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




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





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]

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http://ercweb.wch.org.au/qi/lhs2007/Day1/Session3/ian_glenday.pdf%20%20%5b15

http://www.leanuk.org/downloads/general/moving_to_flow.pdf%20%20%5b11




http://www.goldsim.com/Content.asp?PageID=91

http://www.lean.org.au/-public-workshops/-break-through-to-flow

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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].

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http://www.asq.org/learn-about-quality/project-planning-tools/overview/pdca-cycle.html [16

June 2009]

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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]

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[online] Available from: http://www.web.net/~robrien/papers/arfinal.html [31 May 2009]

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http://learnsigma.com/the-no-nonsense-guide-to-standardized-work/

http://www.asq.org/learn-about-quality/project-planning-tools/overview/pdca-cycle.html

http://learnsigma.com/quality-management-tools/

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http://www.socialresearchmethods.net/tutorial/Mugo/tutorial.htm

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Repetitive Flexible Supply, 2005, “Supply Chain Logic Issue; Buffer Tank” [online].

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from: http://www.niehs.nih.gov/research/resources/bioethics/whatis.cfm [31August 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)

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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


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


Last 1% 0.99% 9 30% 14.52% Red

Copyright UCT


8.4 Appendix 4: Sales data used for the sieve analysis and the categories

that resulted

a) Sales volume data

Copyright UCT


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


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


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


Weekly quantity


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


Copyright UCT


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 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


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


8.7 Appendix 7: The demand behaviour over the period between

January to August 2009

0

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Dem

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SKU 1542 Weekly Demand for the January to August 09 period (Unfiltered)

Actual demand Ave

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Copyright UCT


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Copyright UCT


8.8 Appendix 8: Simulation 1 graphs

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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

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Buffer range





For SKU 12754,



Copyright UCT


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For SKU 1502,



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For SKU 1524,



Copyright UCT



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Copyright UCT


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Copyright UCT



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Copyright UCT


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Copyright UCT



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Simultion 5: weekly Buffer levels and limits calculated from normalised demand data -SKU 1542

Weekly Buffer



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Simultion 5: weekly Buffer levels and limits calculated from normalised demand data - SKU 12754

Weekly Buffer



Copyright UCT


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Copyright UCT


8.12 Appendix 12: Learning Journal

Copyright UCT


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


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


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


8.13 Appendix 13: Graphs that resulted from filtering out spikes

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SKU 12754 Weekly Demand(Filtered)

12754 Ave

Copyright UCT


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Copyright UCT


8.14 Appendix 14: Demand range segments for each green stream SKU

across the 36 week period

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Copyright UCT


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Copyright UCT


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


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


Copyright UCT


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


Copyright UCT


Appendix 16: Segmentation of the observable demand seasons across the 36 week

period

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and

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Demand Level 1Demand Level 2Demand Level 1Demand Level 1 Demand Level 2

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Copyright UCT


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Demand Level 1 Demand Level 2 Demand Level 1

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Demand Level 1 Demand Level 2

Copyright UCT


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


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


b) wk 12 - 16

SKU


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


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


Copyright UCT


d) wk 21 - 25

SKU


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


e) wk 26 - 28

SKU


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


f) wk 29 - 32

SKU


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


Copyright UCT


g) wk 33 - 36

SKU


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


Copyright UCT


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



Weekly quantity


Line 14 output in


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


b) The initial weekly fixed cycle portion that would run on line 15

SKU

AverageTotal weekly


Weekly quantity


Line 15 output in


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


c) The green stream daily quantities and the required run time that would be done

on line 14

SKU



Weekly quantity


Daily quantity


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


d) The green stream daily quantities and the required run time that would be done

on line 15

SKU



Weekly quantity


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


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