Msc Thesis

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TITLE PAGE
BUSINESS PROCESS SIMULATION OF A
PRODUCTION LINE
By
Tony Ponsonby
A report submitted to the
Faculty of Science and Engineering
In partial fulfilment of the requirement for the degree of
Master of Science in Manufacturing with Management
Department of Engineering and Technology
August 2013

Tony Ponsonby Business Process Simulation of a Production Line
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DECLARATION OF ACADEMIC CONFORMITY
I certify that the material contained in this report is my own work and does not
contain significant portions of unreferenced or unacknowledged material. I also
warrant that the above statement applies to the implementation of the project and all
associated documentation.
In the case of electronically submitted work, I also consent to this work being stored
electronically and copied for assessment purposes, including the department’s use
of plagiarism detection systems in order to check the integrity of assessed work.
Name: Tony Ponsonby Signed:
Student ID: 02968984 Dated: 23/08/13

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PROCESS SIMULATION OF A PRODUCTION LINE
ABSTRACT
Thesis directed by Dr Muhammad Latif
This project by Tony Ponsonby, an MSc student in the Department of
Engineering and Technology, focuses upon a production line used to assemble a
range of wire rope hoists. The combination of make-to-order, manual assembly and
variations in size, design and assembly time, means each hoist to enter the
production line presents invariably different manufacturing implications. For this
reason, discrete event simulation is used to analyse the systems complex,
stochastic behaviour. The aim of this piece of work is to determine how the
production capacity can be maximised within the same production line space
constraints. The use of Witnesses Experimenter has provided a number of
optimised solutions within a range of parameters to minimise unnecessary delays
and optimise equipment levels. The solutions allow constraints to be reduced in
stages of financial investment and waste reduction which could be undertaken as
sales increase. The findings show, at 10% year-on-year growth the life of the
existing production line could be extended until May 2022, based on a combination
of financial investment and waste reduction. Given that output could increase from
1590 to 3890 hoists/year, it is dependent upon throughput efficiency increasing from
5.1% to 12.2%. In the event external constraints prevent the efficiency levels to be
surpassed, longevity of the production line could extend until November 2016, to
produce 2293 hoists/year. This analysis in addition to answering the aims and
objectives; provides an unbiased mechanism to assess scenarios before committing
resources, therefore, reducing risk and the potential to make poor decisions.

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ACKNOWLEDGEMENTS
Firstly I would like to thank my partner Erica for the continued support received
throughout this Masters degree.
I would also like to thank Dr Muhammad Latif for supervising this project and
providing advice where needed.
Also, I would like to thank Martin Street for sponsorship of this degree and Tony
Waller from the Lanner Group for providing a loan copy of Witness 12/13

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LIST OF CONTENTS
Title Page.....................................................................................................................i
Declaration of Academic Conformity......................................................................... ii
Abstract......................................................................................................................iii
Acknowledgements.................................................................................................. iv
List of Contents.........................................................................................................v
List Of figures............................................................................................................viii
List of Tables..............................................................................................................xi
List of Equations ...................................................................................................... xi
Glossary....................................................................................................................xii
Chapter 1 Introduction .......................................................................................1
1.1. Project Purpose..........................................................................2
1.2. Scope of Project.........................................................................2
1.3. Aims............................................................................................3
1.4. Objectives...................................................................................3
1.5. Background................................................................................4
Chapter 2 Literature Survey.................................................................................5
2.1. Search Tools Used.....................................................................5
2.2. Search Keywords.......................................................................5
2.3. Literature....................................................................................5
Chapter 3 Approaches and Methods Considered................................................11
3.1. Approach to Maximise Output..................................................11
3.2. Enterprise Resource Planning Systems...................................12
3.3. Spreadsheet Based Systems...................................................12
3.4. Continuous Simulation..............................................................13
3.5. Discrete Event Simulation........................................................13
3.6. Lean Tools................................................................................14
Chapter 4 Methodology......................................................................................16
4.1. Tools Used...............................................................................16
4.2. Data Collection.........................................................................16
4.3. Distributions..............................................................................17

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4.4. Model Structure........................................................................17
4.5. Hoists.......................................................................................19
4.6. Labour and Shift Pattern..........................................................21
4.7. Generator.................................................................................21
4.8. Build-Frames............................................................................22
4.9. Throughput Efficiency...............................................................24
4.10. Entity Feeding..........................................................................26
4.11. Variable Buffer Sizes................................................................26
4.12. Usable Cell Area.......................................................................27
4.13. Warm-Up Period.......................................................................28
4.14. Assumptions.............................................................................30
Chapter 5 Verification and Validation................................................................31
5.1. Verification................................................................................31
5.2. Validation..................................................................................32
Chapter 6 Experimentation and Optimisation....................................................35
6.1. Experiment 1 - Determine the Maximum
Output for the Existing Factory.................................................35
6.2. Experiment 2 - Optimise the Production
Line without Capital Investment................................................37
6.2.1. Part A: Optimise The Production Line
Configuration.....................................................................37
6.2.2. Part B - Quantify the Optimised Configuration..................40
6.3. Experiment 3 - Optimise the Production
Line with Capital Investment....................................................41
6.4. Experiment 4 - Check Optimum Configuration.........................46
6.5. Experiment 5 - Influence of Generator
Requirement on Output............................................................48
6.6. Experiment 6 - Effect of TE on
Lead-time, WIP & Arrival Rate.................................................50
Chapter 7 Discussion.........................................................................................53
Chapter 8 Future work.......................................................................................57

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8.1. Short-Term Capacity Planning.................................................57
8.2. Inventory Scenarios..................................................................57
8.3. Reduce Non-Value Activities....................................................58
Chapter 9 Conclusions .....................................................................................60
References................................................................................................................62
Bibliography..............................................................................................................66
Appendix 1- Sample data used to establish cycle times..........................................67
Appendix 2 - Street Crane Cycle Time Distribution .................................................68
Appendix 3 - Cycle-time Distributions .....................................................................69
Appendix 4 - Confidence Levels of Validation 2 ......................................................82
Appendix 5 - Zero Time Labour Bookings 2012 ......................................................83
Appendix 6 - Experiment 2 Top 40 Scenarios.........................................................84
Appendix 7 - Experiment 3 Results.........................................................................85
Appendix 8 - Iuniform Distributions Governing Hoist Attributes ...............................86
Appendix 9 - Hoist Size Look-Up Table ..................................................................87
Appendix 10 - Experiment 4 Results.......................................................................88
Appendix 11 - Health and Safety Assessment ........................................................89
Appendix 12 - Ethics Check Form...........................................................................90

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LIST OF FIGURES
Figure 1 - Sample Of Hoist Data Used Within A Spreadsheet.................................16
Figure 2 - Code To Input Theoretical Distributions..................................................17
Figure 3 - Flowchart Of Hoist Routing Through Production.....................................18
Figure 4 - Simulated Production Line......................................................................19
Figure 5 - Flowchart To Assign Hoist Attributes ......................................................20
Figure 6 - Code Defining The Type Hoist Variant Entering The Model ....................20
Figure 7 - Witness Coding To Determine The Hoist Size ........................................21
Figure 8 - Code To Push A Build-Frame Entity Into The Model At Zero Time .........23
Figure 9 - Code To Allow Hoists To Link To The Correct Build-Frame Queue ........23
Figure 10 - Output Condition For Buffers Feeding Cells1, 8 And 11........................24
Figure 11 - Sample Witness Code To Control TE ...................................................25
Figure 12 - Logic Governing Hoist Entry Into A Buffer.............................................26
Figure 13 - Layout Of The Production Line & Assembly Cell Sizes .........................27
Figure 14 - Coding To Determine The Usable Cell Area .........................................28
Figure 15 - Warm-Up Period Without Starting Conditions .......................................28
Figure 16 - Code To Inject Hoists Into The Model At Initialisation ...........................29
Figure 17 - Warm-Up Period With Starting Conditions ............................................30
Figure 18 - Model Showing Element Flow Lines To Verify Routings .......................31
Figure 19 - Coding To Prevent The Processing Of Unwanted Attributes.................32
Figure 20 - Confidence Levels For The Existing Production Line Capacity .............36
Figure 21 - Potential Life Of The Existing System...................................................36
Figure 22 - Confidence Levels For The Existing Production Line (No Delay) ..........37
Figure 23 - Production Line Balance Comparison...................................................39
Figure 24 - Experiment 2 Confidence In Capacity (No Forced Delay) .....................40
Figure 25 - Experiment 2 Confidence In Capacity (With Forced Delay)...................40
Figure 26 - Potential Life Of 1st
Optimised System..................................................41
Figure 27 - Experiment 3 Throughput Efficiency .....................................................42
Figure 28 - Experiment 3 Average WIP...................................................................43
Figure 29 - Experiment 3 Hoists / Year ...................................................................44
Figure 30 - Potential Life Of Experiment 3 ..............................................................45
Figure 31 - Experiment 4 Parameter Analysis.........................................................46
Figure 32 - Potential Life Of Experiment 4 ..............................................................48
Figure 33 - Potential For Generator Requirement To Grow With Sales...................49
Figure 34 – Output Vs Generator Requirement.......................................................50
Figure 35 - Effect of Throughput Efficiency on Little’s Law......................................51

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Figure 36 - Experiment Comparison (Dispatch QTY Vs Space Utilisation)..............53
Figure 37 - Experiment Comparison (Lead-Time Vs WIP) ......................................54
Figure 38 - Observed Delays ..................................................................................56
Figure 39 - Summary Sheet Of Distribution Data Sampling ....................................68
Figure 40 - PDF for all variants of ZX10 on End of Line Test ..................................69
Figure 41 - PDF for DT variants of ZX10 on Wire and Test.....................................69
Figure 42 - PDF for SS & ST variants of ZX10 on Wire and Test............................69
Figure 43 - PDF for DT variants of ZX10 on Assembly ...........................................70
Figure 44 - PDF for SS & ST variants of ZX10 on Assembly...................................70
Figure 45 - PDF for CRB & FM variants of ZX8 on Barrel Assembly.......................70
Figure 46 - PDF for LHR variants of ZX8 on Barrel Assembly.................................71
Figure 47 - PDF for CRB & FM 2/4 Fall variants of ZX8 on Frame Assembly..........71
Figure 48 - PDF for 6/8 Fall variants of ZX8 on Frame Assembly ...........................71
Figure 49 - PDF for LHR variants of ZX8 on Trolley Assembly................................72
Figure 50 - PDF for LHR variants of ZX8 on Trolley Assembly 2/3..........................72
Figure 51 - PDF for CRB 2/4 Fall variants of ZX8 on Cable Routing .......................72
Figure 52 - PDF for CRB 6/8 Fall variants of ZX8 on Cable Routing .......................73
Figure 53 - PDF for FM variants of ZX8 on Cable Routing......................................73
Figure 54 - PDF for LHR 2/4 Fall variants of ZX8 on Cable Routing........................73
Figure 55 - PDF for LHR 6/8 Fall variants of ZX8 on Cable Routing........................74
Figure 56 - PDF for all variants of ZX8 on Line Pull Test.........................................74
Figure 57 - PDF for all 2 fall variants of ZX8 on Rope-up........................................74
Figure 61 - PDF for all variants of ZX8 on End-of-Line Test....................................76
Figure 62 - PDF for all variants of ZX8 on Packing .................................................76
Figure 63 - PDF for CRB 2/4 fall variants of ZX8 on Crab Assembly.......................76
Figure 64 - PDF for LHR 6/8 fall variants of ZX8 on Crab Assembly .......................77
Figure 65 - PDF for All variants of ZX6 on Barrel Assembly....................................77
Figure 66 - PDF for All variants of ZX6 on Frame Assembly ...................................77
Figure 67 - PDF for LHR variants of ZX6 on Trolley Assembly 1.............................78
Figure 68 - PDF for LHR variants of ZX6 on Trolley Assembly2/3...........................78
Figure 69 - PDF for LHR variants of ZX6 on Cable Routing ....................................78
Figure 70 - PDF for CRB variants of ZX6 on Cable Routing....................................79
Figure 71 - PDF for FM variants of ZX6 on Cable Routing......................................79

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Figure 72 - PDF for all variants of ZX6 on Line Pull Test.........................................79
Figure 73 - PDF for all variants of ZX6 on Rope-up ................................................80
Figure 74 - PDF for all variants of ZX6 on End-of-Line Test....................................80
Figure 75 - PDF for all CRB variants of ZX6 on Crab Assembly..............................80
Figure 76 - PDF for all variants of ZX6 on packing..................................................81
Figure 77 - 2012 Zero time labour bookings............................................................83

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LIST OF TABLES
Table 1 - Product Throughput Efficiency.................................................................11
Table 2 - Labour Resources Used Within The Simulation.......................................21
Table 3 - Generator Requirement ...........................................................................22
Table 4 - Existing Build-Frame Types And Quantities .............................................22
Table 5 - 2010 to 2012 Output & TE .......................................................................24
Table 6 - Workstation Sizes / Cell ...........................................................................27
Table 7 - Average cycle times.................................................................................33
Table 8 - Actual 2012 Production Data To Validate Against....................................33
Table 9 - Comparison Of Actual To Simulated Products Built .................................34
Table 10 - Comparison Showing The Actual To Simulated Throughput Efficiency ..34
Table 11 - Comparison Of Actual To Simulated Labour Hours................................34
Table 12 - Existing Workstation Quantities / Production Cell...................................35
Table 13 - Experiment 2 Maximum / Minimum Workstation Parameters .................37
Table 14 - Experiment 2 Optimum Scenario ...........................................................38
Table 15 - Experiment 3 Maximum / Minimum Workstation Parameters .................42
Table 16 - Experiment 4 Parameters To Check ......................................................46
Table 17 - Experiment 4 Optimum Scenario ...........................................................47
Table 18 - Iuniform Data to Assign Hoist Model Attribute........................................86
Table 19 - Iuniform Data to Assign ZX6 Attributes ..................................................86
Table 20 - Iuniform Data to Assign ZX8 Attributes ..................................................86
Table 21 - Iuniform Data to Assign ZX10 Attributes ................................................86
LIST OF EQUATIONS
Equation 1 - Throughput Efficiency .......................................................................... 6
Equation 2 - Total non-value added time ................................................................. 6
Equation 3 - Little’s Law..........................................................................................11
Equation 4 - Forced Delay Time .............................................................................25
Equation 5 - Customer Demand..............................................................................39

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GLOSSARY
Autocad Computer aided design software used for engineering design
BPS Business Process Simulation
Cell 1 An inline production cell undertaking barrel assembly on ZX6 & 8 LHR
hoists
Cell 3 An inline production cell undertaking frame assembly on ZX6 & 8 LHR
hoists
Cell 4 An inline production cell undertaking trolley assembly on ZX6 & 8 LHR
hoists
Cell 6 An inline production cell undertaking cable routing on ZX6 & 8 hoists
Cell 7 An inline production cell undertaking line pull test (load test) and rope-
up on ZX6 & 8 hoists
Cell 8 An offline production cell assembling all 2/4 fall, ZX6 & 8 CRB & FM
hoists
Cell 9 An inline production line undertaking End-of-Line test (Inspection) on
all hoists
Cell 10 An inline production line undertaking packing and dispatch on all hoists
Cell 11 An offline production cell assembling all 6/8 fall, ZX8 and ZX10 hoists
CRB A Crab hoist available in all hoist models.
DT A variety of ZX10 hoist that has a double gearbox and true vertical lift
ERP Enterprise Resource Planning
Falls The number of strands of wire rope that connect the hoist to the hook
FIFO First In First Out
FM A Foot Mount hoist available in all hoist models.
LHR A Low Headroom hoist available in the ZX6 and ZX8 hoist models
Matflow Software used for the planning of materials flow
MRP Materials Resource Planning
PDF Probability Density Function
SolidEdge Computer aided design software used for engineering design

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SS A variety of ZX10 hoist that has a single gearbox and a single rope
ST A variety of ZX10 hoist that has a single gearbox and true vertical lift
TE Throughput Efficiency
WIP Work In Progress
Witness Discrete event simulation software developed by the Lanner Group
ZX6 The smallest hoist variant in the range of pre-engineered electric wire
rope hoist with a capacity that ranges from 0.5 to 6.3 tonnes
ZX8 The middle hoist variant in the range of pre-engineered electric wire
rope hoist with a capacity that ranges from 2 to 25 tonnes
ZX10 The largest hoist variant in the range of pre-engineered electric wire
rope hoist with a capacity that ranges from 5 to 50 tonnes

Tony Ponsonby Process Simulation of a Production Line
Chapter 1 Introduction
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CHAPTER 1 INTRODUCTION
StreetCrane Company Ltd is a rapidly growing business specialising in the
manufacture and supply of electrified overhead travelling cranes and wire rope
hoists. With around 60% of the business income generated from the sale of hoist
units, the company aims to develop this market further using a strategic reduction of
selling prices to boost sales volume to an anticipated 10% year on year growth.
Whilst beneficial to the company, this growth potential has led to the speculation by
top management that only by relocating to larger premises will the existing
production levels be surpassed and higher throughput sustained. This notion has led
the board of directors to seek planning permission for a larger building
(highpeak.gov, 2012) to relocate the hoist production line.
The concept that more space allows more products to be produced over a
specified time-frame seems a reasonable assumption. Yet to date, the company has
no mechanism to quantify what the maximum capacity of the existing hoist
production line is, or how elements other than labour constrain the capacity.
Although the company does operate an Enterprise Resource Planning system for
master production scheduling and capacity planning (Sanderson.com, 2012); only
resource and time availability is seen to restrict capacity (Kim & Kim, 2001). This
notion stems from capacity, calculated as the percentage of hours required to
assemble products to the actual labour hours available within the same timeframe.
If 100% capacity is exceeded, only increased levels of overtime, additional labour or
extended lead-times will allow products to be completed within the designated
timeframe.
While the ERP system plays a crucial planning role, the system does not
consider parameters such as the available area, product size, Work-In-Progress
(Bolden et al. 1997) and variation in cycle-times which also influence capacity
(Byrne & Bakir, 1999). Simply by adding more resource availability, the ERP system

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will increase output accordingly without limit. As such, it cannot determine what the
maximum capacity of the existing site is, or when factory expansion should begin;
specifically when influences other than time or resources constrain the output (Kim
& Kim, 2001).
A practical solution could reside with building like products in batches, thereby
minimising the impact of the other parameters and making maximum capacity
determinable under known operating conditions. Unfortunately, the company
operates a make-to-order system (Kumar, 2007) to customer specification. This can
result in each product to enter the production line varying considerably in design or
size to the last. This variation can cause buffer holding capacities and cycle-times to
fluctuate from one product to the next, resulting in imbalances in flow which leads to
bottlenecks and reduced throughput efficiency (Sweeney & Szwejczewski, 1996).
1.1. Project Purpose
Given the desire to increase production volumes, the purpose of this project is
to present a case to maximise the life of the existing production facilities by
identifying ways to become more efficient to increase the maximum capacity. In
doing so, to reduce the risk of uncertainty by simulating probable outcomes to
ensure the right changes are made, at the right time, using data in the absence of
personal bias.
1.2. Scope of Project
Whilst Street Crane Ltd manufacture a wide range of lifting products, the
scope of this project will reside in the assembly of higher volume pre-engineered
wire rope hoists only. Specifically, the analysis will focus upon ZX6, ZX8 and ZX10
hoist models, subdivided further by barrel length, lifting format, and the number of
rope falls from the barrel. To simplify the analysis, the possible product

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configurations are limited to 78. These ensure the impact on the production line is
assessed on the basis of product size, type and cycle-time variations.
The simulation for this piece of work does not use any data to assess the
impact on production capacity from stock outs, the manufacture of sub-assembled
components or the need for more parts storage space when production volumes
increase. Other influences on capacity from rework or operator inefficiency, while
not formally identified or recorded, are assumed to be part of the empirical cycle
times. The use of distribution curves allow for instances where work duration has
deviated from the average, however it is not possible to quantify the effect these
instances have on the overall cycle-time in isolation.
1.3. Aims
The aim of this project is to determine how and by how much, Street Crane
Ltd’s hoist production can be increased within a set of operating conditions, in the
confines of existing production areas. The completion of this aim will extend the life
of the existing production facility and answer investment questions; at what time
should the business buy new equipment or relocate the production line to increase
production levels further?
1.4. Objectives
 To construct a Witness simulation which accurately represents the ZX hoist
production line
 To use the simulation to quantify the maximum production capacity of the
existing production line
 Through experimentation, to quantify how much the capacity could be
increased;
o With changes which do not require high time/cost investment
o With changes which require high time/cost investments

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 To determine how product-mix can alter capacity constraints
All simulations will focus upon cycle-times, WIP, throughput, cell size, product size
and mix to recreate the existing production line.
1.5. Background
Globalisation and competitor pressure are increasingly influencing businesses
to engage in process improvement through changes in the way a business operates
to improve efficiency and competitor advantage, while reducing costs (Hlupic &
Robinson, 1998). Moreover, whilst the motives to change maybe beneficial, the act
of making the change is not guaranteed success (Hammer & Champy, 2009). In
aid, tools such as flowcharts or work flow diagrams are available to analyse and
base decisions upon; yet in reality these tools fall short where processes are
dynamic, stochastic and usually complex (Aguilar-Saven, 2004).
In view of this, Business Process Simulations are employed to improve the
success rate of business process change projects; in part, achieved by enabling
greater understanding of the complex systems with interdependencies (Greasley,
2003). Simulation enables the creation of a virtual model to mimic a real world
system (Gogg & Mott, 1993). Through experimentation, the cause and effect from
specific modifications can be assessed prior to committing actual resources to the
project (Greasley, 2003). With this in mind, BPS allows decision makers to base
judgement on predictions that specific events will occur as opposed to one’s own
personal bias (Hlupic & Robinson, 1998).

Chapter 2 Literature Survey
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CHAPTER 2 LITERATURE SURVEY
2.1. Search Tools Used
Http://scholar.google.co.uk/
Http://metalib5.hosted.exlibrisgroup.com
Http://www.google.co.uk/
2.2. Search Keywords
Witness simulation, discrete event simulation, make to order, continuous simulation,
lean metrics, throughput efficiency, simulation of a production line, work in progress,
business process simulation.
2.3. Literature
In a real world scenario, where changes to business processes justify the use
of simulation, undoubtedly will involve stakeholders from various areas of the
business. Whilst an appreciation of simulation is useful, many stakeholders will have
little or no knowledge of BPS (Gogg & Mott, 1993). With this in mind, simulation
packages with a graphical user interface are more able to engage with stakeholders,
by facilitating an easier understanding of model behaviour without the need for
technical simulation knowledge (Greasley, 2003). Stakeholder interaction is
essential to get the most from the simulation. This communication facilitates the
development and verification of the simulation while enhancing the credibility of
results (Gogg & Mott, 1993).
Given the objective to determine the existing production line’s capacity and
how it could be improved, it does not indicate how efficient the existing or improved
systems are. Using performance measurements to gauge the existing system, the
“as is” state can then be benchmarked against experimental improvements (Mathur,
et al., 2011). The performance metric “Throughput Efficiency” is particularly relevant
due to the impact large products and often lengthy assembly times impose onto

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build cell areas, which influence the amount of products that can be built at any point
in time.
This metric has several derivations including the ratio of actual output to
theoretical output in terms of production units (Muthiah & Huang, 2007) and the ratio
of value added time to the total manufacturing time shown in Equation 1 (Sweeney
& Szwejczewski, 1996). Although related, the later derivative is useful to find the
non-value added time that products will eventually spend in buffers shown in
Equation 2. This metric is particularly apt when considering by what method other
than the number of hoists shipped does the simulation reflect real world.
Equation 1 - Throughput Efficiency (Sweeney & Szwejczewski, 1996)
Equation 2 - Total non-value added time
otal non alue a e ti e otal t ou ut ti e otal alue a e ti e
Using TE will allow comparisons of the existing production output and
efficiency performance to an overall optimum, but also to the performance of an
optimum system which fails to improve efficiency. As such, improvements driven
from waste reduction are quantified yet require solutions, in addition to those found
using the simulation. The level of WIP and floor space required are other lean
metrics which relate to this project to quantify the existing and experimental states
(Andreeva, 2012).
In part, this project will focus on the effect product and buffer sizes have on
the output. The work by Markt & Mayer (1997) has a similar objective, however the
authors use a combination of tools and techniques to undertake tasks where this
project aims to undertake in Witness alone. More precisely Markt & Mayer (1997)
are using Autocad to generate a layout, Matflow to optimise the routing of parts and
to calculate floor space requirements, and Witness to develop a simulation. This

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project uses SolidEdge to create a layout and Witness alone for all simulation
activities. The date when Markt & Mayer undertook this work may explain the use of
other software.
Qu et al, (2001) have also undertaken a comparable project, using simulation
to ascertain how to best design a new production line. They focus upon the cost
implications of tooling and operator requirements and various shift patterns to
determine what pattern works best over a 5-year period with varying levels of
product demand. Their dynamic analysis of costings over this duration could be
applied to this project, however financial information has been unavailable.
Tjahjono & Fernández (2008) present a practical guide to simulation based on
an engine assembly line. Whilst all approaches are relevant, in particular the
analysis of bottlenecks and recommended modelling techniques, it is the validation
study on initial setup that is particularly intriguing. They have used the daily
production output to illustrate graphically the warm up period for the model to reach
steady state and maintain a consistent output per day. Likewise, Mahajan & Ingalls
(2004) focus their work exclusively on methods used to obtain the warmup period.
The authors present a variety of graphical, statistical and heuristic tools to calculate
the warm-up duration to ensure that initialisation bias does not form part of the
results. Witness itself does offer an alternative to reduce the initialisation bias by
importing data from an initialise status file (Lanner, 2012). This has the effect of
starting the simulation as though the model has already run; entities and resources
can be positioned while activities or queues can be set to predefined states that
would not be achieved until reaching a steady state. Regardless of the best method;
Tjahjono & Fernández, (2008) show that initial output must be scrutinised to
understand at what point the output becomes accurate to a specified level before
results can be measured.

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The work by Mehta (2000) provides an exceptionally structured approach to
simulation; as such, the tasks within this project are based upon this work. The
succinct best practice methodology highlights both basic and advanced tools to
enhance the effectiveness of a simulation, in essence providing a reference to guide
this project. The recommended use of Excel to provide input data will be one of the
insights incorporated into this project, allowing the simulation to remain flexible
enabling changes without time consuming modifications to the simulation coding.
To provide credibility in the simulation, the results will need to be analysed to
determine if they are sensible and representative of the real world. The work by
Sargent (2009) provides many techniques that could be used to verify and validate
the simulation. In particular is the use of historical data and sales forecasts to
compare the simulation data against for validation. Cuatrecasas-Arbos et al (2011)
offers a checking process that is undertaken at various simulation stages that can
be applied to this project, including; data validation, conceptual model validation,
simulation model verification and operational validation.
Data validation, being the process of ensuring that the information inputted
into the simulation is correct; in large, falls outside the focus of this project. Data
used will be from the historical record, and whilst the integrity could be affected by
those providing and processing the information, for this study it must be assumed to
be correct. Conceptual model validation focuses on the validity of the underlying
principles that will be used to construct the simulation model. In the project, this will
be undertaken through discussions with the company, observation of the production
line and sampling historical data records. These findings will be compared to a
flowchart to verify the routing logic is correct. Simulation model verification entails
checking the computerised model structure against the conceptual model to ensure
the underlying principles and logic have been applied correctly. In practice, this will
be undertaken in various stages throughout model construction using a controlled

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simulation run to assess for more subtle anomalies rather than after the model is
built; making errors easier to find. Operational validation involves checking that the
simulation produces results that are consistent with real world performance. This
requires the simulation to be replicated a number of times to judge the simulation
output against actual historical output. Whilst it is not expected that the simulation
will exactly match real world, it should be reasonably accurate and repeatable to
provide a reliable source to base decisions.
Considering the random nature of the system there can be no one exact
answer, all experiments should be run over a number of replications so that result
confidence levels are determined (Lanner, 2012). The use of the Witness 13
Experimenter allows the random number stream per scenario replication to change.
Therefore, results are predictable within a upper and lower range.
The quick reference book by Lanner (2012) provides concise descriptions of
modelling actions, attributes, functions, rules, states and simulation features which is
invaluable to aid construction of the Witness simulation and also a point of reference
for technical modelling queries. Additionally, the Lanner.com, (2012) website
provides numerous articles showcasing how Witness has been applied within
industry. These are both inspirational and insightful; providing a background of what
is possible to maximise the project potential.
The papers by Gogg & Mott (1993), Greasley (2003) and Hlupic & Robinson
(1998), whilst dated, do provide a compelling overview of business process
simulation. Although these papers are not suitable to answer technical simulation
queries, they do offer a rationale for the use of simulation which is quite appealing.
In particular; why simulation is used and the importance of financial justification to
support the simulation findings, which has not changed from the time of publication.

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The study by Host, et al (2001) into bottlenecks, outlines the influences a
changing market has on buffers levels. Even though the products between the
authors paper and to this thesis differ, in essence the problems faced are the same.
A drawback in this work is the assumption all resources are of the same
competency. In reality where there are variations in the product-mix (Al-Aomar,
2000), some resources will be more competent than others resulting in bottlenecks
where skills are in short supply.
To enable the simulation to accurately reflect process variation, some data
sampling is required to draw a statistical picture of how the process varies
(Greasley, 2003). Whilst mean values could be used in substitution for theoretical or
empirical distributions, the effects may not always be the same. Law (2007)
demonstrates the differences this can make on a simple queuing system. The use of
mean values to represent the flow into a queue ensures that a bottleneck does not
occur, yet conversely using a distribution causes a queue to backup.
Using theoretical distributions instead of empirical data has the advantage of
providing a complete view of the system, gaps in data are filled in, and irregularities
smoothed out (Gogg & Mott, 1993). To enable the use of theoretical distributions,
statistical analysis must be undertaken to obtain a distribution type that matches the
empirical data best (Greasley, 2003). Unfortunately, Witness does not provide this
feature; it is however available in other simulation packages such as the Input
Analyser module within Arena (Kelton et al. 2002). Instead of using a simulation with
this feature, various standalone statistical analysis tools are available (Law, 2007).
This project uses the Excel add-on module EasyFitXL by MathWave for data
analysis. Albeit not part of the simulation, it does present the advantage of working
inside excel which is used to store all data.

Chapter 3 Approaches and Methods Considered
11
CHAPTER 3 APPROACHES AND METHODS CONSIDERED
Undoubtedly, a project such as this could be undertaken using a range of
approaches, tools and techniques. This section is intended to review some of the
potential ways and present an argument for those chosen to undertake this project.
3.1. Approach to Maximise Output
Referring back to the argument that more floor space could allow more
products to be built, does hold true considering Little’s Law (Equation 3) (Little &
Graves, 2008).
Equation 3 - Little’s Law
Where
L = Average number of items in the queuing system
λ = Average number of items arriving per unit time
W = Average waiting time in the system per item
This idea also appears logical considering that throughput efficiency has remained
fairly consistent over the past 3 years (Table 1). As such, if the average waiting time
remains constant, only by increasing the number of items queuing (WIP) will enable
more products to enter the system. The overarching factor governing the amount of
WIP stored is the available production line space, leading to the conclusion that a
larger area is required.
Table 1 - Product Throughput Efficiency
Whilst investment into larger premises is an option it is not the only approach
available to increase output. If constraints governing the average waiting time are
reduced, WIP could remain fixed whilst output is increased. It is this approach which
Average of
Throughput
Efficiency 2010 2011 2012
ZX10 - 5.4 6.4
ZX6 5.1 4.5 5.1
ZX8 5.0 5.6 5.5
Total 5.1 5.0 5.3

12
is adopted for this project, by targeting constraints which cause unnecessary waiting
time thereby preventing other products from entering the production line. Success
using this approach will extend the life of the existing production site by producing
more hoists from the same overall area.
3.2. Enterprise Resource Planning Systems
ERP systems are used within businesses to undertake a wide variety of tasks
from sales to dispatch (Sanderson.com, 2012). Specifically relating to this project, is
the ability to determine capacity plans. The use of linear programming algorithms in
capacity planning and scheduling, allow an optimised model to be attained to reduce
production costs by manipulating capacity and inventory constraints (Kim & Kim,
2001). Although widely used commercially for capacity planning, limitations for this
option become apparent when the system exhibits queuing and is non deterministic
(Byrne & Bakir, 1999). Whilst ERP systems consider costs, processing times,
available labour or machine capacity, demand and stock (Kim & Kim, 2001), they
on’t take into account product size and variation in cycle-times which also influence
capacity. Both Byrne & Bakir (1999) and Kim & Kim (2001) exceed this fact and
have developed hybrid capacity planning tools using simulations to support the ERP
shortcomings. Unlike this project, the authors do not consider physical size as a
restriction.
3.3. Spreadsheet Based Systems
Cuatrecasas-Arbos et al (2011) developed an operations time chart within a
spreadsheet, to evaluate a production environment in much the same way as a
discrete event simulation package, such as Witness. Given that this method
employs a graphical interface, the presentation differs significantly to a simulation
package with results shown chronologically, similar to a Gantt chart. Conceding this
fact, it can simulate an MRP pull system and measure metrics relevant to this
project including efficiencies, WIP, process and queue times. Unfortunately, this

13
method is a static analysis tool and is unable to process a stochastic input typical of
a make to order environment or a complex production system.
3.4. Continuous Simulation
Continuous simulation, in contrast to discrete event simulation allows inputs to
change continuously with respect to time (Özgün & Barlas, 2009). This is ideally
suited to determine the velocity fluids at any point in time within continuous systems
such as a river or processing plant. Other differences include the method to change
state, occurring at a specified time as opposed to a discrete event and output rule,
operating with a First In First Out basis only (Extendsim.com, 2012).
The research by Özgün & Barlas (2009) compares both approaches by
simulating a crowd control using a simple queuing system. While both systems
could be applied to this problem, the authors note that input rates need to be
significantly high for the continuous simulation to obtain accurate results.
Additionally, where the continuous system is more able to illustrate system
dynamics, it is not so able to make statistical predictions. Considering hoist
production is relatively slow, has output rules which deviate from FIFO, and requires
statistical predictions for analysis, continuous simulation is rendered unsuitable.
3.5. Discrete Event Simulation
Discrete event simulation is a dynamic tool which allows inputs to change at
discrete points in time, ideally suited to simulate stochastic models. Unlike
continuous simulation, discrete event simulation is ideally suited to processes which
involve queuing (Özgün & Barlas, 2009). The simulation package Witness, provides
the ability to model dynamic processes typical of a production line (Lanner.com,
2012). Parameters which influence model performance such as resources,
stoppages, shift patterns or logical routing of entities which are difficult and time
consuming to calculate by other methods, can be readily inputted into the simulation

14
and results displayed on screen (Lanner.com, 2012). Additonally, the use of a
simulation package employing a graphical user interface, provides the suitable
platform to facilitate stakeholder interaction and understanding, while reducing the
risk of misinterpreting results (Greasley, 2003).
It is for these reasons a dynamic simulation will be used to model and analyse
the operation of a production line at StreetCrane. Granted that other discrete event
simulation packages are available, Witness has been used due to availability within
Manchester Metropolitan University for use on this project.
3.6. Lean Tools
Lean methodology could be adopted to improve process performance through
the use of tools designed to eliminate waste to maximise profitability and throughput
(Hicks, 2007). Unlike other systems, lean can be viewed as a way of thinking as
opposed to just a tool for continual improvement. By successfully embedding lean
into an organisation, a culture driven towards process improvement and waste
reduction can develop throughout the workforce (Angelis, et al., 2011). This
presents a possible advantage over alternative solutions; via the potential to
integrate far more people into the improvement process. Furthermore, lean is
documented in many papers and is able to be applied to any system and utilises a
range of techniques geared towards process improvement (Hicks, 2007). Unlike
simulation, lean is not able to analyse and compare stochastic or variable systems,
nor is it able to validate possible solutions prior to execution (Standridge & Marvel,
2006). Whilst not being the focus of this work, it is likely some other tools, lean or
otherwise will be required in addition to simulation, when;
 Insufficient data is available which could be gained using value stream,
process flow or time value mapping (Melton, 2005)

15
 The simulation can quantify a constraint but cannot identify the root-cause
which could be gained using Pareto analysis or the 5 W y’s (Johnson, et al.,
2010)
 Process improvement can be achieved using practical solution such as 5S
(Chapman, 2005)

Chapter 4 Methodology
16
CHAPTER 4 METHODOLOGY
The methodology for this project assumes the reader has some knowledge of
Witness, including basic features and coding language.
4.1. Tools Used
ZXHoistProd Report (Specific to StreetCrane Ltd)
Microsoft Excel
EasyFitXL
Witness 12
Witness 13 including Experimenter Module
4.2. Data Collection
Data used for this project has been gathered ia t e co any’s o uction
operatives over a 3 year period; each specifying the time to complete the operation,
the type of operation undertaken and contract number. It is this information, which is
stored inside t e co any’s ZXHoistProd management Report (Appendix 1) and
used to extract 3-years ZX6 & ZX8 / 2-years ZX10 production data to build the
Witness simulation. Using Excel, this information is input into a spreadsheet
(Hoist.xls) and manipulated to show 1 record per row, with relevant information
placed into defined cells along the row (Figure 1). Pivot tables are used to ascertain
cycle times, throughput efficiency and the percentage each design variant
contributes to the product-mix.
Figure 1 - Sample Of Hoist Data Used Within A Spreadsheet

17
4.3. Distributions
To represent the probable duration hoists spend in each production cell, a
distribution function is used at each activity, to specify the cycle time and allow the
simulation to behave dynamically. Prior to statistical analysis; suspect or erroneous
data such as zero labour hours have been filtered from the empirical cycle times, to
ensure distributions are not distorted. Using EasyFitXL for statistical analysis, 37
theoretical continuous distributions (Appendix 3) have been obtained from the
empirical data. These describe the product and process groups listed in Appendix 2.
To select the closest matching distribution, grading has been made using EasyFitXL
and the Kolmogorov-Smirnov test.
Of the distributions found, none are standard types being incorporated within
Witness. This is overcome by importing a distribution probability density function for
all time increments within an upper and lower limit, into a Witness real distribution at
model initialisation (Figure 2). This method allows any distribution, empirical or
theoretical to be used within the model.
Figure 2 - Code To Input Theoretical Distributions
4.4. Model Structure
Mehta (2000) recommends a map of the existing system should be created to
help fully understand what is to be modelled prior to building the simulation. In
response to this advice, a flow chart (Figure 3) has been developed through direct
observation of the production line and communication with the company. This chart
defines the logic to route hoists through the production facilities based upon the
design of product entering.

18
Figure 3 - Flowchart Of Hoist Routing Through Production
This information has been used to construct a Witness simulation to represent
the production line shown in Figure 4. Whilst the actual production line could deviate
from these routings in exceptional circumstances, the logic defined in Figure 3
dictates the routing of entities inside the simulation without exception.
Hoist Type
Cell 1
Barrel Assembly
Cell 3
Frame Assembly
Cell 4
Trolley
Assembly 1&2
Cell 6 Cable
Routing
Cell 7 Load Test
& Rope Up
Cell 8 CRB / FM
Assembly
Cell 9
End-of-Line
Test
Cell 10
Packing
Cell 11.1
Mechanical
Assembly
Cell 11.3 Cable
Route &Test
Hoist Type
All ZX10 HoistsAll ZX10 Hoists
All ZX8 HoistsAll ZX8 Hoists
ZX86 , 88
OR 10
ZX86 , 88
OR 10
Hoist Type
Any 2or4
Fall CRB
Hoist
Any 2or4
Fall CRB
Hoist
LHR & FM
Hoists
LHR & FM
Hoists
Any 2 or 4 Fall
ZX6 or 8 LHR
Hoist Model
Any 2 or 4 Fall
ZX6 or 8 LHR
Hoist Model
Cell 11.4
Crab
Assembly
OutOut
Any 2 or 4 Fall
ZX6/8 FM / CRB
Hoist Model
Any 2 or 4 Fall
ZX6/8 FM / CRB
Hoist Model
Cell 8
Fit Crab
Frame
to Hoist
Any 6or8
Fall CRB
Hoist
Any 6or8
Fall CRB
Hoist
Order In
In If Hoist
is a ZX8
Model
In If Hoist
is a ZX8
Model
BuildFrame
From Store
InIn
BuildFrame
From Store
OutOut
BuildFrame
To Store
Despatch
Hoist
InIn
Generator used
if hoist is a Non-UK
mains supply
Generator used
if hoist is a Non-UK
mains supply

19
Figure 4 - Simulated Production Line
4.5. Hoists
Upon creation, the hoist entity is assigned a set of attributes (Figure 5) based
upon the probability certain features are likely to occur. Whilst only one entity type is
used, these attributes ensure all 78 hoist combinations are represented. Using 4
Iuniform distributions, the likelihood of characteristics including design type, size,
routing and processing duration per cell are allocated to the hoist by randomly
selecting a number between 1 and;
 The number of hoists produced in the past 12 months
 The number of ZX6 hoists produced in the past 3 years

20
Figure 5 - Flowchart To Assign Hoist Attributes
The numbers selected are used in a logic statement (Figure 6), to cross reference
against Table 18, Table 19, Table 20 and Table 21 (Appendix 8) to assign the
attributes which characterise the hoist entity.
Figure 6 - Code Defining The Type Hoist Variant Entering The Model
Hoist Entity
Arrives
Iuniform distribution
determines the
hoist model
determines the hoist type,
number of falls &
the barrel length
Hoist entity created
having 18 possible
attribute combinations
determines the hoist type,
number of falls &
the barrel length
determines the hoist
type, configuration
& barrel length
"ZX8" Hoist Model
Attribute Assigned
"ZX8" Hoist Model
Attribute Assigned
"ZX10" Hoist Model
Attribute Assigned
"ZX10" Hoist Model
Attribute Assigned
"ZX6" Hoist Model
Attribute Assigned
"ZX6" Hoist Model
Attribute Assigned
Hoist Type,
Hoist Falls &
BarrelType
Attribute Assigned
Hoist Type,
Hoist Falls &
BarrelType
Attribute Assigned
Hoist Type,
Hoist Falls &
BarrelType
Attribute Assigned
Hoist Type,
Hoist Falls &
BarrelType
Attribute Assigned
Hoist Type,
Hoist Configuration &
BarrelType
Attribute Assigned
Hoist Type,
Hoist Configuration &
BarrelType
Attribute Assigned
having 36 possible
having 24 possible

21
Using coding in Figure 7, a numeric value quantifying the size of the hoist is
assigned by cross referencing the attribute combination in Figure 5 to the look-up
table in Appendix 9. Using this method ensures that hoists entering the model
represent the actual products entering the production line.
Figure 7 - Witness Coding To Determine The Hoist Size
4.6. Labour and Shift Pattern
Due to the manual assembly process, labour resources are used at every cell
at the ratio of 1 per workstation. These resources have been grouped into five
categories as shown in Table 2.
Table 2 - Labour Resources Used Within The Simulation
The shift pattern used in the simulation allows resources to operate 45 hours per
week including 8 hours Monday to Friday and 5 Hours on Saturday morning. This
represents the total number of hours normally worked by an employee on the day
shift, excluding breaks.
4.7. Generator
The generator resource provides power to operate all non-UK electrical supply
hoists in Cell7 (Rope-up and Line pull test) and Cell9 (End-of-Line test) for the
duration of the activity (Figure 3). It is assumed product type has no bearing on the
required power supply. As such, each hoist to enter the model is assigned a
Labour Type
Existing
Quantity
Operations Performed Cell Used In
Mechanical 9 All Except Cable Routing, Packing & EoL Test 1,3,4,7,8,11
Electrical 3 Cable Routing 6,11
Despatch 2 Packing 10
Test 2 End of Line Test 9
Mech_Elect 1 All Except Packing & EoL Test 1,2,3,4,6,7,8,11

22
voltage/frequency attribute corresponding to the % split obtained from historical data
in Table 3.
Table 3 - Generator Requirement
4.8. Build-Frames
A Build-Frame is used to assemble every ZX6 and ZX8 hoist model, at the
ratio of 1 per hoist. These are attached to the hoist at the end of barrel assembly in
Cell1, Cell8 or Cell11 and remain so until removal at the packing stage in Cell10
(Figure 3). It is assumed the time to fit or remove these items is part of the barrel
assembly and packing distribution duration.
In total, eight types of build-frame are available in quantities as per Table 4.
This quantity will remain unchanged for this project to help control WIP levels. It is
recognised, increased production levels or variation in the production mix may
cause insufficient availability, causing delays for hoists to enter into the production
line, however this will not form part of this analysis.
Table 4 - Existing Build-Frame Types And Quantities
Voltage Frequency % Split
Generator
Requirement
380 50 2.10 Yes
400 50 75.94 No
230 60 0.03 Yes
380 60 2.13 Yes
440 60 0.03 Yes
460 60 11.43 Yes
575 60 8.35 Yes
Grand Total 100
Model
Design
Type
Number
of Falls
ZX6 LH ZX6 LHR ALL 35
ZX6 FM ZX6 FM ALL 6
ZX6 CRB ZX6 CRB ALL 8
ZX8 LH 2/4Fall ZX8 LHR 2 & 4 16
ZX8 FM ZX8 FM ALL 5
ZX8 CRB 2/4Fall ZX8 CRB 2 & 4 9
ZX8 CRB 6/8Fall ZX8 CRB 6 & 8 11
ZX8 LH 6/8Fall ZX8 LHR 6 & 8 2
92Total
BuildFrame
Simulation
Name
Hoists The BuildFames
are Designed to Fit
Qty

23
Whilst each build-frame must be attached to the correct hoist (Table 4), only one
entity is used to represent all build-frame types. This reduces the number of entities,
allows build-frame pre-emption and simplifies entry into and out of the model. At
time zero, build-frame entities are pushed into eight queues (Figure 8).
Figure 8 - Code To Push A Build-Frame Entity Into The Model At Zero Time
To ensure build-frames enter and exit the same queue, each hoist is assigned an
attribute at creation with the name of the queue that contains the required build-
frame (Figure 8).
Figure 9 - Code To Allow Hoists To Link To The Correct Build-Frame Queue

24
This attribute is used to push and pull build-frames to and from the correct queue,
and to ensure hoists only enter barrel assembly if the corresponding build-frame is
available (Figure 10). Whilst pre-emption of build-frame availability prior to starting
work may not always be realistic, it does prevent blockages inside the simulation.
Figure 10 - Output Condition For Buffers Feeding Cells1, 8 And 11
4.9. Throughput Efficiency
Throughput efficiency is used within the simulation to benchmark against the
existing production line (Equation 1). Where the total value added time is the
number of labour hours booked to the hoist, and the total throughput time is the
entire duration from start to the completion date.
TE for hoists produced in 2012 (Table 5) indicate a large proportion of the total
throughput time is the total non-value added time (Equation 2).
Table 5 - 2010 to 2012 Output & TE
Hoists
Produced
Average
ThroughPut
Efficiency
Hoists
Produced
Average
ThroughPut
Efficiency
Hoists
Produced
Average
ThroughPut
Efficiency
ZX10 1 3.7 61 5.4 60 6.4
ZX6 614 5.1 749 4.5 863 5.1
ZX8 459 5.0 532 5.6 667 5.5
Total 1074 5.1 1342 5.0 1590 5.3
2010 2011 2012
Model

25
Through assessment of the simulation, it is evident the duration an entity
spends inside the production line as WIP, waiting to enter next activity or off-shift,
does not account for the total non-value added time. As such, forced delays
(Equation 4) have been used to allow the simulation to benchmark throughput
efficiency levels recorded in 2012.
Equation 4 - Forced Delay Time
o ce elay ti e otal non alue a e ti e otal ueuin ti e otal ff ift ti e
The additional delay could stem from any one of the seven deadly wastes of
lean manufacturing (Melton, 2005) (Ramaswamy, et al., 2002). Yet for this
simulation, no data has been available to indicate the causes, or where in the
production process is affected most. For this reason, it is assumed delays occur
equally across the production line. The simulation seeks to maintain TE through the
use of an output rule, used to control the exit from a buffer at each production cell
(Figure 11). Only if an entities TE is less than or equal to 2012 TE will the entity be
able to leave the buffer.
Figure 11 - Sample Witness Code To Control TE
To enable this method, three attributes are assigned to every hoist which define;
 The historic Throughput Efficiency
 The cumulative value added time
 The time spent waiting to enter the production line.

26
This approach will maintain 2012 TE levels, with the exception of some system
configurations which exhibit significantly large queuing, causing the simulated TE to
fall below the historical value.
4.10. Entity Feeding
The hoist arrival rates are set sufficiently high for all scenarios to ensure the
output is not constrained. To mimic the release of works instructions into the
production line, hoist entities are released in batches of 10. Like production
management, this allows the simulation to select from the batch which entity can
enter the production line.
4.11. Variable Buffer Sizes
Through observation, it is apparent the maximum hoist storage capacity of each cell
will vary depending on the area available within the cell and the size of products
inside the cell. The simulation governs product entry into a cell using the logic
controlling size in Figure 12.
Figure 12 - Logic Governing Hoist Entry Into A Buffer
Unfortunately, this entry logic does not account for gaps between products. As such,
more hoists potentially could be stored inside the simulated area than is realistic. An
alternative method could be constructed using an array of dummy activities to
represent the usable area. Using product length and width attributes to represent the
hoists size, the entry into each dummy activity could be controlled using similar logic
to Figure 12. While the alternative approach has proven more accurate by allowing

27
for wasted space, multiple dummy arrays complicate the model significantly and
restrict change. It is for these reasons it has not been adopted for this project.
4.12. Usable Cell Area
The usable area within each cell is the maximum space available minus the total
area imposed by the number of workstations. For this project, the maximum area of
a cell will remain fixed, shown in Figure 13.
Figure 13 - Layout Of The Production Line & Assembly Cell Sizes
The size of a workstation varies depending on the process undertaken. Whilst some
processes do not require any workstation area, others require physical items such a
workbench or test equipment which occupy space in the cell regardless of WIP
(Table 6).
Table 6 - Workstation Sizes / Cell
Given that too few workstations can cause bottlenecks via insufficient
processing capacity, too many can also reduce the storage capacity within the cell,
yet both can also lead to a reduction in output. The Witness coding used to calculate
the usable cell area at “initialise actions” is shown in Figure 14.
Cell 11
Cell 9
Cell 10
Cell 1
Cell 9
Cell 3
Cell 4
Cell 6Cell 7Cell 7Cell 8
Cell 10
Cell 7
12m
11.6m
4.4m
9.8m
7.6m 7.2m 6.3m
3.9m
2.3m
5.9m
5m
6.5m
8.3m5.4m
6.5m
4.9m
3.6m
4.7m
7.4m
7.5m
10.8m
Cell Cell4 Cell 6 Cell 8 Cell 9 Cell 10 Cell 11
Activity
Barrel
Assembly
Frame
assembly
Trolley
Assembly
Cable
Routing
Line Pull
Test
Rope
Up
ZX6 & ZX8
2/4 Fall Crab
Assembly
End of
Line
Test
Packing
ZX8 6/8
Fall &
ZX10
Assembly
Cell Area (m²) 29.2 32.5 54.0 35.1 115.8 61.0 139.2
Workstation
Requirements
Bench Bench Bench None Load Test Rig None Bench Bench None Bench
Area Imposed Per
Workstation (m²)
2 0 4 0 2 2 0 2
Cell1 & 3
2
57.7
Cell 7

28
Figure 14 - Coding To Determine The Usable Cell Area
4.13. Warm-Up Period
The daily operation of the production line requires a constant presence of WIP
to ensure resources have tasks to perform at the start of each day. In contrast, the
simulation will start with zero WIP, allowing levels to build up over a period. The use
of a simulation warm-up period ensures behaviour at initialisation does not bias the
results (Lanner, 2012).
To determine the warm-up duration, an assessment of labour utilisation has
been made in the early stages of model start-up, using the existing production line
parameters. It has been found without any initial starting conditions; the simulation
must run for 2184hours before the labour utilisation reaches a steady condition
(Figure 15).
Figure 15 - Warm-Up Period Without Starting Conditions
73.5 - Avg
72.1 - Min
74.9 - Max
0
10
20
30
40
50
60
70
80
90
0
168
336
504
672
840
1008
1176
1344
1512
1680
1848
2016
2184
2352
2520
2688
2856
3024
3192
3360
3528
3696
3864
4032
4200
4368
4536
4704
4872
LabourUtilisation%
Time (Hours)
Warm-Up Period (Without Starting Conditions)
Labour Utilisation
Average Labour
Utilisation
Minimum Labour
Utilisation
Maximum Labour
Utilisation
Transient SteadyState

29
Although the warm-up duration is necessary to remove bias, it does add
additional real-time onto the length of the experimentation. As a remedy, initial
conditions have been set to reduce the warm-up duration and overall simulation
running time for subsequent experiments.
On start-up, 60 hoist entities are input into the model at a zero time, those
arriving at cell 1 are processed at zero time and pushed to buffers at cell 4, 6 and 7
Figure 16.
Figure 16 - Code To Inject Hoists Into The Model At Initialisation
Once these initial hoists have entered the model, the arrival rate is increased and
normal operation starts. In effect, this method injects entities into the model thereby
ramping up labour utilisation and dispatch rate much faster whilst reducing
simulation time by approximately 16%.By this initialisation method, the warm-up
duration can reduce to 500hours (Figure 17).

30
Figure 17 - Warm-Up Period With Starting Conditions
4.14. Assumptions
 Only direct labour is required to operate the production line
 Entities arriving into the model are not part of a batch of the same product
 No account has been made for holidays or sickness
 Labour resources use the same shift pattern for the duration of the simulation
 The product-mix will remain constant
 Unless specified, experiments are run for 12-months (8760hour) duration plus a
500 hour warm-up, with the mean value taken over 10 replications.
 To find the maximum capacity of a system, labour resources are set sufficiently
high to ensure labour availability does not constrain the output.
 Forecasted sales projections grow at 10% year on year
75.1 - Avg
72.5 - Min
78.85 - Max
0
10
20
30
40
50
60
70
80
90
0
168
336
504
672
840
1008
1176
1344
1512
1680
1848
2016
2184
2352
2520
2688
2856
3024
3192
3360
3528
3696
3864
4032
4200
4368
4536
4704
4872
LabourUtilisation%
Time (Hours)
Warm-Up Period (With StartingConditions)
Labour Utilisation
Average Labour
Utilisation
Minimum Labour
Utilisation
Maximum Labour
Utilisation
Transient SteadyState

Chapter 5 Verification and Validation
31
CHAPTER 5 VERIFICATION AND VALIDATION
5.1. Verification
In order to verify the simulation operates in the way intended, the model has
been observed running after each incremental change. The use of element flow
lines shown in Figure 18 allows a visual verification to determine if cells are correctly
linked. Due to the number of attribute combinations from a single hoist entity, this
method is not suitable to check the routing of specific products.
Figure 18 - Model Showing Element Flow Lines To Verify Routings
Whilst the individual routing can be checked manually via inspection of the
input and output rules, in practice this has proven time consuming and prone to
error. Instead, 4 additional lines of code used at “actions on input”, activate should a
hoist entity with an unwanted attribute arrive. This code verifies product routings
automatically and reduces the time to remove errors by causing the simulation to
stop and highlight the activity name, the unwanted attribute and the time (Figure 19).

32
Figure 19 - Coding To Prevent The Processing Of Unwanted Attributes
5.2. Validation
To validate if the simulation data is representative of the production line, two
tests have been undertaken to compare cycle-times, overall production hours,
product-mix and throughput efficiency.
5.2.1. Validation 1 - Comparison of simulated to actual processing time
For this exercise, the model was run once for 100,000hrs to record the total
processing time for every hoist entity exiting the packing stage at cell 10. To allow
like-for-like comparison, the results from both historical and simulated data have
been grouped into 11 product categories. Table 7 shows the comparisons between
the historical and simulated average processing times. It can be seen that the
simulated times compare closely to the historical times, with a maximum error of
2.79%.

33
Table 7 - Average Cycle-Times
5.2.2. Validation 2: Comparison of simulation to 2012 production figures
To compare against historical data, the simulation was run over 50
replications, to quantify; product-mix, total produced, labour hours and throughput
efficiency (Table 8). For comparison, only performance indicators of hoists built in
2012 are used. The confidence levels for all results of validation 2 are shown in
Appendix 4 .
Table 8 - Actual 2012 Production Data To Validate Against
Table 9 shows the comparison of actual to simulated mean quantity of
products dispatched. Whilst these results do not show an exact correlation, the
product split and the total number despatched are similar. This validates the
Iuniform distributions, which control the model split are correct.
Model Historical
ZX6 CRB 2 TO 4 14.52 14.66 -0.94%
ZX6 FM 2 TO 4 11.81 11.98 -1.34%
ZX6 LHR 2 TO 4 13.76 13.91 -1.05%
ZX8 CRB 2 TO 4 23.29 23.44 -0.66%
ZX8 FM 2 TO 4 19.78 20.24 -2.27%
ZX8 LHR 2 TO 4 19.63 19.99 -1.81%
ZX8 CRB 6 TO 8 35.91 35.00 2.59%
ZX8 FM 6 TO 8 29.87 30.09 -0.72%
ZX8 LHR 6 TO 8 35.99 35.24 2.12%
ZX10 DD ALL 42.93 41.77 2.79%
ZX10 ST OR SS ALL 32.05 31.93 0.40%
Hoist
Model
Average Processing
Time (hrs)
Error %
Hoist
Type
Number of
Falls
Model
Number of
Hoists
Produced
Model
Split (%)
Total Hours
to Complete
Average
Throughput
Efficiency (%)
ZX10 60 3.77% 1652.0 6.4
ZX6 863 54.28% 11952.2 5.1
ZX8 667 41.95% 12509.4 5.5
Total 1590 26113.6
2012 Hoist Production

34
Table 9 - Actual Vs Simulated Products Built
Table 10 shows the comparison of actual throughput efficiency to the
simulated mean throughput efficiency. Although the simulated TE values are all
slightly less than the actual values, this has not restricted the output, as shown in
the quantity of hoists despatched (Table 9) being 4.78 more than the actual. This
indicates that the TE errors are not significant to influence the results.
Table 10 - Actual Vs Simulated Throughput Efficiency
Table 11 shows the comparison of actual to simulated total labour hours.
These results show a significant difference, with the simulation being 2887.6 hours
or 11.06% more. On closer inspection of the data used to find the total labour hours
booked in 2012, it was found that 1171 operations were recorded with a zero labour
time. This means that the total actual time used to validate the simulation against is
questionable. To quantify this effect, the 1171 instances of zero labour time have
been multiplied by the average process time (Appendix 5). When the average times
for the missing data are added to the actual labour hours, the simulation is only
125.6 hours or 0.43% less.
Table 11 - Actual Vs Simulated Labour Hours
Model
Number
of Hoists
Produced
Product
Split (%)
Average
Number
of Hoists
Produced
Product
Split (%)
ZX10 60 3.77% 47.56 2.98%
ZX6 863 54.28% 885.4 55.52%
ZX8 667 41.95% 661.72 41.49%
Total 1590 1594.78
Validation Model2012 Hoist Production
Model
Actual Average
Throughput
Efficiency in
2012 (%)
Simulation
Average
Throughput
Efficiency (%)
% Error
From
Actual
ZX10 6.4 6.222 -2.8408%
ZX6 5.1 5.004 -2.0135%
ZX8 5.5 5.269 -4.7702%
2012Actual Simulation Difference Error (%)
Labour Hours Recorded 26113.6 29001.261 2887.6 11.06%
Calculated Time Not Recorded 3013.2 - - -
Total Labour Hours 29126.8 29001.261 -125.6 -0.43%

Appendix 9
35
CHAPTER 6 EXPERIMENTATION AND OPTIMISATION
Experimentation has been undertaken in a number of stages using the Witness
13 Experimenter module, to analyse the effect system parameters have on maximum
output. This module provides the ability to optimise the simulated model using a
range of inbuilt algorithms to determine which parameter combination provides the
optimum object function (Lanner.com, 2012). In addition, it allows the same
experiment to be run over a number of replications, to test for variation by using a
different number stream per replication. This generates confidence levels in the
results, so the overall variability of the system can be gauged. The experiments in
this piece of work use the “all combinations” method, to test all the possible
parameter combinations, with the object function or goal to maximise the number of
hoists shipped
6.1. Experiment 1 - Determine the Maximum Output for the Existing Factory
The first experiment aims to quantify the maximum output of the existing
factory, without any changes other than allowing sufficient labour to work. The
purpose is to establish a benchmark to measure future improvements. The quantity
of simulation elements is set to reflect the existing quantities shown in Table 12.
Table 12 - Existing Workstation Quantities / Production Cell
Cell 1 ZX6 & 8 LHR 2/4 Fall Barrel Assembly 2
Cell 3 ZX6 & 8 LHR 2/4 Fall Frame Assembly 2
Cell 4 ZX6 & 8 LHR 2/4 Fall Trolley Assembly 2
Cell 6 Cable Routing 3
Cell 7 Line Pull Test 1
Cell 7 Rope-Up Only 2
Cell 8 ZX6 & 8 2/4 Fall Crab / Foot mount Assembly 1
Cell 9 End of Line Test 3
Cell 10 Packing Assembly 2
Cell 11 ZX10 & 6/8 Fall ZX8 Assembly 4
Generator Power supply for non 400Volt / 50Hz Products 1
BuildFrame ZX6 LHR Buildframe 35
BuildFrame ZX6 FMBuildframe 6
BuildFrame ZX6 CRB Buildframe 8
BuildFrame ZX8 2/4 Fall LHR Buildframe 16
BuildFrame ZX8 FMBuildframe 5
BuildFrame ZX8 2/4 Fall CRB Buildframe 9
BuildFrame ZX8 6/8 Fall LHR Buildframe 11
BuildFrame ZX8 6/8 Fall CRB Buildframe 2
DescriptionElement
QTY

36
The mean value of 10 replications shows the capacity of the existing production
line is 1718.8. At 99% confidence levels; the system achieves a minimum output of
1690 at the lower limit, yet this could increase to 1748 at the upper limit (Figure 20).
Figure 20 - Confidence Levels For The Existing Production Line Capacity
Given production levels of 1590 hoists in 2012 and 10% growth, the existing
production line would require changes other than just additional labour between
August 2013 and January 2014 (Figure 21).
Figure 21 - Potential Life Of The Existing System
In contrast, and to demonstrate the requirement for forced delays, this
experiment has run under the same conditions but without forced delays applied.
1580
1600
1620
1640
1660
1680
1700
1720
1740
1760
Jan13
Feb13
Mar13
Apr13
May13
Jun13
Jul13
Aug13
Sep13
Oct13
Nov13
Dec13
Jan14
Feb14
Mar14
AnnualDespatchVolume
Date
Potential Life of Existing System
10% Year
on Year
Sales
Growth
Mean
Capacity
99%Min
Capacity
99%Max
Capacity

37
Using this scenario the same model could achieve a mean output of 1801.8
hoists/year; with 99% confidence levels achieving 1756.7 at the lower limit and
1846.9 at the upper limit (Figure 22).
Figure 22 - Confidence Levels For The Existing Production Line (No Delay)
This additional capacity stems from the throughput efficiency increasing from 5.12%
to 5.38%, which reduces the average total production time from 351 to 331hours.
6.2. Experiment 2 - Optimise the Production Line without Capital Investment
The purpose of this experiment is to determine how to increase the production
output without investment in equipment such as generators or line pull testrigs. In
discussion with the company, it has been decided that the possible changes to
workstation quantities should be limited to those given in Table 13.
Table 13 - Experiment 2 Maximum / Minimum Workstation Parameters
6.2.1. Part A: Optimise The Production Line Configuration
The first part of this experiment will ascertain the optimum configuration based
upon forced delays being removed. Considering the lengthy duration to perform this
experiment; each scenario is run for a shorter duration of 4380 hours with only 5
replications. The top 40 scenarios for this experiment are shown in Appendix 6 with
From To
Cell 1 ZX6 & 8 LHR 2/4 Fall Barrel Assembly 2 4 3 3
Cell 3 ZX6 & 8 LHR 2/4 Fall Frame Assembly 2 4 3 9
Cell 4 ZX6 & 8 LHR 2/4 Fall Trolley Assembly 2 4 3 27
Cell 6 Cable Routing 3 6 4 108
Cell 7 Line Pull Test 1 1 1 108
Cell 7 Rope - Up 2 4 3 324
Cell 8 ZX6 & 8 CRB & FMAssebly 1 3 3 972
Cell 9 End of Line Test 3 4 2 1944
Cell 10 Packing Assembly 2 4 3 5832
Cell 11 ZX10 & 6/8 Fall ZX8 Assembly 4 6 3 17496
Configuration
Combinations
Total
Combinations
Element to
change
Description

38
the optimum scenario, achieving a mean of 976.2 hoists shipped in 6 months shown
in Table 14.
Table 14 - Experiment 2 Optimum Scenario
Using a comparison showing the average cycle-time per cell, the work load for
existing and optimised cell configurations, it becomes apparent why this is the
optimum solution (Figure 23). On inspection of this chart, it is clear a significant
imbalance in cycle-times occur from one cell to the next. Without being able to
redistribute work, increase working hours or buffer sizes to balance workload; the
simulation has optimised the production line via the quantity of workstations per cell.
This has reduced Takt-time (Simons & Zokaei, 2005) by 0.67 hours, and imbalance
by 0.45 hours in the optimised configuration.
Mean Quantity Dispatched 976.2
Cell1 Quantity 3
Cell3 Quantity 4
Cell4 Quantity 3
Cell6 Quantity 6
Cell8 Quantity 1
Cell9 Quantity 4
Cell11 Quantity 6
Cell10 Quantity 4
Cell7_RopeUp Quantity 4
Generator Utilisation 96.2
90% Min 937.6
90% Max 1015
95% Min 925.8
95% Max 1027
99% Min 892.8
99% Max 1060
ConfidenceOptimisedConfiguration

39
Figure 23 - Production Line Balance Comparison
Whilst this analysis does not consider potential variation in cycle-times, it does
indicate that production levels could increase significantly (Equation 5).
Equation 5 - Customer Demand
Even though this experiment is 6 months duration, the output is 744 hoists less
than Equation 5 would suggest is possible to produce. This analysis does not
consider lower volume products such as CRB and FM designs or any ZX10 hoists
therefore, 3441 hoists/year, is potentially an under estimate of what can be produced.
The explanation for this could stem from the generator resource being used nearly all
of the time (Table 14), which probably has a significant influence on the output. One
could speculate that output will increase by maximising the number of
workstations/cell, by creating redundancy to cope with variation; however excessive
quantities constrain the output by reducing floor space and storage capacity
unnecessarily.
0.00
0.25
0.50
0.75
1.00
1.25
1.50
1.75
2.00
2.25
2.50
2.75
3.00
3.25
3.50
Cell 1
Barrel Assy
Cell 3
Frame
Assy
Cell 4
Trolley
Assy
Cell 6
Cable
Routing
Cell 7 Load
Test
Cell 7
Rope-Up
Cell 9 EOL
test
Cell 10
Packing
ProductionHours/WorkStation
Production Cells
Time / Hoist
Line Balance Comparison Average Cycle-
Time / Cell
Existing
Production Line
Configuration
Optimised
Production Line
Configuration
Existing
Minimum Takt
Time
Optimised
Minimum Takt
Time

40
6.2.2. Part B - Quantify the Optimised Configuration
To allow comparison, the optimised scenario has been repeated for a 1-year
duration, with and without forced delays. This enables the output to be quantified at
the worst case scenario; maintaining the existing TE or best case scenario; with the
removal of forced delays.
At best case, the removal of forced delays increases the mean output to 1978
hoists/year. This is 260 more than is possible from the existing production line when
maximum capacity is reached. The confidence levels for this are shown in Figure 24.
Figure 24 - Experiment 2 Confidence In Capacity (No Forced Delay)
With TE set to ensure it does not exceed existing levels; at the worst case the
mean output increases to 1915 hoists/year, 197 extra than the existing production
line. The confidence levels for this are shown in Figure 25.
Figure 25 - Experiment 2 Confidence In Capacity (With Forced Delay)
Assuming 10% growth, the optimised configuration will extend the life of the
existing production facilities until at least August 2014 based upon 99% minimum
confidence levels at the existing TE. If forced delays are removed, the longevity could
be extended until August 2015 based upon 99% maximum confidence levels (Figure

41
26). To achieve higher production volumes, further changes are required such as
working additional hours, or by removing other constraints.
Figure 26 - Potential Life Of 1
st
Optimised System
6.3. Experiment 3 - Optimise the Production Line with Capital Investment
This experiment will quantify the effect investment in additional line pull test
equipment and generators have on production output at best and worst case
scenarios. Using 2012 TE levels to constrain the model at the worst case, and
unconstrained TE at the best case, a definitive lifespan and capacity can be assigned
to the production facility. The possible investment configurations are shown in Table
15. The purpose of this experiment is to show that changes driven by waste
reduction can make a significant contribution to the output.
Table 15 - Experiment 3 Maximum / Minimum Workstation Parameters
1800
1850
1900
1950
2000
2050
2100
Jun
14
Jul
14
Aug
14
Sep
14
Oct
14
Nov
14
Dec
14
Jan
15
Feb
15
Mar
15
Apr
15
May
15
Jun
15
Jul
15
Aug
15
Sep
15
Oct
15
Nov
15
Date
Potential Life of Optimised System
10% Year on Year
Sales Growth
Mean capacity -
no delay
99% Max capacity
-no delay
99% Min capacity -
no delay
Mean capacity -
delay
99% Max capacity
-delay
99% Min capacity -
delay
From To
Generator Generator Resource 1 11 11 33
Element
To Change
Description
Configuration
Combinations
Total
Combinations

42
When forced delays constrain the system, it is clear the optimum configuration
from the last experiment does not achieve the average TE from 2012 until a second
generator resource is used (Figure 27). Furthermore, a third or fourth generator or
any additional line pull testrig provides no additional benefit.
Figure 27 - Experiment 3 Throughput Efficiency
Interestingly if the forced delays are removed; the use of a second and third
line pull testrig does improve TE using a single generator, yet with additional
generators and a higher output, a third testrig does not. This stems from hoist entities
arriving at the line pull test and blocking the activity until the generator resource is
free, thus additional testrigs offer the potential to test more products and improve TE.
In reality, this scenario would not manifest unless the operator at the line pull test had
no UK electrical supply hoists to test. As such, the requirement for the third line pull
testrig can be discounted, yet in all cases, a second unit does offer improvement.
Referring to Little’s Law, the approach adopted for this project was to increase
output by maintaining WIP but reducing the overall throughput time. Figure 28 shows
4%
5%
6%
7%
8%
9%
10%
11%
12%
13%
1 2 3
ThroughputEfficiency%
Number of
Line Pull
Test Rigs
ThroughputEfficiency 1 Generator
(With Delay)
2 Generators
(With Delay)
3 Generators
(With Delay)
4 Generators
(With Delay)
1 Generator
(No delay)
2 Generators
(No delay)
3 Generators
(No delay)
4 Generators
(No delay)

43
WIP levels, instead of being maintained, can reduce whilst production output
increases (Figure 29)
Figure 28 - Experiment 3 Average WIP
Comparison of Figure 27 and Figure 29; shows the overall duration a hoist
spends inside the production line has a significant impact on the number of hoists
that are able to be produced. Under the same operating conditions, the output can be
seen to differ by as much as 1139 hoists/year. This amount depends entirely on TE
ranging from 5.16% to 12.6% (Figure 29).
55
60
65
70
75
80
85
90
95
1 2 3
AverageQuantityofWIP
Number of
Line Pull
Test Rigs
Average WIP 1 Generator
(With Delay)
2 Generators
(With Delay)
3 Generators
(With Delay)
4 Generators
(With Delay)
1 Generator
(No delay)
2 Generators
(No delay)
3 Generators
(No delay)
4 Generators
(No delay)

44
Figure 29 - Experiment 3 Hoists / Year
In concurrence, the use of a 3rd
or 4th
generator is pivotal to achieve the
optimum output, TE and WIP. Yet in all cases, a second generator provides a notable
increase in output to exceed existing levels.
If 2012 TE levels are not able to be surpassed, the production line could only
achieve an average 2293 hoists/year, irrespective of any other investments. Should
these circumstances become evident, the company should look to move into larger
premises.
If factors constraining TE are eliminated, a second unit will support production
output without constraint until levels approach 3144 hoists/year. Further investment
would then be required in a 3rd
generator to allow the configuration to achieve a
maximum of 3425 hoists/year.
Figure 33 shows the difference this could have on a timeline; making
comparison of 5.16% TE using 2 generators to 11.74% TE using 3 generators.
Clearly without TE improvement this configuration can be expected to serve
1900
2100
2300
2500
2700
2900
3100
3300
3500
1 2 3
NumberofHoistsShipped
Number of
Line Pull
Test Rigs
Hoists Produced / Year 1 Generator
(With Delay)
2 Generators
(With Delay)
3 Generators
(With Delay)
4 Generators
(With Delay)
1 Generator
(No delay)
2 Generators
(No delay)
3 Generators
(No delay)
4 Generators
(No delay)

45
production until November 2016 at maximum confidence levels, yet improvement in
TE could extend the lifespan until February 2021 (Figure 30).
Figure 30 - Potential Life Of Experiment 3
This experiment shows that throughput has a significant bearing on both output
and longevity of the existing facilities. Despite the ability to develop an optimum
production configuration to cope with high output, the simulation is unable to provide
a solution to show how waste reduction is achieved or even if it is possible.
2200
2300
2400
2500
2600
2700
2800
2900
3000
3100
3200
3300
3400
3500
Jun16
Aug16
Oct16
Dec16
Feb17
Apr17
Jun17
Aug17
Oct17
Dec17
Feb18
Apr18
Jun18
Aug18
Oct18
Dec18
Feb19
Apr19
Jun19
Aug19
Oct19
Dec19
Feb20
Apr20
Jun20
Aug20
Oct20
Dec20
Feb21
Apr21
Jun21
Date
Potential Life of Optimised System withInvestment 10% Year
on Year
Sales
Growth
99% Max
capacity -
11.74% TE
Mean
capacity -
11.74% TE
99% Min
capacity -
11.74% TE
99% Max
capacity -
5.16% TE
Mean
capacity -
5.16% TE
99% Min
capacity -
5.16% TE

46
6.4. Experiment 4 - Check Optimum Configuration
Given that additional generators allow the output to grow significantly if forced
delays are removed, Experiment 4 will re-evaluate the optimum configuration from
experiment 2 using 4 generators. The purpose is to determine if this configuration is
still valid to produce the maximum output, or if some other elements now constrain
the system. The possible parameter combinations are shown in Table 16.
Table 16 - Experiment 4 Parameters To Check
Using the parameter analysis feature within Witness Experimenter (Figure 31), it is
clear that cell 8 was constraining the output in the last experiment, with cell 7 (line
pull test) being close behind.
Figure 31 - Experiment 4 Parameter Analysis
If workstations within these cells are increased, the production line could achieve an
average of 3890 hoists/year using the configuration highlighted in Table 17.
From To
Cell 1 ZX6 & 8 LHR 2/4 Fall Barrel Assembly 3 4 2 2
Cell 4 ZX6 & 8 LHR 2/4 Fall Trolley Assembly 3 4 2 4
Cell 8 ZX6 & 8 CRB & FMAssebly 1 3 3 24
Element
to
change
Description
Configuration
Combinations
Total
Combinations

47
Table 17 - Experiment 4 Optimum Scenario
As suspected using Takt time to analyse the balance of work (Figure 23), the
use of additional workstations in cells 1 and 4 do not contribute to additional output.
Interestingly the use of additional workstations has proven to reduce the output. This
effect, described by Lepore & Cohen (1999:9) as “local optimization at the expense
of global optimization”, is caused by a reduction in the available buffer storage space
due to unnecessary workstations.
The results from experiment 4 are shown in
Appendix 10, from which the optimum configuration allows the life of the
production facility to increase to at least April 2022 based on 10% year on year
growth (Figure 32). At 3890 hoists/year, this configuration represents the maximum
output achievable using the possible parameter combinations.
Optimum Scenario 6 18 12 24
Mean Hoists Shipped 3890.2 3890.1 3888.1 3884.9
Cell1 Quantity 3 4 3 4
Cell7 Rope-Up Quantity 4 4 4 4
Generator Quantity 4 4 4 4
Cell7 LinePull Quantity 2 2 2 2
Generator Utilisation (%) 47.1 47.1 46.8 46.9
ZX6 Throughput Efficiency (%) 9 9 9 9
ZX8 Throughput Efficiency (%) 23 23.4 23.1 23
ZX10 Throughput Efficiency (%) 14 14 14 14
Line Pull Utilisation (%) 51.6 51.4 51.4 51.5

48
Figure 32 - Potential Life Of Experiment 4
Fundamentally, it is the extra workstations inside cell 8 which has enabled the
output to grow. The additional line pull testrig in cell 7 has reduced the loading on the
existing unit, allowing throughout efficiency to improve. It is foreseeable that the
output could increase further if additional hours be applied to the same timeframe
through shift patterns or overtime.
6.5. Experiment 5 - Influence of Generator Requirement on Output
Until this point, the percentage of non-UK electrical supply products was
assumed to remain constant throughout growth. Experiment 5 illustrates how product
voltage and frequency requirement could affect the output in the absence of any
constraint other than generator requirement.
In 2012, hoists produced with non UK electrical systems contributed 24.06% of
total sales (Table 3). With hoist sales forecast to grow at 10% year on year, it is
feasible that UK sales may not contribute to this growth. Should this scenario occur;
expansion into overseas markets could result in a disproportionate increase of non-
3800
3820
3840
3860
3880
3900
3920
3940
3960
3980
4000
Mar 22 Apr 22 May 22 Jun 22 Jul 22 Aug 22
Date
Potential Life of Experiment 4
10% Year on Year
Sales Growth
Mean capacity
99% Min capacity
99% Max capacity

49
UK electrical systems. If this scenario is true, by 2023 as many as 3328 hoists or
73% of all products sold will require the use of the generator (Figure 33).
Figure 33 - Potential For Generator Requirement To Grow With Sales
With this possibility in mind, experiment 5 will run the optimised solution from
experiment 4 to determine the quantity of generators required to sustain sales growth
with the percentage of non-UK mains supply products ranging from 0 to 70%.
The results of this experiment have been plotted onto Figure 34; analysis
shows a single generator will allow sales up to 2100 units/year at the existing
percentage requirement. If the sales of non-UK electrical systems continue at
24.06% of total sales; 4 generators are required to allow the system to reach a
maximum output of 3890 hoists/year. If the generator requirement grows
disproportionately with respect to sales volume (shown in Figure 34), 7 generators
would be required by 2021, to allow the system to reach maximum output.
0%
10%
20%
30%
40%
50%
60%
70%
80%
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
%GeneratorReuirement
HoistsShipped/Year
Year
Potential Growth of Hoists With Non UK Mains Supply
Total Hoists
Produced / Year
Total Non UK
Suppy Hoists /
Year
Overall Generator
Requirement(%)

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