
that could have resulted are also shown as well as their duration and meaning. The optimum fixed cycle
was therefore developed as shown in table 3 above.
The above results confirms that the Glenday sieve could be used as a way of generating a fixed cycle
and as indicated in the literature section, fixed cycle production is an early step in creating levelled
production. From the resultant fixed cycle, sudden plan changes would be avoided and an optimum
change over sequence has been developed which consequently would result in reduced changeover
time hence increasing the actual production capacity.
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Sizing of the buffer for each SKU
From the findings, the fact that lower buffer limits were negative most of the time is a sign that the
Glenday buffer limits are not suitable for the demand behaviour similar to that displayed, for company
X, in the first 36 weeks of 2009.
When analysing the data that was obtained, in order to explain the negative lower buffer limits, the
demand behaviour showed an erratic behaviour for all the SKUs (refer to appendix 7 for the plots). The
normality (i.e. chi squared goodness-of-fit) on the data revealed that the data was not normally
distributed. The p-values obtained were 3.93525x10-50
, 5.07633x10-40
, 4.2302x10-30
and 7.53402x10-14
for SKUs 1542, 12754, 1502 and 1524 respectively, which are all so much small that is would be
reasonable to assume that the p-values for all the SKUs are equal to zero. The p values obtained were
all less than 0.05, i.e. they were statistically significant, hence the null hypothesis could not be
accepted; i.e. the weekly average demand data cannot be claimed to be normally distributed.
Upon discussions with the company‟s Supply Chain Manager, it was highlighted that the data came
from the distribution centres (i.e. stock that left the distribution centres to customers) and high
variability in the data (which is their biggest problem), was attributed to several factors that includes:
Unexpected promotional activities from some key customers that the company did not plan for.
This, in a way, results in serious demand distortions. It was indicated that these promotions
were even more frequent during the current economic conditions.
Some customers sometimes deviate from their buying behaviour patterns and stock-up for
certain periods / festivities.
The company‟s pricing strategy is very much influenced by the economic conditions, e.g. the
oil price. This is because they procure their raw materials overseas and fluctuations in the oil
price result in fluctuations in both the transportation costs as well as the actual price of the raw
materials which ultimately affect the price of their products. As a result, the fluctuations in the
customer buying pattern is tightly linked to the oil price fluctuations; a situation that the
company cannot predict or control.
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From the above reasons, it looks like the behaviour that is observed on these data is as a result of a
“bullwhip effect‟. In other words, the stock outs as well as the negative Glenday buffer limits
observed could be attributed to the highly variable demand behaviour. These stock outs are an
indication that the Glenday sieve (in particular a fixed cycle), cannot work in a highly variable
demand environment. However from the results, it cannot be claimed that overall Glenday sieve
does not work.
Simulations 3 and 5 were done mainly to try and investigate the environment in which the Glenday
Sieve works. Starting off at a higher initial buffer level, in simulation 3, only pushed the demand
level curve upwards, to avoid stock out, but it was observed that the buffer starting level was way
outside the Glenday buffer limits. Therefore, the question that one could ask is „is this not the
inventory muda that Glenday is trying to avoid?” According to Glenday‟s definition of inventory
muda, i.e. the according to the established buffer limits, this is inventory muda.
Again as explained in the findings, the fact that most of the time the buffer level was outside the
Glenday buffer limits makes one wonder, “Is the Glenday buffer limits the optimal way of
calculating buffer limits to absorb these kinds of demand fluctuations? What is the right level of
inventory to absorb the demand fluctuations and avoid stock outs?”
In response to both questions, it was decided to investigate how the company would cope with a
fixed production cycle at its current buffer limit rules. The results also showed that the buffer limits
were way above the actual buffer limits that resulted from a fixed cycle (refer to section 4.2 for the
statistics). This implies that, if the company have to adopt a fixed cycle production, its current
buffer rules would result in too much inventory muda suggesting that another way to calculate
inventory in a highly erratic demand environment is required. Appendices 8 to 11 show the graphs
that resulted from the various buffer simulations.
In further trying to investigate the environment in which Glenday‟s buffer rules would work, the
data was normalised as highlighted in the above sections (simulation 5). The analysis of the results
indicated that the sieve works better with normal distributions, i.e. even though all the Glenday
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calculated limits were negative, the demand levels were generally within the limits at all times with
only one or two exceptions for each of SKUs 1542 and 1524. Refer to appendix 11 for the graphs.
Overall, the results show that for company X, the buffer sizing method suggested by Glenday is not
suited given the demand data behaviour. Therefore, for the company to benefit fully from the
Glenday sieve implementation, it needs to address its demand variability problem.
4.4 Limitations of the study
This study was limited to simulation instead of a real life implementation due to time
constraints. This could have resulted in missing some opportunities that would have been
discovered in a real life implementation.
Hypothesis 2 could not be tested because a value stream map of the process could not be
conducted due to, as mentioned, research time constraints and the fact that the research was
simulation based and limited to the green stream simulations. The limitation on this was that
more opportunities especially those resulting from the reduction of muda could have been
missed.
Obtaining the data was extremely difficult; hence the study was only limited to the 36 week
data that was obtained. It would have been more interesting to do the sieve as well as
simulations using historic data from several years in order to test the sieve under a wider range
of demand patterns.
5 RESEARCH CONCLUSIONS
Overall the results show that the Glenday sieve could be successfully applied to the company‟s sales
data to categorize the product range into the green, yellow, blue and the red streams and consequently
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create a fixed production cycle. The research revealed that there are a lot of opportunities that could
result from implementation of the Glenday sieve, including:
The identification of the product categories that could help in focusing improvement efforts.
The fact that the red stream products, i.e. slow movers, could be identified using the sieve gives
the company the opportunity to start a healthy debate as to what to do with these products, i.e.
whether to give the business away or to re-engineer the supply chain such that they increase
sales volumes.
The identification of the green stream products and creating a fixed cycle for them also
indicates that the company does not have to start with all the products in order to start creating
levelled production; the benefits could still be achieved with starting off small and progressing
step by step with the rest of the streams.
The research also confirmed that the Glenday sieve could be used as a way of generating a
fixed cycle which is an early step in creating levelled production. A fixed production cycle sets
a platform for economies of repletion and both have the following benefits:
overall improved process stability
reduced change over times as an optimal sequence was selected
However, other opportunities that were expected like reduced inventory levels, the actual cost
reduction and reliable delivery to customers were not explicitly seen from the simulations mainly
because of the behaviour of the buffer levels and limits that were observed. It was observed that
overall the Glenday sieve does not work for a demand pattern that is not normally distributed;
hence, for this particular case, it cannot be claimed that a reduction of inventory levels resulted. At
the same time, it can also not be claimed that there would be a reliable customer delivery as stock
outs were seen during simulations. What is apparent though is that under a normally distributed
demand pattern, the Glenday sieve would work perfectly. This therefore means that, to benefit fully
from the Glenday sieve, the company needs to work with its customers to try and negotiate better
buying patterns that would stabilise or normalise the high demand variability that was observed.
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6 FUTURE RESEARCH DIRECTIONS
The future direction that is recommended for this research would be to investigate a way of applying
the Glenday Sieve under very variable demand conditions.
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7 REFERENCES AND BIBLIOGRAPHY
Bayer R, n.d., “Employee Morale: Corporate Mental Health” [Online],
http://www.upperbay.org/employee_morale.htm, [23 August 2009]
Coughlan P & Coghlan D, 2002, “Action Research for Operations management”,
International Journal of Operations and Production Management, Vol. 22, No. 2, pp. 220 –
240.
Drew J, McCallum B & Roggenhofer S, 2004, Journey to Lean, Palgrave Macmillan, New
York
French S, 200, “AR for practising Managers”, Journal of Management Development, Vol. 28
No.3, pp. 187-204
Glenday I & Brunt D, 2007 “Principles of Lean Value Stream Design” [online]
http://ercweb.wch.org.au/qi/lhs2007/Day1/Session3/ian_glenday.pdf [15 April, 2009]
Glenday I (2006), “Moving to flow” [online]. Available from:
http://www.leanuk.org/pages/download_flow.htm [11 April 2009)
Glenday I, 2004, “Moving to flow” [online]. Available from:
http://www.leanuk.org/pages/download_flow.htm [11 April 2009)
Glenday I, 2005, “Breaking through to flow: Banish fire fighting and increase customer
service”, Lean Enterprise Academy, Version 1.0.
GoldSim, 2009, “What is Simulation?” [Online], Available from:
http://www.goldsim.com/Content.asp?PageID=91 [28 August 2009]
Jones D, 2006 “Breaking through to flow” [online]. Available from:
http://www.leanuk.org/pages/download_flow.htm (11 April 2009)
Lean Enterprise Australia, 2006, “Breaking Through to Flow” [online]. Available from:
http://www.lean.org.au/-public-workshops/-break-through-to-flow [11 April 2009]
Copyright UCT
[MBA Thesis] Page 47
Lean Enterprise Institute, 2007, “What is Lean” [online]. Available from:
http://www.lean.org/WhatsLean/ [29 March 2009]
Lean Summit Africa, 2007, “The lean life cycle” [online]. Available from:
www.upavon.co.za/.../Lean_Summit_DRAFT_Programme_4.pdf [28 March 2009]
Leansixsigma, 2009, “The no-nonsense guide to standardized work” [Online], Available
from: http://learnsigma.com/the-no-nonsense-guide-to-standardized-work/ [20 August 2009].
Learn about Quality, “Project Planning and Implementing Tools” [online]. Available from:
http://www.asq.org/learn-about-quality/project-planning-tools/overview/pdca-cycle.html [16
June 2009]
Learn Sigma, 2008, “LearnSigma Handbook: Lean & quality 101” [Online], Available from:
http://learnsigma.com/quality-management-tools/ [15 August 2009]
Liker K.J.2004, The Toyota way, McGraw-Hill, New York
Mitchel A, n.d. “The magic of levelled scheduling” [Online], Available from:
http://www.leanuk.org/downloads/general/the_magic_of_levelled_scheduling.pdf” [30
March 2009]
Mugo F, n.d. “Sampling in research” [Online], Available from:
http://www.socialresearchmethods.net/tutorial/Mugo/tutorial.htm [30 August 2009]
NHS Scotland, 2007, “Glenday Sieve - Runners Repeaters Strangers” [online]. Available
from:
http://www.nodelaysscotland.scot.nhs.uk/ServiceImprovement/Tools/Pages/IT045_Glenday_
Sieve_Runners_Repeaters_Strangers.aspx [16 June 2009]
O‟Brien R, 1998, “An Overview of the Methodological Approach of Action Research”
[online] Available from: http://www.web.net/~robrien/papers/arfinal.html [31 May 2009]
Copyright UCT
[MBA Thesis] Page 48
Repetitive Flexible Supply, 2005, “Supply Chain Logic Issue; Buffer Tank” [online].
http://www.ecr.se/upload/PDF%20filer/Lean-workshop%2020%20mars%202007.pdf (18
April 2009)
Resnik B, n.d. “What is Ethics in Research & Why is It Important?” [Online], Available
from: http://www.niehs.nih.gov/research/resources/bioethics/whatis.cfm [31August 2009]
Wikipedia “Action research” [online] Available from:
http://en.wikipedia.org/wiki/Action_research [31 May 2009]
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8 APPENDICES
8.1 Appendix 1: An AR criteria/methodology checklist
Perry and Zuber-Skerritt (1991, p. 70) checklist cited in French (2009)
If yours is a situation in which . . .
1. people reflect and improve (or develop) their own work and their own situations by
tightly interlinking their reflection and action and also making their experience public
not only to other participants but also to other persons interested in and concerned
about the work and the situation, i.e. their (public) theories and practices of the work
and the situation;
and, if yours is a situation in which there is increasingly . . .
1. data gathering by participants themselves (or with the help of others) in relation to
their own questions;
2. participation (in problem posing and in answering questions) in decision making;
3. power-sharing and the relative suspension of hierarchical ways of working towards
industrial democracy;
4. collaboration among members of the group as a “critical community”: self-reflection,
self-evaluation, and self-management by autonomous and responsible persons and
groups learning progressively (and publicly) by doing and making mistakes in a “self-
reflective spiral” of planning, acting, observing, reflecting, re-planning, etc.
5. reflection, which supports the idea of the “(self-)reflective practitioner”;
. . . then yours is a situation in which action research is occurring.
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8.2 Appendix 2: The seven-part structure for AR analysis
1. Diagram: Diagrammatic representation of the action research cycles.
2. The notion: An AR process begins with a notion in the practitioner‟s mind that a
change in work practice is desirable. The notion is then articulated and used to
develop the “thematic concern” and “research question”.
3. The AR cycles: The AR cycles are enumerated and objectives set for each cycle. As
planning is the first element of each of the AR cycles, a set of objectives for each
cycle is articulated. The first AR cycle will include the development and articulation
of the “thematic concern” (the action element) and the “research question” (the
research element) of the project.
4. The AR criteria/methodology checklist: An AR criteria/methodology checklist,
utilising the thinking of Perry and Zuber-Skerritt (1991, p. 70), is applied at the start
of each analysis chapter to confirm that an AR project is occurring.
5. The Dick (1999b) documentation model: Each of the AR cycles of plan, act, observe,
reflect, and re-plan is described with the use of Dick‟s (1999b) frame for each cycle,
including: “Before the event: The outcomes you hope to achieve in this next cycle,
and why you think they are worth pursuing. The contribution you expect those
outcomes to make to your long-term goals, and why you expect it. The actions you
plan to take to achieve those outcomes and why you think those actions will achieve
those outcomes in that situation.
“Then, after the event: What actions you carried out, and what outcomes you
achieved. How and why these differed (if they did) from what you expected. What
you learned about the client system, your methodology, yourself and so on”.
6. Other AR characteristics: Each chapter will conclude with a discussion of how the
project demonstrated the six elements: collaboration, problem-solving, change in
practice, theory development, publication of results, and power.
7. Conclusion. A conclusion is provided in response to the “action” outcomes and to
provide an answer to the “research” question.
French (2009)
Copyright UCT
[MBA Thesis] Page 51
8.3 Appendix 3: sieve analysis results for both the sales volume and sales
value
Table 4: Sieve Analysis results for the sales volume (tons)
Glenday's
Proposed
Cumulative % of
sales (Vol)
Actual
Cumulative % of
sales (Vol)
Number of
Product range
(Vol)
Glenday's
Proposed
Cumulative % of
product range
(Vol)
Actual
Cumulative %
of product
range (Vol) Colour code
50% 51.57% 7 6% 11.29% Green95% 95.41% 32 50% 62.90% Yellow99% 99.11% 13 70% 83.87% Blue
Last 1% 0.89% 10 30% 16.13% Red
Table 5: Sieve Analysis results for the sales value (Rands per tons sold)
Glenday's
Proposed
Cumulative % of
sales (Value)
Actual
Cumulative %
of sales (Value
Number of
Product range
(Value)
Glenday's
Proposed
Cumulative % of
product range
(Value)
Actual
Cumulative % of
product range
(Value)
Colour
code
50% 51.30% 8 6% 12.90% Green95% 95.01% 32 50% 64.52% Yellow99% 99.01% 13 70% 85.48% Blue
Last 1% 0.99% 9 30% 14.52% Red
Copyright UCT
[MBA Thesis] Page 52
8.4 Appendix 4: Sales data used for the sieve analysis and the categories
that resulted
a) Sales volume data
Copyright UCT
[MBA Thesis] Page 53
SKU Average weekly
sales (tons) Tons per case
Average weekly
sales (Cases)
Cumulative sales
(Tons)
% Cumulative
sales (Tons)
1542 301.20 0.015 20079.94 301.20 14.80%12754 191.57 0.015 12771.19 492.77 24.21%1502 153.10 0.015 10206.94 645.87 31.73%1522 128.29 0.016 8018.36 774.16 38.03%20060142 124.92 0.015 8328.11 899.09 44.17%2700539 79.55 0.896 88.74 978.64 48.08%1524 71.02 0.015 4734.64 1049.65 51.57%1520 65.33 0.012 5443.78 1114.98 54.78%1025 62.91 0.008 7604.81 1177.89 57.87%10007 55.15 0.788 69.95 1233.03 60.58%11441 50.52 0.018 2748.12 1283.55 63.06%1546 48.12 0.005 9623.89 1331.67 65.42%1538 47.06 0.012 3921.81 1378.73 67.73%10945 44.34 0.016 2771.16 1423.07 69.91%1537 44.04 0.012 3670.14 1467.11 72.08%1544 39.10 0.018 2172.22 1506.21 74.00%1547 34.53 0.006 5754.81 1540.74 75.69%1508 33.44 0.015 2229.58 1574.18 77.34%12687 31.64 0.012 2636.28 1605.82 78.89%12686 30.55 0.012 2545.47 1636.37 80.39%1509 29.47 0.018 1637.39 1665.84 81.84%12689 28.81 0.012 2400.42 1694.64 83.25%12690 27.42 0.012 2284.89 1722.06 84.60%9783 18.41 0.021 887.17 1740.47 85.50%2700535 17.32 0.021 834.72 1757.79 86.36%20060140 16.61 0.012 1384.33 1774.40 87.17%2700251 15.67 0.025 626.88 1790.08 87.94%1574 15.61 0.006 2602.16 1805.69 88.71%10240 14.57 0.017 877.51 1820.26 89.42%11849 13.88 0.021 668.88 1834.13 90.11%1540 13.86 0.013 1108.78 1847.99 90.79%20046139 13.81 0.012 1151.08 1861.81 91.47%1662 13.71 0.008 1657.07 1875.51 92.14%11504 13.32 0.016 832.66 1888.84 92.79%20060141 12.13 0.012 1010.43 1900.96 93.39%10640 10.95 0.025 438.00 1911.91 93.93%1563 10.11 0.012 842.50 1922.02 94.42%12556 10.09 0.017 607.63 1932.11 94.92%1532 9.92 0.012 826.25 1942.02 95.41%12369 9.81 0.012 817.47 1951.833388 95.89%2700813 7.53 0.025 301.00 1959.358388 96.26%2700536 6.75 0.021 325.16 1966.105447 96.59%2700641 6.35 0.025 254.00 1972.455447 96.90%12007 6.25 0.016 390.36 1978.701225 97.21%2700231 6.20 0.021 298.80 1984.901225 97.51%20046083 5.78 0.006 963.00 1990.679225 97.80%2700210 5.62 0.021 270.68 1996.295892 98.07%2700421 4.66 0.025 186.50 2000.958392 98.30%2700870 4.50 0.180 25.00 2005.458392 98.52%20046082 4.11 0.012 342.68 2009.570558 98.72%2700284 4.00 0.025 160.00 2013.570558 98.92%12685 3.80 0.006 633.42 2017.371058 99.11%2700219 3.66 0.021 176.44 2021.032169 99.29%2700915 2.96 0.190 15.58 2023.993169 99.43%12688 2.75 0.006 459.14 2026.748003 99.57%11790 2.75 0.005 550.90 2029.502521 99.70%11013 2.18 0.012 182.00 2031.686521 99.81%11151 1.79 0.016 112.09 2033.479976 99.90%10349 1.09 0.005 218.11 2034.570501 99.95%11014 0.60 0.005 119.32 2035.167092 99.98%11328 0.33 0.025 13.00 2035.492092 100.00%11865 0.03 0.021 1.53 2035.523739 100.00%
Copyright UCT
[MBA Thesis] Page 54
b) Sales value data
SKUPrice per case
(R)Tons per case Price per ton (R) Average weekly
sales (tons)
Average weekly
sales (Value)
Cumulative sales
(Value)
% Cumulative
sales (Value)
1542 R 127.35 0.015 R 8,490.00 301.20 R 2,557,179.75 2557179.75 13.30%1025 R 229.20 0.008 R 27,708.27 62.91 R 1,743,021.61 4300201.36 22.37%12754 R 116.20 0.015 R 7,746.67 191.57 R 1,484,012.79 5784214.15 30.09%1522 R 128.74 0.016 R 8,046.25 128.29 R 1,032,283.81 6816497.96 35.46%1502 R 81.62 0.015 R 5,441.33 153.10 R 833,090.81 7649588.77 39.79%2700539 R 9,081.10 0.896 R 10,130.63 79.55 R 805,879.24 8455468.01 43.99%20060142 R 93.20 0.015 R 6,213.33 124.92 R 776,179.96 9231647.97 48.02%11441 R 229.04 0.018 R 12,460.02 50.52 R 629,430.06 9861078.03 51.30%10007 R 8,496.06 0.788 R 10,776.33 55.15 R 594,297.21 10455375.24 54.39%1520 R 99.44 0.012 R 8,286.67 65.33 R 541,329.26 10996704.50 57.21%1524 R 105.74 0.015 R 7,049.33 71.02 R 500,640.72 11497345.22 59.81%1538 R 115.80 0.012 R 9,650.00 47.06 R 454,145.08 11951490.30 62.17%1546 R 46.97 0.005 R 9,394.00 48.12 R 452,034.06 12403524.36 64.52%1537 R 119.60 0.012 R 9,966.67 44.04 R 438,948.61 12842472.98 66.81%10945 R 139.05 0.016 R 8,690.63 44.34 R 385,329.53 13227802.50 68.81%1544 R 161.60 0.018 R 8,977.78 39.10 R 351,031.11 13578833.62 70.64%12687 R 131.57 0.012 R 10,964.17 31.64 R 346,855.07 13925688.68 72.44%12686 R 135.21 0.012 R 11,267.50 30.55 R 344,173.30 14269861.98 74.23%12689 R 134.49 0.012 R 11,207.50 28.81 R 322,832.04 14592694.02 75.91%1547 R 54.97 0.006 R 9,161.67 34.53 R 316,341.66 14909035.68 77.56%12690 R 133.61 0.012 R 11,134.17 27.42 R 305,284.00 15214319.69 79.15%1508 R 125.80 0.015 R 8,386.67 33.44 R 280,481.58 15494801.27 80.60%1509 R 158.97 0.018 R 8,831.67 29.47 R 260,295.71 15755096.98 81.96%1662 R 122.40 0.008 R 14,797.08 13.71 R 202,825.75 15957922.73 83.01%10240 R 222.47 0.017 R 13,401.81 14.57 R 195,219.66 16153142.39 84.03%1574 R 73.87 0.006 R 12,311.67 15.61 R 192,221.81 16345364.20 85.03%2700535 R 225.29 0.021 R 10,857.35 17.32 R 188,054.23 16533418.43 86.01%9783 R 209.78 0.021 R 10,109.88 18.41 R 186,111.47 16719529.89 86.98%20060140 R 134.08 0.012 R 11,173.33 16.61 R 185,611.41 16905141.31 87.94%20046139 R 152.29 0.012 R 12,690.83 13.81 R 175,298.48 17080439.79 88.85%2700251 R 235.42 0.025 R 9,416.80 15.67 R 147,580.64 17228020.43 89.62%11849 R 218.67 0.021 R 10,538.31 13.88 R 146,263.01 17374283.44 90.38%11504 R 162.57 0.016 R 10,160.63 13.32 R 135,364.93 17509648.36 91.09%20060141 R 132.18 0.012 R 11,015.00 12.13 R 133,558.45 17643206.81 91.78%1540 R 108.66 0.013 R 8,692.80 13.86 R 120,479.79 17763686.61 92.41%1532 R 136.37 0.012 R 11,364.17 9.92 R 112,675.71 17876362.32 92.99%12556 R 174.42 0.017 R 10,507.23 10.09 R 105,982.92 17982345.23 93.54%1563 R 117.47 0.012 R 9,789.17 10.11 R 98,968.48 18081313.71 94.06%12369 R 113.67 0.012 R 9,472.50 9.81 R 92,922.07 18174235.78 94.54%10640 R 205.74 0.025 R 8,229.60 10.95 R 90,114.12 18264349.90 95.01%20046083 R 82.25 0.006 R 13,708.33 5.78 R 79,206.75 18343556.65 95.42%2700641 R 297.66 0.025 R 11,906.40 6.35 R 75,605.64 18419162.29 95.82%2700536 R 217.81 0.021 R 10,496.87 6.75 R 70,822.98 18489985.27 96.19%2700813 R 226.69 0.025 R 9,067.72 7.53 R 68,234.59 18558219.86 96.54%2700231 R 211.79 0.021 R 10,206.75 6.20 R 63,281.83 18621501.69 96.87%20046082 R 180.57 0.012 R 15,047.50 4.11 R 61,877.83 18683379.52 97.19%2700210 R 225.77 0.021 R 10,880.48 5.62 R 61,112.04 18744491.56 97.51%2700870 R 2,170.64 0.180 R 12,059.11 4.50 R 54,266.00 18798757.56 97.79%12685 R 82.32 0.006 R 13,720.00 3.80 R 52,142.86 18850900.42 98.06%12007 R 126.69 0.016 R 7,918.13 6.25 R 49,454.85 18900355.27 98.32%2700421 R 262.73 0.025 R 10,509.20 4.66 R 48,999.15 18949354.42 98.57%11790 R 81.43 0.005 R 16,286.00 2.75 R 44,860.09 18994214.50 98.81%12688 R 83.19 0.006 R 13,865.00 2.75 R 38,195.76 19032410.27 99.01%2700284 R 236.54 0.025 R 9,461.60 4.00 R 37,846.40 19070256.67 99.20%2700219 R 211.83 0.021 R 10,208.67 3.66 R 37,375.09 19107631.76 99.40%2700915 R 2,299.74 0.190 R 12,103.89 2.96 R 35,839.63 19143471.39 99.58%11013 R 146.43 0.012 R 12,202.50 2.18 R 26,650.26 19170121.65 99.72%11151 R 173.76 0.016 R 10,860.00 1.79 R 19,476.92 19189598.57 99.82%10349 R 83.54 0.005 R 16,708.00 1.09 R 18,220.49 19207819.06 99.92%11014 R 102.64 0.005 R 20,528.00 0.60 R 12,246.84 19220065.89 99.98%11328 R 223.92 0.025 R 8,956.80 0.33 R 2,910.96 19222976.85 100.00%11865 R 222.92 0.021 R 10,743.13 0.03 R 339.98 19223316.84 100.00%
Copyright UCT
[MBA Thesis] Page 55
8.5 Appendix 5: Capacity calculations
a) Table 6: Total available capacity on each of line 14 and 15
Total working days in a cycle (Days) 7Number of Shifts 2Hours worked by a shift per day (hrs) 12Planned stoppages for a shift per day (min) 0Total available capacity for a
cycle (hrs per cycle) 168
b) Table 7: The initial weekly fixed cycle portion that would run on line 14
SKU
Average Total weekly
Demand in cases (Acual)
Weekly quantity
allocated to the line
Line 14 output in
Cases per hour
Run time in
hours
1542 20079.94 9685.38 224.25 43.1912754 12771.19 6160.07 224.25 27.471502 10206.94 4923.23 224.25 21.951524 4734.64 2283.71 224.25 10.18
102.80Total Required run time (hours)
c) Table 8: The initial weekly fixed cycle portion that would run on line 15
SKU
AverageTotal weekly
Demand in cases (Acual)
Weekly quantity
allocated to the line
Line 15 output in
Cases per hour Run time in hours
1542 20079.94 10394.56 240.67 43.1912754 12771.19 6611.12 240.67 27.471502 10206.94 5283.72 240.67 21.951524 4734.64 2450.93 240.67 10.18
102.80Total Required run time (hours)
Copyright UCT
[MBA Thesis] Page 56
8.6 Appendix 6: Product compatibility Matrix for the products that
could run on both line 14 and 15
Product Running
Product running 1542
Non Green Stream products type 1 12754 2006014 1524
Non Green Stream products type 2
Non Green Stream products type 3
Non Green Stream products type 4
Non Green Stream products type 5
Non Green Stream products type 6 1502
1542 push push push push Full CIP push push Hot Flush Hot Flush Full CIP Full CIP Full CIP Hot Flush
Non Green Stream products type 1 push push push push Full CIP push push push push push push Full CIP Full CIP Full CIP Hot Flush
12754 push push push push Full CIP push push push push push push Full CIP Full CIP Full CIP Hot Flush
2006014 Full CIP Full CIP Full CIP Full CIP Full CIP Full CIP Full CIP Full CIP Full CIP Full CIP
1524 Hot Flush push push Hot Flush Full CIP Hot Flush Hot Flush Full CIP Full CIP Full CIP Hot Flush
Non Green Stream products type 2 push push push push push push Full CIP Hot Flush push push push push Full CIP push push Hot Flush
Non Green Stream products type 3 push push push push push push Full CIP Hot Flush push push push push Full CIP push push Hot Flush
Non Green Stream products type 4 Hot Flush Hot Flush Hot Flush Full CIP Hot Flush push push push push Full CIP push push Full CIP
Non Green Stream products type 5 Hot Flush Hot Flush Hot Flush Full CIP Hot Flush Hot Flush Hot Flush Full CIP Full CIP Full CIP
Non Green Stream products type 6 Full CIP Full CIP Full CIP Full CIP Hot Flush Full CIP Full CIP Hot Flush Full CIP Hot Flush
1502 push push push push push push Full CIP push push Hot Flush Hot Flush Full CIP Full CIP Full CIP
(Source: Company planning department, Company records)
Copyright UCT
[MBA Thesis] Page 57
In the figure above, “Hot Flush” and “Push Push” refers to the type of cleaning that has to be
done to ensure that there is no contamination from the previous SKU that was run. To use the
figure, the product that is running is located in the yellow part on the top raw of the figure
and the product that would run next is located in the orange vertical column; drawing a line
from any product that is running down to which ever product that would run next, would end
on a square with a colour that would give a guide as to what type of cleaning would be
required. Added to the above cleaning types, there is another type of cleaning that is normally
done, again depending on the types of products involved, called a “Full CIP”, which at this
stage, according to the figure, is not necessary especially for these four green stream
products. These acronyms, according to the company Planner (Personal Interview), indicate:
Full CIP: means that a full Cleaning In Process (CIP) needs to be done before change
over. According to the company planner this type of cleaning takes two hours to
complete. This is the longest change over cleaning type and it is strongly
recommended that SKU runs be sequenced in such a way that this is avoided as much
as possible.
Hot Flush: means that the system needs to be flushed with hot water for change over.
It is the next longest change over cleaning type after “Full CIP”, lasting for about 1
hour. In other words, this is preferred to a “Full CIP” in terms of time saving.
Push Push: This means the only cleaning involved is pushing the product that was
running previously with the new one; meaning that the product are in a way
compatible. This is the most preferred type of change over cleaning as it saves the
most time, i.e. takes only 20 minutes. It therefore recommended that any cycle
planning should strive to have SKU sequencing that would result in a Push Push
change over cleaning type.
Copyright UCT
[MBA Thesis] Page 58
8.7 Appendix 7: The demand behaviour over the period between
January to August 2009
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Dem
and
(Cas
es)
Week
SKU 1542 Weekly Demand for the January to August 09 period (Unfiltered)
Actual demand Ave
0
5000
10000
15000
20000
25000
30000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Dem
and
(Cas
es)
Week
SKU 12754 Weekly Demand for the January to August 09 period (Unfiltered)
Actual Demand Ave
Copyright UCT
[MBA Thesis] Page 59
0
5000
10000
15000
20000
25000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Dem
and
(Cas
es)
Week
SKU 1502 Weekly Demand for the January to August 09 period (Unfiltered)
Actual Demand Ave
0
1000
2000
3000
4000
5000
6000
7000
8000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Dem
and
(Cas
es)
Week
SKU 1524 Weekly Demand for the January to August 09 period (Unfiltered)
Actual Demand Ave
Copyright UCT
[MBA Thesis] Page 60
8.8 Appendix 8: Simulation 1 graphs
-10000.00
0.00
10000.00
20000.00
30000.00
40000.00
50000.00
60000.00
70000.00
80000.00
90000.00
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Pro
du
ct q
uan
tity
(ca
ses)
Week
Current Company Buffer sizes relative to Glenday's proposed Buffer sizes and Simultion 1 weekly Buffer levels - SKU 1542
Weekly Buffer
Current Company Buffer upper level
Glenday
Buffer range
Current
Company
Buffer range
Current Company Buffer lower level
Current Company Buffer target level
Glenday Buffer upper level
Glenday Buffer lower level
For SKU 1542,
Company buffer LL / Glenday buffer UL = 0.914
Company buffer LL / Glenday buffer UL =1.827
-20000.00
-10000.00
0.00
10000.00
20000.00
30000.00
40000.00
50000.00
60000.00
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Pro
du
ct q
uan
tity
(ca
ses)
Week
Current Company Buffer sizes relative to Glenday's proposed Buffer sizes and Simultion 1 weekly Buffer levels - SKU 12754
Weekly Buffer
Current Company Buffer upper level
Glenday
Buffer range
Current
Company
Buffer range
Current Company Buffer lower level
Current Company Buffer target level
Glenday Buffer upper level
Glenday Buffer lower level
For SKU 12754,
Company buffer LL / Glenday buffer UL = 0.886
Company buffer LL / Glenday buffer UL = 1.772
Copyright UCT
[MBA Thesis] Page 61
-5000.00
0.00
5000.00
10000.00
15000.00
20000.00
25000.00
30000.00
35000.00
40000.00
45000.00
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Pro
du
ct q
uan
tity
(ca
ses)
Week
Current Company Buffer sizes relative to Glenday's proposed Buffer sizes and Simultion 1 weekly Buffer levels - SKU 1502
Weekly Buffer
Current Company Buffer upper level
Glenday
Buffer range
Current
Company
Buffer range
Current Company Buffer lower level
Current Company Buffer target level
Glenday Buffer upper level
Glenday Buffer lower level
For SKU 1502,
Company buffer LL / Glenday buffer UL = 0.933
Company buffer LL / Glenday buffer UL = 1.867
-5000.00
0.00
5000.00
10000.00
15000.00
20000.00
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Pro
du
ct q
uan
tity
(ca
ses)
Week
Current Company Buffer sizes relative to Glenday's proposed Buffer sizes and Simultion 1 weekly Buffer levels - SKU 1524
Current Company Buffer upper level
Glenday
Buffer range
Current Company
Buffer range
Current Company Buffer lower level
Current Company Buffer target level
Glenday Buffer upper level
Glenday Buffer lower level
For SKU 1524,
Company buffer LL / Glenday buffer UL = 0.999
Company buffer LL / Glenday buffer UL = 1.997
Copyright UCT
[MBA Thesis] Page 62
8.9 Appendix 9: Simulation 2 graphs
-80000.00
-60000.00
-40000.00
-20000.00
0.00
20000.00
40000.00
60000.00
80000.00
18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Pro
du
ct q
uan
tity
(ca
ses)
Week
Current Company Buffer sizes relative to Glenday's proposed Buffer sizes and Simultion 2 weekly Buffer levels - SKU 1542
Weekly Buffer
Current Company Buffer upper level
Glenday
Buffer range
Current
Company
Buffer rangeCurrent Company Buffer lower level
Current Company Buffer target levelGlenday Buffer upper level
Glenday Buffer lower level
-60000.00
-40000.00
-20000.00
0.00
20000.00
40000.00
60000.00
18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Pro
du
ct q
uan
tity
(ca
ses)
Week
Current Company Buffer sizes relative to Glenday's proposed Buffer sizes and Simultion 2 weekly Buffer levels - SKU 12754
Weekly Buffer
Current Company Buffer upper level
Glenday
Buffer range
Current
Company
Buffer range
Current Company Buffer lower level
Current Company Buffer target level
Glenday Buffer upper level
Glenday Buffer lower level
Copyright UCT
[MBA Thesis] Page 63
-50000.00
-40000.00
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-20000.00
-10000.00
0.00
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30000.00
40000.00
50000.00
18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Pro
du
ct q
uan
tity
(ca
ses)
Week
Current Company Buffer sizes relative to Glenday's proposed Buffer sizes and Simultion 2 weekly Buffer levels - SKU 1502
Weekly Buffer
Current Company Buffer upper level
Glenday
Buffer range
Current
Company
Buffer range
Current Company Buffer lower level
Current Company Buffer target level
Glenday Buffer upper level
Glenday Buffer lower level
-20000.00
-15000.00
-10000.00
-5000.00
0.00
5000.00
10000.00
15000.00
20000.00
18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Pro
du
ct q
uan
tity
(ca
ses)
Week
Current Company Buffer sizes relative to Glenday's proposed Buffer sizes and Simultion 2 weekly Buffer levels - SKU 1524
Current Company Buffer upper level
Glenday
Buffer range
Current Company
Buffer range
Current Company Buffer lower level
Current Company Buffer target level
Glenday Buffer upper level
Glenday Buffer lower level
Copyright UCT
[MBA Thesis] Page 64
8.10 Appendix 10: Simulation 3 graphs
-10000.00
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30000.00
40000.00
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60000.00
70000.00
80000.00
90000.00
18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Pro
du
ct q
uan
tity
(ca
ses)
Week
Current Company Buffer sizes relative to Glenday's proposed Buffer sizes and Simultion 3 weekly Buffer levels - SKU 1542
Weekly Buffer
Current Company Buffer upper level
Glenday
Buffer range
Current
Company
Buffer range
Current Company Buffer lower level
Current Company Buffer target level
Glenday Buffer upper level
Glenday Buffer lower level
0.00
10000.00
20000.00
30000.00
40000.00
50000.00
60000.00
18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Pro
du
ct q
uan
tity
(ca
ses)
Week
Current Company Buffer sizes relative to Glenday's proposed Buffer sizes and Simultion 3 weekly Buffer levels - SKU 12754
Weekly Buffer
Current Company Buffer upper level
Glenday
Buffer range
Current
Company
Buffer range
Current Company Buffer lower level
Current Company Buffer target level
Glenday Buffer upper level
Glenday Buffer lower level
Copyright UCT
[MBA Thesis] Page 65
0.00
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30000.00
35000.00
40000.00
45000.00
18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Pro
du
ct q
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tity
(ca
ses)
Week
Current Company Buffer sizes relative to Glenday's proposed Buffer sizes and Simultion 3 weekly Buffer levels - SKU 1502
Weekly Buffer
Current Company Buffer upper level
Glenday
Buffer range
Current
Company
Buffer range
Current Company Buffer lower level
Current Company Buffer target level
Glenday Buffer upper level
Glenday Buffer lower level
0.00
2000.00
4000.00
6000.00
8000.00
10000.00
12000.00
14000.00
16000.00
18000.00
20000.00
18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Pro
du
ct q
uan
tity
(ca
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Week
Current Company Buffer sizes relative to Glenday's proposed Buffer sizes and Simultion 3 weekly Buffer levels - SKU 1524
Current Company Buffer upper level
Glenday
Buffer range
Current Company
Buffer range
Current Company Buffer lower level
Current Company Buffer target level
Glenday Buffer upper level
Glenday Buffer lower level
Copyright UCT
[MBA Thesis] Page 66
8.11 Appendix 11: Simulation 5 graphs
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0.00
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60000.00
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Week
Simultion 5: weekly Buffer levels and limits calculated from normalised demand data -SKU 1542
Weekly Buffer
Glenday Buffer upper level
Glenday Buffer lower level
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-5000.00
0.00
5000.00
10000.00
15000.00
20000.00
25000.00
30000.00
35000.00
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ses)
Week
Simultion 5: weekly Buffer levels and limits calculated from normalised demand data - SKU 12754
Weekly Buffer
Glenday Buffer upper level
Glenday Buffer lower level
Copyright UCT
[MBA Thesis] Page 67
-10000.00
0.00
10000.00
20000.00
30000.00
40000.00
50000.00
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Pro
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ct q
uan
tity
(ca
ses)
Week
Simultion 5: weekly Buffer levels and limits calculated from normalised demand data - SKU 1502
Weekly Buffer
Glenday Buffer upper level
Glenday Buffer lower level
-10000.00
-5000.00
0.00
5000.00
10000.00
15000.00
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36Pro
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ct q
uan
tity
(ca
ses)
Week
Simultion 5: weekly Buffer levels and limits calculated from normalised demand data - SKU 1524
Weekly Buffer
Glenday Buffer upper level
Glenday Buffer lower level
Copyright UCT
[MBA Thesis] Page 68
8.12 Appendix 12: Learning Journal
Copyright UCT
[MBA Thesis] Page 69
DATE PLAN PURPOSE EXPECTED OUTCOME ACTUAL OUTCOME REFLECTION 03-Jul-09 Call the company Supply Chain Director To discuss my intentions and ask permission to
do my research with the company
To be granted permission Permission was granted verbally Everything went as expected and but I was given
the contact details of the Supply Chain Manager
to contact and discuss my requirements.
08-Jul-09 Call to the company Supply Chain Manager To indicate to her what I have discussed with the
Supply Chain Director.To also discuss my
intentions and ask for her permission to do my
research with her department. To set up a meeting
date
To be granted permission and a
meeting date to be agreed
Permission was granted verbally. But the meeting was not
set up as the Supply Chain manager was not available for
the next couple of weeks. Relevant data to the research
was request in the mean time.
Eerything went as expected. However, the data
obtained was not the one that was asked for.
Plan forecasts were obtained instead and this
was after about a month of constant follow ups.
This could have been handled better insisting on
a meeting first before requesting the data so that
what was needed could be fully understood. A
meeting with the Supply Chain manager was
therefore requested.
10-Aug-09 Meeting with the Supply Chain Manager To meet face to face and to explain the purpose of
my research and what assistance I would need
from the company. And relevant data was asked
for.
To meet the Supply Chain Manager
and gain her support for the research.
And to get an overview of the
comppany's planning and production
process as well as to get the data asked
for inorder to start applying the
Glenday sieve.
The meeting did happen and the over view of the
planning and production rocess was given; however the
Supply Chain Manager promised that she would e-mail
the data late on in the day.
The meeting went very well, but the data was
not obtained later on in the day as it was
promised. Only historic production planning
data for the first 24 weeks of 2009 was obtained
in stead of the requested sales data. A request
was again put for the sales data instead.
02-Oct-09 Call to the Company planner To introduce myself and ask for the relevant data,
i.e. sales data, product list, change over times, etc
To get comitment that the data sked
for would be sent
The data was sent late on in the day with the exception of
the sales data that was indicated to accsessed by the
Supply Chain Manager only
This stratergy seemed to have worked better.
This could be due to the fact that the planner is
looking at these values at all times and therefore
can readily access them. The major learning was
that is you want action, do not talk to managers;
jus ensure that they are aware of what you are
doing, but work with people who do the actual
job as managers are always busy.
07-Oct-09 Follow-up (telephonically and thrruogh a text
message) on the sales data from the Supply Chain
Manager
To get the required sales data. To express the
urgency of the matter and express the difficulty in
going forward with my research as it depended on
this data
To get the required data The data was sent later that weekend. However, the units
of the data was not expresses, i.e. Slaes volume (cases or
tons) or sales value.
This stratergy seemed to have worked better.
This could be due to the fact the Supply Chain
Manager sensed the urgency of the matter from
our communication. The units of the data was
asked for, but it was not obtained until I
requested to go and work for about three days
from the planning office.
24-Nov-09 Ask for permission to work from the planning office
for about three days
Gain permission to work from planning office so
that I could be closser to all the resources that I
needed
Access to be granted Access was granted by the Supply Chain Manager The plan worked well though the major
resource, the company planner was not made
aware of my visit the first day. She was
however made aware later, but unfortunately she
had her day full of meetings and could not give
me the required attention. But she was now
aware that I would spend time with her for the
next couple of days. All the data was eventually
obtained by the 27th November 2009
Copyright UCT
[MBA Thesis] Page 70
Learning journal Cont...
DATE PLAN PURPOSE EXPECTED OUTCOME ACTUAL OUTCOME REFLECTION 27-Nov-09 Start analysing the data obtained To apply the Glenday Sieve to the data obtained
and see if it would work for these data
The Glenday sieve to be able to
categorise the company sales data
according to the green, yellow, blue
and the red streams.
The sieve was able to categorise both the sales volume
and value as expected.
The Glenday sieve does work for high volume
operations demand data. The sieve results were
close foe both the sales volume and value and it
was decided to proceed with the research using
the sales volume as it was easier to work with
units consistant with those used in the
production environment.
28-Nov-09 Fix the initial cycle To establish a fixed production cycle A fixed volume and sequence
production cycle to be established on
the green stream. To decide on
dedicated production line to be used
for the fixed cycle.
The fixed sequence was established and it was decided to
run this cycle on one production line, line 15 as it has the
highest output.
It was found out that the required capacity
198.58 hours was greater than the available
capacity, 168 hours. It was realised that this was
due to variability on the demand.
Filter out abnormally high demand values To filter out outliers from the demand inorder to
establish a weekly demand average that would
form the fixed quantity to be produced weekly to
avoid over production muda.
To resultant demand data to have a
required production capacity less than
the available capacity.
The new required run time was 157.38 hours (refer to
appendix 13 for the graphs and appendix 15a for the
calculations)
Though the resultant run time was within the
avalable capacity, it was realised that this
method of filtering the data was not scientific.
To establish a scientific way of filtering the demand
data. The damand data was plotted on histograms
and the different demand data ranges were
established and demand averages calculated for each
range.
To come up with a cycle that would fall within
the available capacity. To establish a base demand
that would be used for the fixed cycle over
different demand periods (ranges), i.e. to establish
a fixed cycle for each demand period.
The calculated required run time to be
less than 168 hours after the data has
been filtered and a fixed cycle to be
established for each demand period
Three demand periods were established with the resultant
required run times of 162.66, 246.78 and 368.13 hours for
the low, medium and high demand seasons respectively
(refer to appendix 14 for the established periods and
appendix15b for the calculations)
Only the low demand run time would be catered
for in the available capacity. To meet the
medium and high demand periods extra capacity
would be introduced, i.e. Each demand period
was had its own fixed demand cycle with its on
required capacity. However, it was realised that
the model was very different from Glenday's
proposed model as three different fixed cycles
were established. Another method to cater for
the different demand levels and still use
Glenday's principles was needed.
To establish a base demand that would be used for
the whole period without filtering out any peaks;
while any excess demand would be planned to run
on other lines.
To have only one fixed cycle To establish a base demand that would
be used with the fixed cucle so that
inventory muda is not experienced
Different demand "seasons" were established as shown in
appendix 16. The based demand required run time was
calculated to be 171.87 hours which was more than the
available capacity of 168 hours.
This model was found to be more scienntific
relative to the above two. However, the required
run time was still found to be more than the
available capacity. It was therefore decided to
introduce extra capacity.
To establish a fixed cycle on two lines instead of 1 To meet the required run time for the fixed cycle A fixed volume and sequence cycle
over lines 14 and 15 as these two lines
were designed to run these products
A weekly fixed production cycle was established on these
two lines and it was going to require 88.97 hours run time
on each line.
Even though these run time was within the
available capacity, it was decided to fixe the
cycle daily instead of weekly as this cycle was
only going to run for about 4 days in the 7
available working days. The major problem
forseen was that the 3 day gap would interfear
with continous repetition that was needed.
Copyright UCT
[MBA Thesis] Page 71
Learning journal Cont...
DATE PLAN PURPOSE EXPECTED OUTCOME ACTUAL OUTCOME REFLECTION To establish the fixed cycle daily To ensure that the fixed cycle runs daily and
operators are exposed to daily repetion of taks
The cycle to run daily Daily fixed cycle was established as shown in the
calculations in appendix 19
Athough this model appeared to be more
scientific, compared to the previous two, it was
again realised that the model deviated from what
Glenday proposes should be done, i.e. To use the
data as it is and establish a fixed cycle
To calculate the overall average demand over the 36
weeks for each green stream SKU
To establish a fixed cycle with the data unfiltered
that could have been used in the 36 weeks
To establish a fixed volume and
sequence cycle on production lines 14
and 15
The fixed cycle was established as shown in table 3 of the
report
It was found that the cycle would require 104.13
hours on each line weekly to run. Glenday Sieve
proved to be efficient in fixing cycles over high
volume operations
To establish the buffer limits that were to be used
with the fixed cycle. Glenday's way of calculating
the buffer was used. Buffer levels were calculated
and plotted on the same axis as the buffer limits.
The buffer limits were to ensure that demand
variability was absorbed.
The buffer limits calculated would
absorb the demand fluctuations very
well, i.e the calculated buffer levels
would lie within the limits.
There were stock out observed on both the buffer limits
and buffer levels
The stock outs seemed to be a result of the high
variability in the demand data. It was therefore
decided to simulate the buffer tanks to try and
establish compatible buffer limits that would be
used with the established fixed cycle.
Similation 2: The demand dat for the first 18 weeks
of the available data was used to fix the weekly
production volumes and size the buffers
To establish how the buffer limits would handle
the resultant buffer levels
The buffer limits calculated would
absorb the demand fluctuations very
well, i.e the calculated buffer levels
would lie within the limits.
Stock out amd negative buffer limits were still observed
for this simulation
As above, it seemed stock outs were a result of
the high variability in the demand data. A
different condition that would ensure that no
stockout occurs needed to be established.
Simulation 3: Start off with a higher buffer level
using simulation 2 data
To establish the initial buffer level needed to
avoid stock outs
The buffer limits to be able to handle
the resulting buffer levels.
Buffer levels were all positive, but the Glenday lower
limits were still negative. The initial buffer levels
required were up to 4.5 times the Glenday buffer limits.
Stock out were avoided on actual buffer levels
but the limits still allowed for stock outs ang the
initial buffer levels were found to be too high
Simulation 4: To analyse the current company buffer
targets against the Glenday's buffer targests for
simulations 1 - 3.
To try and establish buffer limits that would work
for the kind of demad experienced by the
company.
The company's established buffer
limits to be much higher than the
Glenday's buffer limits
As expected. The company buffer levels were found to be
upto two times that recommended by Glenday
The company is currently generating inventory
muda. It was decided to establish an
environment in which the Glenday sieve buffer
limits work
To normalise the available data To investigate the Glenday sieve under a normally
distributted demand pattern
Glenday sieve to work under for
normal distributions
As expected. Glenday sieve cannot hanlde very variable
demand fluctuations. For the company to benefit
from the Glenday sieve, they need to work with
their customers to try and stabilise the demand
pattern.
Copyright UCT
[MBA Thesis] Page 72
8.13 Appendix 13: Graphs that resulted from filtering out spikes
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SKU 1542 Weekly Demand (Filtered)
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SKU 12754 Weekly Demand(Filtered)
12754 Ave
Copyright UCT
[MBA Thesis] Page 73
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SKU 1502 Weekly Demand (Filtered)
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SKU 1524 Weekly Demand (Filtered)
Copyright UCT
[MBA Thesis] Page 74
8.14 Appendix 14: Demand range segments for each green stream SKU
across the 36 week period
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Dem
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Week
SKU 1542 Weekly Demand for the January to August 09 period (Unfiltered)
Actual demand Ave
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and
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SKU 12754 Weekly Demand for the January to August 09 period (Unfiltered)
Actual Demand Ave
Copyright UCT
[MBA Thesis] Page 75
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Dem
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SKU 1502 Weekly Demand for the January to August 09 period (Unfiltered)
Actual Demand Ave
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Dem
and
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Week
SKU 1524 Weekly Demand for the January to August 09 period (Unfiltered)
Actual Demand Ave
Copyright UCT
[MBA Thesis] Page 76
8.15 Appendix 15: Required capacity calculated from manipulated data
a) Total run time calculated after filtering out spikes in the data
SKU
Average weekly
Demand in cases
(Acual)
Line 15 output
in Cases per
hour
Run time in
hours
1542 15061.50 240.67 62.58
12754 9812.92 240.67 40.77
1502 8541.96 240.67 35.49
1524 4497.12 242.67 18.53
157.38Total Required run time
b) Calculating the base demand using the demand data ranges
Low demand range
SKU
Average weekly
Demand in cases
(Acual)
Line 15 output in
Cases per hour
Run time in
hours
1542 15544.00 240.67 64.59
12754 11676.00 240.67 48.51
1502 8340.00 240.67 34.651524 3587.00 240.67 14.90
162.66Total Required run time (hours)
Medium demand range
SKU
Average weekly
Demand in cases
(Acual)
Line 15 output in
Cases per hour
Run time in
hours
1542 25292.18 240.67 105.09
12754 16429.69 240.67 68.27
1502 12288.31 240.67 51.061524 5382.75 240.67 22.37
246.78Total Required run time (hours)
Copyright UCT
[MBA Thesis] Page 77
High demand range (spikes)
SKU
Average weekly
Demand in cases
(Acual)
Line 15 output in
Cases per hour
Run time in
hours
1542 37612.50 240.67 156.28
12754 25424.00 240.67 105.64
1502 18215.00 240.67 75.681524 7347.33 240.67 30.53
368.13Total Required run time (hours)
Copyright UCT
[MBA Thesis] Page 78
Appendix 16: Segmentation of the observable demand seasons across the 36 week
period
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Dem
and
(Cas
es)
Week
SKU 1542 Weekly Demand for the January to August 09 period (Unfiltered)
Actual demand Ave
Demand Level 1Demand Level 2Demand Level 1Demand Level 1 Demand Level 2
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Dem
and
(Cas
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Week
SKU 12754 Weekly Demand for the January to August 09 period (Unfiltered)
Actual Demand Ave
Demand Level 1 Demand Level 2 Demand Level 1
Copyright UCT
[MBA Thesis] Page 79
0
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Dem
and
(Cas
es)
Week
SKU 1502 Weekly Demand for the January to August 09 period (Unfiltered)
Actual Demand Ave
Demand Level 1 Demand Level 2 Demand Level 1
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Dem
and
(Cas
es)
Week
SKU 1524 Weekly Demand for the January to August 09 period (Unfiltered)
Actual Demand Ave
Demand Level 1 Demand Level 2
Copyright UCT
[MBA Thesis] Page 80
Appendix 17: The resulting SKU demand levels for each identified period after super
imposing the demand for the different seasons for each SKU
SKU wk 1 - 11 wk 12 - 16 wk 17 - 20 wk 21 - 25 wk 26 - 28 wk 29 - 32 wk 33 - 36
1542 level 1 level 2 level 1 level 2 level 2 level 1 level 112754 level 1 level 2 level 2 level 2 level 2 level 1 level 11502 level 1 level 1 level 1 level 1 level 2 level 2 level 11524 level 1 level 2 level 2 level 2 level 2 level 2 level 2
NB: The level for each SKU per identified period can be obtained by super imposing the
graphs in appendix 16.
Copyright UCT
[MBA Thesis] Page 81
Appendix 18: Required run time calculations for each of the periods in appendix 18
above
a) wk 1 - 11
SKU
Average weekly Demand
in cases (Acual)
Line 15 output in
Cases per hour
Run time in
hours
1542 17348.94 240.67 72.09
12754 10789.42 240.67 44.83
1502 9431.00 240.67 39.191524 3795.63 240.67 15.77
171.87Total Required run time (hours)
b) wk 12 - 16
SKU
Average weekly Demand
in cases (Acual)
Line 15 output in
Cases per hour
Run time in
hours
1542 27270.92 240.67 113.31
12754 16825.53 240.67 69.91
1502 9431.00 240.67 39.191524 5338.15 240.67 22.18
244.59Total Required run time (hours)
c) wk 17 - 20
SKU
Average weekly Demand in
cases (Acual)
Line 15 output in
Cases per hour
Run time in
hours
1542 17348.94 240.67 72.09
12754 16825.53 240.67 69.91
1502 9431.00 240.67 39.191524 5338.15 240.67 22.18
203.36Total Required run time (hours)
Copyright UCT
[MBA Thesis] Page 82
d) wk 21 - 25
SKU
Average weekly Demand
in cases (Acual)
Line 15 output in
Cases per hour
Run time in
hours
1542 27270.92 240.67 113.31
12754 16825.53 240.67 69.91
1502 9431.00 240.67 39.191524 5338.15 240.67 22.18
244.59Total Required run time (hours)
e) wk 26 - 28
SKU
Average weekly Demand in
cases (Acual)
Line 15 output in
Cases per hour
Run time in
hours
1542 27270.92 240.67 113.31
12754 16825.53 240.67 69.91
1502 15695.29 240.67 65.211524 5338.15 240.67 22.18
270.62Total Required run time (hours)
f) wk 29 - 32
SKU
Average weekly Demand in
cases (Acual)
Line 15 output in
Cases per hour
Run time in
hours
1542 17348.94 240.67 72.09
12754 10789.42 240.67 44.83
1502 15695.29 240.67 65.211524 5338.15 240.67 22.18
204.31Total Required run time (hours)
Copyright UCT
[MBA Thesis] Page 83
g) wk 33 - 36
SKU
Average weekly Demand in
cases (Acual)
Line 15 output in
Cases per hour
Run time in
hours
1542 17348.94 240.67 72.09
12754 10789.42 240.67 44.83
1502 9431.00 240.67 39.191524 5338.15 240.67 22.18
178.28Total Required run time (hours)
Copyright UCT
[MBA Thesis] Page 84
Appendix 19: The initial weekly and daily fixed cycle established for both lines 14 and
15
a) The initial weekly fixed cycle portion that would run on line 14
SKU
Average Total weekly
Demand in cases (Acual)
Weekly quantity
allocated to the line
Line 14 output in
Cases per hour Run time in hours
1542 17348.94 8368.10 224.25 37.32
12754 10789.42 5204.18 224.25 23.21
1502 9431.00 4548.96 224.25 20.291524 3795.63 1830.79 224.25 8.16
88.97Total Required run time (hours)
b) The initial weekly fixed cycle portion that would run on line 15
SKU
AverageTotal weekly
Demand in cases (Acual)
Weekly quantity
allocated to the line
Line 15 output in
Cases per hour Run time in hours
1542 17348.94 8980.83 240.67 37.32
12754 10789.42 5585.24 240.67 23.21
1502 9431.00 4882.04 240.67 20.291524 3795.63 1964.84 240.67 8.16
88.97Total Required run time (hours)
c) The green stream daily quantities and the required run time that would be done
on line 14
SKU
Average Total weekly
Demand in cases (Acual)
Weekly quantity
allocated to the line
Daily quantity
allocated to the line
Line 14 output in
Cases per hour
Run time in
hours
1542 17348.94 8368.10 1195.44 224.25 5.33
12754 10789.42 5204.18 743.45 224.25 3.321502 9431.00 4548.96 649.85 224.25 2.90
1524 3795.63 1830.79 261.54 224.25 1.17
12.71Total Required run time (hours)
d) The green stream daily quantities and the required run time that would be done
on line 15
SKU
Average Total weekly
Demand in cases (Acual)
Weekly quantity
allocated to the line
Daily quantity
allocated to the
line
Line 15 output in Cases
per hour Run time in hours
1542 17348.94 8980.83 1282.98 240.67 5.33
12754 10789.42 5585.24 797.89 240.67 3.321502 9431.00 4882.04 697.43 240.67 2.90
1524 3795.63 1964.84 280.69 240.67 1.17
12.71Total Required run time (hours)