msc thesis

i

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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ii

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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iii

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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iv

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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v

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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vi

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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viii

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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x

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

1

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



2

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



3

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



4

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

5

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

http://scholar.google.co.uk/

http://metalib5.hosted.exlibrisgroup.com/

https://www.google.co.uk/



6

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



7

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.



8

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



9

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.



10

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

OutOutBuildFrame

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 TypeExisting

QuantityOperations 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 FitQty



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 3Cell 4

Cell 6Cell 7Cell 7Cell 8Cell 10

Cell 7

12m

11.6

m

4.4

m

9.8m

7.6m 7.2m 6.3m

3.9

m

2.3m

5.9

m

5m

6.5

m

8.3m5.4m

6.5

m

4.9m 3.6

m

4.7m

7.4

m

7.5m

10.8

m

Cell Cell4 Cell 6 Cell 8 Cell 9 Cell 10 Cell 11

ActivityBarrel

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

RequirementsBench 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 - Avg72.1 - Min

74.9 - Max

0

10

20

30

40

50

60

70

80

90

0

16

8

33

6

50

4

67

2

84

0

10

08

11

76

13

44

15

12

16

80

18

48

20

16

21

84

23

52

25

20

26

88

28

56

30

24

31

92

33

60

35

28

36

96

38

64

40

32

42

00

43

68

45

36

47

04

48

72

Lab

ou

r U

tilis

atio

n %

Time (Hours)

Warm-Up Period (Without Starting Conditions)

Labour Utilisation

Average Labour Utilisation

Minimum Labour

Utilisation

Maximum Labour Utilisation

Transient Steady State



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 - Avg72.5 - Min

78.85 - Max

0

10

20

30

40

50

60

70

80

90

0

16

8

33

6

50

4

67

2

84

0

10

08

11

76

13

44

15

12

16

80

18

48

20

16

21

84

23

52

25

20

26

88

28

56

30

24

31

92

33

60

35

28

36

96

38

64

40

32

42

00

43

68

45

36

47

04

48

72

Lab

ou

r U

tilis

atio

n %

Time (Hours)

Warm-Up Period (With Starting Conditions)

Labour Utilisation

Average Labour

Utilisation

Minimum Labour Utilisation

Maximum Labour

Utilisation

Transient Steady State


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%

2012 Actual 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 FM Buildframe 6

BuildFrame ZX6 CRB Buildframe 8

BuildFrame ZX8 2/4 Fall LHR Buildframe 16

BuildFrame ZX8 FM Buildframe 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

Jan 1

3

Feb

13

Mar 1

3

Ap

r 13

May 1

3

Jun

13

Jul 1

3

Au

g 13

Sep

13

Oct 1

3

No

v 13

De

c 13

Jan 1

4

Feb

14

Mar 1

4

An

nu

al D

esp

atch

Vo

lum

e

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 & FM Assebly 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

changeDescription



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

Co

nfi

de

nce

Op

tim

ise

d C

on

figu

rati

on



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

Pro

du

ctio

n H

ou

rs /

Wo

rk S

tati

on

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 1st

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

An

nu

al D

esp

atch

Vo

lum

e

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 ChangeDescription

Configuration

CombinationsTotal

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

Thro

ugh

pu

t Ef

fici

en

cy %

Number of Line Pull Test Rigs

Throughput Efficiency 1 Generator (With Delay)

2 Generators (With Delay)



1 Generator (No delay)

2 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

Ave

rage

Qu

anti

ty o

f W

IP


Average WIP 1 Generator (With 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

Nu

mb

er

of

Ho

ists

Sh

ipp

ed


Hoists Produced / Year 1 Generator (With 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

Jun

16

Au

g 1

6

Oct

16

De

c 1

6

Fe

b 1

7

Ap

r 1

7

Jun

17

Au

g 1

7

Oct

17

De

c 1

7

Fe

b 1

8

Ap

r 1

8

Jun

18

Au

g 1

8

Oct

18

De

c 1

8

Fe

b 1

9

Ap

r 1

9

Jun

19

Au

g 1

9

Oct

19

De

c 1

9

Fe

b 2

0

Ap

r 2

0

Jun

20

Au

g 2

0

Oct

20

De

c 2

0

Fe

b 2

1

Ap

r 2

1

Jun

21

An

nu

al D

esp

atch

Vo

lum

e

Date

Potential Life of Optimised System with Investment 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 & FM Assebly 1 3 3 24

Element

to

change

DescriptionConfiguration

CombinationsTotal

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

An

nu

al D

esp

atch

Vo

lum

e

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

20

12

20

13

20

14

20

15

20

16

20

17

20

18

20

19

20

20

20

21

20

22

20

23

% G

en

era

tor

Re

uir

em

en

t

Ho

ists

Sh

ipp

ed

/ Y

ear

Year

Potential Growth of Hoists With Non UK Mains Supply

Total Hoists Produced / Year

Total Non UK Suppy Hoists / Year

Overall Generator Requirement (%)



50

Figure 34 – Output Vs Generator Requirement

The purpose of this experiment is to demonstrate that factors other than those

considered in the ERP system can have a significant impact on what can be

produced, within a given timeframe. A danger could arise if the company were to

accept a large overseas order on a standard lead-time without considering the

generator available capacity. This would cause a bottleneck to develop, and only

resolved through working additional hours in all processes requiring the generator. If

additional time is not worked, the company will fail to make an on-time delivery.

6.6. Experiment 6 - Effect of TE on Lead-time, WIP & Arrival Rate

The purpose of this experiment is to illustrate the relationship between

Throughput Efficiency and Little’s Law when the production line operates at

maximum capacity. The aim is to quantify the maximum output of a system when the

system is constrained to a lower throughput than is theoretically achievable. This

idea applies to the time directly after a production line change, when the system is

constrained by elements external to change such as waste, until such time when

waste is reduced and system again constrains the output.

20122013

2014

2015

2016

2017

2019

2020

2021

2022

0

250

500

750

1000

1250

1500

1750

2000

2250

2500

2750

3000

3250

3500

3750

4000

0% 10% 20% 30% 40% 50% 60% 70%

Nu

mb

er

of

Ho

ists

Sh

ipp

ed

% Generator Requirement

Output vs Generator Requirement

Future Generator Requirement8 Generators

7 Generators

6 Generators

5 Generators

4 Generators

3 Generators

2 Generators

1 Generator



51

As such, when only throughput efficiency at the “as is” state is known, the

reserved capacity at the “as is” state and at the point when the system configuration

constrains the output is quantifiable.

Using the optimum configuration from experiment 4 (Table 17), this experiment

shows the manufacturing lead-time, WIP & arrival rate when the average throughput

efficiency increases from 4 to 12% (Figure 35).

For clarity these terms will be defined as;

Arrival rate is the mean number of hoists produced divided by the total hours

in 1 year (8760 hours)

Average lead-time is the total number of hours from barrel assembly in cells

1, 8 or 11 to dispatch at cell 10

WIP is the number of entities from barrel assembly in cells 1, 8 or 11 to

packing at cell 10

Figure 35 - Effect of Throughput Efficiency on Little’s Law

From analysis of Figure 35; the arrival rate is a linear response to throughput

efficiency until 9.2% TE, showing only external constraints are present up until this

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0

50

100

150

200

250

300

350

400

450

500

4% 5% 6% 7% 8% 9% 10% 11% 12%

Hoi

sts

/ hr

WIP

Qua

ntit

y H

ours

Avg Throughput Efficiency (%)

Effect of Throughput Efficiency on Littles Law Avg Wip

Avg Leadtime (hrs)

Avg Wasted Time (hrs)

Arrival Rate (Hoists/hr)



52

time. Between 9.2% and 10.2%, the system undergoes a transition from external to

system constraint. Albeit less noticeable in WIP; this transition phase affects both the

arrival rate and WIP responses in coincidence. Beyond 10.2% TE, no further

increase in the arrival rate is possible, yet WIP continues to reduce as lead-times

decay.

In experiment 4; the maximum output achieved 3890 hoists/year at an average

TE of 12.2%


Chapter 6 Experimentation and Optimisation

53

Appendix 10), yet analysis of Figure 35 shows the same number of hoists

could be produced at a lower efficiency but with higher WIP levels.

The main advantage using this analysis is the ability to quantify the maximum arrival

rate and WIP for a given system when only throughput efficiency is known. If this is

combined with an efficiency improvement rate; the potential life of a system can be

ete ine not just by t e t eo etical ca acity but also at any oint f o t e “as is”

TE to the theoretical TE.


Chapter 7 Discussion

54

CHAPTER 7 DISCUSSION

The purpose of this project; to extend the life of the existing production

facilities through the ability to produce more from the same area, has proven

possible through simulation. Experimentation has shown the output can be

increased from 1590 to 3890 hoists/year (Figure 36) and longevity extended until

2022 using the same hours of operation. Under the approach adopted, the annual

output/m² of production floor space could rise from 3.28 to 7.42 hoists/m². This has

been achieved using an improved production line configuration and sufficient

availability in equipment, which permit throughput efficiency to increase from 5.1%

to 12.2%.

Figure 36 - Experiment Comparison (Dispatch QTY Vs Space Utilisation)

Whilst this is theoretically possible, no data has been available to quantify the

external constraints as the output increases. In experiment 1, these influences have

been observed to restrict the throughput efficiency via increasing the manufacturing

lead-time by an average of 20 hours/hoist. Going forward when there are clear

merits to reconfigure the line and invest in a 2nd generator, to improve flow and

3.0

3.5

4.0

4.5

5.0

5.5

6.0

6.5

7.0

7.5

8.0

1500

1750

2000

2250

2500

2750

3000

3250

3500

3750

4000

Existing System

Exp 1 -Max

Output

Exp 1 -Max

Output

Exp 2 - 1st Opt Conf


Exp 3 -Investment

Exp 3 -Investment

Exp 4 - 2nd Opt Conf

Delayed Delayed No Delay Delayed No Delay Delayed No Delay No Delay

Ho

ists

Dis

pat

che

d /

m ²

Ho

ists

Dis

pat

che

d /

Ye

ar

ExperimentScenario

Experiment Comparison (Dispatch Quantity Vs Space Utilisation)QTY Dispatched

Hoists / m² of building



55

reduce waiting time; the likelihood the changes will achieve full efficiency potential is

unclear.

In the worst case scenario when external constraints limit throughout

efficiency to exceed existing levels, an optimised production line could yield 1915

hoists/year, whilst the addition of a generator could increase this to 2293 hoists/

year. Despite delaying the requirement for further change until December 2014 or

November 2016 respectively, these solutions on’t come without cost implications.

In both cases, WIP and lead-times will increase (Figure 37). From a financial

perspective; these will tie-up extra working capital over a longer period thus

reducing the liquidity of the business (Gupta & Boyd, 2008). From an operations

perspective; these will congest the production line and potentially reduce on-time

delivery and customer goodwill (Yücesan & Groote, 2000), however no data is

available to verify or quantify lateness.

Figure 37 - Experiment Comparison (Lead-Time Vs WIP)

Clearly, the only viable way to grow the business within the same space

constraints is via a sustained improvement in throughput efficiency. To achieve 3890

50.0

55.0

60.0

65.0

70.0

75.0

80.0

85.0

90.0

95.0

100.0

6

7

8

9

10

11

12

13

14

15

16

Existing System

Exp 1 -Max

Output

Exp 1 -Max

Output



Exp 3 -Investment

Exp 3 -Investment

Exp 4 - 2nd Opt Conf

Delayed Delayed No Delay Delayed No Delay Delayed No Delay No Delay

WIP

Lead

-Tim

e (

Day

s)

ExperimentScenario

Experiment Comparison (Lead-time Vs WIP)Lead-Time (days)

WIP Quantity



56

hoists/year, this represents a significant reduction in the lead-time. To put this into

context; a hoist with an average assembly time of 18.1 hours, needs to be

manufactured in an average lead-time of 6.2-days as opposed to 14.6-days

currently (Figure 37).

Even if the business increases throughput efficiency as experiment 4

demonstrates, other factors which influence cycle-time variations, could still threaten

system stability. Reduced redundant capacity and reliance on known efficiency

levels could yield the system susceptible to localised spikes in variation (Fearne &

Fowler, 2006). In the event of a series of defects; a short delay in dispatch will cause

WIP to build up significantly faster in the optimised model than the existing. Such

that a 2-day delay in experiment 4 causes all available floor space to be consumed,

in contrast the existing model could survive 6-days.

Evidently, limitation to analysis using discrete event simulation resides in the

extents of simulated model and data available to describe the state of the real

system. Given that delays other than waiting rate are known to occur, in the

absence of data to specify what the cause of the delay could be, a solution to

guarantee improved throughput efficiency using Witness is not possible. Whilst it is

argued, Lean needs simulation for analysis of complex systems (Standridge &

Marvel, 2006); the simulation needs tools, lean or otherwise to provide data. In this

instance, sufficient data was only available to allow the simulation to quantify the

effect of forced delays. Like the concept of the Hidden Factory (Kesting & Ulhø,

2010), this does show that improved performance is achievable if wastes are

removed.

With this concept in mind, an investigation outside of Witness has been

undertaken throughout July 2013 using Pareto Analysis, to identify why throughput

efficiency is lower that the simulation would suggest possible. By taking a headcount

of the reasons why WIP was not being processed, both the cause and frequency of



57

delay are identified (Figure 38). To reflect meaningful problems seen by the

company, the cause of delays are categorised as;

Waiting in a queue

Waiting for In-house parts

Waiting for bought-In parts

Incorrect product planning (technical defects)

Waiting for people

Waiting for information

Figure 38 - Observed Delays

Conceding the fact that this investigation does not provide data suitable for

simulation, from a waste reduction point of view, it does allow management to

prioritise what to deal with first (Johnson, et al., 2010). Intriguingly and

unexpectedly, the number of delay occurrences for In-house made components is

double those for bought-In components. These findings suggest the company is its

own worst supplier in terms of the quantity of delays.

86

2722

13 10 7 60%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0

10

20

30

40

50

60

70

80

90

100

Wai

ting

in

Que

ues

Wai

ting

for

In-

Hou

se P

arts

Inco

rrec

t Pl

anni

ng

Wai

ting

for

Boug

ht-In

Par

ts

Wai

ting

for

Peop

le

Wai

ting

for

Equi

pmen

t

Wai

ting

for

Info

rmat

ion

Num

ber

of D

elay

s Obs

erve

d

Delay Category

Instances of Delay

Delay Occurances

Cummulative Percentage


Chapter 8 Future Work

58

CHAPTER 8 FUTURE WORK

The work undertaken in this project highlights a myriad of other opportunities

for further work to improve production output. It is believed that the following

recommendations are relevant to this project and have the potential to benefit a

wider audience at Street Crane Ltd.

8.1. Short-Term Capacity Planning

As opposed to optimising the production line to produce a higher production

output over the long-term, there is also potential benefit to focus the simulation

towards short-term production. Considering MRP systems are known to have

limitations regarding variations, queuing and complexity, (Byrne & Bakir, 1999)(Kim

& Kim, 2001) the use of dynamic simulation in conjunction could prove

advantageous (Al-Aomar, 2000).

At planning and control; the use of Witness could help to fine-tune the master

production schedule by illustrating the effect the actual schedule is likely to have.

This could be achieved by modifying the simulation developed in this project to input

hoists which reside on the order book, with experimentation designed to schedule

hoists into production in the best sequence. In this instance, Experimenter could not

just improve production flow (Melton, 2005) and resource utilisation but also on-time

delivery, which could be affected through a poor sequence of work.

After the publication of the production schedule, this simulation could then

facilitate sound decision making by production management when daily events

deviate from the planned schedule. In this application, the simulation should be

carefully designed to reduce the requirement for in-depth Witness knowledge of

users, who ultimately will use simulation as a support tool.

8.2. Inventory Scenarios

In reality, inventory also has a significant role in the production process

(Figure 38). The inclusion of inventory in the simulation, would provide an insight



59

into how stocking levels affect the behaviour of the production line as sales volumes

increase or the product-mix alters (Kleijnen, 2005). These insights could include

determining an optimised stocking level of components (Petrovic, 2001), the space

required to store components and the effect storage space has on usable cell

space. Given that the MRP system can set stock levels based upon historical usage

and lead-time, it cannot allow for possible variation of products, delivery

performance variability or production size.

To optimise stock levels; the simulation will need linking to the bill of materials

for each hoist configuration and process stage, the existing stocking levels,

component lead-time and on-time delivery data. Using random number streams

governing product entry, confidence levels can be determined in the quantity of

specific parts held to prevent stock-outs and delays in output (Kleijnen, 2005). By

introducing a component size into the bill of materials, the required storage space

can also be determined as hoist volumes grow and stocking levels change. Should

additional storage space become a requirement, the effect on usable space and

production output can be quantified. The outcome of this work should facilitate

improved on-time delivery, reduced WIP and lower operational costs (Jammernegg

& Reiner, 2007).

8.3. Reduce Non-Value Activities

This project has only considered direct labour activities within the production

line; in reality operation requires indirect labour to service direct activities and

sustain output. Further work could therefore focus upon quantifying these non-value

added indirect activities to allow simulation and optimisation of the layout

configuration to reduce indirect labour costs. Specifically these activities stem from

transportation, motion and inventory issues, identified in the seven deadly wastes

(Ramaswamy, et al., 2002).



60

The use of lean tools such as value stream mapping, process flow, time-value

or spaghetti diagrams could form a basis of this work to gather data to identify and

quantify waste (Melton, 2005). This information could then be used inside Witness to

optimise the production line by reducing the indirect workload without adverse effect

on the production output.


Chapter 9 Conclusions

61

CHAPTER 9 CONCLUSIONS

Through the course of this work, it has been confirmed that it is possible to

extend the lifespan of the existing production line, although the duration and

practicality depend heavily on increased throughput efficiency. If influences outside

of this study constrain TE, an optimised configuration (Table 14) using 2 generators

could be expected to produce 2293 hoists/year sufficing sales levels until November

2016 (Figure 30). Unfortunately, as volumes increase, this scenario is at risk of

reduced on-time delivery performance (Figure 37). In contrast; if TE is improved, a

2nd optimised configuration (Figure 17) could increase the output to 3890 hoists/year

sufficing sales levels until April 2022 (Figure 32).

Mindful that future sales could be composed from a greater proportion of non-

UK electrical systems; analysis has shown the actual number of generators could

vary from 1 to 7 as sales increase (Figure 34). Using sales volumes verses

anticipated requirement, this analysis enables the exact requirement to be

determined to help prevent wasted investment or unforeseen overloading.

Albeit not part of this work, it is clear through observation the cycle-times are out-of-

balance between production cells (Figure 23). Whilst balancing can be achieved

using an optimum number of workstations, unless cycle-time/Takt-time equates to

an integer, cells will be unbalanced and require buffers or displacement of

operatives to balance load. Knowing unbalance could result in excessive WIP, under

utilisation of resources, quality defects and conflict (Simons & Zokaei, 2005); it is

recommended workload is redistributed.

Undeniably, the simulation developed could have been constructed in a

countless number of ways, yet through the course of this work; the use of Excel to

store and transfer data to and from Witness has proven advantageous. Code linking

the simulation to a spreadsheet has reduced the model size, the time to make


Chapter 9 Conclusions

62

changes and model complexity. In addition, Excel provides functionality beyond

Witness to analyse transient behaviour typical of initialisation.

Given that manual analysis proved useful for verification and rudimentary

understanding, for lengthy experiments it becomes burdensome. Instead, Witness

Experimenter for semi-automated experimentation and optimisation has shown to be

essential to understand how the production line can be improved within a large

number of possible scenarios.

The use of Experimenter in conjunction with Excel requires data to be

imported into a Witness variable at model initialisation, to allow Experimenter to alter

the variable at the start of each scenario. In contrast, data can be imported directly

into an action or rule without use of a Witness variable; whilst enabling faster

modelling, this does limit analysis to manual experimentation.

Similar to the Theory of Constraints (Goldratt, 1990) objective, Witness

Experimenter is able quantify at what point a variable becomes a constraint to the

object function. In being able to identify the weakest link, its influence on other

interdependent links, and how the link limits the goal of the system, one can answer

Theory of Constraint uestions “w at s oul be c an e ?” an “w at s oul it be

c an e to?”(Lepore & Cohen, 1999). Considering the stochastic nature and

general complexity the production line, undeniably this ability is both powerful and

desirable.


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63

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__________________________________________________________ Appendix1

68

APPENDIX 1- SAMPLE DATA USED TO ESTABLISH CYCLE TIMES

Job

Number

Part

Number Op Description

First

Labour

Last

Labour Completed

Actual

Hours

28,746.94

A73709 836-00783 ZX064-3FoLM5K052-CRB09A1-40050 13/07/2010 10.09

836-00783 BARREL ASSY 22 Jun 10 23 Jun 10 2.67

836-00783 ZX FRAME ASSY 23 Jun 10 23 Jun 10 1.67

836-00783 CABLE ROUTING 13 Jul 10 13 Jul 10 0

836-00783 LINE PULL TEST 13 Jul 10 13 Jul 10 0

836-00783 ROPE UP BTM BLK 24 Jun 10 30 Jun 10 2

836-00783 END LINE TEST 30 Jun 10 30 Jun 10 1

836-00783 CRAB ASSY 10 Jul 10 13 Jul 10 2.75

836-00783 PACKING 13 Jul 10 13 Jul 10 0

A73710 836-00783 ZX064-3FoLM5K052-CRB09A1-40050 13/07/2010 12.59

836-00783 BARREL ASSY 22 Jun 10 23 Jun 10 2.67

836-00783 ZX FRAME ASSY 23 Jun 10 23 Jun 10 1.67

836-00783 CABLE ROUTING 24 Jun 10 24 Jun 10 2.5

836-00783 LINE PULL TEST 01 Jul 10 01 Jul 10 0.5

836-00783 ROPE UP BTM BLK 01 Jul 10 01 Jul 10 1

836-00783 END LINE TEST 02 Jul 10 02 Jul 10 1.5

836-00783 CRAB ASSY 10 Jul 10 13 Jul 10 2.75

836-00783 PACKING 13 Jul 10 13 Jul 10 0

A69957 836-00879 ZX064-3SONM4K031-LHR0002-40050 10/02/2010 10.68

836-00879 BARREL ASSY 01 Feb 10 01 Feb 10 1.59

836-00879 ZX FRAME ASSY 02 Feb 10 03 Feb 10 2

836-00879 TROLLEY ASSY 1 04 Feb 10 04 Feb 10 1

836-00879 TROL ASSY 2/3 04 Feb 10 05 Feb 10 1.09

836-00879 CABLE ROUTING 08 Feb 10 08 Feb 10 1.5

836-00879 LINE PULL TEST 08 Feb 10 08 Feb 10 0.5

836-00879 ROPE UP BTM BLK 08 Feb 10 08 Feb 10 1

836-00879 END LINE TEST 08 Feb 10 10 Feb 10 2

836-00879 PACKING 10 Feb 10 10 Feb 10 0

A70800 836-00879 ZX064-3SONM4K031-LHR0002-40050 24/02/2010 11.75


836-00879 ZX FRAME ASSY 19 Feb 10 19 Feb 10 2.5


836-00879 TROL ASSY 2/3 22 Feb 10 22 Feb 10 1



836-00879 ROPE UP BTM BLK 24 Feb 10 24 Feb 10 0.5


836-00879 PACKING 24 Feb 10 24 Feb 10 0

A70080 836-00903 ZX064-3SoNM5K041-LHR0002-40050 10/02/2010 10.17


836-00903 ZX FRAME ASSY 02 Feb 10 02 Feb 10 2


836-00903 TROL ASSY 2/3 04 Feb 10 04 Feb 10 1



836-00903 ROPE UP BTM BLK 08 Feb 10 08 Feb 10 0.5


836-00903 PACKING 10 Feb 10 10 Feb 10 0

A70882 836-00903 ZX064-3SoNM5K041-LHR0002-40050 05/03/2010 10.85


836-00903 ZX FRAME ASSY 01 Mar 10 01 Mar 10 1.5

836-00903 TROLLEY ASSY 1 01 Mar 10 01 Mar 10 1

836-00903 TROL ASSY 2/3 01 Mar 10 01 Mar 10 0.92

836-00903 CABLE ROUTING 01 Mar 10 01 Mar 10 1.59

836-00903 LINE PULL TEST 03 Mar 10 03 Mar 10 0.5

836-00903 ROPE UP BTM BLK 03 Mar 10 03 Mar 10 1

836-00903 END LINE TEST 04 Mar 10 05 Mar 10 2

836-00903 PACKING 04 Mar 10 05 Mar 10 0.84


__________________________________________________________Appendix 2

69

APPENDIX 2 - STREET CRANE CYCLE TIME DISTRIBUTION

Figure 39 - Summary Sheet Of Distribution Data Sampling

Description Distribution Type

Hoist Type Where

Distribution Is Used

Cell Where

Distribution Is

Used

ZX10 En of Line est – All Burr ZX10 - All models 9

ZX10 Wire & Test - DT Gamma ZX10 DT 11

ZX10 Wire & Test – SS & ST Kumaraswamy ZX10 SS & ST 11

ZX10 Assembly – DT Burr ZX10 DT 11

ZX10 Assembly – SS & ST Inverse Gaussian ZX10 SS & ST 11

ZX8 Barrel Assembly Time – ZX8 CRB & FM Burr ZX8 CRB & FM 1,8 or 11

ZX8 Barrel Assembly Time – LHR General Extreme ZX8 LHR 1,8 or 11

ZX8 Frame Assembly Time – All 2/4 Fall Wakeby ZX8 All 2/4 Fall 3 or 8

ZX8 Frame Assembly Time – All 6/8 Fall Burr ZX8 All 6/8 Fall 11

ZX8 Trolley Assembly Time – LHR Log Logistic ZX8 LHR 2/4 Fall 4

ZX8 Trolley Assembly 2/3 Time – LHR Log Logistic ZX8 LHR 2/4 Fall 5

ZX8 Cable routing Time – CRB 2/4 Fall Inverse Gaussian ZX8 CRB 2/4 Fall 6

ZX8 Cable routing Time – CRB 6/8 Fall Log Logistic ZX8 CRB 6/8 Fall 11

ZX8 Cable routing Time – FM Generalised Pareto ZX8 FM 6 or 11

ZX8 Cable routing Time – LHR 2/4 Fall Burr ZX8 LHR 2/4 Fall 6

ZX8 Cable routing Time – LHR 6/8 Fall Fatigue Life ZX8 LHR 6/8 Fall 11

ZX8 Line Pull Test Time – All Log Pearson ZX8 - All models 7

ZX8 Rope-up Time – All 2 Fall General Gamma ZX8 - All 2 Fall models 7

ZX8 Rope-up Time – All 4 Fall Burr ZX8 - All 4 Fall models 7

ZX8 Rope-up Time – All 6 Fall Wakeby ZX8 - All 6 Fall models 6 or 11

ZX8 Rope-up Time – All 8 Fall Log Logistic ZX8 - All 8 Fall models 6 or 11

ZX8 End of Line Test Time – All Gamma ZX8 - All models 9

ZX8 Packing Time – All General Extreme ZX8 - All models 10

ZX8 Crab Assembly Time – CRB All Falls Wakeby ZX8 CRB All Falls 8 or 11

ZX8 Crab Assembly Time – LHR 6/8 Fall Burr ZX8 LHR 6/8 Fall 11

ZX6 Barrel Assembly Time – All General logistic ZX6 All Models 1 or 8

ZX6 Frame Assembly Time – All General Extreme ZX6 All Models 3 or 8

ZX6 Trolley Assembly Time – LHR Log Logistic ZX6 LHR 4

ZX6 Trolley Assembly 2/3 Time – LHR Log Pearson ZX6 LHR 5

ZX6 Cable Routing Time – LHR Burr ZX6 LHR 6

ZX6 Cable Routing Time – CRB Log Logistic ZX6 CRB 6

ZX6 Cable Routing Time – FM Burr ZX6 FM 6

ZX6 Line Pull Test Time – All Rice ZX6 All Models 7

ZX6 Rope-up Time – All Burr ZX6 All Models 7

ZX6 End of Line Test Time – All General Extreme ZX6 All Models 9

ZX6 Crab Assembly Time – All Johnson SB ZX6 All Models 8

ZX6 Packing Time – All Pearson 5 ZX6 All Models 10


Appendix 3

70

APPENDIX 3 - CYCLE-TIME DISTRIBUTIONS

ZX10 End of line test - all variants

Figure 40 - PDF for all variants of ZX10 on End of Line Test

ZX10 Wire & test - DT

Figure 41 - PDF for DT variants of ZX10 on Wire and Test

ZX10 Wire & test - SS & ST

Figure 42 - PDF for SS & ST variants of ZX10 on Wire and Test

ZX10 End of Line Test - All Variants

Histogram Burr

Cycle time (hrs)

765432

f(x)

0.52

0.48

0.44

0.4

0.36

0.32

0.28

0.24

0.2

0.16

0.12

0.08

0.04

0

Distribution – Burr k = 1.08147 α 4.534334 β 3.505067 # Distribution Kolmogorov

Smirnov

Anderson Darling

Chi-Squared

Statistic Rank Statistic Rank Statistic Rank 2 Burr 0.1347304 1 0.4329841 10 1.762955 21

25 Gumbel Max 0.1411854 2 0.4121944 5 1.820167 27 40 Lognormal 0.1428626 3 0.4250525 8 1.801488 25

ZX10 Wire & Test - DT

Histogram Gamma (3P)

Cycle time (hrs)

3632282420161284

f(x)

0.55

0.5

0.45

0.4

0.35

0.3

0.25

0.2

0.15

0.1

0.05

0

Distribution – Gamma α = 0.7591906 β 11.22939 γ = 2.5 # Distribution Kolmogorov

Smirnov

Anderson Darling

Chi-Squared

Statistic Rank Statistic Rank Statistic Rank 19 Gamma (3P) 0.1071618 1 7.548347 45 N/A 23 Gen. Pareto 0.1086214 2 0.4170786 2 0.2680201 2 29 Johnson SB 0.1104032 3 0.3838761 1 1.085856 5

ZX10 Wire & Test - SS & ST

Histogram Kumaraswamy

Cycle time (hrs)

252015105

f(x)

0.44

0.4

0.36

0.32

0.28

0.24

0.2

0.16

0.12

0.08

0.04

0

Distribution – Kumaraswamy α1 = 1.206512 α2 = 222.266 a = 0.7219992 b = 580.5437 # Distribution Kolmogorov

Smirnov

Anderson Darling

Chi-Squared

Statistic Rank Statistic Rank Statistic Rank 31 Kumaraswamy 0.0692777 1 0.5915563 1 6.950295 10 58 Weibull (3P) 0.0696535 2 0.5930856 2 6.952396 11 23 Gen. Gamma (4P) 0.0697532 3 0.5934718 3 6.952486 12


__________________________________________________________Appendix 3

71

ZX10 Assembly - DT

Figure 43 - PDF for DT variants of ZX10 on Assembly

ZX10 Assembly - SS & ST

Figure 44 - PDF for SS & ST variants of ZX10 on Assembly

ZX8 Barrel assembly time - CRB & FM variants

Figure 45 - PDF for CRB & FM variants of ZX8 on Barrel Assembly

ZX10 Assembly - DT

Histogram Burr

Cycle time (hrs)

4035302520

f(x)

0.5

0.45

0.4

0.35

0.3

0.25

0.2

0.15

0.1

0.05

0

Distribution – Burr k = 0.517107 α 13.73048 β 22.88117 # Distribution Kolmogorov

Smirnov

Anderson Darling

Chi-Squared

Statistic Rank Statistic Rank Statistic Rank

2 Burr 0.079528 1 0.1944329 1 1.089862 18

37 Log-Logistic (3P) 0.081613 2 0.2041672 2 1.10868 20

3 Burr (4P) 0.0818452 3 0.2056219 3 1.108064 19

ZX10 Assembly - SS & ST

Histogram Inv. Gaussian

Cycle time (hrs)

5040302010

f(x)

0.44

0.4

0.36

0.32

0.28

0.24

0.2

0.16

0.12

0.08

0.04

0

Distribution – Inverse Gaussian λ = 248.8677 μ = 19.88852

# Distribution Kolmogorov Smirnov

Anderson Darling

Chi-Squared


28 Inv. Gaussian 0.0481849 1 0.3791777 14 2.687003 16

53 Rayleigh (2P) 0.0490625 2 0.4105187 19 1.790949 8

15 Fatigue Life 0.0505466 3 0.341572 2 1.484754 7

ZX8 Barrel Assembly Time - CRB & FM Variants

Histogram Burr (4P)

Cycle time (hrs)

12108642

f(x)

0.5

0.4

0.3

0.2

0.1

0

Distribution – Burr k = 0.5280175 σ 10.77693 β 4.669118 γ -2.146674

# Distribution Kolmogorov

Smirnov

Anderson

Darling

Chi-Squared


3 Burr (4P) 0.1074594 1 3.554914 1 50.23415 1

37 Log-Logistic (3P) 0.1077604 2 3.976809 4 65.63102 4

2 Burr 0.1079956 3 3.950913 2 65.69478 6


__________________________________________________________Appendix 3

72

ZX8 Barrel assembly time - LHR

Figure 46 - PDF for LHR variants of ZX8 on Barrel Assembly

ZX8 Frame assembly time - CRB & FM 2/4 falls

Figure 47 - PDF for CRB & FM 2/4 Fall variants of ZX8 on Frame Assembly

ZX8 Frame assembly time - 6/8 fall all variants

Figure 48 - PDF for 6/8 Fall variants of ZX8 on Frame Assembly

ZX8 Barrel Assembly Time - LHR & LHC

Histogram Gen. Extreme Value

Cycle time (hrs)

121086420

f(x)

0.8

0.72

0.64

0.56

0.48

0.4

0.32

0.24

0.16

0.08

0

Distribution – General Extreme k = 0.2397529 δ 0.3778651 μ 1.669713 # Distribution Kolmogorov

Smirnov

Anderson Darling

Chi-Squared


21 Gen. Extreme Value 0.1201122 1 32.61526 21 N/A

2 Burr 0.1347907 2 9.304144 2 382.8457 2

3 Burr (4P) 0.1377839 3 9.249433 1 377.1777 1

ZX8 Frame Assembly Time - All Variants

Histogram Wakeby

Cycle time (hrs)

2015105

f(x)

0.72

0.64

0.56

0.48

0.4

0.32

0.24

0.16

0.08

0

Distribution – Wakeby α = 278.4285 β = 83.92139 γ 1.391011 δ 0.1258894 ζ -1.116262

# DISTRIBUTION KOLMOGOROV

SMIRNOV

ANDERSON

DARLING

CHI-SQUARED


60 WAKEBY 0.0700419 1 11.33769 3 N/A

3 BURR (4P) 0.0777804 2 6.865691 1 133.2115 2

2 BURR 0.0813036 3 8.858123 2 130.3759 1

ZX8 Frame Assembly Time - 6/8 Fall Variants

Histogram Burr

Cycle time (hrs)

141210864

f(x)

0.28

0.26

0.24

0.22

0.2

0.18

0.16

0.14

0.12

0.1

0.08

0.06

0.04

0.02

0

Distribution – Burr k = 496.8598 α = 5.743046 β = 34.4693 # DISTRIBUTION KOLMOGOROV

SMIRNOV

ANDERSON

DARLING

CHI-SQUARED


2 BURR 0.0471006 1 0.5438014 4 8.804365 1

21 GEN. EXTREME VALUE 0.0507058 2 0.6432017 7 14.74508 20

31 JOHNSON SU 0.0544405 3 0.4967668 1 13.85122 13


__________________________________________________________Appendix 3

73

ZX8 Trolley assembly time - LHR

Figure 49 - PDF for LHR variants of ZX8 on Trolley Assembly

ZX8 Trolley assembly 2/3 time - LHR

Figure 50 - PDF for LHR variants of ZX8 on Trolley Assembly 2/3

ZX8 Cable routing time - CRB 2/4 Fall

Figure 51 - PDF for CRB 2/4 Fall variants of ZX8 on Cable Routing

ZX8 Trolley Assembly Time - LHR 2/4 Fall Variants

Histogram Log-Logistic (3P)

Cycle time (hrs)

543210

f(x)

0.72

0.64

0.56

0.48

0.4

0.32

0.24

0.16

0.08

0

Distribution – Log-Logistic α = 4.812424 β 0.6778694 γ 0.1972457 # Distribution Kolmogorov

Smirnov

Anderson Darling

Chi-Squared


34 Log-Logistic (3P) 0.1549023 1 22.44336 4 842.7591 11

6 Dagum (4P) 0.1575338 2 22.3139 3 843.7098 12

3 Burr (4P) 0.1598991 3 21.87751 1 848.6234 14

ZX8 Trolley Assembly 2/3 Time - LHC / LHR


Cycle time (hrs)

1086420

f(x)

1

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

Distribution – Log-Logistic α = 4.135496 β 0.8182648 γ 0.1514899 # Distribution Kolmogorov

Smirnov

Anderson Darling

Chi-Squared


35 Log-Logistic (3P) 0.1428849 1 16.09746 5 617.5289 8

20 Gen. Extreme Value 0.1460525 2 20.45221 13 618.8348 10

6 Dagum 0.1487524 3 15.6568 2 617.496 6

ZX8 Trolley Assembly 2/3 Time - LHC / LHR

Histogram Inv. Gaussian

Cycle time (hrs)

2520151050

f(x)

0.55

0.5

0.45

0.4

0.35

0.3

0.25

0.2

0.15

0.1

0.05

0

Distribution - Inverse Gaussian λ = 6.96671 μ 4.405886 # Distribution Kolmogorov

Smirnov

Anderson Darling

Chi-Squared


28 Inv. Gaussian 0.07598 1 0.6281517 15 4.32158 1

40 Lognormal (3P) 0.0774355 2 0.5433269 11 6.909811 9

29 Inv. Gaussian (3P) 0.0777099 3 0.6091184 14 7.946264 13


__________________________________________________________Appendix 3

74

ZX8 Cable routing time - CRB 6/8 falls

Figure 52 - PDF for CRB 6/8 Fall variants of ZX8 on Cable Routing

ZX8 Cable routing time - FM

Figure 53 - PDF for FM variants of ZX8 on Cable Routing

ZX8 Cable routing time - LHR 2/4 falls

Figure 54 - PDF for LHR 2/4 Fall variants of ZX8 on Cable Routing

ZX8 Cable routing Time - CRB 6/8 Fall


Cycle time (hrs)

12108642

f(x)

0.32

0.28

0.24

0.2

0.16

0.12

0.08

0.04

0

Distribution – Log-Logistic α = 5.243512 β 9.09024 γ -3.451846 # DISTRIBUTION KOLMOGOROV

SMIRNOV

ANDERSON

DARLING

CHI-SQUARED


36 LOG-LOGISTIC (3P) 0.0601989 1 0.282607 9 3.886376 5

7 DAGUM 0.0702104 2 0.2504889 1 6.880988 14

8 DAGUM (4P) 0.0710318 3 0.2528404 2 8.084905 26

ZX8 Cable routing Time - FM

Histogram Gen. Pareto

Cycle time (hrs)

121110987654321

f(x)

0.4

0.36

0.32

0.28

0.24

0.2

0.16

0.12

0.08

0.04

0

Distribution – Generalised Pareto k = -0.3291256 σ 5.223236 μ 0.8931126 # DISTRIBUTION KOLMOGOROV

SMIRNOV

ANDERSON

DARLING

CHI-SQUARED


24 GEN. PARETO 0.0730794 1 0.2020629 2 1.68992 25

30 JOHNSON SB 0.0739067 2 0.1846372 1 0.7024844 6

38 LOG-PEARSON 3 0.094907 3 0.2827201 3 0.9205102 9

ZX8 Frame Assembly Time - LHR 6/8 Fall Variants

Histogram Burr (4P)

Cycle time (hrs)

161412108642

f(x)

0.6

0.55

0.5

0.45

0.4

0.35

0.3

0.25

0.2

0.15

0.1

0.05

0

Distribution – Burr k = 0.4092073 α 9.503123 β 4.464578 γ -2.098865 # DISTRIBUTION KOLMOGOROV

SMIRNOV

ANDERSON

DARLING

CHI-SQUARED


3 BURR (4P) 0.0503108 1 1.79384 1 33.15232 3

2 BURR 0.0556799 2 2.105094 2 31.33453 1

7 DAGUM 0.0587015 3 2.97295 4 64.88283 5


__________________________________________________________Appendix 3

75

ZX8 Cable routing time - LHR 6/8 falls

Figure 55 - PDF for LHR 6/8 Fall variants of ZX8 on Cable Routing

ZX8 Line pull test time - all variants

Figure 56 - PDF for all variants of ZX8 on Line Pull Test

ZX8 Rope-up time - all 2 fall variants

Figure 57 - PDF for all 2 fall variants of ZX8 on Rope-up

ZX8 Cable routing Time - LHR / LHC 6/8 Fall

Histogram Fatigue Life (3P)

Cycle time (hrs)

108642

f(x)

0.32

0.28

0.24

0.2

0.16

0.12

0.08

0.04

0

Distribution – Fatigue Life Distribution α = 0.007506 β 307.9398 γ = -301.0421 # DISTRIBUTION KOLMOGOROV

SMIRNOV

ANDERSON

DARLING

CHI-SQUARED


16 FATIGUE LIFE (3P) 0.1078151 1 0.2531651 11 0.0739317 8

29 INV. GAUSSIAN (3P) 0.1081493 2 0.2476341 10 0.0682815 5

39 LOGISTIC 0.1091111 3 0.2669197 15 0.8406902 40

ZX8 Line Pull Test Time - All Variants

Histogram Log-Pearson 3

Cycle time (hrs)

6543210

f(x)

1.1

1

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

Distribution – Log-Pearson 3 Parameter α = 1.969122 β 0.2518998 γ -0.9536435 # DISTRIBUTION KOLMOGOROV

SMIRNOV

ANDERSON

DARLING

CHI-SQUARED


34 LOG-PEARSON 3 0.2650797 1 167.5913 39 N/A

14 FRECHET 0.2674476 2 84.79278 1 1185.382 2

38 NORMAL 0.270496 3 142.9538 35 3542.422 38

ZX8 Rope-Up Time - 2 Fall Variants

Histogram Gen. Gamma

Cycle time (hrs)

108642

f(x)

0.36

0.32

0.28

0.24

0.2

0.16

0.12

0.08

0.04

0

Distribution – General Gamma k = 0.9778099 δ 3.025703 μ 1.387396 # DISTRIBUTION KOLMOGOROV

SMIRNOV

ANDERSON

DARLING

CHI-SQUARED



40 LOGNORMAL (3P) 0.0529645 2 0.1742001 8 4.561529 21

45 PEARSON 5 (3P) 0.0531989 3 0.1588757 3 4.197518 14


__________________________________________________________Appendix 3

76








Histogram Burr (4P)

Cycle time (hrs)

161412108642

f(x)

0.6

0.56

0.52

0.48

0.44

0.4

0.36

0.32

0.28

0.24

0.2

0.16

0.12

0.08

0.04

0


SMIRNOV

ANDERSON

DARLING

CHI-SQUARED


3 BURR (4P) 0.0477191 1 1.767108 1 60.80933 4

2 BURR 0.0523837 2 2.062476 2 52.41425 1

7 DAGUM 0.0554853 3 2.956326 4 60.82049 5


Histogram Wakeby

Cycle time (hrs)

108642

f(x)

0.22

0.2

0.18

0.16

0.14

0.12

0.1

0.08

0.06

0.04

0.02

0

Distribution – Wakeby α = 54.26124 β 31.997 γ 7.465259 δ 0.7871496 # DISTRIBUTION KOLMOGOROV

SMIRNOV

ANDERSON

DARLING

CHI-SQUARED


61 WAKEBY 0.0610328 1 0.1362901 1 1.384995 22

31 JOHNSON SB 0.0631668 2 0.2188883 4 1.737394 24

38 LOG-PEARSON 3 0.0635461 3 0.2139905 3 1.142141 16



Cycle time (hrs)

12108642

f(x)

0.3

0.28

0.26

0.24

0.22

0.2

0.18

0.16

0.14

0.12

0.1

0.08

0.06

0.04

0.02

0

Distribution – Log-Logistic α = 9.178967 β 14.59555 γ = 8.819733 # DISTRIBUTION KOLMOGOROV

SMIRNOV

ANDERSON

DARLING

CHI-SQUARED


37 LOG-LOGISTIC (3P) 0.0569572 1 0.3112037 5 7.368598 35

24 GEN. LOGISTIC 0.0593257 2 0.3235013 11 6.126104 24

61 WAKEBY 0.0640833 3 0.2639663 1 6.920481 32


__________________________________________________________Appendix 3

77

ZX8 End of line test time - all variants

Figure 61 - PDF for all variants of ZX8 on End-of-Line Test

ZX8 Packing time - all variants

Figure 62 - PDF for all variants of ZX8 on Packing

ZX8 Crab assembly time - CRB all variants

Figure 63 - PDF for CRB 2/4 fall variants of ZX8 on Crab Assembly

ZX8 End of Line Test Time - All Variants

Histogram Gamma

Cycle time (hrs)

987654321

f(x)

0.6

0.55

0.5

0.45

0.4

0.35

0.3

0.25

0.2

0.15

0.1

0.05

0

Distribution – Fatigue Life Distribution α = 5.856415 β 0.396903 # DISTRIBUTION KOLMOGOROV

SMIRNOV

ANDERSON

DARLING

CHI-SQUARED


19 GAMMA 0.1876646 1 84.2315 24 1924.812 30

26 GUMBEL MAX 0.1901087 2 73.96984 17 1879.493 25

40 LOGNORMAL 0.1904525 3 64.5147 9 1836.516 19

ZX8 Packing Time - All Variants


Cycle time (hrs)

252015105

f(x)

1.6

1.4

1.2

1

0.8

0.6

0.4

0.2

0

Distribution – General Extreme k = 0.3637871 δ 0.332108 μ 1.108131 # DISTRIBUTION KOLMOGOROV

SMIRNOV

ANDERSON

DARLING

CHI-SQUARED



8 DAGUM (4P) 0.1776 2 38.65548 4 1479.531 4

15 FATIGUE LIFE 0.1779939 3 58.53319 19 1522.193 17

ZX8 Crab Assembly Time - All Variants

Histogram Wakeby

Cycle time (hrs)

108642

f(x)

0.48

0.44

0.4

0.36

0.32

0.28

0.24

0.2

0.16

0.12

0.08

0.04

0

Distribution – Wakeby α = 30.1711 β = 9.185647 γ = 0.7287804 δ = 0.2093373 ζ = -0.3216961

# DISTRIBUTION KOLMOGOROV

SMIRNOV

ANDERSON

DARLING

CHI-SQUARED


61 WAKEBY 0.1249245 1 3.451145 1 134.5576 2

4 CAUCHY 0.1275343 2 4.405837 2 51.5366 1

37 LOG-LOGISTIC (3P) 0.140773 3 8.723243 7 194.8182 20


__________________________________________________________Appendix 3

78

ZX8 Crab assembly time - LHR 6/8 fall variants

Figure 64 - PDF for LHR 6/8 fall variants of ZX8 on Crab Assembly

ZX6 Barrel assembly time - all variants

Figure 65 - PDF for All variants of ZX6 on Barrel Assembly

ZX6 Frame assembly time - all variants

Figure 66 - PDF for All variants of ZX6 on Frame Assembly

ZX8 Crab Assembly Time - LHR Variants

Histogram Cauchy

Cycle time (hrs)

8.887.26.45.64.843.2

f(x)

0.56

0.52

0.48

0.44

0.4

0.36

0.32

0.28

0.24

0.2

0.16

0.12

0.08

0.04

0

Distribution – Cauchy σ 0.5177225 μ 5.060881 # DISTRIBUTION KOLMOGOROV

SMIRNOV

ANDERSON

DARLING

CHI-SQUARED


4 CAUCHY 0.1843189 1 0.4587954 1 0.2806318 15

33 LAPLACE 0.1850915 2 0.492071 2 3.123549 49

11 ERROR 0.1854464 3 0.4961597 3 3.123531 48

ZX6 Barrel Assembly Time - All Variants

Histogram Gen. Logistic

Cycle time (hrs)

6.45.64.843.22.41.60.8

f(x)

0.4

0.36

0.32

0.28

0.24

0.2

0.16

0.12

0.08

0.04

0

Distribution – General Logistic k = 0.3529654 σ = 0.2685148 μ = 1.423823 # DISTRIBUTION KOLMOGOROV

SMIRNOV

ANDERSON

DARLING

CHI-SQUARED


24 GEN. LOGISTIC 0.133047 1 141.5019 36 N/A

2 BURR 0.1345875 2 25.78711 2 1038.536 4

60 WAKEBY 0.1358823 3 24.79037 1 1338.58 9

ZX6 Frame Assembly Time - All Variants


Cycle time (hrs)

12108642

f(x)

0.64

0.56

0.48

0.4

0.32

0.24

0.16

0.08

0

Distribution – General Extreme k = 0.3023477 α 0.5467881 β 1.88475 # DISTRIBUTION KOLMOGOROV

SMIRNOV

ANDERSON

DARLING

CHI-SQUARED


21 GEN. EXTREME VALUE 0.0762871 1 36.94412 6 N/A

2 BURR 0.0770891 2 10.38397 2 526.4907 12

3 BURR (4P) 0.0778827 3 9.752139 1 503.5971 11


__________________________________________________________Appendix 3

79

ZX6 Trolley assembly time - LHR

Figure 67 - PDF for LHR variants of ZX6 on Trolley Assembly 1

ZX6 Trolley assembly 2/3 time - LHR

Figure 68 - PDF for LHR variants of ZX6 on Trolley Assembly2/3

ZX6 Cable routing time - LHR

Figure 69 - PDF for LHR variants of ZX6 on Cable Routing

ZX6Trolley Assembly Time - All Variants

Histogram Log-Logistic

Cycle time (hrs)

32.521.510.5

f(x)

0.48

0.44

0.4

0.36

0.32

0.28

0.24

0.2

0.16

0.12

0.08

0.04

0

Distribution – Log-Logistic α = 5.610915 β 0.7961095 # DISTRIBUTION KOLMOGOROV

SMIRNOV

ANDERSON

DARLING

CHI-SQUARED


34 LOG-LOGISTIC 0.1699777 1 45.42452 6 1922.832 24

3 BURR (4P) 0.1703063 2 40.9354 1 877.1647 4

13 FATIGUE LIFE 0.1703818 3 55.01549 16 1920.795 22

ZX6 Trolley Assembly Time - LHR / LHC

Histogram Log-Pearson 3

Cycle time (hrs)

3.532.521.510.5

f(x)

0.72

0.64

0.56

0.48

0.4

0.32

0.24

0.16

0.08

0

Distribution – Log-Pearson 3 Parameter α = 208.9847 β = -0.0213544 γ 4.313397 # DISTRIBUTION KOLMOGOROV

SMIRNOV

ANDERSON

DARLING

CHI-SQUARED


35 LOG-PEARSON 3 0.1663461 1 44.89431 11 1936.342 14

46 PEARSON 6 (4P) 0.1668951 2 43.09965 7 1936.346 18


ZX6 Cable Routing Time - LHR / LHC

Histogram Burr (4P)

Cycle time (hrs)

20151050

f(x)

0.8

0.72

0.64

0.56

0.48

0.4

0.32

0.24

0.16

0.08

0


SMIRNOV

ANDERSON

DARLING

CHI-SQUARED


3 BURR (4P) 0.0718834 1 6.002953 1 328.7607 8

2 BURR 0.0727731 2 7.159316 2 316.7859 5

7 DAGUM 0.0767745 3 8.707279 4 318.4076 7


__________________________________________________________Appendix 3

80

ZX6 Cable routing time - CRB

Figure 70 - PDF for CRB variants of ZX6 on Cable Routing

ZX6 Cable routing time - FM

Figure 71 - PDF for FM variants of ZX6 on Cable Routing

ZX6 Line pull test time - all variants

Figure 72 - PDF for all variants of ZX6 on Line Pull Test

ZX6 Cable Routing Time - CRB / CRE


Cycle time (hrs)

20151050

f(x)

0.64

0.56

0.48

0.4

0.32

0.24

0.16

0.08

0

Distribution – Log-Logistic α = 3.634933 β 3.498207 γ -0.8902334 # DISTRIBUTION KOLMOGOROV

SMIRNOV

ANDERSON

DARLING

CHI-SQUARED


36 LOG-LOGISTIC (3P) 0.0909658 1 1.225602 2 14.76913 22

7 DAGUM 0.0985709 2 1.057907 1 4.51993 2

50 RAYLEIGH 0.1036765 3 3.344808 27 6.019246 3

ZX6 Cable Routing Time - FM

Histogram Burr (4P)

Cycle time (hrs)

108642

f(x)

0.56

0.52

0.48

0.44

0.4

0.36

0.32

0.28

0.24

0.2

0.16

0.12

0.08

0.04

0


SMIRNOV

ANDERSON

DARLING

CHI-SQUARED


3 BURR (4P) 0.0626822 1 0.3112994 1 8.630546 5

36 LOG-LOGISTIC (3P) 0.0684314 2 0.3928862 2 8.432349 2

8 DAGUM (4P) 0.0684537 3 0.4013878 3 8.550448 4

ZX6 Line Pull Test Time - All Variants

Histogram Rice

Cycle time (hrs)

4.543.532.521.510.5

f(x)

0.88

0.8

0.72

0.64

0.56

0.48

0.4

0.32

0.24

0.16

0.08

0

Distribution – Rice ν = 0.4836036 σ 0.2727408 # DISTRIBUTION KOLMOGOROV

SMIRNOV

ANDERSON

DARLING

CHI-SQUARED

Statistic Rank Statistic Rank Statistic Rank 50 RICE 0.3491075 1 362.963 26 5133.871 30

22 GUMBEL MAX 0.3498859 2 326.8881 23 11010.06 35

47 RAYLEIGH 0.3542044 3 383.8424 29 5348.803 31


__________________________________________________________Appendix 3

81

ZX6 Rope-up time - all variants

Figure 73 - PDF for all variants of ZX6 on Rope-up

ZX6 End of line test time - all variants

Figure 74 - PDF for all variants of ZX6 on End-of-Line Test

ZX6 Crab assembly time - all variants

Figure 75 - PDF for all CRB variants of ZX6 on Crab Assembly

ZX6 Rope-up Time - All Variants

Histogram Burr (4P)

Cycle time (hrs)

1086420

f(x)

1

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0


SMIRNOV

ANDERSON

DARLING

CHI-SQUARED


3 BURR (4P) 0.215375 1 80.93651 1 3423.532 14

22 GEN. GAMMA 0.2232415 2 141.1277 19 3604.71 21

2 BURR 0.2235395 3 81.3424 2 3419.742 13

ZX6 End of Line Test Time - All Variants


Cycle time (hrs)

8.887.26.45.64.843.22.41.60.8

f(x)

0.64

0.56

0.48

0.4

0.32

0.24

0.16

0.08

0

Distribution – General Extreme k = 0.0835149 δ 0.4115712 μ 1.809864 # DISTRIBUTION KOLMOGOROV

SMIRNOV

ANDERSON

DARLING

CHI-SQUARED



45 PEARSON 5 0.1813326 2 91.7465 26 2656.549 14

46 PEARSON 5 (3P) 0.1843568 3 113.8879 33 2555.488 6

ZX6 Crab Assembly Time - All Variants

Histogram Johnson SB

Cycle time (hrs)

43.532.521.5

f(x)

0.6

0.56

0.52

0.48

0.44

0.4

0.36

0.32

0.28

0.24

0.2

0.16

0.12

0.08

0.04

0

Distribution – Johnson SB γ = 1.268061 δ 0.9716015 λ 3.756405 ξ 1.537274 # DISTRIBUTION KOLMOGOROV

SMIRNOV

ANDERSON

DARLING

CHI-SQUARED


30 JOHNSON SB 0.1948007 1 8.423895 52 N/A

24 GEN. PARETO 0.1970093 2 8.426445 53 N/A

17 FRECHET 0.2012252 3 1.184864 24 6.330685 29


__________________________________________________________Appendix 3

82

ZX6 Packing time - all variants

Figure 76 - PDF for all variants of ZX6 on packing

ZX6 Packing Time - All Variants

Histogram Pearson 5 (3P)

Cycle time (hrs)

654321

f(x)

0.8

0.72

0.64

0.56

0.48

0.4

0.32

0.24

0.16

0.08

0

Distribution – Pearson Type 5 α = 16.00812 β 19.49298 γ = -.1446756 # DISTRIBUTION KOLMOGOROV

SMIRNOV

ANDERSON

DARLING

CHI-SQUARED


45 PEARSON 5 (3P) 0.270043 1 151.4917 7 1873.001 2

47 PEARSON 6 (4P) 0.270477 2 151.6131 8 1873.001 1

1 BETA 0.2709203 3 183.7113 22 4926.114 18


Appendix 4

83

APPENDIX 4 - CONFIDENCE LEVELS OF VALIDATION 2


Appendix 5

84

APPENDIX 5 - ZERO TIME LABOUR BOOKINGS 2012

Figure 77 - 2012 Zero time labour bookings

Model Type Falls Cell Operation

Time /

Operation

Number of

Zero Time

bookings

Time Not

Booked

ZX10 DT All 11 Assembly 25.6 4 102.3

ZX10 SS/ST All 11 Assembly 19.9 1 19.9

ZX10 DT All 11 Wire & Test Time 11.0 6 66.2

ZX10 SS/ST All 11 Wire & Test Time 6.9 9 61.9

ZX10 All All 9 End Of Line Test 3.7 38 139.2

ZX10 All All 10 Packing 1.5 58 86.6

ZX6 All All 1 or 8 Barrel Assembly 1.6 26 42.5

ZX6 All All 3 or 8 Frame Assembly 2.4 45 109.8

ZX6 LHR All 4 Trolley Assembly 1 0.9 15 13.0

ZX6 LHR All 4 Trolley Assembly 2/3 0.9 15 13.7

ZX6 CRB All 6 Cable Routing 3.1 4 12.2

ZX6 FTM All 6 Cable Routing 2.9 10 28.6

ZX6 LHR All 6 Cable Routing 3.0 25 75.5

ZX6 All All 7 Line Pull 0.6 67 38.2

ZX6 All All 7 Rope Up 1.2 64 78.2


ZX6 CRB All 8 Crab Assembly 2.5 97 241.4


ZX8 CRB /FM All 8 or 11 Barrel Assembly 3.3 101 330.9

ZX8 LHR All 1 or 11 Barrel Assembly 2.0 22 44.1

ZX8 All 2&4 1 or 8 Frame Assembly 3.8 20 75.1

ZX8 All 6&8 11 Frame Assembly 11.2 8 89.8

ZX8 LHR All 4 Trolley Assembly 1 1.0 20 19.5

ZX8 LHR All 4 Trolley Assembly 2/3 1.1 16 17.4

ZX8 CRB 2&4 6 Cable Routing 4.4 0 0.0

ZX8 CRB 6&8 11 Cable Routing 6.1 1 6.1

ZX8 FM All 6 or 11 Cable Routing 4.8 64 308.7

ZX8 LHR 2&4 6 Cable Routing 3.8 13 49.2

ZX8 LHR 6&8 6 or 11 Cable Routing 6.9 6 41.3

ZX8 All All 7 Line Pull 0.7 150 105.4

ZX8 All 2&4 7 Rope Up 3.8 13 49.9

ZX8 All 6&8 7 Rope Up 6.2 62 385.7



ZX8 CRB All 6 or 11 Crab Assembly 3.6 37 133.8

Total Not Booked 1171 3013.2

Tony Ponsonby siness Process Simulation of a Production Line

Appendix 6

85

APPENDIX 6 - EXPERIMENT 2 TOP 40 SCENARIOS

Sce

nar

io

Me

an Q

uan

tity

Dis

pat

che

d

Ce

ll1

Qu

anti

ty

Ce

ll3

Qu

anti

ty

Ce

ll4

Qu

anti

ty

Ce

ll6

Qu

anti

ty

Ce

ll8

Qu

anti

ty

Ce

ll9

Qu

anti

ty

Ce

ll1

1 Q

uan

tity

Ce

ll1

0 Q

uan

tity

Ce

ll7

_R

op

eU

p Q

uan

tity

Ge

ne

rato

r U

tili

sati

on

90

% M

in

90

% M

ax

95

% M

in

95

% M

ax

99

% M

in

99

% M

ax

248 976.2 3 4 3 6 1 4 6 4 4 98.6 945.5 1006.9 936.1 1016.3 909.9 1042.5

1094 970.7 4 4 3 5 1 4 5 3 4 98.4 935.3 1006.2 924.5 1017.0 894.2 1047.3

486 969.1 4 4 2 5 1 4 5 4 4 98.6 942.4 995.8 934.2 1003.9 911.4 1026.7

1118 968.7 4 4 3 6 2 4 5 3 4 98.4 915.4 1022.1 899.1 1038.4 853.4 1084.1

824 968.3 3 4 3 6 1 4 6 3 4 98.6 945.3 991.3 938.2 998.3 918.6 1017.9

1144 968.1 4 4 4 6 1 4 6 3 4 98.8 936.6 999.6 927.0 1009.2 900.1 1036.1

462 967.6 4 3 4 5 2 4 5 4 4 97.8 925.8 1009.4 913.0 1022.2 877.3 1057.9

1126 966.9 4 4 4 5 1 4 5 3 4 98.6 934.0 999.8 924.0 1009.8 895.9 1037.9

264 965.6 3 4 4 5 1 4 6 4 4 98.6 929.9 1001.3 919.0 1012.2 888.4 1042.8

792 965.6 3 4 2 6 1 4 6 3 4 98.6 922.6 1008.6 909.5 1021.7 872.7 1058.5

1096 964.9 4 4 3 5 1 4 6 3 4 98.4 940.4 989.5 932.8 997.0 911.8 1018.0

808 964.8 3 4 3 5 1 4 6 3 4 98.2 930.6 999.0 920.1 1009.4 890.9 1038.6

544 964.6 4 4 3 6 2 4 6 4 4 98.6 918.8 1010.5 904.8 1024.4 865.6 1063.6

1120 963.3 4 4 3 6 2 4 6 3 4 98.6 951.4 975.1 947.8 978.7 937.7 988.9

782 962.3 3 4 2 5 2 4 5 3 4 98.2 931.5 993.1 922.1 1002.5 895.8 1028.8

512 962.1 4 4 2 6 2 4 6 4 4 98.6 907.2 1017.0 890.5 1033.8 843.6 1080.7

214 961.5 3 4 2 6 1 4 5 4 4 98.6 935.1 987.8 927.1 995.8 904.6 1018.3

286 960.5 3 4 4 6 2 4 5 4 4 98 924.9 996.1 914.0 1006.9 883.6 1037.3

1008 960.3 4 3 3 5 2 4 6 3 4 98.4 937.5 983.1 930.5 990.1 911.0 1009.6

816 960.0 3 4 3 5 2 4 6 3 4 98.4 940.5 979.5 934.5 985.4 917.9 1002.1

552 957.8 4 4 4 5 1 4 6 4 4 99 949.3 966.3 946.7 968.9 939.4 976.2

520 957.7 4 4 3 5 1 4 6 4 4 98.6 946.9 968.4 943.6 971.7 934.4 980.9

174 957.2 3 3 4 5 2 4 5 4 4 98.2 938.1 976.2 932.3 982.0 916.0 998.3

550 957.2 4 4 4 5 1 4 5 4 4 98 934.7 979.6 927.8 986.5 908.6 1005.7

864 956.8 3 4 4 6 2 4 6 3 4 98.4 924.9 988.8 915.1 998.5 887.9 1025.8

534 956.7 4 4 3 6 1 4 5 4 4 98.4 917.8 995.5 905.9 1007.4 872.7 1040.6

830 956.5 3 4 3 6 2 4 5 3 4 98.8 913.5 999.5 900.4 1012.6 863.7 1049.3

1078 956.2 4 4 2 6 1 4 5 3 4 99 920.4 991.9 909.5 1002.8 879.0 1033.4

1134 956.0 4 4 4 5 2 4 5 3 4 98.4 923.7 988.3 913.9 998.1 886.3 1025.7

126 955.3 3 3 2 6 2 4 5 4 4 98 917.3 993.4 905.7 1005.0 873.1 1037.5

206 955.2 3 4 2 5 2 4 5 4 4 98.6 911.9 998.4 898.7 1011.6 861.8 1048.6

1088 955.2 4 4 2 6 2 4 6 3 4 98.8 911.0 999.4 897.5 1012.9 859.7 1050.7

510 954.8 4 4 2 6 2 4 5 4 4 98.2 900.1 1009.6 883.4 1026.3 836.6 1073.0

518 954.5 4 4 3 5 1 4 5 4 4 98.6 907.2 1001.8 892.8 1016.2 852.4 1056.6

528 954.5 4 4 3 5 2 4 6 4 4 98.4 905.7 1003.3 890.8 1018.2 849.1 1060.0

568 954.3 4 4 4 6 1 4 6 4 4 98.2 942.6 966.1 939.0 969.7 928.9 979.8

774 953.8 3 4 2 5 1 4 5 3 4 98.6 916.2 991.5 904.7 1003.0 872.6 1035.1

270 953.5 3 4 4 5 2 4 5 4 4 98.6 911.1 995.9 898.2 1008.8 862.0 1045.0

110 953.2 3 3 2 5 2 4 5 4 4 98.4 930.6 975.7 923.8 982.6 904.5 1001.9

1128 952.9 4 4 4 5 1 4 6 3 4 99 929.0 976.7 921.7 984.0 901.2 1004.5


Appendix 7

86

APPENDIX 7 - EXPERIMENT 3 RESULTS

Scenario

Mean Hoists Shipped

Generator Quantity

Cell7 LinePull Quantity

Generator Utilisation (%)

ZX6 Throughput Efficiency (%)



Line Pull Utilisation (%)

Combined Average Total Time

Combined Average Process Time

Combined Average Wasted Time

Combined TE

Combined Arrival Rate (Hoists/Hour)

Combined Avg WIP

Combined Hoists / m² of building

90% Min

90% Max

95% Min

95% Max

99% Min

99% Max

De

lay1

1968.31

193.7%

3.6%6.%

4.%51.5

41819

3994.5%

0.22594.0

41935

20021927

20101909

2028

De

lay2

1991.91

296.2%

3.7%6.%

4.%25.4

41319

3944.5%

0.22793.9

41968

20161962

20211949

2034

De

lay3

2006.41

396.9%

3.9%6.%

4.1%17

41019

3924.5%

0.22994.0

41970

20431961

20511942

2071

De

lay4

2284.92

154.5%

4.%6.%

5.%60.5

35618

3385.2%

0.26192.9

42278

22912277

22932273

2297

De

lay5

2293.22

254.7%

4.%6.%

5.%29.8

35618

3385.2%

0.26293.2

42284

23022282

23042278

2309

De

lay6

2289.42

354.8%

4.%6.%

5.%19.9

35618

3375.2%

0.26193.0

42280

22992278

23012273

2306

De

lay7

2293.43

137.%

4.%6.%

5.%60.4

35618

3385.2%

0.26293.3

42286

23012284

23032280

2307

De

lay8

2289.33

236.7%

4.%6.%

5.%29.5

35618

3385.2%

0.26193.1

42282

22972280

22992276

2303

De

lay9

2292.73

336.6%

4.%6.%

5.%19.8

35618

3385.2%

0.26293.2

42284

23022281

23042276

2309

De

lay10

2285.54

127.4%

4.%6.%

5.%60.2

35718

3385.2%

0.26193.0

42279

22922277

22942273

2298

De

lay11

2286.24

227.3%

4.%6.%

5.%29.7

35618

3385.2%

0.26193.0

42279

22932277

22952274

2299

De

lay12

2290.14

326.9%

4.%6.%

5.%19.8

35618

3375.2%

0.26193.0

42282

22982280

23002276

2304

No

De

lay1

2044.41

195.1%

4.8%23.9%

5.8%52.9

30018

2826.1%

0.23370.1

42008

20811999

20901979

2109

No

De

lay2

2212.81

298.9%

5.5%22.4%

5.3%28.2

26817

2516.4%

0.25367.8

42144

22822128

22982091

2335

No

De

lay3

2319.91

399.%

6.1%21.9%

5.8%19.7

23716

2216.9%

0.26562.7

42270

23692259

23812232

2408

No

De

lay4

3144.62

173.9%

8.%24.3%

9.9%82.9

18218

1649.8%

0.35965.3

63109

31803101

31893081

3208

No

De

lay5

3386.52

281.%

9.%23.4%

13.1%44.3

15618

13811.5%

0.38760.1

63371

34023367

34063359

3414

No

De

lay6

3259.62

375.3%

9.1%23.9%

12.3%28.1

14917

13211.5%

0.37255.6

63233

32863227

32923213

3306

No

De

lay7

3421.23

154.7%

9.%24.5%

13.4%91

15318

13511.7%

0.39159.8

73404

34393399

34433390

3453

No

De

lay8

3412.63

255.2%

9.1%24.4%

14.1%44.9

14518

12612.5%

0.39056.3

73396

34293392

34333383

3442

No

De

lay9

3411.33

355.2%

9.7%24.6%

14.4%30

14418

12612.5%

0.38956.0

73395

34273391

34313383

3440

No

De

lay10

34254

141.9%

9.%24.4%

14.%91

14818

13012.2%

0.39157.9

73409

34413406

34443397

3453

No

De

lay11

3415.74

241.3%

9.4%24.6%

14.5%45

14318

12512.6%

0.39056.0

73400

34313396

34353388

3444

No

De

lay12

3406.84

341.%

9.5%24.7%

14.5%29.9

14318

12512.6%

0.38955.7

63393

34203390

34233383

3431


Appendix 8

87

APPENDIX 8 - IUNIFORM DISTRIBUTIONS GOVERNING HOIST ATTRIBUTES

Table 18 - Iuniform Data to Assign Hoist Model Attribute

Table 19 - Iuniform Data to Assign ZX6 Attributes



Hoist

Model

Qty in

2012

Iuniform

from (>)

Iuniform

to (<=) % Split

ZX10 60 0 60 3.77

ZX6 863 60 923 54.28

ZX8 667 923 1590 41.95

Total 1590

Barrel

Length

No

Of

Falls

CRB

QTY

Iuniform

from (>)

Iuniform

to (<=)

FTM

QTY

Iuniform

from (>)

Iuniform

to (<=)

LHR

QTY

Iuniform

from (>)

Iuniform

to (<=) Total

2 1 0 1 6 1 7 34 7 41

4 9 41 50 29 50 79 45 79 124

2 1 124 125 1 125 126 24 126 150

4 86 150 236 52 236 288 575 288 863

2 1 863 864 17 864 881 51 881 932

4 49 932 981 49 981 1030 1206 1030 2236

Total 147 154 1935 2236

ZX6 Hoist Configurations

E

L

N

Barrel

Length

No

Of

Falls

CRB

QTY

Iuniform

from (>)

Iuniform

to (<=)

FTM

QTY

Iuniform

from (>)

Iuniform

to (<=)

LHR

QTY

Iuniform

from (>)

Iuniform

to (<=) Total

2 2 0 2 1 2 3 21 3 24

4 20 24 44 14 44 58 36 58 94

6 28 94 122 6 122 128 5 128 133

8 110 133 243 8 243 251 12 251 263

2 263 263 4 263 267 14 267 281

4 92 281 373 41 373 414 157 414 571

6 24 571 595 7 595 602 3 602 605

8 32 605 637 3 637 640 3 640 643

2 13 643 656 3 656 659 44 659 703

4 202 703 905 54 905 959 670 959 1629

6 16 1629 1645 1645 1645 5 1645 1650

8 1 1650 1651 1651 1651 1 1651 1652

Total 540 141 971 1652


N

L

E

Barrel

Length

ZX10

Configuration

CRB

QTY

Iuniform

from (>)

Iuniform

to (<=)

FTM

QTY

Iuniform

from (>)

Iuniform

to (<=) Total

E SS 0 0 0 2 0 2

DT 14 2 16 2 16 18

SS 8 18 26 3 26 29

ST 9 29 38 38 38

DT 3 38 41 41 41

SS 30 41 71 3 71 74

ST 3 74 77 77 77

SS 12 77 89 5 89 94

ST 0 94 94 94 94

DT 16 94 110 1 110 111

SS 1 111 112 1 112 113

ST 12 113 125 125 125

Total 108 17 125


S

V

L

N


Appendix 9

88

APPENDIX 9 - HOIST SIZE LOOK-UP TABLE

Ho

ist

Mo

de

l

Ho

ist

De

sign

Fall

s

Bar

rel

Len

gth

Co

de

Are

a m

²

Wid

th (

m)

De

pth

(m

)

Ho

ist

Mo

de

l

Ho

ist

De

sign

Fall

s

Bar

rel

Len

gth

ZX1

0

Co

nfi

gura

tio

n

Co

de

Are

a m

²

Wid

th (

m)

De

pth

(m

)

ZX6 CRB 2 E ZX6CRB2E 2.20 1.80 1.22 ZX8 FM 6 E ZX8FM6E 2.39 2.64 0.90

ZX6 FM 2 E ZX6FM2E 3.45 2.40 1.44 ZX8 LHR 6 E ZX8LHR6E 6.36 3.14 2.03

ZX6 LHR 2 E ZX6LHR2E 1.92 1.68 1.15 ZX8 CRB 6 L ZX8CRB6L 3.86 2.02 1.92

ZX6 CRB 2 L ZX6CRB2L 1.77 1.45 1.22 ZX8 FM 6 L ZX8FM6L 3.45 2.40 1.44

ZX6 FM 2 L ZX6FM2L 1.92 1.68 1.15 ZX8 LHR 6 L ZX8LHR6L 5.21 2.57 2.03

ZX6 LHR 2 L ZX6LHR2L 1.52 1.33 1.15 ZX8 CRB 6 N ZX8CRB6N 3.73 1.95 1.92

ZX6 CRB 2 N ZX6CRB2N 1.62 1.33 1.22 ZX8 FM 6 N ZX8FM6N 3.08 2.14 1.44

ZX6 FM 2 N ZX6FM2N 1.52 1.33 1.15 ZX8 LHR 6 N ZX8LHR6N 4.68 2.31 2.03

ZX6 LHR 2 N ZX6LHR2N 1.26 1.10 1.15 ZX8 CRB 8 E ZX8CRB8E 4.95 2.59 1.92

ZX6 CRB 4 E ZX6CRB4E 2.20 1.80 1.22 ZX8 FM 8 E ZX8FM8E 2.39 2.64 0.90

ZX6 FM 4 E ZX6FM4E 3.45 2.40 1.44 ZX8 LHR 8 E ZX8LHR8E 6.91 3.41 2.03

ZX6 LHR 4 E ZX6LHR4E 1.92 1.68 1.15 ZX8 CRB 8 L ZX8CRB8L 3.86 2.02 1.92

ZX6 CRB 4 L ZX6CRB4L 1.77 1.45 1.22 ZX8 FM 8 L ZX8FM8L 3.45 2.40 1.44

ZX6 FM 4 L ZX6FM4L 1.92 1.68 1.15 ZX8 LHR 8 L ZX8LHR8L 5.21 2.57 2.03

ZX6 LHR 4 L ZX6LHR4L 1.52 1.33 1.15 ZX8 CRB 8 N ZX8CRB8N 3.73 1.95 1.92

ZX6 CRB 4 N ZX6CRB4N 1.62 1.33 1.22 ZX8 FM 8 N ZX8FM8N 3.08 2.14 1.44

ZX6 FM 4 N ZX6FM4N 1.52 1.33 1.15 ZX8 LHR 8 N ZX8LHR8N 4.68 2.31 2.03

ZX6 LHR 4 N ZX6LHR4N 1.26 1.10 1.15 ZX10 CRE 6 E SS ZX10CREESS 8.39 4.10 2.04

ZX8 CRB 2 E ZX8CRB2E 3.97 2.35 1.69 ZX10 FM 6 E SS ZX10FMESS 3.62 2.94 1.23

ZX8 FM 2 E ZX8FM2E 2.25 2.51 0.90 ZX10 CRE 6 L SS ZX10CRELSS 8.15 3.99 2.04

ZX8 LHR 2 E ZX8LHR2E 3.45 2.40 1.44 ZX10 FM 6 L SS ZX10FMLSS 3.37 2.74 1.23

ZX8 CRB 2 L ZX8CRB2L 3.01 1.78 1.69 ZX10 CRE 6 N SS ZX10CRENSS 6.85 3.33 2.06

ZX8 FM 2 L ZX8FM2L 3.45 2.40 1.44 ZX10 FM 6 N SS ZX10FMNSS 2.88 2.34 1.23

ZX8 LHR 2 L ZX8LHR2L 2.64 1.84 1.44 ZX10 CRE 6 S SS ZX10CRESSS 5.48 2.66 2.06

ZX8 CRB 2 N ZX8CRB2N 2.57 1.52 1.69 ZX10 FM 6 S SS ZX10FMSSS 2.39 1.94 1.23

ZX8 FM 2 N ZX8FM2N 2.64 1.84 1.44 ZX10 CRE 6 V SS ZX10CREVSS 10.06 4.89 2.06

ZX8 LHR 2 N ZX8LHR2N 2.27 1.58 1.44 ZX10 FM 6 V SS ZX10FMVSS 4.24 3.44 1.23

ZX8 LHR 2 V ZX8LHR2V 4.03 2.80 1.44 ZX10 CRE 12 L ST ZX10CRELST 7.58 3.33 2.28

ZX8 CRB 4 E ZX8CRB4E 3.97 2.35 1.69 ZX10 FM 12 L ST ZX10FMLST 5.04 3.39 1.49

ZX8 FM 4 E ZX8FM4E 2.25 2.51 0.90 ZX10 CRE 12 L DT ZX10CRELDT 8.60 3.77 2.28

ZX8 LHR 4 E ZX8LHR4E 3.45 2.40 1.44 ZX10 FM 12 L DT ZX10FMLDT 5.63 3.77 1.49

ZX8 CRB 4 L ZX8CRB4L 3.01 1.78 1.69 ZX10 CRE 12 N ST ZX10CRENST 6.21 2.73 2.28

ZX8 FM 4 L ZX8FM4L 3.45 2.40 1.44 ZX10 FM 12 N ST ZX10FMNST 4.14 2.79 1.49

ZX8 LHR 4 L ZX8LHR4L 2.64 1.84 1.44 ZX10 CRE 12 N DT ZX10CRENDT 7.23 3.17 2.28

ZX8 CRB 4 N ZX8CRB4N 2.57 1.52 1.69 ZX10 FM 12 N DT ZX10FMNDT 4.74 3.17 1.49

ZX8 FM 4 N ZX8FM4N 2.64 1.84 1.44 ZX10 CRE 12 V DT ZX10CREVDT 10.88 4.77 2.28

ZX8 LHR 4 N ZX8LHR4N 2.27 1.58 1.44 ZX10 FM 12 V DT ZX10FMVDT 7.13 4.77 1.49

ZX8 LHR 4 V ZX8LHR4V 4.03 2.80 1.44 ZX10 CRE 16 V ST ZX10CREVST 9.15 4.33 2.12

ZX8 CRB 6 E ZX8CRB6E 4.95 2.59 1.92 ZX10 FM 16 V ST ZX10FMVST 6.51 4.39 1.49


Appendix 10

89

APPENDIX 10 - EXPERIMENT 4 RESULTS

Scenario

Mean Hoists Shipped

Cell1 Quantity

Cell3 Quantity

Cell4 Quantity

Cell6 Quantity

Cell8 Quantity

Cell9 Quantity

Cell11 Quantity

Cell10 Quantity

Cell7 Rope-Up Quantity

Generator Quantity

Cell7 LinePull Quantity

Generator Utilisation (%)




Line Pull Utilisation (%)

ZX6QTY

ZX8QTY

ZX10QTY

90% Min

90% Max

95% Min

95% Max

99% Min

99% Max

183890.1

44

36

34

64

44

247.1

923.4

1451.4

2210.81561.1

118.13875

39053872

39083864

3916

63890.2

34

36

34

64

44

247.1

923

1451.6

2206.61565.3

118.33878

39023875

39053869

3912

243884.9

44

46

34

64

44

246.9

923

1451.5

2209.51557.1

118.63873

38973870

39003864

3906

123888.1

34

46

34

64

44

246.8

923.1

1451.4

2205.31564.5

118.63872

39043868

39083860

3916

223710.1

44

46

24

64

44

245.1

9.424.1

14.249.1

21251466.4

118.53693

37273689

37313680

3740

43705.8

34

36

24

64

44

245.1

9.324

14.749.1

2115.31471.9

118.43687

37253682

37293672

3739

163711.9

44

36

24

64

44

244.8

9.323.9

14.249.1

2127.91465.7

118.33692

37323687

37373676

3748

103700.6

34

46

24

64

44

244.7

9.224.2

14.949

2108.71473.7

118.23683

37193678

37233669

3733

33658.6

34

36

24

64

44

144.1

924

12.397.3

2160.31380.7

117.83647

36703644

36733638

3679

93650

34

46

24

64

44

143.9

924.1

12.297.3

21601371.8

118.33640

36603638

36623633

3667

213650.8

44

46

24

64

44

143.8

924

11.997.3

2159.71372.4

118.73637

36653634

36683626

3675

153656.9

44

36

24

64

44

143.8

924.1

12.197.3

2161.61376.8

118.23646

36683643

36703638

3676

113674

34

46

34

64

44

143.6

8.824.1

10.897.9

2217.11338.2

118.63665

36833662

36863657

3691

233668.1

44

46

34

64

44

143.4

8.324.2

10.297.8

22241326.1

118.13655

36813652

36843645

3691

173668.6

44

36

34

64

44

143.4

8.524.1

10.397.6

2222.11328.1

118.53656

36813653

36843646

3691

53669

34

36

34

64

44

143.2

8.724.2

10.697.7

2221.61329.2

118.33658

36803655

36833649

3689

13425

34

36

14

64

44

141.9

924.4

1491

20031303.4

118.33409

34413406

34443397

3453

203417.4

44

46

14

64

44

241.5

1024.4

1445

1991.51307.7

118.23404

34313401

34343394

3441

73424.6

34

46

14

64

44

141.3

924.6

1491.1

20051301.8

1183410

34393406

34433398

3451

23415.7

34

36

14

64

44

241.3

9.424.6

14.545

1978.61319.2

117.93400

34313396

34353388

3444

133428.7

44

36

14

64

44

141.2

924.3

1491.1

2018.81292.1

117.83410

34473406

34523396

3462

193422.8

44

46

14

64

44

141.2

924.4

1491

2008.41296.2

118.13409

34373405

34403397

3448

143415.8

44

36

14

64

44

241.1

9.824.7

14.144.9

1989.31308.5

118.33401

34303398

34343390

3441

83416.9

34

46

14

64

44

240.9

9.624.4

14.244.9

1982.71316.3

1183402

34323398

34363390

3444


Appendix 11

90

APPENDIX 11 - HEALTH AND SAFETY ASSESSMENT

The MANCHESTER METROPOLITAN UNIVERSITY

Faculty of Science and Engineering

RISK ASSESSMENT COVER SHEET

SCHOOL: Science and Engineering

TITLE OF WORK: Process Simulation of a Production Line

LOCATION OF WORK: John Dalton E345 & E328

If off-site give contact details: NA

INTENDED ACTIVITIES: Simulation of business processes using Witness simulation

software within the MMU computer laboratory.

PERSONS AT RISK: Tony Ponsonby

HAZARDS: Prolonged exposure to display screen equipment.

Are these hazards necessary in order to achieve the objectives of the activity? Yes

Hazard Rating: Low

HAZARDOUS SUBSTANCES/MATERIALS USED AND HAZARD CLASSIFICATION: None ALL CONTAINERS OF HAZARDOUS SUBSTANCES SHOULD BEAR CORRECT HAZARD

WARNING LABELS.

NAME OF MATERIAL N/A

HAZARD CLASS

HAZARD LABEL

DISPOSAL N/A

PROCEDURE FOR EMERGENCY SHUT-DOWN: N/A

IF OFF-SITE INDICATE ANY OTHER ISSUES (e. . associate wit : in i i ual’s health and dietary requirements (obtain off-site health forms for all participating individuals and indicate where this information will be located); social activities, transportation, ID requirements; permissions for access and sampling).

NAME STAFF/STUDENT No. DATE

Originator Tony Ponsonby 02968984 16/11/12

Supervisor Mohammed Latif

Technical Manager - - -

Divisional / School Health and Safety Coordinator (p.p. HoS)

DATE TO BE REVIEWED BY:


Appendix 12

91

APPENDIX 12 - ETHICS CHECK FORM

Before completing the Ethics Check Form the person undertaking the activity should consider the following:

YES NO N/A

1. Is the size of sample proposed for any group enquiry larger than justifiably necessary?

N/A

2 Will any lines of enquiry cause undue distress or be impertinent?

N/A

3 Has any relationship between the researcher(s) and the participant(s), other than that required by the academic activity, been declared?

N/A

4 Have the participants been made fully aware of the true nature and purpose of the study? If NO is there satisfactory justification (such as the likelihood of the end results being affected) for withholding such information? (Details to be provided to the person approving the proposal).

N/A

5 Have the participants given their explicit consent? If NO is there satisfactory justification for not obtaining consent? (Details to be provided to the person approving the proposal).

N/A

6 Have the participants been informed at the outset that they can withdraw themselves and their data from the academic activity at any time?

N/A

7 Are due processes in place to ensure that the rights of those participants who may be unable to assess the implications of the proposed work are safeguarded?

N/A

8 Have any risks to the researcher(s), the participant(s) or the University been assessed? If YES to any of the above is the risk outweighed by the value of the academic activity?

YES

NO

9 If any academic activity is concerned with studies on activities which themselves raise questions of legality is there a persuasive rationale which demonstrates to the satisfaction of the University that: i the risk to the University in terms of external (and internal) perceptions of the worthiness of the work has been assessed and is deemed acceptable;

N/A

YES

ii arrangements are in place which safeguard the interests of the researcher(s) being supervised in pursuit of the academic activity objectives;

YES

iii special arrangements have been made for the security of related documentation and artifacts.

N/A

10 Have the ethical principles and guidelines of any external bodies associated with the academic activity been considered?

N/A


Appendix 12

92

Faculty of Science and Engineering Department of Engineering and Technology

Ethics Check Form

1 Name(s) of Applicant: Tony Ponsonby 2 Department: Engineering and Technology 3 Name of Supervisor: Dr Muhammad Latif 4 Title of Project: Business Process Simulation of a Production Line 5 Resume of ethical issues: 6 Does the project require the approval of any external agency?

YES If YES has approval been granted by the external agency? YES

7 Statement by Applicant

I confirm that to the best of my knowledge, I have made known all relevant information, and I undertake to inform my supervisor of any such information, which subsequently becomes available whether before or after the research has begun

Signature of Applicant:

Date: 6/12/12

8 Statement by Supervisor

Approval for the above named proposal is granted I confirm that there are no ethical issues requiring further consideration. (Any subsequent changes to the nature of the project will require a review of the ethical considerations): Signature of Supervisor: Date: I confirm that any issues identified overleaf as requiring further consideration have been satisfactorily addressed: Signature of Supervisor: Date:

Approval for the above named proposal is not granted I confirm that there are ethical issues requiring further consideration and will refer the project proposal to the appropriate Committee** Signature of Supervisor: Date:

msc thesis

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