FACILITY LAYOUT IMPROVEMENT USING SYSTEMATIC LAYOUT PLANNING (SLP) AND ARENA
CHEE AILING
A thesis submitted in fulfillment of the requirements for the award degree of Masters of Engineering (Industrial Engineering)
Faculty of Mechanical Engineering Universiti Teknologi Malaysia
May 2009
iii
ACKNOWLEDGEMENT
I would like to express my utmost gratitude and appreciation to my thesis lecturer, Dr Wong Kuan Yew, with his dedication and invaluable guidance through out the challenging process of completing this Masters Project. I would like to thank him for his prompt response in providing guidelines whenever I m faced with obstacles which has been an important source of motivation.
I also wish to thank the staff of Agilent, especially my operating manager who agrees for me to disclose the information of Agilent product line only for the purpose of this Masters project. Not to forget also the production line operators who have spent their valuable time in assisting me to collect the cycle time data for the process.
Last but not least, is to my family who has been the source of motivation, support and care. Their support has always been the pillar of strength for me throughout this project.
iv
ABSTRACT
The objective of this thesis is to improve the production floor layout of the MTA department and to evaluate the proposed alternative layouts using ARENA simulation. This project is conducted at Agilent Technologies, Inc., an Electronics Manufacturing company located in Bayan Lepas, Penang. The major problem faced by the company is high cross-over frequency for E-Cal and Coaxial Waveguide Adapter products between two buildings. There is high flow intensity between departments which have high interrelationship. This leads to high travelling time and high travelling cost. Two alternative layouts are proposed using the 11 steps in Systematic Layout Planning, which is a systematic way of generating layout alternatives. The proposed alternative layout involves transferring the departments which have high interrelationship close to each other. The proposed alternative layouts are evaluated using ARENA simulation student version. The best alternative is chosen based on the performance measures which have the most significant improvement, which are total travel distance, total travel time, total travel cost, number of cross-over, output, average resource utilization, total average WIP level, total average waiting time and total time spent in the system. The best alternative layout is Layout Design 2, which does not need extra space for re-layout. Total travel distance for Coaxial Waveguide Adapter will reduce significantly by 78.1% and for E-Cal the total travel distance will reduce by 62.87%. Total travel time for coaxial waveguide adapter is reduced by 86.42 % while for e-cal is reduced by 75.17%. This will subsequently reduce cost of travel for coaxial waveguide adapter by 86.42% and for E-cal is reduce by 68.09%. The output for coaxial waveguide adapter will increase 55.30 % as well. For e-cal the output will increase by 9.05 %.
v
ABSTRAK
Objektif projek ini adalah untuk memperbaiki susunatur jabatan produksi MTA dan memilih cadangan layout terbaik menggunakan simulasi ARENA. Kajian kes ini dilaksanakan di Agilent Technologies, Inc.,sebuah kilang elektronik di Bayan Lepas, Pulau Pinang. Masalah utama adalah bilangan ulang alik yang tinggi antara dua bangunan bersebelahan bagi produk Coaxial Waveguide Adapter dan E-Cal. Bahagian yang mempunyai hubungan ulang alik yang banyak terletak berjauhan. Ini mengakibatkan masa pengangkutan yang panjang dan kos yang tinggi. Kaedah Systematic Layout Planning digunakan untuk menghasilkan 2 cadangan alternatif susunatur.Dua cadangan ini bertujuan untuk menukar kedudukan stesen yang terletak berjauhan . Simulasi digunakan untuk memilih antara dua alternatif ini. Alternatif terbaik dipilih berdasarkan jumlah jarak pengangkutan,jumlah kos pengangkutan manual, bilangan ulang alik,jumlah masa pengangkutan,output, purata penggunaan sumber, purata WIP, purata masa menunggu dan purata masa dalam sistem. Susunatur cadangan kedua merupakan pilihan yang terbaik kerana tidak memerlukan ruangan yang lebih. Jumlah jarak pengangkutan bagi Coaxial Waveguide Adapter akan kurang 78.1% manakala bagi E-Cal akan kurang 62.87%. Jumlah masa pengangkutan bagi coaxial waveguide adapter akan kurang sebanyak 86.42% dan untuk E-Cal akan kurang 75.17%.Ini akan mengurangkan kos untuk mengangkut coaxial waveguide adapter sebanyak 86.42, untuk E-Cal akan kurang sebanyak 68.09%. Output untuk coaxial waveguide adapter akan bertambah sehingga 55.30% manakala untuk E-Cal akan bertambah 9.05%.
vi
TABLE OF CONTENTS
CHAPTER
1
TITLE
PAGE
DECLARATION
ii
ACKNOWLEDGEMENTS
iii
ABSTRACT
iv
ABSTRAK
v
TABLE OF CONTENTS
vi
LIST OF TABLES
xii
LIST OF FIGURES
xiii
LIST OF APPENDICES
xvi
INTRODUCTION
1.1
Background of the problem
1
1.2
Statement of problem
3
1.3
Objective
3
1.4
Scope
4
1.5
Methodology
4
1.6
Relevant literature
7
1.7
Significance of study
8
1.8
Arrangement of thesis
9
1.9
Conclusions
11
vii 2
LITERATURE REVIEW
2.1
Introduction
12
2.2
Plant layout
12
2.3
Facility layout planning
13
2.3.1
Objectives of facility layout planning
14
2.3.2
Factors affecting facility layout planning
15
2.3.2.1 Material
15
2.3.2.2 Machinery
15
2.3.2.3 Labor
16
2.3.2.4 Material handling
16
2.3.2.5 Waiting time
16
Importance of plant layout
17
2.3.3 2.4
Traditional types of facilities layout
17
2.4.1
Process ( Job shop ) layout
18
2.4.1.1 Advantages of process layout
19
2.4.1.2 Disadvantages of process layout
19
Product (Flow shop ) layout
20
2.4.2.1 Advantages of product layout
21
2.4.2.2 Disadvantages of product layout
21
Fixed position layout
22
2.4.3.1 Advantages of fixed position
22
2.4.2
2.4.3
layout 2.4.3.2 Disadvantages of fixed position
23
layout 2.4.4
Group technology layout
23
2.4.4.1 Advantages of cellular layout
24
2.4.4.2 Disadvantages of cellular layout
25
2.5
Non-traditional types of facility layout
26
2.6
Review on previous layout planning techniques
26
2.7
Systematic layout planning
29
2.8
Tools for layout design
39
viii 2.8.1
Definition of simulation
40
2.8.2
Trends in simulation
40
2.8.3
Uses of simulation
42
2.8.4
When should simulation be used
42
2.8.5
Simulation methodology
43
2.8.5.1 Problem formulation and setting
44
of objectives 2.8.5.2 Model boundary and scope
45
2.8.5.3 Conceptual model and assumption
45
Document 2.8.5.4 Model development
46
2.8.5.5 Data collecting cleansing and
47
analysis 2.8.5.6 Model verification
47
2.8.5.7 Model validation
48
2.8.5.8 Experimental design
48
2.8.5.9 Experimentation
49
2.8.5.10 Analysis
49
2.8.5.11 Reporting
50
2.9
Advantages and disadvantages of simulation
50
2.10
ARENA
51
2.10.1 ARENA reputation
52
2.10.2 ARENA methodology
53
2.10.3 ARENA technology
53
Previous projects
55
2.11.1 Systematic layout plan for Baystate
57
2.11
services 2.11.2 Systematic layout planning : A study on
58
Semiconductor wafer fabrication facilities. 2.11.3 An empirical study using a modified SLP
58
procedure 2.11.4 Facility planning for a gas manufacturing plant
59
ix 2.11.5 The carbolite case study: Lean approach
60
To Systematic layout planning 2.12
Comparisons between my work and previous
62
works 2.13
3
62
COMPANY BACKGROUND
3.1
Introduction
64
3.2
General information
64
3.2.1
Business group
65
3.2.2
Strategy
66
3.2.3
Market leadership
66
3.2.4
History
66
3.2.5
Microwave test accessories(MTA)
67
3.2.6
Strategy
67
3.2.7
MTA Charter
68
3.2.8
MTA vision
68
3.2.9
Key products
68
3.3
Company Structure
69
3.4
Factory layout
70
3.5
Manufacturing process
70
3.5.1
Electronic Calibration Kit
71
3.5.2
Coaxial waveguide adapter
72
3.6
4
Conclusions
Conclusions
73
PROBLEM IDENTIFICATION
4.1
Introduction
74
4.2
Cross-over diagram
74
4.2.1
Electronic calibration kit
74
4.2.2
Coaxial waveguide adapter
76
4.3
Quantity of cross-over chart
78
4.4
Travel cost
78
x
5
6
4.5
From-To-Chart
79
4.6
Overall From-To Chart
81
4.7
Overall From To-Chart(with closeness ratings)
82
4.8
Conclusions
83
SYSTEMATIC LAYOUT PLANING
5.1
Introduction
84
5.2
Input data
85
5.2.1
Standard time
85
5.2.2
Flow of materials
88
5.3
Activity relationship chart
90
5.4
Relationship diagram
92
5.5
Space requirements
93
5.6
Space available
94
5.7
Space relationship diagram
96
5.8
Modifying constraints
98
5.9
Practical limitations
98
5.10
Develop layout alternatives
99
5.11
Conclusions
100
DATA ANALYSIS AND MODELLING
6.1
Introduction
101
6.2
Conceptual Model
101
6.3
Performance measures
102
6.4
Conceptual model validation
103
6.5
Model description
105
6.6
Assumptions
106
6.7
Model construction
107
6.7.1
Basic process
108
6.7.2
Advanced transfer
108
6.8
Model verification
109
6.9
Model validation
113
xi 6.10
7
8
Steady state system
117
6.10.1 Warm up period
117
6.10.2 Obtaining sample observation
119
6.10.3 Simulation run length
119
6.11
Number of replication determination
120
6.12
Conclusions
122
SIMULATION EXPERIMENTATION AND RESULTS
7.1
Introduction
123
7.2
Experimentation
123
7.3
Experiment 1 : Layout Design 1
124
7.4
Experiment 2 : Layout Design 2
129
7.5
Discussion
134
7.6
Conclusion
138
CONCLUSIONS AND RECOMMENDATIONS
8.1
Introduction
139
8.2
Project Summary
139
8.3
Findings
140
8.4
Further recommendations
141
8.5
Conclusions
141
REFERENCES
142
APPENDICES
146
xii
LIST OF TABLES
TABLE NO.
2.1
TITLE
Summary of Layout Planning
PAGE
27
Techniques 2.2
Simulation Software
52
2.3
Summary of previous projects
56
5.1
PQRST Analysis for E-Cal
87
5.2
PQRST Analysis for Coaxial
87
Waveguide Adapter 5.3
Space requirements information
94
6.1
Output of 10 replications
121
7.1
Performance measures for coaxial waveguide
127
adapter Layout Design 1 7.2
Performance measures for E-cal Layout Design 1
128
7.3
Performance measures for coaxial waveguide
132
adapter Layout Design 2 7.4
Performance measures for E-cal Layout Design 2
133
7.5
Performance measures for Coaxial Waveguide
135
Adapter 7.6
Performance measures for E-cal
136
xiii
LIST OF FIGURES
FIGURE NO.
TITLE
PAGE
1.1
Project Methodology
5
2.1
Process Layout
18
2.2
Product Layout
20
2.3
Fixed Position Layout
22
2.4
Group Technology Layout
24
2.5
Types of Layout
25
2.6
Non traditional types of facilities layout
26
2.7
Steps for Systematic Layout Planning
30
2.8
Activity Relationship Chart
32
2.9
Relationship Chart
33
2.10
Relationship Chart
34
2.11
Relationship Diagram
35
2.12
Space Requirements
35
2.13
Space Available
36
2.14
Activity Relationship Diagrams
37
2.15
Block Plan
38
2.16
Detailed Layout
39
2.17
Tools and Techniques for Layout Design
39
2.18
Simulation Methodology
44
3.1
Department Structure
69
3.2
Production Plant Layout
70
4.1
Cross-over diagram for e-cal
77
4.2
Cross-over diagram for coaxial waveguide
77
adapter
xiv 4.3
Flow Intensity Matrix
79
4.4
Inter department Distance Matrix
80
4.5
Calculation of Overall From-To-Chart
81
4.6
Overall From-To Chart (with closeness ratings)
82
5.1
Product Flow for E-Cal
89
5.2
Product Flow for Coaxial Waveguide Adapter
90
5.3
Activity Relationship Diagram
91
5.4
Relationship Diagram
92
5.5
Extra space available
95
5.6
No Extra Space Available
96
5.7
Space relationship diagram(Option 1)
97
5.8
Space relationship diagram(Option 2)
97
5.9
Layout Alternative 1
99
5.10
Layout Alternative 2
100
6.1
Conceptual Model For Coaxial Waveguide
102
Adapter 6.2
Conceptual Model For E-Cal
102
6.3
Statistical Input of ARENA Input Analyzer
104
6.4
Block modules for coaxial waveguide adapter
105
6.5
Block modules for E-Cal
106
6.6
Basic Process and Advanced Transfer Panel
107
6.7
Station marker placement for coaxial waveguide
109
adapter 6.8
Step button location in ARENA
110
6.9
Set maximum arrival
110
6.10
Screen shot of increase / decrease IAT
111
6.11
SIMAN language window
113
6.12
Historical Data Comparisons
114
6.13
Average resource utilization
115
6.14
Graphical comparisons between actual output
116
and model output 6.15
Comparisons of actual total travel time for coaxial waveguide adapter and simulated total travel time
116
xv 6.16
Average resource utilization versus time(minutes)
118
for coaxial waveguide adapter( Design 1) 6.17
Average resource utilization versus time(minutes)
118
for coaxial waveguide adapter (Design 2) 6.18
Average resource utilization versus time(minutes)
118
for E-Cal (Design 1) 6.19
Average resource utilization versus time(minutes)
119
for E-Cal (Design 2) 7.1
Simulation model for coaxial waveguide adapter
125
design 1 7.2
Simulation model for e-cal design 1
125
7.3
Route and station placement for coaxial
126
waveguide adapter design 1 7.4
Route and station placement for e- cal design 2
126
7.5
Simulation model for coaxial waveguide adapter
129
design 2 7.6
Simulation model for e-cal design 2
130
7.7
Route and station placement for coaxial
130
waveguide adapter design 2 7.8
Route and station placement for e-cal design 2
131
xvi
LIST OF APPENDICES
APPENDIX
A
TITLE
Process flow diagram of e-cal and coaxial
PAGE
146
waveguide adapter B
Calculation of number of cross-over
148
C
Travelling cost calculation
150
D
Department Distance Calculation
151
E
Cycle Time and Sample Size Calculation
152
F
Additional process cycle time (40 observations)
160
G
ARENA Input Analyzer Analysis for E-Cal and
167
H
ARENA Input Analyzer Analysis for
175
Coaxial Waveguide Adapter I
Data Collection for walking time Design 1
181
J
Input Analyzer distribution for walking time
188
Design 1 K
Data Collection for walking time Design 2
191
L
New process cycle time Design 1
197
M
New process cycle time Design 2
198
CHAPTER I
INTRODUCTION
1.1
Background of the problem
In the 21st century business world, companies are exposed to continuous challenges. One of it is to equip organizations with the ability to compete in a global marketplace. (Schonberger, 1986) states
..world class performance is dedicated to
serving the customer. Thus, in order to keep track of performance, organizations must develop measures of performance. The current trend in the electronics industry, which is experiencing very competitive era like many others is striving hard to reduce manufacturing costs, improve quality and customer satisfaction.
Materials handling equipment and the facilities it operates can contribute to as much as 70 percent of the total cost of the manufactured product (Tompkins et al, 1996). Facilities layout design is part of facilities planning (Tompkins et al, 1996). It is the arrangement of work space which, in general terms smoothes the way to access facilities that have strong interactions. The main concern with the plant facility layout planning is to reduce the cost of materials handling as poor materials handling can generate business problems. As Sims (Industrial Engineering May 1990) states The best material handling is no handling . Subsequently, a good layout will enable the manufacture of the product economically in the required volume and variety.
2 Other objectives can be stated as effective utilization of manpower, space and infrastructure, as well as providing overall wellbeing and morale of the worker.
Today s manufacturing industry is facing problems that have been growing in size and complexity over the last several years. As a result, there is an immediate need for procedures or techniques in solving various problems encountered in today s manufacturing arena without extended shutdown s or expensive modifications ( Clark ,1996). Computer simulation is a powerful tool that allows experimentation with various manufacturing techniques and layout without actual implementation. Simulation can be used as a stochastic model to evaluate the randomness of events which exists. Simulation predicts the behavior of complex manufacturing systems by determining the movement and interactions of system components. It is capable of aiding in the design of the most complex layout and also allows the user to evaluate alternative solutions to examine the flexibility of a design ( Eneyo and Pannirselvam,1998).
Based on the above facts, it is obvious that layout optimization and simulation are two tasks that are crucial to any facility planning and layout study (Grajo, 1996). If not tackled in the early phases, it can generate logistics implications for the company involved.
1.2
Statement of the problem
Agilent Technologies, Inc. is an electronics manufacturing company located in Bayan Lepas. It has numerous business units where the problem area which is Microwave Test Accessories (MTA) department will be discussed here. MTA has 2 main buildings, Building 5 and Building 6.Frequency of cross over is high between the two buildings as stations for some products are located in both buildings.
3 Processes which have high interdependency are not located close to each other. This causes high travelling time for the operator as they have to travel to and fro from building 5 to building 6. The labor cost of each product is also high due high travelling cost.
In response to the above problems, the need for facilities layout optimization and a model capable of simulating workstation production on new layout proposals to evaluate the performance measures related to the manufacturing goals of the company is needed. This thesis proposes to use Systematic Layout Planning (SLP) as the infrastructure for layout optimization. Subsequently simulation using ARENA is then used to systematically to examine the role and impact of product complexity and other key variables on factory performance. The factory performance improvements are in terms of cycle time reduction, productivity increase, reduction in travelling cost and reduction in travelling distance.
1.3
Objective
i. To improve the production floor layout at the MTA department. ii. To evaluate the proposed alternative layouts using simulation.
1.4
Scope
In this thesis, the case study is limited to the MTA production floor of Agilent Technologies, Inc. The products selected will be only Electronic Calibration Kit and Coaxial Waveguide Adapter. This work focuses on improving the facilities design of the production floor. The layout of the production is process oriented layout. The
4 Systematic Layout Planning (SLP) methodology will be utilized in this case study as part of the strategy to portrait the relationship between each department to generate improved layout alternatives. The future layout alternative will be evaluated using simulation software
1.5
ARENA.
Methodology
This project is divided into Masters Project I and Masters Project II, which has to be completed in Semester I and Semester II respectively. The methodology for this project is stated in Figure 1.1 on the next page.
5
Figure 1.1 Project Methodology
6 Literature review would be done throughout the 2 semesters to have a more detailed background and theoretical knowledge regarding Facilities Planning, Systematic Layout Planning and simulation ARENA. This would provide evidence of familiarity with the areas covered in this study and its classifications. Apart from that, current trends, direction and research issues were identified. Previous studies, journal papers, online articles were reviewed critically. Subsequently, evidence of not repeating what others have done will guide in the formulation of problem statement and the justification of proper selection of tools and techniques to be used.
After reviewing previous journals and studies, a company is selected to be studied. The company background is discussed briefly. It covers the company profile, organization structure of the company, understanding the current layout of the company, its manufacturing process and its policies.
Subsequently, the problems faced by the company will be identified. The scope will be limited to the facilities layout of the company and two of the major high revenue products. The process flow for each of the product will be observed and documented. The distance travelled by the operator is calculated. Tools such as crossover chart/spaghetti diagram, From-To-Chart are used. Time studies will be used as the method of cycle time calculation.
Following that, the Systematic Layout Planning (SLP) will be used for the generation of layout alternatives in Chapter 5. Systematic Layout Planning is used in this case study as it is a procedural approach which incorporates both qualitative and quantitative data. It is a proven tool in providing layout design guidelines in practice in the past few decades. At the end of the first semester, new layout proposals will be proposed to improve the facilities layout of the company. Other than that, simulation software, ARENA will be learnt to enhance skills of constructing the model of the layout plan and flow of product for semester two. A draft report will be send to the supervisor.
7
In Masters Project II, the model of the proposed improvement layouts will be modeled using ARENA. The types of data distribution will be justified. The models will be validated and verified. Experimentation of each layout proposal will be done using the simulation model. The results will be analyzed and compared with the existing layout.
Finally the results of each alternative layout are compared to select the one with the most significant improvement to the company. The full report will be submitted to the supervisor upon completion.
1.6
Relevant Literature
Global competitiveness and advances in technology have given rise to the need for effective space utilization ( Muther, 1976). Immer (1950) presented the basic steps in the layout planning as related to material handling and outlined the need for the representation of the flow and its depiction in terms of the output produced by equipment. In the early days of research pertaining to plant layout, the approach was typically one of minimizing the distance traveled between work centers.
Reed (1961) devised a layout planning chart as the single most important phase of systematic planning in plant layout. Other approaches are such as Systematic Layout Planning (Muther, 1973), steepest descent search method by pairwise exchange, graph based construction method, Tabu search, simulated annealing and genetic algorithms.
8 Other computer aided layout techniques have been developed as well such as CRAFT, ALDEP, COFAD, CORELAP, MULTIPLE, BLOCPLAN. There are also a few commercial packages available for facility layout design namely, PROMODEL, LayOPT, FactoryPLAN and Factory modeler.
From the review of the literature, it can be concluded that layout design problem has been an active research area in the past few decades ( Meller and Gau, 1996). However, most of the research does not integrate the layout improvement methodology together with simulation. Therefore the following paragraph summarizes the significance of this case study by using the SLP methodology and simulation using ARENA for optimization.
1.7
Significance of Study
An approach from Muther (1973), Systematic Layout Planning (SLP) is used as the improvement method. It uses a graphical representation and builds up a proximity matrix which represents the closeness of each facility. Flowcharts can also be developed showing quantitative relationships.
From the above proximity matrix a trial and error process can be used to generate the layout but again this approach has not been shown to be a particularly efficient method in practice. Simulation provides a more powerful tool (a 6 sigma capable tool) than those commonly used in a lean
6 sigma process. Simulation is
uniquely able to support achieving a corporate goal of finding a correct or at least a very good solution that meets the system design and operation requirements before implementation at minimal cost. Therefore simulation using ARENA is used as tool in this study to address issues that the Systematic Layout Planning approach failed to identify and could not solve.
9
This case study which focuses on manufacturing activities in the electronics industry can also be easily applied with minimal modification in other types of facilities such as offices where workflow processes may be present. Thus the model described possesses a general applicability in other domains that can be achieved through mapping of equivalent governing parameters to those that have been identified in the manufacturing sector.
1.8
Arrangement of Thesis
This project report consists of 8 chapters. Chapter 1 begins with an overview of Facilities Layout Planning definitions and its principles. Objectives and Scope of the case study are also well defined. Subsequently, some relevant literature is reviewed to justify the significance of this study.
Chapter 2 will be the literature review on facilities layout planning with the focus area in Systematic Layout Planning methodology, simulation techniques and its importance. Other than that the integration of Layout design and Simulation will also be discussed. Finally, relevant research and previous journals will be summarized with emphasis on the strengths and gaps. Subsequently evidence of the strength of this project compared to the previous studies will be highlighted.
Chapter 3 will discuss about the methodology of the thesis, including types of data to be collected, tools and techniques used to solve the problem and performance measures.
10 Chapter 4 will review on the background of the company. The company s profile, policies, current layout structure will be described. The problem identification will also be discussed. Cross over diagrams, process flow mapping and travelling cost calculation will be utilized to describe the problem of the production line.
Chapter 5 will adopt the Systematic Layout Planning (SLP) methodology to generate preliminary proposed layout alternatives to the current production line.
Chapter 6 will be the data analysis and modeling. Cycle time data will be collected and the distribution for each product will be determined. The existing and proposed layout will be modeled. Verification and validation of the model will be included as well.
Chapter 7 will discuss on the simulations experiments which also covers the results generated for the existing layout and the proposed layout. The results of the simulation using ARENA are discussed. The comparisons of the existing model and the improved model will be conducted
Chapter 8 will discuss on the best model (layout) to be selected It will summarize the findings from this study and recommendation for future work will be proposed.
11 1.9
Conclusion
In the beginning of this chapter, an overview of the facilities layout planning and its importance to existing companies is written to further enhance the importance for using it as the main principle for this project. The objectives are also defined to be linked to the deliverables in this case study. The boundary of this project is also defined based on the statement of problem .Some justifications of conducting this case study and its importance is also discussed. At the end of this chapter, the overall structure of the thesis is stated to provide the reader a helicopter s view of the whole thesis.
Subsequently, the literature review of facilities planning, SLP and simulation will be discussed in the following chapter to further enhance the reader s understanding.
12
CHAPTER II
LITERATURE REVIEW
2.1
Introduction
In this chapter an overview of Facilities Layout Planning, types of layout, Systematic Planning methodology and simulation will be discussed. This will give a brief overview on the tools and techniques used for this case study. Integration of layout and simulation will also be written to project the benefits of using simulation in this study.
2.2
Plant Layout
Plant layout planning includes decisions regarding the physical allocation of the economic activity centers in a facility. An economic activity center is any entity occupying space. The plant layout process starts at an aggregate level, taking into account the different departments. As soon as the details are analyzed, different issues arise and the original configuration maybe changed through a feedback
13 process. Most layouts are designed properly for the initial conditions of the business, although as long as the company grows and has adapted to internal and external changes, a re-layout is necessary. Symptoms that allow us to detect the need for a relayout: Congestion and bad utilization of space. Excessive stock in process at the facility Long distances in the work flow process Simultaneous bottle necks and workstations with idle time Qualified workers carrying out too many simple operations Labor anxiety and discomfort. Accidents at the facility. Difficulty in controlling operations and personnel.
2.3
Facility Layout Planning
A facility layout is an arrangement of everything needed for production of goods or delivery of services. A facility is an entity that facilitates the performance of any job. It may be a machine tool, a work centre, a manufacturing cell, a machine shop, a department, a warehouse, etc. ( Heragu,1997). It means planning for the locations of all machines, utilities, employee workstations, customer service areas, material storage area, aisles, restrooms, lunchrooms, internal walls, offices and computer rooms. This is for the flow patterns of materials and people around, into and within buildings.
The layout design generally depends on the products variety and the production volumes. Four types of organization are referred to, namely fixed product layout, process layout, product layout and cellular layout (Dilworth, 1996)
14
2.3.1 Objectives of Facility Layout Planning
The main objective consists of organizing equipment and working areas in the most efficient way, and at the same time satisfactory and safe for the personnel doing the work. Product design and Volume.( Product strategy) Process equipment and capacity (process strategy ) Quality of work life( human resource strategy) Building and site constraints( location strategy)
These main objectives are reached through the attainment of the following facts: Congestion reduction. Elimination of unnecessary occupied areas. Reduction of administrative and indirect work. Improvement on control and supervision. Better adjustment to changing conditions. Better utilization of the workforce, equipment and services. Reduction of material handling activities and stock in process. Reduction on parts and quality risks. Reduction on health risks and increase on workers safety. Moral and workers satisfaction increase. Reduction on delays and manufacturing time, as well as increase in production capacity.
All these factors will not be reached simultaneously, so the best solution will be a balance among them
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2.3.2 Factor Affecting Facilities Layout Planning
The final solution for a Plant Layout has to take into account a balance among the characteristics and considerations of all factors affecting plant layout, in order to get the maximum advantages. The factors affecting plant layout can be grouped into 5 main categories: Materials Machinery Labor Material Handling Waiting Time
2.3.2.1 Material
The layout of the productive equipment will depend on the characteristics of the product to be managed at the facility, as well as the different parts and materials to work on. Main factors to be considered: size, shape, volume, weight, and the physical-chemical characteristics, since they influence the manufacturing methods and storage and material handling processes. The sequence and order of the operations will affect plant layout as well, taking into account the variety and quantity to produce.
2.3.2.2 Machinery
Having information about the processes, machinery, tools and necessary equipment, as well as their use and requirements is essential to design a correct layout. The methods and time studies to improve the processes are closely linked to
16 the plant layout. Regarding machinery, the type, total available for each type, as well as type and quantity of tools and equipment has to be considered. It s essential as well to know about space required, shape, height, weight, quantity and type of workers required, risks for the personnel, requirements of auxiliary services, etc
2.3.2.3 Labor
Labor has to be organized in the production process (direct labor, supervision and auxiliary services). Environment considerations: employees safety, light conditions, ventilation, temperature, noise, etc. Process considerations: personnel qualifications, flexibility, number of workers required at a given time as well as the type of work to be performed by them.
2.3.2.4 Material Handling
Material handling does not add value to the product; it s just waste. Objective: Minimize material handling as well as combining with other operations when possible, eliminating unnecessary and costly movements.
2.3.2.5 Waiting Time
Objective: Continuous Material Flow through the facility, avoiding the cost of waiting time and demurrages that happen when the flow stops. On the other hand, the material waiting to flow through the facility not always represents a cost to avoid. As stock sometimes provides safety to protect production, improving customer service, allowing more economic batches, etc. It s necessary then to consider space for the required stock at the facility when designing the layout. Resting time to cool down or heating up.
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2.3.3 Importance of plant layout
Plant layout can be varied and can significantly impact the overall effectiveness of production systems. Since 1955, approximately 8 percent of the gross national product (GNP) has been spent annually on new facilities, and it is generally accepted that effective facilities planning can reduce material handling cost by at least 10 to 30 percent (Tompkins et al, 1996). The magnitude of the investment in the new facilities each year renders the criticality to the plant layout generations function. The main objectives of the plant layout function are to enable the manufacture of the product economically in the required volume and variety. Other objectives can be stated as effective utilization of manpower, space and infrastructure, as well as providing for the overall wellbeing and morale of the worker.
2.4
Traditional types of facilities layout
Traditionally 4 types of layout are considered appropriate for a manufacturing facility: Process ( Job Shop ) Layout Product (Flow Shop ) Layout Fixed Position Layout Group technology Layout
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2.4.1 Process ( Job Shop ) Layout
In the job shop layout, machines are grouped according to function to machine centers. Orders for individual products are routed through the various machine centers to obtain the required processing. Designed to facilitate processing items or providing services that present a variety of processing requirements. The layout includes departments or other functional groupings in which similar kinds of activities are performed. This type of plant layout is useful when the production process is organized in batches. Personnel and equipment to perform the same function are allocated in the same area. The different items have to move from one area to another one, according to the sequence of operations previously established. The variety of products will lead to diversity of flows through the facility. Variations in the production volumes from one period to the next one (short period of time) may lead to modifications in the manufactured quantities as well as the types of products to be produced.
Diagram of process layout is shown in Figure 2.1
Lathe S t o r a g e
Lathe
Drill
Weld
Lathe
Lathe
Drill
Paint
Mill
Mill
Grind
Assembly
Mill
Mill
Grind
Assembly
Figure 2.1: Process Layout
Weld
Paint
W a r e h o u s e
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2.4.1.1 Advantages of Process Layout
A high degree of flexibility exists relative to equipment or manpower allocation for specific tasks. Smaller investment in equipment as duplication is not necessary unless volume is large. The diversity of tasks offers a more interesting and satisfying occupation for the operator. Supervisors for each department become highly, knowledgeable about their functions. Better utilization of machines can result in fewer machines used.
2.4.1.2 Disadvantages of Process Layout
Lack of process efficiency as back tracking and long movements may occur in the handling of materials. Lack of efficiency in timing as workers must wait between tasks. Complications of production planning and control Workers must have broad skills and must be paid higher wages than assembly line workers. Comparatively large amounts of in process inventory as space and capital are tied up by work in process. Lowered productivity as each job requires different setups and operator training.
20 2.4.2 Product (Flow Shop) Layout
Here the product (or products) follows a fixed path through the production resources. The resources are arranged to minimize the material movement. This type of plant layout is useful when the production process is organized in a continuous or repetitive way.
Continuous flow: The correct operations flow is reached through the layout design and equipment and machinery specifications. Repetitive flow (assembly line): The correct operations flow will be based in a line balancing exercise, in order to avoid problems generated by bottle necks.
The plant layout will be based in allocating a machine as close as possible to the next one in line, in the correct sequence to manufacture the product. A job is divided into a series of standardized tasks, permitting specialization of both labor and equipment. Because of the high volume of production, the machines on the line can be designed with a high level of fixed automation, with very little manual labor. Operations are arranged in the sequence required to make the product.
Diagram of Product Layout is shown in Figure 2.2
Lathe S t o r a g e
Drill
Press
Bend
Mill
Drill
Lathe
Lathe
Grind
Drill
Drill
Drill
Figure 2.2: Product Layout
A s s e m b l y
W a r e h o u s e
21
2.4.2.1 Advantages of Product Layout
Since the layout corresponds to the sequence of operations, smooth and logical flow lines result. Since the work from one process is fed directly into the next, small in-process inventories result. Total production time per unit is short. Since the machines are located so as to minimize distances between consecutive operations, material handling is reduced. Little skill is usually required by operators at the production line; hence, training is simple, short, and inexpensive. Simple production planning control systems are possible. Less space is occupied by work in transit and for temporary storage
2.4.2.2 Disadvantages of Product Layout
A breakdown of one machine or absence of enough operators to staff all work stations may stop the entire line. Lack of process flexibility, since the layout is determined by the product, a change in product design may require major alternations in the layout. Lack of flexibility in timing, as the product cannot flow through the line faster then the slowest task can be accomplished unless that task is performed at several stations. Supervision is general, rather than specialized. Comparatively high investment is required, as identical machines (a few not fully utilized) are sometimes distributed along the line. Worker fatigue as workers may become bored by the endless repetition of simple tasks.
22
2.4.3 Fixed Position Layout
For tasks on large objects such as the manufacture of an electrical generator, the construction of a building, or the repair of a large airplane, the machines implementing the operation must come to the product, rather than the product moving to the machine. In fixed position layouts, the item being worked on remains stationary and workers, materials and equipment are moved as needed. Fixed positions layouts are used in large construction projects (buildings, power plants and dams), shipbuilding and production of large aircraft and space mission rockets. Fixed position are widely used for farming, firefighting, road building, home building, remodeling and repair and drilling for oil. Diagram of Fixed Position Layout is shown in Figure 2.3
Lathe
Press
Grind W a r e h o u s e
S t o r a g e Weld
Paint
Assembly
Figure 2.3: Fixed Position Layout
2.4.3.1 Advantages of Fixed Position Layout
Material movement is reduced, minimizes damage or cost of moving. Promotes job enlargement by allowing individuals or teams to perform the whole job .
23 Continuity of operations and responsibility results from team. This reduces the problems of re-planning and instructing people each time a new type of activity is to begin. Highly flexible; can accommodate changes in product design, product mix, and product volume. Independence of production centers allowing scheduling to achieve minimum total production time.
2.4.3.2 Disadvantages of Fixed Position Layout
Increased movement of personnel and equipment may be expensive. The necessary combination of skills may be difficult to find and high pay levels maybe necessary. Equipment duplication may occur. Higher skill requirements for personnel as they are involved in more operations. General supervision required. Cumbersome and costly positioning of material and machinery. Low equipment utilization as equipment may be left at a location where it will be needed again in a few days rather than moved to another location where it would be productive.
2.4.4 Group Technology Layout / Cellular Layout
Definition of Group Technology: Group technology us the technique of indentifying and bringing together related or similar parts in a production process in order to utilize the inherent economy of flow production methods. V.B Solaja,
24 Institute of Machine Tools, Belgrade, Yugoslavia. Group technology is also called cellular layout. Cellular layout is a type of layout in which machines are grouped into what is referred to as a cell. Groupings are determined by the operations needed to perform work for a set of similar items or part families that require similar processing. It is the physical division of the manufacturing facilities into production cells. Each cell is designed to produce a part family. A part family is a set of parts that require similar machinery, tooling, machine operations and jig or fixtures. The parts within the family normally go from raw material to finished parts within a single cell. Things to consider during implementation are reorganization of machine layout. Supervisors must also be expert in several field .Cell concepts leads to unbalanced workload on machines and needs to be reviewed from time to time
Diagram of Group Technology Layout is shown in Figure 2.4
Lathe S t o r a g e
Drill
Grind
Assembly
Mill
Assembly
Weld
Paint
Press
Lathe
Drill
Press
Assembly
Grind
Drill
Assembly
Drill
Grind
Figure 2.4: Group Technology Layout
2.4.4.1 Advantages of Cellular Layout
Reduced material handling Reduced set up time Reduced tooling
W a r e h o u s e
25 Reduced in process inventory Increase operator expertise Improved human relations, job enlargement tend to occur. Supports the use of general purpose equipment
2.4.4.2 Disadvantages of Cellular Layout
General supervision required. Higher skills level required of employees than for product layout. Reduced shop flexibility Depends on balanced material flow between product layout and process layout, other wise buffers and work in process storage are required. Lower machine utilization than for process layout Extended job flow times.
Types of layout in relations to volume and product variety are shown in Figure 2.5.
Volume High Product Planning Department
Medium
Low
Product Layout Fixed Location Layout Fixed Materials Location Planning Department
Low
Product Family Planning Department Group Technology Layout
Medium
Figure 2.5: Types of layout
Process Layout
Process Planning Department High
Variety
26 2.5
Non Traditional Types of Facilities Layout
In an extension to the traditional layouts, many non traditional layouts have been introduced. The below figure presents a classification of traditional and non traditional facility layout using a product versus process focus as the basis for grouping and placement of machines in a manufacturing facility layout. The prevailing new concepts that appeared in recent literature on facility layout include Agile, Flexible, Fractal, Holonic, Hybrid Cellular, Modular, Multi- Channel Manufacturing and responsibility Networks layout. An overview on these layouts is shown in Figure 2.6 below:
Figure 2.6: Nontraditional types of facilities layout
2.6
Review on Previous Layout Planning Techniques
The trends and history of previous layout planning techniques is reviewed. Table 2.1 in the next page shows a summary of the layout planning techniques and the timeline.
27
Table 2.1: Summary of Layout Planning Techniques
28 From the review of the literature indicated in the above section, it can be concluded that there have been numerous research activities in the area of layout design. There also have been a number of algorithms developed. Existing literature for a layout design problem often fall unto two major categories as algorithmic and procedural approaches Algorithmic approaches usually simplify both design constraints and objectives in order to reach a surrogate objective function which solution can then be obtained (Peters and Yang 1997;Cardarelli and Pelagagge,1995;Geiger et al,1997).These approaches usually involve quantitative input data. Their design solutions are easier to be evaluated by comparing their objectives functions.
The output from algorithmic approaches often need further modifications in order to satisfy detailed design requirements such as departmental shapes, utilities supply ,material handling system, ergonomics concerns, work in process storage, space utilization, etc. Advance training in mathematical modeling techniques are often pre-requisites for a designer to use algorithmic approaches. Accordingly many companies hesitate to adopt algorithmic approaches as their design methodologies.
Procedural approaches can incorporate both qualitative and quantitative objectives in the design process ( Padilli et al,1997;Apple ,1997;Muther 1973) For these approaches, the design process is divided into several steps that are then solved sequentially. The success of a procedural approach implementation is dependant on the generation of quality design alternatives that are often from the output of an experienced designer.
Systematic Layout Planning (SLP) is a procedural layout design approach. The process involved in performing SLP is relatively straight forward; however, it is a proven tool in providing layout design guidelines in practice in the past few decades. This case study proposes to use Muther s systematic layout planning (SLP)(Muther 1973) as the infrastructure to solve an electronic layout problem.
29 Based on the review of literature on previous study most of them do not utilize the integration of SLP and simulation Simulation is widely used as a stochastic model to evaluate a proposed materials handling system in which a randomness of events exists. Simulation predicts the behavior of complex manufacturing systems by determining the movement and interaction of system components. It is capable of aiding in the design of the most complex automated materials handling system and also allows the user to evaluate alternative solutions and to examine the flexibility of a design ( Eneyo and Pannieselvam,1998)
2.7
Systematic Layout Planning
In 1973, Richard Muther proposed the Systematic Layout Planning ( SLP) method that formalizes the whole layout process as a pattern of procedures through which each layout project passes. The design of process is being treated stepwise starting from the generation of alternatives, evaluation, selection and implementation. It has been widely used since its introduction which has proven to work well in many situations. Overall there are 11 stages required to complete an SLP. 1. Gather input data 2. Identify flow of material/information 3. Identify relationships between activities and resources. 4. Create a string diagram. 5. Determine space requirements 6. Quantify space availability 7. Create a space relationship diagram 8. Identify modifying considerations 9. Apply practical limitation 10. Developing layout alternatives 11. Evaluation of final design
30
Figure 2.7: Step for Systematic Layout Planning
The above Figure 2.7 shows the steps involved in SLP (Muther, 1973)
31 Step 1 : Input Data and Activities
The input variables for every SLP and P, Q, R, S and T. P (Product), material or service that will be processed. Q (Quantity), is the volume each item to be processed. R (Routing), is the path an item travels to be processed. S (Services), refers to services required to complete this processing and T (Time), refers to the overall time required to complete processing should be scrutinized in order to assure the validness of the input data at the design stage. This requires gathering and analyzing data required for the project. This must occur before any planning of relationships, space or adjustment. The preliminary data gathering-and analysis step is termed as Input Data & Activities and follows the general sequence found below: 1. Identify specific elements of input data needed as design criteria for the project. 2. Project this data into the future. (This involves restructuring information supplied by others in the organization.) 3. Seek general approval and top management endorsement of the input data. 4. Examine the data for distinctive dissimilarities to arrive at a basic layout. 5. Identify and define the activities to be used in subsequent planning.
Step 2 : Flow of Materials Analysis
All material flows from the whole production line are aggregated into a fromto-chart that represents the flow intensity among different departments. The analysis of material flow involved determining the most effective sequence of work and material. An effective flow means that the materials move progressively through the process and should always advance without excessive detours. In traditional manufacturing applications, the flow is determined from either the product or the process as shown in Figure2.8.
32
Figure 2.8: Material Flow Analysis
Step 3: Activity Relationship Diagram
The step of activity relationships performs qualitative analysis towards the closeness relationship decision between activities and resources. The results will be displayed into an activity relationship chart. The relationship chart displays which entities are related to others and it also rates the importance of the closeness between them. These ratings make the relationship chart one of the most effective tools for layout planning and are the best way of planning the arrangement of facilities. The activity relationship chart itself is a record keeping tool to organize data into a usable form. With this data and Activity Relationship Diagram was generated where proximity and relationship are visually evident. The relationship is defined by a closeness rating system: A meaning that it is absolutely necessary that the activities be next to each other. E meaning that it is especially necessary that the activities be close to each other. I meaning that it is important the activities be close to each other. O meaning that ordinary closeness be maintained (meaning that it is only necessary that these activities be in the same facility). U meaning that it is unimportant the activities be close to each other and
33 X meaning that the activities should not be close to each other.
For each relationship defined, the reason s why a specific closeness ratings was used is also noted. Example of relationship chart is shown in Figure 2.9 and Figure 2.10.
Figure 2.9: Relationship Chart
34 .
Figure 2.10: Relationship Chart
Step 4 : Relationship Diagram
This step positions departments spatially. For those departments that have strong interactions and/or closeness relationships are placed in proximity. The activity relationship diagram or string diagram is essentially a visual display of the activity relationship chart. Each entity on the chart is translated to a symbol to be place on the diagram and then lines are connected to show the value of the relationship. The string diagram shows near optimal placement without consideration for space requirements and exposes possible clustering of departments. As shown in Figure 2.11
35
Figure 2.11: Relationship Diagram
Step 5: Space Requirements
Now that relationships have been identified, special requirements must be analyzed and then applied to a spatial relationship diagram. The information to be included in terms of amount of space, equipment and operational improvements for each activity has to be determined as shown in Figure 2.12.
Figure 2.12: Space Requirements
36 Step 6 : Space Available
During this step, a square footage is assigned to each activity. The space assigned to each activity is predicated previously in the space requirements step. The total available space at the plant is reviewed. The area is divided at first approach to estimate the space required for each department. When performing the detailed layout, it is required to have more accurate shapes adjusted to the reality. Example in Figure 2.13.
Figure 2.13: Space Available
Step 7: Space Relationship Diagram
Adds departmental size information into the relationship diagram from step 4. At this point, the space requirements are applied to the space available. The purpose of the space relationship diagram is to combine established spatial constraints with the activity relationship diagram in Figure 2.14.
37
Figure 2.14: Activity Relationship Diagrams
Step 8: Modifying Constraints
These are additional constraints for the department during the initial stages of the new layout design. It is in terms of space requirement or department personnel needs.
Step 9: Practical Limitations
Practical limitations can be in terms of budget or space.
Step 10: Develop Layout Alternatives
This step involves development of layout alternatives as design candidates. These initial designs were created using the requirements and constraints described before. This is a layout of facility using blocks of space, no details. The block plan is developed y using the space available information and the relationship chart that
38 have been previously developed. With this information, blocks of space are developed and positioned according to their relationships defined in the relationship chart. The pros and cons of each layout are compared as each layout had good traits that are combined into a final block plan layout. Usually these designs are brought to the management for further inputs and comments in Figure 2.15.
Figure 2.15: Block Plan
Step 11: Evaluation
Chooses the final design from the design candidates. Once a final block plan layout has been selected, the equipment layout can then be developed. Equipment and machinery layout within each department is presented in the detailed layout in Figure 2.16.
39
Figure 2.16: Detailed Layout
2.8
Tools for Facility Layout Design
There are many tools and techniques for design of facility layouts. Figure 2.17 below summarizes it. ARENA is chosen as the tools for this case study improvement.
Figure 2.17: Tools and Techniques for Layout Design
40 2.8.1 Definition of simulation
Simulation is the process of designing a model of a real system and conducting experiments with this model for the purposes either of understanding the behavior of the system or of evaluating various strategies (within the limits imposed by a criterion or set of criteria) for the operation of the system ( R.E. Shannon,1975). A model is a representation of a system or process. A simulation model is a representation that incorporates time and the changes that occur over time. A discrete model is one whose state changes only at discrete points in time, not continuously.
2.8.2 Trends in Simulation
Approaches that looks promising and which has just begun to be explored in the simulation optimization context are model-based methods. These are contrasted with what are called instance-based approaches, which generate new solutions based only on the current solution (or population of solutions) ( Dorigo and Stützle 2004 ). The metaheuristics described earlier generally fall into this latter category, with the exception of tabu search, because it uses memory. Model-based methods, on the other hand, are not dependent explicitly on any current set of solutions, but use a probability distribution on the space of solutions to provide an estimate of where the best solutions are located. The following are some examples:
Swarm Intelligence
This approach is perhaps best known under the name of Ant Colony Optimization," because it uses ant behavior (group cooperation and use of pheromone updates and evaporation) as a paradigm for its probabilistic workings.
41 Because there is memory involved in the mechanisms, like tabu search, it is not instance-based; see Dorigo and Stützle (2004) for more details.
Estimation of Distribution Algorithms (EDAs).
The goal of this approach is to progressively improve a probability distribution on the solution space based on samples generated from the current distribution. The crudest form of this would utilize all samples generated to a certain point, hence the use of memory, but in practical implementation, parameterization of the distribution is generally employed, and the parameters are updated based on the samples; see Larrañaga and Lozano (2002) for more details.
Cross-Entropy (CE) Method.
This approach grew out of a procedure to find an optimal importance sampling measure by projecting a parameterized probability distribution, using cross entropy to measure the distance from the optimum measure. Like EDAs, samples are taken that are used to update the parameter values for the distribution. Taking the optimal measure as a point mass at the solution optimum of an optimization problem, the procedure can be applied in that context; see De Boer et al. (2005) and Rubinstein and Kroese (2004) for more details.
Model Reference Adaptive Search.
As in EDAs, this approach updates a parameterized probability distribution, and like the CE method, it also uses the cross-entropy measure to project a parameterized distribution. However, the particular projection used relies on a stochastic sequence of reference distributions rather than a single fixed reference
42 distribution (the final optimal measure) as in the CE method, and this result in very different performance in practice.
2.8.3 Uses of Simulation
A simulation model is a descriptive model of a process or system, and usually includes parameters that allow the model to be configurable, that is, to represent a number of somewhat different systems or process configurations. Simple examples include parameters that allow a user to vary the number of workers at a workstation, the speed of a machine or vehicle, the timing characteristics of a conveyor control system, and so on. As a descriptive model, a simulation model is used to experiment with, and evaluate and compare, any number of system alternatives. Evaluation, comparison and analysis are the key reasons for doing simulation. Prediction of system performance and identification of system problems and their causes are the key results.
2.8.4 When Should Simulation be Used?
Simulation is most useful in the following situations: ( Michael C Fu. et al,2005)
There is no simple analytic model, spreadsheet model or back of the envelope calculation that is sufficiently accurate to analyze the situation. The real system is regularized; that is, it is not chaotic and out of control. System components can be defined and characterized and their interaction defined.
43 The real system has some level of complexity, interaction or interdependence between various components, or pure size that makes it difficult to grasp in its entirety. In particular, it is difficult or impossible to predict the effect of proposed changes. A new system is designed, considering major changes in physical layout or operating rules in an existing system, or being faced with new and different demand. A large investment is needed in a new or existing system, and it represents a system modification of a type for which the user has little or no experience and hence faces considerable risk. A tool where all the people involved can agree on a set of assumptions, and then see (both statistically and with animation) the results and effects of those assumptions. That is, the simulation process as well as the simulation model can be used to get all members of a team onto a (more) common understanding. Simulation with animation is an excellent training and educational device, for managers, supervisors, engineers and labor. In fact, in systems of large physical scale, the simulation animation may be the only way in which most participants can visualize how their work contributes to overall system success or creates problems for others.
2.8.5 Simulation Methodology
Every simulation project proceeds through a set of phases and steps whose goal is a successful project. A successful simulation project can be divided into 4 phases which are Project Initiation, Model Verification and Validation, Experimentation, Analysis and Reporting (Carson II, 2005) as shown in Figure 2.18.
44
Figure 2.18: Simulation Methodology
2.8.5.1 Problem formulation and setting of Objectives.
All modeling activities should be focused on the objective. Often, the actual problem maybe unknown or little understood and problem formulation may initially be stated in terms of observed symptoms. A list of specific questions that the model should address, measures of performance that will be used to evaluate or compare the alternatives being modeled should be developed. There would be a goal in mind, example the new system under a certain level of resources and manning will achieve a expected throughput. If the case study finds that the proposed system design or set of operating rules does not achieve the expected throughput, then the model is
45 expected to provide information and insight to the causes, so that better alternatives can be developed.
2.8.5.2 Model boundary and scope
At this phase, a set of working assumptions that will form the basis for model development is developed: Model boundary and scope Level of detail Project Scope
The model boundary or scope determines what is in the model, and what is out. The model level of detail specifies how in-depth one component or entity is modeled; it is determined by the questions being asked and data availability. Think of model boundary as width and level of detail as depth . Overall project scope deals with the breadth of the questions that the model will be used to address; that is, it deals more broadly with how the model will be used during the experimentation and analysis phase. As more and more questions can be asked of a given model (especially a parameterized one), a common understanding of project scope needs to be achieved to avoid scope creep and a project with no end.
2.8.5.3 Conceptual Model and Assumptions Document
The set of agreed-upon assumptions and data is, in essence, the conceptual model. These assumptions and data requirements should be detailed in an Assumptions Document or Functional Specifications Document. The Assumptions Document should be written in the language of the real system and the people who work in that system. It should not use modeling language or jargon peculiar to any particular simulation software or language. After all, its purpose is to communicate a
46 set of assumptions and data requirements among all members of the simulation team, not all of whom will be, or even need to be, simulation experts. With this common document, the team can revise the assumptions until all members agree to a common set of working assumptions, or at least to note disagreement until agreement can be reached. In summary, project initiation has these essential activities: Get all interested parties involved in project kickoff, initial problem formulation and meetings discussing model assumptions. If a person on the customer or client side will be present at any review meetings or final presentations, that person must be present at these initial meetings. If a person expects the model to address certain questions, that person must put the questions on the table at project initiation. Put all assumptions and data requirements into writing. Include objectives, specific questions to address, and measures of system performance. A written Assumptions Documents is essential. A reviewed, and signed-off, Assumptions Document is critical.
2.8.5.4 Model Development
Model development consists, in a nutshell, of two major activities: (1) development of data structures to represent the data needed by the model, and (2) translation of the modeling assumptions in the assumptions Document into the language or representation required by the simulation package. The simulation analyst must design data structures that represent the data and its inter-relationships as well as fit into those allowed by the simulation software. For example, almost all packages allow variable arrays, most allow tabular displays of data (and referencing of that data by model entities and processes), and some allow lists of object and data.
47 2.8.5.5 Data Collection Cleansing and Analysis
Data sources include databases, manual records, automatic data collection systems, sampling studies and time studies. Unfortunately, it seldom happens that all or even much of the needed data is readily available, or when available that it is of the desired quality. In these circumstances, much effort and expense may be required to collect the data or extract it from existing databases. After collecting it, a further effort may be required to validate and cleanse the data. When data on an activity is available, and the data exhibits random variability, that is, variability for which no immediate cause is evident, then the activity duration is usually modeled by a statistical distribution. Sometimes the empirical distribution of the data is used; sometimes a statistical package is used to fit a distribution to the data. With some types of data, the analyst may decide to use the actual data itself as input to the simulation. For example, customer order files are often used as input to a model of a distribution center or order fulfillment center. Each customer order may consist of a number of line items, and each line item has a quantity of one or more. There is almost always a correlation between number of line items and quantity, a correlation that would be difficult to characterize and represent with a statistical distribution. In this and similar situations, actual order files is used to drive models. To get representative variation, the customer is asked to provide several different samples of order files (that is, orders from several different days). If there is a need to experiment with greater demand (more orders), different order files are combined into a single order file. If there is a need for a different order profile (perhaps more small orders and fewer large orders), the order file is partitioned appropriately and sample or recombine to get a desired profile.
2.8.5.6 Model Verification
In model verification, the model is checked using a number of different techniques, to verify that the running model agrees with the Assumptions Document. This is more than debugging in the programming sense. All model outputs should
48 make sense and be reasonable over a range of the input parameters. Numerous techniques should be applied, including but not limited to: (1) stress testing, or testing with a wide range of parameters and different random numbers; (2) a thorough review of all model outputs, not just the primary measures of performance, but numerous secondary measures; (3) using the software s debugger, animation and any other tools provided; (4) using selective traces, especially for complex portions of the logic; and (5) review by a more senior simulation professional (especially valuable for the relatively new practitioners). First, make a hypothesis: the model is correct . Second, try to prove the hypothesis is false; If only after great effort, confirmations are made and no evidence of a faulty model, then conclude (tentatively) that the model is verified.
2.8.5.7 Model Validation
After convinced that the model is accurate and verified, a thorough analysis of the model should be reviewed. Numerous techniques, similar to those used during verification, may be used during model validation, including: (1) use of animations and other visual displays to communicate model assumptions, (2) output measures of performance for a model configuration representing an existing system or an initial design. If sufficient data has been collected on a realworld system that matches one of the model s possible configurations, more formal tests may be conducted comparing the real system to the model.
2.8.5.8 Experimental Design
Below are some issues to be considered before running a simulation experiment: The input parameters to be varied, their range and legitimate combinations, Model run length (how long to run the simulation), For steady-state analyses, the model warm-up period, Number of statistical replications.
49 In earlier phases, the analyst should explore inherent model variability range of short-term behavior
the
which should provide at least initial insight into
appropriate model run length and number of replications needed for later experiments. Model run length may be dictated by the nature of the system or the available data. In contrast, inherent and high system variability together with a desire for a certain level of statistical accuracy (width of confidence intervals) combined to require upwards of 100 statistical replications for each point in the experimental design (each system configuration). Other models with less inherent variability have required only 3 to 5 replications. The number of replications affects statistical accuracy of performance measures; specifically, it affects the width of any confidence interval estimators.
2.8.5.9 Experimentation
Usually simulation models are used to compare a number of alternatives, The Assumptions Document should include a description of expected model variations, including the range of each model input parameter, to be simulated to represent the alternatives of interest. In practice, initial model experiments often raise new questions and may change the direction of the study after initial experiments are run and analyzed. In each phase of the experimentation, actual model configurations should be guided by an experimental design that lays out the model parameters being varied, the range of each parameter, and the parameter combinations that make sense.
2.8.5.10 Analysis
Analysis is based on the agreed-upon measures of system performance. Typically in manufacturing and logistics applications, there are measures of throughput, resource utilization, queuing and bottlenecks. It often happens that initial experiments produce outputs that identify a problem, or symptoms of a problem, but do not readily provide the causes of the problem or provide enough information to
50 give insight into the nature of the problem. In this perplexing situation, the model will be used as a basis for forming hypotheses regarding the causes of any identified problems. Then the analyst may need to add auxiliary measures of performance to further pinpoint the cause, and most importantly, to confirm that the hypothesis is correct.
2.8.5.11 Reporting
Reporting of the results of experimentation and analyses usually includes one or more presentations and a written report. Presentations allow question and answer and expansion of explanations. The response to the presentation should be used to finalize the report and to address issues and questions that arose during the presentation. The final report should include the Assumptions Document, appropriately revised to include any changes that arose during the course of the project, as well as, of course, the key results and recommendations of the study.
2.9
Advantages and Disadvantages of Simulation
A good simulation model provides not only numerical measures of system performance, but provides insight into system performance. There are several advantages and disadvantages of simulation:
Advantages: Simulation allows experimentation with a model of a system, without a model, experimentation with real system will probably cause major disruptions at a potential risk.
51 Allows identification of problems, bottlenecks and design shortfalls before building or modifying a system Allows comparisons of many alternative designs and rules of operation before committing resources and investments. Allows studies on dynamics of a system, how it changes over time and how subsystem and components interact. The only method to study new, nonexistent complex dynamic system for which analytic or static models provide with corresponding low accuracy. Hypotheses about how or why certain phenomena occur can be tested for feasibility.
Disadvantages: Simulations are time consuming data is not available or costly to obtain and the time available before decisions must be made is not sufficient for a reliable study. Inexperienced simulation analysts or those too focused on the simulation software and technology may add too much detail to a model and spend too much time in model development, resulting in original goals and project time lines being side tracked. Animations and visual displays, combined with time pressures, may mislead decision makers into premature conclusions based on insufficient evidence.
2.10 ARENA
Table 2.2 summarizes a few simulation optimization software packages currently available and summarizes their search strategies. Comparing with table 1in Fu (2002), one observes that Promodel and SIMUL8 have both migrated to OptQuest from their previous simulation optimization packages ( SimRunner and OPTIMIZ, respectively).
52 Table 2.2: Simulation Softwares Optimization Package
Vendor
Primary Search
(simulation platform)
(URL)
Strategies
AutoStat
AutoSimulations, Inc.
Evolutionary, genetic
(AutoMod)
(www.autosim.com)
algorithms
Evolutionary Optimizer
AutoSimulations,Inc
Evolutionary, genetic
(Extend)
(www.imaginethatinc.com)
algorithms
Opt Quest
OptTek Systems, Inc.
Scatter search, tabu
(ARENA, Crystal Ball,
(www.opttek.com)
search, neural networks.
RISKOptimizer
Palisade Corp.
Genetic algorithms
(@RISK)
(www.palisade.com)
Optimizer
Lanner Group, Inc.
(WITNESS)
(www.lanner.com/corporate) tabu search.
Promodel, SIMULUS8,et al)
Simulated annealing,
2.10.1 ARENA reputation
Arena has been on the market for nearly a quarter century. Many other simulation vendors have come and gone out of business during this time. Others simulation companies have consolidated with former competitors as the simulation industry has shaken out the fringe players. No other simulation vendor has withstood the test of time and triumphed like Arena has. With 300,000 users world-wide and growing, Arena advice and support is easy to find. Arena is supported by academic as well as commercial users. In addition to Rockwell s technical support and consulting services, our unique user zone enables modelers from around the world to communicate and share knowledge.
53 2.10.2 ARENA methodology
Arena uses an entity-based, flowcharting methodology for modeling dynamic processes. Most other commercial simulation products are code-based and require programming in proprietary scripting languages, and many simulation products force the user to concentrate primarily on animating a process rather than documenting it. Arena is a Visio-compatible, flowcharting tool. Entities in an Arena model proceed through a flow chart of the process and seize control of resource capacity as they are processed. The flowchart approach to model building makes the most sense to engineers and to process designers who must be able to carefully document a process in order to accurately model it and analyze it. This results in models that become highly detailed documents of the processes being studied. Arena's flowcharting methodology makes Arena: Easier to learn than other simulation tools Easier to validate, verify, and debug Easier to communicate the intricacies of complex processes to others Ease of use translates to rapid model development.
2.10.3 ARENA Technology
Arena Runtime feature allows analysts to perform what-if simulation analysis using an Arena model built by someone else. When a model enters runtime mode, an analyst can modify the characteristics of any objects in the model, including module data, object positions, animation pictures, etc., but may not add or delete objects in the model or items in a module repeat group.
54 Import Visio Flowcharts and translate any Visio shape into an Arena module. This leverages all process documentation already done in Visio and your corporate investment in Visio flowcharting software.
Import AutoCAD drawings plus objects, clipart, pictures, video clips, etc, for convincing 2d animation.
More than 5,000 intricate animation objects are included in Arena's animation library. Custom animation can also be created by the user. Clipart, bitmaps, AutoCad drawings and many other animation file types can be imported into Arena.
ODBC data compatibility. Import and export data from/to any ODBC data file. File types include: Excel, Access, XML, text, Sequential, LOTUS, and Active X Data Object (ADO).
Visual Basic scripting. Unlike other tools that use proprietary scripting languages, Arena uses a standard VBA editor (included) and the Arena object model to build custom user interfaces and custom data interfaces to Arena models.
VB Automation. All Arena functions can be automated with Visual Basic Programming. Both the building and the execution of Arena models can be fully automated through VB programming and the Arena Object Model.
VB Macro Recorder. Arena includes a Visual Basic macro recorder that produces Visual Basic programs of all mouse movement and keyboard actions, which is used to automate the building of Arena models from external data files.
55 General-purpose DES modeling tools. Arena is the leading general-purpose Discrete Event Simulation package. Any process described can be modeled in Arena, including customer service, finance, order & fulfillment, billing, logistics, etc.
Real Time modeling. Arena Real Time allows the model to run in real time or some multiple of real time, and to communicate asynchronously with external devices or external applications. This technology is useful for system testing and operator training, using Arena as the "virtual" system that communicates with actual devices.
SIMAN simulation language engine. The SIMAN simulation language is the engine in Arena. Arena is a language-based simulation tool, it's not a simulator that has 'canned' or prescribed functionality. Having a simulation language as its engine makes Arena models run extremely fast and makes it possible for the user to model any complex process that can be verbalized and described. SIMAN was the predecessor to Arena and was the world's first PC-based simulation language when it was introduced in 1982.
OptQuest for Arena optimization software included. Most Arena packages includes the state-of-heart solver for Discrete Event Simulation: OptQuest. OptQuest is developed by OptTek Systems, Inc. in Boulder, CO.
2.11 Previous Projects
Several previous projects which were related to facilities planning improvement have been reviewed. Among these projects, five most relevant will be summarized. Table 2.3 shows summary of previous project.
56
Table 2.3: Summary of previous project
57 2.11.1 Systematic Layout Plan for Baystate Benefit Services
The problems facing a small and growing business are often self inflicted. Lack of attention paid to facility planning and work flow design, as the company grows, is common. Baystate Benefit Services is such as company. Baystate begin in 1993 as a two person operation and has grown become one of the regions largest insurance brokerages.
As their business grew, Baystate needed to add to their workforce to accommodate the increase in business. The expansion happened with no regard of workflow and location of resources. Departments with similar functions are not being located close to each other. Also, resources not being located near the staff that uses them. Besides, there is lack of storage space for materials, equipment and personnel due to expansion of business.
5S methodology is implemented for space optimization. Systematic Layout Planning is employed to identify the work and information flow through the office. Using this information, 3 design alternatives were created and a final design which reduces the most travelling time and has the most cost savings is selected.
The results are a significant improvement of productivity in the office as much as 3.5 percent, ability to bring on four new clients without other changes and reduction of 5 minutes of travelling time per day.
The limitations of this study are that it does not use simulation in the evaluation of the layout alternatives. The types of distribution of data are not considered. Capacity information has to be calculated manually which is time consuming and might lead to errors.
58 2.11.2 Systematic Layout Planning: A study on semiconductor wafer fabrication facilities. ( Taho Yang, Chan-Ton Su & Yuan-Ru Hsu)
This project is conducted in a semiconductor wafer fabrication facility which has DRAM products as its major product line and is planning to expand its capacity through the establishment of a new 8 fab with only limited floor space available.
The opportunity for improvement lies in the need for increase in capacity, productivity, WIP flow and layout flexibility. 3 alternatives layout proposal were developed using the Systematic Layout Planning (SLP). Departments with close relationship are placed close to each other. The best solution here is that this case study has multiple objectives including quantitative and qualitative, moreover they are subjective in nature. Thus, Analytic Hierarchy Process(AHP), is used to choose the final design layout based on the alternative design which has the highest grand weight.
The limitations of this study are that quantitative data is not presented result of the improvement performed on the layout. Besides, it does not utilize the simulation tool in evaluating the layout alternatives. Therefore, no comparisons on quantitative data for the design objectives can be performed. The selected design alternative might not be the optimum solution in this case though.
2.11.3 An empirical study of facility layout using a modified SLP procedure. (Te-king Chien)
Oosaki Precicion, which specializes in manufacturing of tools for patching up pipe leakages will be setting up a new plant in Tokyo, Japan. The area within the new factory is limited due to lack of space. Therefore a practical methodology is needed to maximize the
59 new layout at the plant. Utilized modified SLP methodology by proposing concepts such as grouping, compounding and hypothetical distance to modify the procedures and enhance practicality in traditional SLP. The steps involved are making the improved activity relationship chart, calculating the average relationship rating, calculating the hypothetic distance, mastering the design principles, making the hypothetic distance relationship diagram, developing layout alternatives and finally evaluating the layout alternatives. The best solution in this study is that is uses the aggregation approach, compound characteristic and hypothetic distance to optimize layout of a new plant.
Results of this study generated 3 layout alternatives based on modified SLP procedure. Managed to group together activities with same proximity and improve those activities with separateness. However ,no significant figures of improvement was provided.
The limitation of this study is that no simulation is utilized. The evaluation of case study is solely based on approaching and separation rating and no quantitative data regarding the improvement is done. It would be less convincing comparing to a study which uses tools such as simulation where quantitative figures are displayed in the report.
2.11.4 Facility Planning for a Gas Manufacturing Plant (Chui Wing Cheong & Chu Lap Keung)
This case study is regarding facilities planning carried out in a manufacturing plant, named Hong Kong Oxygen. Oxygen supply in tanks is the main products of this company. Plant relocation is needed due to new town planning scheme, by the Hong Kong government. The new town ship will be a residential area replacing the area of this plant. Some of the objectives of this relocation are also better safety could be achieved in the new
60 plant; considerable profit could be derived by re-developing the existing piece of land into a residential and commercial area.
There are 3 locations whereby the company needs to select the lowest transportation cost among 3 locations to minimize delivery cost. After the selection of the strategic location, Systematic Layout Planning is used to develop block plans based on the data input, activity relationship diagrams, activity relationships charts. The best solution is this case study utilized computer aided planning (CORELAP),which is a construction type of layout program, to generate layout alternatives, to generate a new layout from the activity relationship diagram, space requirements and shape. The best layout is selected based on the most favorable compromise among a list of competing criteria. The layout alternative which has the highest score of competing criteria is selected.
Results are relocation of new plant to new location, Tseung Kwan, which has minimum transportation cost. The best layout selected has the most compromising among the competing criteria in terms of economy of material handling, safety, ease of supervision, room for expansion, flow of material and convenience.
The limitations however, are it did not published the quantitative improvements as it only uses the ratings. No actual data of performance measures indicators. The reader will not have a clear idea on the improvements before and after re-layout.
2.11.5 The Carbolite Case Study: Lean Approach to Systematic Layout Planning
This case study is performed in Carbolite which is a UK-based, world leading manufacturer and supplier of electric laboratory chamber and tube furnaces. The main challenge faced by the company is that cycle time of finished product through the total
61 value delivery system at Carbolite was extremely long. Lead times for products varied from between eight weeks to six months. Large direct and indirect cost savings were expected to arise if the cycle time of the products could be significantly reduced. Site layout and resultant material flow issues were identified as a major contributor to long cycle times.
Hence, value stream mapping is used to identify the major waste contribution. Systematic layout planning is also used for improvement in factory layout. Current basic data is obtained. Flow process charts, material flow diagrams current product volumes, activity relationships diagram were established between production processes within the logical groups. Storage requirements, materials handling requirements were also determined. Alternative layouts were evaluated to identify the most suitable layout.
One piece flow and batch size reduction is used to reduce the waste generated in the supply chain, pull system and elimination of all cross flow by re-layout. Results are summarized which are: Increase of profit margin by 20% with new layout, reduction of 3 headcounts, potential reduction of WIP by 80%, transportation time reduced by 90%.
Some limitations of the case study are it does not use simulation to compare the alternative layout generated. It did no show comparisons between layout 1 and layout 2. The final selected layout is not discussed in this project. It also does not project clearly the actual layout issue of the company.
From the literature review, we can see that much research has been carried out in the areas of Systematic Layout Planning. However, combining both systematic layout planning and simulation in greater detail is less explored. Moreover, little documented research exists in combining these topics with an optimization approach. Simulation is widely used as a stochastic model to evaluate a proposed materials handling system in which a randomness of events exists. Simulation predicts the behavior of complex manufacturing systems by determining the movement and interaction of system
62 components. It is capable of aiding in the design of the most complex automated materials handling system and also allows the user to evaluate alternative solutions and to examine the flexibility of a design ( Eneyo and Pannieselvam,1998).
2.12
Comparisons Between My Work and Previous Projects
Comparisons between my project and previous projects have been done. The similarity is that Systematic Layout Planning is justified as a universal method for layout improvement. The main difference is that my project incorporates both systematic layout planning together with simulation
ARENA, whereas the 5
projects uses only the SLP method in layout generations. Besides, in my project, variation is addressed and data fully analyzed to help understand the random nature of the system behavior, other projects do not addressed this type of variation. Another difference is that the future layout proposed by my project is validated in terms of quantitative performance measures before it is implemented to minimize the period of trial and errors, while 5 other projects either calculate the performance measures manually or some not calculated. Thus my layout proposal would be more convincing compared to the 5 other projects. My case study is also focused on the manufacturing of electronics parts which have not been done by others. Last but not least my project involves process oriented layout improvement which it not found in the previous 5 projects.
2.13
Conclusions
In this chapter the literature review of facilities planning and types of layout is discussed. Apart from that, some review on previous trends of layout improvement
63 methodology is reported, followed by systematic planning layout (SLP) methodology. Simulation applications, advantages and disadvantages and step by step methodology are discussed. Lastly some previous studies is reviewed and compared with my thesis.
64
CHAPTER III
COMPANY BACKGROUND
3.1
Introduction
This chapter provides and overview of the case study company, Agilent Technologies, Inc. General information of the company such as company mission, business groups, strategy, history, products, factory size, will be discussed in this chapter. Besides, the company structure and factory layout is also included. The details of the selected product s process flow will be discussed.
3.2
General Information
Agilent Technologies Inc. (NYSE: A) is the world s premier measurement company and a technology leader in communications, electronics, life sciences and chemical analysis. The company operates two primary businesses
electronic
65 measurement, and life sciences and chemical analysis -- supported by a central research group, Agilent Laboratories. Its businesses excel in applying measurement technologies to develop products that sense, analyze, display and communicate data.
The company s 20,000 employees serve customers in more than 110 countries. These customers include many of the world s leading high-technology firms, which rely on Agilent s products and services to increase profitability and competitiveness, from research and development through manufacturing, installation and maintenance. Agilent enables its customers to speed their time to market and achieve volume production and high-quality precision manufacturing.
Agilent had net revenue of $5.4 billion in fiscal year 2007. More than half of this revenue was generated from outside the United States. Agilent maintains facilities in about 30 countries, with worldwide headquarters in Santa Clara, Calif. Its global presence offers a distinct competitive advantage, with R&D, manufacturing, sales and support capabilities serving customers around the world.
3.2.1 Business Groups
Agilent s business groups are organized around the customers and markets they serve: Electronic Measurement provides products, services and solutions to industry-leading customers in the communications and electronics industries. Life Sciences and Chemical Analysis provides detection and measurement solutions for research, testing and quality control applications to leading chemical, pharmaceutical, biotech, government and academic organizations.
66 3.2.2 Strategy
As the world s premier measurement company, Agilent focuses on growth market opportunities in the communications, electronics and life sciences industries. Continuing a legacy of technological innovation, Agilent leverages the benefits of scale and global presence to capture and create business opportunities.
3.2.3 Market Leadership
Agilent holds many product and market leadership positions. Agilent is first worldwide in overall test and measurement products and first worldwide in gas chromatographs and liquid chromatography/mass spectrometry. Agilent is also a leading supplier to the telecommunications industry.
3.2.4 History
Agilent spun off from Hewlett-Packard Company in 1999 as part of a corporate realignment that created two separate companies. Its roots date back to 1939, when Bill Hewlett and Dave Packard started a company that helped shape Silicon Valley and the technology industry. The two founders are renowned for their visionary approach to management (known as the HP Way ) and for their commitment to making products that contribute to advances in science and
67 technology. Agilent s headquarters is located at 5301 Stevens Creek Boulevard in Santa Clara, California.
3.2.5 Microwave Test Accessories (MTA)
Agilent s Electronic Measurement business provides standard and customized solutions that are used in the design, development, manufacture, installation, deployment and operation of electronic equipment and systems and communications networks and services. The Microwave Test Accessories (MTA) Department, which falls under the electronic measurement group, will be the main focus for this project
3.2.6 Strategy
Address the needs of the wireless, wire line and Internet communications market. Identify customers business and technology needs, then leverage across the value chain. Satisfy customers through operational and product excellence Focus on top-tier customers Build a new global capability in solutions, systems and services
68 3.2.7 MTA Charter
To enhance Agilent instrument measurement experience and potential. To enable and support Agilent instrument growth.
3.2.8 MTA Vision
To be the dominant supplier of a broad range, premier grade microwave test accessories and solutions with competitive value for performance. To be an innovation center for Electrical and Mechanical Metrology and Accessory.
3.2.9 Key Products
RF and microwave switches, Fixed variable attenuators, switch drivers, adapters, connectors, amplifier, DC block, detectors, directional couplers, bridges, power dividers, power limiter, splitters, frequency meters, termination, waveguide accessories, bias network, cable assemblies, comb generators, electronic calibration kit. The focus product for this study will be electronic calibration kit and coaxial waveguide adapter.
69 3.3
Company Structure
MTA is headed by an Integrating Manager who reports to a Senior Manager. This department is divided into 3 divisions which are Product engineering, Process engineering and production engineering. Each of these divisions has their own engineers and operators. The general organization of the company is shown in the below Figure 3.1. The focus for this case study is more to the production engineering department as production layout falls under that category.
Figure 3.1: Department Structure
70 3.4
Factory Layout
MTA operates in a two section department which is Building 5 and Building 6 The total build up area for Building 5 is (308*83) = 25,563 sq ft whereas for Building 6 is (312*102.25 ) = 31902 sq ft. Operators for certain products have to walk to and fro within these 2 departments during assembly and testing process. The layout for these two buildings is shown in the Figure 3.2.
Figure 3.2: Production Plant Layout
3.5
Manufacturing Process
The scope of this case will cover only 2 products in MTA which are the electronic calibration kit (E-Cal) and T2 products. The process flow will be described below. Only the key terms for the process are described as some information is private and confidential
71 3.5.1
Electronic Calibration Kit (E-cal)
The electronic calibration (E-Cal) modules consist of connector-specific calibration standards that measure the known devices of the system over the frequency range of interest to detect systematic errors. With simple one-connection operation, they offer excellent accuracy without sacrificing time to calibrate. There are over 50 types of electronic calibration kit. However, the process flow for each of the part is almost the same. The overview of the process flow is briefly described. Refer to Appendix A for the process flow diagram of E-Cal.
All incoming material are inspected especially the connector mating surfaces per general mating surface criteria. If parts fail the mating surface specifications, the parts are failed per the non con form process. The next process will be the microcircuit package assembly which is performed in building 5. In this process, wire bonding will be done base on the circuit diagram. Subsequently, connectors will be torque to the microcircuit body. The bulkhead assembly is then screwed to the body. The microcircuit is then inspected for debris and skin flakes.
The next process will be the laser welding. It is performed in a centralized enclosed area in building 5 due to it uses beta waves generated which will be harmful to the human body. The purpose is to weld the housing to the microcircuit body to protect the circuit assembly.
After that will be the module assembly which is performed in building 6, two bulkhead assemblies will be torque on both sides using screws. After that loctite will be drop on the 4 remaining screw holes of the microcircuit assembly. A printed circuit board will then be attached to the micro-circuit assembly. After that the top half of the casing enclosure will be place upon the bottom half of the casing enclosures to seal the whole PC board and microcircuit assembly. Labels with
72 product model names will be attached to the casing. Then the operator has to walk all the way to the control room at the far end of building 5 to run electrical testing and performance tests. If fails, the units will be send to building 5 for rework.
After passed the performance testing, the product will be sent to the centralized or chamber testing room. Temperature cycling according to certain profile given will be performed for 3 full days.
At the end of the third day, the product will be packed on sent to the outgoing quality inspection. If passed, the product will be shipped.
3.5.2
Coaxial Waveguide Adapter
The Agilent waveguide adapter transforms waveguide transmission line into 50-ohm coaxial line. Power can transmitted in either direction, and each adapter covers the full frequency range of its waveguide band. There are over 50 types of waveguide adapter and the process flow for each product is almost the same. Refer to Appendix A for process flow diagram of coaxial waveguide adapter.
The process begins with the assembly of the center conductor assembly to the body of the waveguide. The assembly is then tightened with torque driver. This process is performed in the assembly department, in building 5. After that, the unit is transferred to another assembly area whereby the housing is screwed to the
73 waveguide body. Subsequently, the operator has to walk all the way to building 6 to perform pin depth measurement inspection using special equipment. If there is rework, they have to walk to building 5 to re-assemble the unit. Once the measurement inspection is completed, the units are then transferred again to building 5 testing department for performance evaluation. If there happens to be rework again, the operator has to walk back to the assembly department to re-assemble. If passed testing, units are then transferred back again to building 5 to perform packaging. Last but not least the units are sent to outgoing quality area for inspection and then sent to shipping department.
3.6
Conclusions
The background of the case study company is briefly discussed in this chapter. This covers the organization chart, company s mission, strategy, products, factory layout and product manufacturing process. The subsequent chapter will discuss on the problems face by this company.
74
CHAPTER IV
PROBLEM DEFINITION
4.1
Introduction
This chapter discusses about the identification of area where facilities layout planning is to be implemented. Cross over diagrams will be used to illustrate the current movement of the operator, quantity of cross-over will also be displayed in a table, and from-to-chart, travelling cost incurred and process flow diagram will also be used.
4.2
Cross-Over Diagram
4.2.1
Electronic Calibration Kit
Constraints: Laser welding department, chamber testing department. Scope: Microcircuit assembly to the packaging process only.
75
Process begins with incoming material at store. However this process is not included in the scope of this case study. After that, the microcircuit assembly is performed by the operator in building 5. After that they have to walk 5 meters to the next department which is laser welding department. The laser welding process will take about 4 hours. Upon completion, operator has to cross-over to building 6, which is the next building, 238 feet away, to continue with the module assembly process. This process requires the usage of epoxy at the end stage. Epoxy has to be stored in a freezer which is maintained at -40 degrees Celsius. Therefore although the freezer is located next to the laser welding room, the operator is not allowed to pick the epoxy before proceeding to building 6, as the epoxy can only be 10 minutes in normal room temperature. Therefore long travelling distance and time occurs when the operator has to use the epoxy and travelling occurs to-and fro from the module assembly to the freezer.
After module assembly, the operator has to walk back again to building 5 testing department, which is 334.3 feet away, for performance test. The unit has to pass all testing before proceeding to the next process. If the unit fails, it has to be sent back to the module assembly, in building 6 for rework. Cross-over for this process is also frequent especially during high rework. After rework and unit passed all the testing, it is send over to the chamber testing department for temperature cycling. After chamber testing for 3 days, it is the responsibility of the testing department to retrieve the units and pass them back to the operator , which is located in building 6, for packaging. The units are then sent to Outgoing quality inspection and shipped. However for this case study the scope covers until the packaging process.
4.2.2
Coaxial Waveguide Adapter
Constraints: Freezer Scope: From Center conductor installation to packaging process.
76
The process of assembling the coaxial waveguide adapter begins with the incoming material from store. However this process is not included in the scope of this case study. After that the units have to go through the center conductor assembly and housing to body assembly in building 6. This process requires the usage of epoxy as well, which is located in the freezer in building 5, therefore the operator has to walk to and fro from building 6 to building 5 to retrieve epoxy, as the epoxy can last only for 10 minutes under normal room temperature. After that, operator has to walk back again to building 6 to continue the assembly.
After assembly, will be the pin depth gauging process, which is located quite far from the assembly process. The department is a centralized area, as other products will also use the equipment. However, we found that the equipment used for the coaxial waveguide adapter is unique and not shared by other products. If pin depth gauging fails, the operator has to walk to-and fro from the pin depth gauging department back to the assembly process for rework. If passed pin depth gauging, the operator has to cross-over to building 5 to the testing department. Cross-over here is frequent as the volume of the coaxial waveguide adapter is high compared to other products. If the units fail, the operator has to cross-over back to building 6 to either the pin depth gauging department or the assembly department for rework depending on the types of failure. Cross-over here during rework is also very high. After the units passed the testing the units will then again be transported back to the packaging process in building 6 before sending to OQA and shipping. Figure 4.1 and Figure 4.2 shows the cross-over diagram for electronic calibration kit and coaxial waveguide adapter.
77
Rework flow E-cal E-Cal process flow
Figure 4.1: Cross-over diagram for E-Cal
Rework flow Coaxial Waveguide Adapter Coaxial Waveguide Adapter Process flow
Figure 4.2: Cross-over diagram for Coaxial Waveguide Adapter
78 4.3
Quantity of Cross-Over Chart
Cross-over in the context of this case study is defined when the operator has to walk over from one building to the other. Cross-over also happens when rework needed to be done. The more the cross-over, the more travelling time incurred during the manufacturing of each product. Similarly, the cost of manufacture for each product will increase as well. In this section the quantity of cross-over is summarized below:
Quantity of cross-over for electronic calibration kit = 9 times per unit Quantity of cross-over for coaxial waveguide adapter = 6 times per unit
If there are 100 units to be produced each month, the cross-over will increase 100 times. This will cause unnecessary waste of travelling. Please refer to Appendix B.
4.4
Travel cost
The labor salary in this company is calculated as RM 105.30 per hour. The travelling time by the operator is also considered in the assembly process as labor hours. The chart below shows the travel cost for e-cal and coaxial waveguide adapter
Cost of travelling for E-cal = RM 50.84 per unit Cost of travelling for coaxial waveguide adapter = RM 52.59 per unit Please refer to Appendix C for cost calculation.
79
4.5
From To
Chart
The From-To-Chart is a popular tool for material flow analysis. It represents the flow intensity between each process. The more the flow intensity, the more important the relationship between each process.
From the chart we can see that the assembly, testing and packaging have the highest flow intensity due to both products also shares the same process as shown in Figure 4.3.
The micro-circuit assembly, laser welding, pin depth gauging has lesser flow intensity as there is only one product for each of the process.
Figure 4.3: Flow Intensity Matrix (Frequency Per Month)
80
Figure 4.4: Inter department Distance Matrix
The distance between each department is calculated using the rectilinear method. A rectilinear distance is the generalized distance between two points. In a plane with point p1 at (x1, y1) and p2 at (x2, y2), it is (|x1 - x2|² + |y1 - y2|²)½. Figure 4.4 shows the distance between two departments. The details of the calculation is attached in the Appendix D
81 4.6
Overall From-To-Chart
The overall From-To-Chart is obtained by multiplying the flow intensity matrix and interdepartmental distance matrix as shown in Figure 4.5
Figure 4.5: Calculation of Overall From-To-Chart
82
4.7
Overall From-To Chart (with closeness ratings)
Figure 4.6: Overall From-To-Chart (with closeness ratings)
Based on the rule of thumb for closeness ratings, A represents absolutely necessary relationship and cannot be more than 5 % of the relationships. E represents especially important relationship and cannot be more then 10% of the relationships. I represents important relationship and cannot be more than 15% of the relationships. O represents ordinary relationship and cannot be more than 20% of the relationships. U represents unimportant relationship and consists about 50% of the relationships. Lastly, X represents undesirable relationship and consists of not more than 5% of the relationships.
83
4.8
Conclusions
From the analysis : Cross-Over Chart, Quantity of cross-over chart, From To- Chart , it is obvious that the layout of the products is a major contribution to the high cost and the high cross-over quantity. This is further justified by the from-tochart where significant flow intensity occurs within processes which are currently located far apart. Therefore in the following chapter, systematic layout planning will be used as a methodology to define, analyze and synthesize the current problem faced by the company.
84
CHAPTER V
SYSTEMATIC LAYOUT PLANNING
5.1
Introduction
In this chapter, the Systematic Layout Planning methodology is analyzed in detail for different layout alternatives generations. Basically the SLP methodology literature has been reviewed in Chapter 2. It has a total of 11 steps. First is input data, followed by flow of materials, activity relationships, relationship diagram, space requirements, space available, space relationship diagram, modifying constraints, practical limitation, developing layout alternatives and lastly evaluation. However in this chapter only the first 10 steps will be discussed as evaluation will be discussed in Chapter 6 using ARENA simulation.
85 5.2
Input data
The first step of Systematic Layout Planning requires gathering and analyzing data required for the case study. This must occur before any planning of relationships, space or adjustments. The input variables for every SLP are P, Q, R, S and T. Product (P) is the material that will be processed. For this case study, the products are Electronic Calibration Kit and Coaxial Waveguide Adapter. The general usage and process flow of these 2 products have been discussed in Chapter 4. Quantity (Q) is the volume of each product to be processed. The volume in this case study refers to the output of each product. It relies on the total time used to build a unit of product. Routing (R) is the path a product travels to be processed. The routing (R) in this case study is obtained from the company s process specific document (PSD) for electronic calibration kit and coaxial waveguide adapter respectively. Time (T) refers to the overall time required to complete processing. Data collection of the cycle time for each process is done by time studies.
5.2.1
Standard Time
Standard provides information essential for the successful operation of an organization. Standard time is the time required by a typical operator, working at a normal pace, to perform a specified task using a prescribed method, with time for personal, fatigue and delay allowed.
Time study is the analysis of a given operation to determine the elements of work required to perform it, the order in which these elements occur and the times which are required to perform them effectively. Time study is most effective for
86 developing standards for highly repetitive tasks which have relatively short cycle times.
The standard must be adjusted to reflect the personnel, fatigue and delays that are part of every job. The personal fatigue and delay (PFD) allowance is usually expressed as a percentage of the standard time and added to the time allowed to complete the particular task being studied.
Standard Time Determination The observed time (OT) of the given element is determined by taking the mean of 10 observations. The sample size, n, is then calculated to show its sufficiency.
Normal Time (NT) is found by multiplying the observed time by the average rating Normal Time = Observed Time*Average Rating The rating used here is 85% = 0.85
Standard Time = Normal Time/ ( 1- Allowances) Allowances for this project are Personal needs
5%
Unavoidable delay 1% Basic Fatigue
4%
Total = 10%
Tables 5.1 and 5.2 show the process analysis for both electronic calibration kit and coaxial waveguide adapter.
87 Table 5.1: PQRST Analysis for E-Cal
Table 5.2: PQRST Analysis for Coaxial Waveguide Adapter
From the data gathered, the total time of processing for E-cal is 201.3 minutes. With the standard working days per month of 22 days, the output yields 157 pieces per month. It can be seen that the travelling time (pink highlighted) is high due to frequent travelling between 2 buildings. Same as for the coaxial waveguide adapter which requires 56.9 minutes of processing time, has an output of 556 pieces per month. The travelling time is about 6 minutes which is higher then the assembly
88 and queuing time. Further details of the cycle time and sample size calculation are attached in Appendix E.
5.2.2
Flow of Materials
The flow of material involves in determining the most effective sequence of work and material. An effective flow means that the material moves progressively through the process and should always advance forward without excessive detours and cross-overs. In traditional manufacturing applications, the flow is determined from either the product or the process. In this case study, process flow was used to establish flow. There are altogether 8 departments involved in processing these 2 products. Microcircuit assembly, assembly, pin depth gauging, freezer, laser welding, chamber testing, testing and packaging. As each function was defined and added to the flow chart, it has been apparent that the flow of materials was never formally planned. This can be seen by the extensive cross-over between both departments which has been mentioned in Chapter 4. The flow of each product is shown in Figure 5.1 and Figure 5.2.
For electronic calibration kit, the flow starts with microcircuit assembly in building 5, it is then transferred to the laser welding department before crossing over to building 6 for the next assembly. During that period, frequent travelling occurs between both buildings when the operator retrieves epoxy from the freezer, the transport time is as much as 5 minutes. After completing the assembly process, the operator has to walk all the way to the end of building 5 to perform testing. Sometimes when rework occurs, the operator has to walk all the way back to building 5 to perform rework before going back again to building 6 to re-test the units. After that the product will travel to the next process, chamber testing. After that it will go
89 back again to testing. Lastly the product is transferred back again to building 6 for packaging. The batch size of each is 10 pieces per transfer.
Rework flow E-cal E-Cal process flow
Figure 5.1: Product Flow for E-Cal
For Coaxial Waveguide Adapter, the assembly process starts in building 5, after that it is transferred to the next process to perform pin depth gauging. Subsequently, the product is transferred back again to building 6 to perform testing. Same as for the electronic calibration kit, sometimes rework also occurs in this process causing extensive travelling to and fro between buildings. Lastly, the product is sent in batches of 10 pieces for packaging before proceeding to OQA and then shipping.
90
Rework flow Coaxial Waveguide Adapter Coaxial Waveguide Adapter Process flow
Figure 5.2: Product Flow for Coaxial Waveguide Adapter
In general, the product flow for the 2 products is unsystematic with required resources not being located close to each other, there are also many cross-over between two buildings which are building 5 and building 6. One of the reasons is due to the short time frame provided during the launch of these 2 products causing assembly tables to be simply located wherever there is empty space.
5.3
Activity Relationship Chart
In this stage, the identification of the relationships between resources is discussed. The resources are the various processes involved in the manufacturing of the product. The information regarding where a product is received from and the destination of the product upon completion is gathered. The results are tabulated in
91 an activity relationship chart. The relationship chart displays which entities are related to others and it also rates the importance of the closeness between them. This makes the chart the most effective tool for layout planning and is the best way of planning the arrangement of a plant layout having little flow of materials. It is a good record keeping tool to organize data into a usable form. With this, Activity Relationship Diagram is generated where proximity and relationships are visually evident. Figure 5.3 below shows the diagram.
Figure 5.3: Activity Relationship Diagram
A- Absolutely Necessary E- Especially Important I - Important O- Ordinary U- Unimportant X- Undesirable
92 5.4
Relationship Diagram
The activity relationship diagram is a visual display of the activity relationship chart. Each entity on the chart is translated to a symbol to be placed on the diagram and then lines are connected to show the value of the relationship. Figure 5.4 shows the relationship diagram for this case study. Different colored lines are used to distinguish the importance between each process.
Figure 5.4: Relationship Diagram
Absolutely necessary relationship occurs between assembly and testing. Locating them close together is absolutely necessary as both the products (E-cal and coaxial waveguide adapter) share these 2 processes. Rework process, which requires travelling to and fro among these 2 processes, is also the highest .Especially important relationship occurs among assembly process, freezer and testing and packaging as these processes occur back to back and are also shared between two products.
93 Important relations, occur between the pin depth gauging, testing and chamber testing as these processes involve only E-cal. Relationships rated as ordinary closeness involve laser welding, assembly, pin depth gauging ,microcircuit assembly and chamber as these are fixed equipment owned by the company and their current position is satisfactory ( huge investment is needed if relocation is absolute necessary.)
Through the analysis of the Activity Relationship Diagram, a better understanding of the processing functions for both the products has been achieved. This can be applied to the layout of the physical building in a space relationship diagram.
5.5
Space requirements
After defining the relationships among processes, the next step is determining the space requirements needed for each process to translate it into the actual layout. The first process is the Microcircuit Assembly for the electronic calibration kit. This process is located in building 5. The microcircuit process requires 165.90 square feet of space. The assembly process takes up 437.52 square feet, whereas pin depth gauging requires 262.78 square feet. Testing area requires the most space 575.75 square feet due to the huge size in test stations. Packaging takes up the least space which is 242.15 square feet.
All the above spaces include a minimum of 6 feet clearance between assembly tables and clearance of 1 foot for electrical utilities purposes and LAN points. This is so that operators can be seated during work comfortably without knocking the person behind.
94 The information of space requirements for both products is summarized in Table 5.3.
Table 5.3: Space Requirements Information
5.6
Space Available
Building 5 is a shared building between MTA department and Micro department. Both the electronic calibration kit and waveguide adapter kit belong to the MTA department. Building 6 is also a shared building between MTA department and Instrument department. Current scenario is that there occurs frequent travelling of operator between building 5 and building 6.Thus, the idea of improvement is to consolidate the processes which are currently sprawled across 2 buildings to one building. Building 5 is chosen as the laser welding, freezer and chamber testing are all located in building 5, these are also centralized processes which are shared between MTA department and Micro department. Therefore the overall idea of improvement is to bring over the assembly, pin depth and packaging stations belonging only to coaxial waveguide adapter and electronic calibration kit.(other products are not using these stations.)
95 There are 2 scenarios. First is there would be extra space provided by Micro Department beside the Instrument area as shown in Figure 5.5. The size is 168 square feet, this happens if some stations (which initially owned by MTA) are transferred back to building 6. Therefore, the stations which are beside the cal kit assembly line (owned by Micro) will replace those moved stations. The instrument area, 942.51 square feet, will be moved back to building 6 as well.
Figure 5.5: Extra space available
Second scenario is no extra space provided by Micro Department as shown in Figure 5.6 This is much more challenging as it requires consolidation of some existing cal kit assembly stations to accommodate the packaging, assembly and pin depth stations The size of this area is 942.15 square feet.
96
Figure 5.6: No Extra Space Available
5.7
Space Relationship Diagram
The space requirements discussed in 5.4 and space available discussed in 5.5 are then combined to a space relationship diagram. The purpose of the space relationship diagram is to combine established spatial constraints with the activity relationship diagram. There are 2 different space relationship diagrams. One is with extra space and another is without extra space.
Basically in the first option, the micro-circuit assembly, assembly, pin depth measurement and packaging process are moved to building 5, beside the testing process. The space relationship diagram is a combination of the relationship diagram and space available.
97
Figure 5.7: Space relationship diagram (Option 1)
In the second option, without extra space provided, the microcircuit assembly remains at the same location beside the laser welding process. However, some stations in the existing cal kit assembly line have to be consolidated to make way for the assembly, pin depth station and packaging station from building 6.
The space relationship diagram is shown in Figure 5.8 below:
Figure 5.8: Space relationship diagram (Option 2)
98 5.8
Modifying Constraints
There are a few constraints in this case study. First is the laser welding process which requires a laser welding machine. This process is performed in a controlled environment and requires a huge investment if relocated. Apart from that, the Freezer is also a fixed equipment which cannot be moved, as it is not a property of MTA department (belongs to Micro department). The chamber testing is also a shared resource among MTA and Micro and thus, cannot be moved.
5.9
Practical Limitations
Systematic Layout Planning is best employed when creating a new facility starting from scratch and the design is not yet finalized. In the case of this case study, the existing facilities were established and there was limited ability to expand the area for extra space.
Other that that, some negotiations is needed between Micro department ( or other departments) if the layout with extra space is more productive as compared to the improvement without extra space.
Also the testing process is performed in a control environment ( 23 degrees) to maintain the performance of certain products.
99 5.10
Develop Layout Alternatives
Design I
In the first design, Figure 5.9, the microcircuit assembly, assembly, packaging and pin depth gauging are placed in a straight line. This requires extra space beside the existing cal kit production line. The testing department, laser welding, chamber testing and freezer remain at the same location. There will be a major re-layout between Micro department as well since it involves their stations. However the scope of this project is only limited to Agilent s MTA department
Figure 5.9: Layout Alternative 1
Design II
In this design, shown in Figure 5.10, the microcircuit assembly line remains at the same location beside the laser welding process. No extra space is needed as the
100 assembly of the existing cal kit line is consolidated to make way for the assembly, pin depth measurements and packaging stations.
Figure 5.10: Layout Alternative 2
5.11
Conclusions
In this chapter, the SLP is used as a tool for defining, analyzing and synthesizing to generate two different layout alternatives. These alternatives will be evaluated using simulation-ARENA and quantitative comparisons will be made in the next chapter.
101
CHAPTER VI
DATA ANALYSIS AND MODELLING
6.1
Introduction
The conceptual model for both the E-Cal and Coaxial Waveguide Adapter is discussed in this chapter. Besides, the data distribution analysis of process cycle time is discussed thoroughly as well. Model description, model construction, verification and validation will be included. This is followed by determining the warm up period and number of replications to be used.
6.2
Conceptual Model
An initial model has to be built prior to the simulation
the conceptual
model. This model is vital in visualization of the manufacturing process. In general, the model will consist of input, output, queue and process. There will be 2 conceptual models used for this simulation. One belongs to the E-Cal and the other is Coaxial Waveguide Adapter. Both models are independent as the two products are
102 manufactured separately. Figure 6.1 shows the conceptual model for the E-Cal. Figure 6.2 shows the conceptual model for the coaxial waveguide adapter.
Figure 6.1: Conceptual Model for Coaxial Waveguide Adapter
Figure 6.2: Conceptual Model for E-Cal
6.3
Performance Measures
Quantitative data are required to measure the performance of the manufacturing process. This is to perform comparisons on certain key performance indices between the existing layout and the 2 alternative layouts as proposed in Chapter 5. Below is the list of performance measures used to evaluate the effectiveness of the improved layout:
Total Travel Distance Total Travel Time Travelling Cost Cross-Over Output
103 Operator Utilization Total Average Work In Progress (WIP) Level Total Average Waiting Time
6.4
Conceptual model validation
This type of validation is determining that:
1) The theories and assumptions underlying the conceptual model are correct and 2) the model representation of the problem entity and the model s structure, logic, and mathematical and causal relationship are reasonable for the model
The theories and assumptions underlying the model should be tested using mathematical analysis and statistical methods on problem entity data. Example of applicable statistical methods are fitting distributions to data and estimating parameter values from the data. ARENA uses the Input Analyzer to fit a probability distribution to the existing data. Example of how the input analyzer analyzes data is shown in Figure 6.3.
104
Figure 6.3: Statistical Input of ARENA Input Analyzer
Cycle time such as the process time, interarrival time, travel time, operator route schedules and machine setup time has to be collected. Initially 10 data are collected for each process. The sample size is determined. Example of calculation is shown in Appendix E.
The distribution of data is determined using the ARENA input analyzer. The input analyzer is a standard tool that accompanies ARENA and is designed specially to fit distributions to observed data, provide estimates of their parameters and measure how well they fit the data. The variation of the process time is large, as the process cycle time is in minutes, therefore the input analyzer is used to determine the distribution for all the processes. A total of 40 observations are taken for all processes (excluding walking time as it will be shown in Appendix I, J, K, and L) for e-cal and coaxial waveguide adapter. We could use this information to select which distribution to be used in the model. The process cycle time is shown in Appendix F. The screen shot of the cycle time distribution for each process using input analyzer is attached in Appendix G and Appendix H.
105 6.5
Model Description
There are 2 simulation models for this case study. This is due to two products are manufactured independently. One of them models the manufacturing process of the coaxial waveguide adapter while the other models the process of the e-cal. Each model consists of parts, processes, operators and queues. Firstly, the parts will arrive at the station and then it will be pushed to other processes and finally shipped. The simulation model of coaxial waveguide adapter and E-cal models is shown in Figure 6.4 and Figure 6.5 respectively.
Figure 6.4: Block modules for coaxial waveguide adapter
106
Figure 6.5: Block modules for E-Cal
6.6
Assumptions
The following assumptions for the simulation are:
1. Operators are always available during the 2 shifts (1 shift = 8 hours), an hour lunch break was taken by the operators. 2. There are no reject and rework. 3. There are no significant equipment / station failure. 4. All machines can process only one lot at a time. 5. Both products processed for e-cal and coaxial waveguide adapter are independent. 6. Materials are always available at each assembly station.
107 6.7
Model Construction
ARENA model basic building blocks are called modules. Modules come in 2 basic flavors flowchart and data. Flowchart modules describe the dynamic process in the model. The flowchart modules used for this simulation are taken from the Basic Process Panel: Create, Process, Dispose and Advanced Transfer Panel: Station and Route. Data modules define the characteristic of various process elements. Entity, Resource and Schedule are used for this simulation. Refer to Figure 6.6 for these 2 panels.
Figure 6.6: Basic Process and Advanced Transfer Panel
108 6.7.1 Basic Process
The Create flowchart module is for the arrival of entity to the model s boundary. In this simulation is the arrival of coaxial parts / arrival of e-cal. The Process Flowchart module represents the process in each station including the resource, its queue and the entity delay time. In this simulation, the process cycle time is the entity delay time. The dispose module represents entities leaving the model boundaries which represent the parts being shipped.
As for the entity data module, the parts are modeled by Picture Man. which represents parts being transferred in the production line by operators. As for the resource module, it consists of operators, testers and gauging machine. It is based on schedule as there is setup time after every shift and a lunch break for each shift. The production schedule runs on a 8 hour, 2 shift per day basis. The schedule module is used to define the capacity versus the time of usage on a daily basis. It is modeled based on the actual production run which is 15 minutes setup time for each machine and 1 hour lunch break for each operator. A day is defined as 16 hours in this simulation.
6.7.2 Advanced Transfer
Stations module represents the location for the part arrivals, and part departures. Each station is assigned a unique name. Example: assembly station, pin depth gauging station, testing station. The route module allows the movement of entities from one station to another. In this simulation the route time is expressed as a constant, after data collection of 40 observations, and using the standard time.
109 The station and the route module provide the driving force to the model s animation by displaying the movement of operators between the stations as the model run progresses. The station module can be represented in the production layout using station markers. These station markers establish locations on the production where station transfers are initiated or terminated. The movement of operators between the stations is defined by route path objects, which visually connects the stations to each other for the animation and establish the path of movement for operators that are routed between the stations.
An example of station marker placement and the route path for coaxial waveguide adapter in layout design 1 is shown in Figure 6.7 below.
Figure 6.7 : Station marker placement for coaxial waveguide adapter
6.8
Model Verification
Model verification is often defined as ensuring that the computer program of the computerized model and its implementation are correct in other words debugging the model.
110 i ) Step button
The Step button in Figure 6.8 below:
Figure 6.8: Step button location in ARENA
This will control the model execution and step the entity through the system. It creates a single instance of a part type and watched that solitary part flows through the system. This method is used to verify on the model and no errors were found.
ii) Set Max Arrival field
Figure 6.9: Set maximum arrival
Apart from that, the Max Arrival field is set to 1 to release limited number of entities to the system. This is to review for any errors before execution of Infinite number of entities.
111 iii)
Change model times to constant value
Some parts of the model data are replaced with constant values. Using deterministic data will allow prediction of the system more accurately.
iv)
Increasing / decreasing part interarrival time
Figure 6.10: Screen shot of increase / decrease IAT
The Interarrival Time (IAT) has been increased and decreased. The model still runs smoothly. If there are errors in the model, it will most likely show up during these kinds of stressed out conditions.
v)
Animation
An animation is often useful during the verification process as it allows viewing the entire system being modeled as it operates. The model s operational
112 behavior is displayed graphically as the model moves through time. A model which is free of bugs will operate smoothly to the way described in the system.
vi )
Face Validity
Throughout the simulation, consultations from individuals knowledgeable about the system whether the model and/or its behavior are performed to ensure the logic in the conceptual model is correct. Consultation from various department heads have been done to get a better understanding of the whole system. Also valuable inputs from my thesis supervisor regarding the simulation techniques used for this software are gathered.
vii )
SIMAN language
The SIMAN code could also be viewed through the Run>SIMAN> View option. The general logic is possible to be followed. The logic used in this model is the arrival of entity at the process module, increments some internal counters, enters a queue, waits to seize a resource, delays for the process time, releases the resource, decrements the internal counters and exits the module. Example of the SIMAN language is shown in Figure 6.11. The script is verified for any possible error or bug.
113
Figure 6.11: SIMAN language window
6.9
Model Validation
Model validation is the process of ensuring that the model created behaves the same as the real system.
i)
Historical Data Validation
If historical data exist, (or data collected on a system specifically for building and testing a model), part of the data is used to build the model and the remaining data are used to determine whether the model behaves as the system does. For this simulation, historical standard time is obtained from and compared with statistical analysis from the ARENA input analyzer. Example in Figure 6.12 shows the standard cycle time for the coaxial waveguide adapter and the sample mean data.
114
Data Validation of Process Cycle Time 16 14
Time(minutes)
12 10
Actual Model
8 6 4 2 0 0
1
2
3
4
5
6
7
Process
Figure 6.12: Historical Data Comparisons (Coaxial Waveguide Adapter Design 1)
ii)
Internal Validity
Several replications or runs of a stochastic model are made to determine the amount of variability in the model. A large amount of variability may cause the model s results to be questionable. Therefore several replications of the model for coaxial waveguide adapter and e-cal are done.
iii)
Operational Graphics(Change the graph not stepped)
Values of various performance measures for example, the number in queue and output of the system, are shown graphically as the model runs through time. The dynamical behavior of performance indicators are visually displayed as the simulation model runs through time to ensure they are correct.
115 The simulation software ARENA is user friendly for testing the model in visual way ( Kelton, et al., 2004). ARENA uses dynamic plots which will dynamically draw themselves as the simulation runs. The expression builder is used to select which parameters to be plotted. An example of the average resource value of the coaxial waveguide adapter is shown in Figure 6.13 below.
Figure 6.13: Average resource utilization
iv)
Graphical comparisons of data
The behavior data of the simulation model and the system are graphed for various sets of experimental conditions to determine of the model s output behavior has sufficient accuracy for the model s intended purpose.
Example in Figure 6.14 shows comparisons of quantity of shipped products for the coaxial waveguide adapter shown below.
116
Comparisons of Simulation Model and Actual Output for Coaxial Waveguide Adapter(Design1) 800 700
Output Quantity
600 500 400
Model System
300 200 100 0 1
2
3
4
5
Month
Figure 6.14: Graphical comparisons between actual output and model output
4.15 Model
Actual
Figure 6.15: Comparisons of actual total travel time for coaxial waveguide adapter and simulated total travel time
117 6.10
Steady State System
A steady state is one in which the quantities to be estimated are defined in the long run: that is over an infinite time frame. In principle, the initial conditions for the simulation do not matter. The system does not start and stop idle.
Three items to determine for steady state system are the warm up period, obtaining sample observations and determining the run length.
6.10.1 Warm Up Period
Warm up period is the duration needed by the simulation model to transform from transient behavior to steady state. The results generated by the simulation model during the warm up period should be disregarded. A preliminary simulation of the system is run. The average resource utilization at each time step is plotted. The time when the system reaches stability is observed. The time when the average utilization begins to flatten out is the warm up period.
Figures 6.16 and 6.17 below show the plotted graph of the warm up period for coaxial waveguide adapter (Design 1 and Design 2). Time unit is in minutes. The warm up period is approximately 60 minutes.
118
Figure 6.16: Average resource utilization versus time (minutes) for coaxial waveguide adapter (Design 1)
Figure 6.17: Average resource utilization versus time (minutes) for coaxial waveguide adapter (Design 2)
Figure 6.18: Average resource utilization versus time (minutes) for ECal (Design 1)
119
Figure 6.19: Average resource utilization versus time (minutes) for ECal (Design 2)
Based on Figure 6.18 and Figure 6.19, the warm up period for E-Cal Design 1 and Design 2 is 140 minutes.
6.10.2 Obtaining sample observation
Independent observations must be created. Independent simulations are run to determine the number of replication. For this simulation, 10 simulation runs are conducted. This will be further elaborated in section 6.11.
6.10.3 Simulation Run Length
Model run length may be dictated by the nature of system or the available data. The run length has to be long enough to reflect the actual scenario of the
120 system. Example is when simulating one day s operation of a distribution center, one shift ramp-up of a manufacturing line or any data driven model where the data represents a fixed period of time. For this simulation the run length is 3 months which equals to 63360 minutes.
6.11
Number of Replication Determination
The number of replications determines the sample size required based on a predefined relative error from simulation output to estimate the output parameter. Systems with high system variability will desire a certain level of statistical accuracy up to 100 statistical replications for each point in the experimental design (each system configuration). Other models with less inherent variability require only 3 to 5 replications. The number of statistical replications affects the statistical accuracy of performance measures; specifically, it affects the width of any interval estimators.
In order to determine the number of replications, the simulation model is initially run for 10 replication with run length of 63360 minutes (3 months) and the outputs generated are recorded. The output for 10 replications are shown in Table 6.1
121 Table 6.1: Outputs of 10 replications
Observation
Output
1
1752
2
1770
3
1710
4
1680
5
1740
6
1680
7
1725
8
1680
9
1740
10
1770
Mean,
1725 35.79
Std Dev, s
For 95% Confidence Level, For error , k =
t 0 .025 , 9 = 2.262
5%
2
Number of replication, n =
2
ts = 2.262*35.79 = 0.9386 = 1 kx 0.05*1725
Based on the calculation, 1 observation is sufficient for allowable error of 5% with 95% confidence level. This is due to small variance in the results generated from the simulation model.
122 6.12
Conclusions
In the beginning part of this chapter, conceptual model for coaxial waveguide adapter and e-cal has been constructed. The distributions of the data collected (cycle time) are determined using ARENA input analyzer. The assumptions used for this model are also listed down. The conceptual model and the cycle time data collected are then used to construct the simulation model. Subsequently various methods of verification and validation are discussed. This is followed by determining the nature of the model which is steady state. The model is then run for certain period of time to determine the warm up period, run length and number of replications. Last but not least, the model can be used for experimentation to generate results which will be discussed in the next chapter.
123
CHAPTER VII
SIMULATION EXPERIMENTATION AND RESULTS
7.1
Introduction
This chapter discusses about the simulation for the 2 layout alternatives proposed using the Systematic Layout Planning (SLP) methodology as discussed in Chapter 5.The results generated will be compared to the current layout and the best alternative will be chosen.
7.2
Experimentation
The simulation experimentation is conducted to evaluate the effect of 2 different layout proposals generated in Chapter 5. The performance measures which will be evaluated are total travel time, total travel cost, number of cross-overs, output, average resource utilization, total average WIP level ,total average waiting time and total time spent in the system. The 2 alternative layouts will involve relocating the processes (assembly, pin depth gauging and packaging) which were previously in building 6 to building 5.
124 The simulation is conducted under steady state condition with a warm-up period of 60 minutes for coaxial waveguide adapter and 140 minutes for e-cal. The maximum simulation run length for coaxial waveguide adapter is 120 minutes and for e- cal is 150 minutes. The simulations are conducted for a period of 3 months. The number of replications for both products is 1 due to small variation for each observation. Also the 2 products are created in separate process flow modules are they are manufactured independently. However, for this case study a total of 5 replications are conducted.
The simulated results which are the output and total travel time are then compared to manual calculations. This would be discussed later in this chapter.
7.3
Experiment 1: Layout Design 1
In the first experiment, the results of the first proposed layout design is evaluated in terms of the total travel time, output, total average WIP level , average resource utilization, total average waiting time and total time spent in system. The simulation model for coaxial waveguide adapter and e-cal are shown in below Figure 7.1 and Figure 7.2.
125
Figure 7.1: Simulation model for coaxial waveguide adapter design
Figure 7.2: Simulation model for e-cal design 1
126 In order to enhance the visualization of product flow through the new layout, station and route transfer modules are used to animate the flow. The graphical flow for coaxial waveguide adapter and e-cal are shown in Figure 7.3 and Figure 7.4 respectively
The route time in the simulation model is equivalent to the walking time of the operator. The walking time is collected through 40 observations, Appendix I. As the variation is large (due to cycle time in minutes), the ARENA input analyzer is used to determine the distribution of walking time, Appendix J.
Figure 7.3: Route and station placement for coaxial waveguide adapter
Figure 7.4: Route and station placement for e- cal
127 Based on the experiment conducted, the results generated for Design 1 coaxial waveguide adapter and e-cal are tabulated. These results are also compared with the current performance measures calculated manually for the current design as shown in Table 7.1 and Table 7.2. The duration of the simulation results are for 3 months.
Table 7.1 Performance measures for coaxial waveguide adapter Layout Design 1
Coaxial Waveguide Adapter Current Layout Total Travelling Distance(feet) Total Travelling Time (minutes/unit) Travelling Cost (RM / unit) Number of cross over (times/unit) Output ( 3 months)
Average resource utilization Total average WIP Level Total Average waiting time Total time spent in system
Layout Design 1 (Simulation results)
1100.9
246.79
22.54
3.64
52.59
8.49
6
0
766.98
1584
0.44
59.15
48.16
83.87
128 Table 7.2 Performance measures for E-cal Layout Design 1
E-cal Current Layout Total Travelling Distance(feet) Total Travelling Time (minutes/unit) Travelling Cost (RM / unit) Number of cross over (times/unit) Output (3 months)
Layout Design 1 (Simulation results)
1614.08
607.08
27.99
10.55
50.84
24.62
9
0
387.36
422.4
Average resource utilization Total average WIP Level Total Average waiting time Total time spent in system
0.55
59.71
0.00
141.47
From the above 2 tables, it can be seen that in layout design 1, no cross-over are needed for both products. Total travelling time for coaxial waveguide adapter is reduced by 83.85% while for e-cal is reduced by 62.30%. This will subsequently reduce the travelling cost for coaxial waveguide adapter by 83.85%, E-cal is reduced by 51.57%. The output for coaxial waveguide adapter will increase 51.58% as well. For E-cal the output will increase by 9.05%.
129 Thus by comparing the existing layout and the new proposed design 1, it can be seen that design 1 yield great improvement.
7.4
Experiment 2: Layout Design 2
In experiment 2 the Microcircuit Assembly process will remain in its location. But similar to design 1, the assembly, pin depth gauging process and packaging process are shifted from building 6 to building 5. The effect of the second proposed layout design is evaluated in terms of the total travel time, output, average WIP, average resource utilization and average waiting time. The simulation model for coaxial waveguide adapter and e-cal is shown in Figure 7.5 and Figure 7.6.
Figure 7.5: Simulation model for coaxial waveguide adapter design 2
130
Figure 7.6: Simulation model for e-cal design 2
The graphical flow for coaxial waveguide adapter and e-cal are shown in Figure 7.7 and Figure 7.8 respectively.
Figure 7.7: Route and station placement for coaxial waveguide adapter design 2
131
Figure 7.8: Route and station placement for e-cal design 2
Due to huge variations in route time, 40 observations are collected to determine the distribution of each route time. Please refer to Appendix K.
Based on the experiment conducted, the results generated for Design 2 coaxial waveguide adapter and e-cal are tabulated. These results are also compared with the current performance measures calculated manually for the current design. Results are shown in Table 7.3 and Table 7.4. The duration of the simulation results are for 3 months. The overall process and walking time are tabulated in process flow diagrams shown in Appendix L and Appendix M.
132 Table 7.3: Performance measures for coaxial waveguide adapter Layout Design 2
Coaxial Waveguide Adapter Current Layout
Layout Design 2 (Simulation results)
Total Travelling Distance(feet) Total Travelling Time (minutes/unit) Travelling Cost (RM / unit)
Number of cross over (times/unit) Output ( 3 months )
Average resource utilization
Total average WIP Level
1100.9
242.1
22.54
3.06
52.59
7.14
6
0
766.98
1716
0.45
59.66
Total Average waiting time 47.63 Total Time Spent In System
83.14
133 Table 7.4: Performance measures for e-cal Layout Design 2
E-cal Current Layout
Layout Design 2 (Simulation results)
Total Travelling Distance(feet) Total Travelling Time (minutes/unit) Travelling Cost (RM / unit) Number of cross over (times/unit) Output (3 months)
1614.08
599.29
27.99
6.95
50.84
16.22
9
0
387.36
422.4
Average resource utilization Total average WIP Level
Total Average waiting time Total Time Spent In System
0.56
68.69
0.00
137.62
From the above 2 tables, it can be seen that in layout design 2, no cross-over are needed for both products. Total travel time for coaxial waveguide adapter is reduced by 86.42 % while for e-cal is reduced by 75.17%. This will subsequently reduce cost of travel for coaxial waveguide adapter by 86.42% and e-cal is reduced by 68.09%. The output for coaxial waveguide adapter will increase 55.30 % as well. For e-cal the output will increase by 9.05 %.
134 Thus by comparing the existing layout and the new proposed design 2, it can be seen that design 2 also improves the performance measures compared to the existing layout.
7.5
Discussion
After running the simulation model for both experiments, the results are compared in terms of total travel time, travel cost, number of cross-over, output, resource utilization, WIP level and average waiting time. Table 7.5 summarizes the performance measures for coaxial waveguide adapter.
135 Table 7.5: Performance measures for Coaxial Waveguide Adapter
Coaxial Waveguide Adapter
Total Travelling Distance(feet) Total Travelling Time (minutes/unit) Travelling Cost (RM / unit) Number of cross over (times/unit) Output ( 3 months )
Average resource utilization Total average WIP Level
Total Average waiting time Total Time Spent in System
Current Layout
Layout Design 1
Layout Design 2
1100.9
246.79
242.1
22.54
3.64
3.06
52.59
8.49
7.14
6
0
0
766.98
1584
1716
0.44
0.45
59.15
59.66
48.16
47.63
83.87
83.14
136 Table 7.6: Performance measures for E-cal
E-cal
Total Travelling Distance(feet) Total Travelling Time (minutes/unit) Travelling Cost (RM / unit) Number of cross over (times/unit) Output (3 months )
Average resource utilization Total average WIP Level Total Average waiting time(minutes) Total Time Spent in System(minutes)
Current Layout
Layout Design 1
Layout Design 2
1614.08
607.08
599.29
27.99
10.55
6.95
50.84
24.62
16.22
9
0
0
387.36
422.4
422.4
0.55
0.56
59.71
68.69
0.00
0.00
141.47
137.62
Based on the above simulation results comparisons for layout design 1 and layout design 2 for coaxial waveguide adapter product, the total travel distance for layout design 2 is less by 7.79 feet compared to design 1. Subsequently, total travel time will be shorter for layout design 2. This will be converted into resource cost as time is taken by the operator to transport the products from one station to another. Therefore travelling cost would be cheaper compared to design 1. Output of design 2 is greater by about 8% compared to design 1. Average resource utilization (humans and tester) for design 2 is also greater by 0.01. However this will not have significant
137 impact on the overall results. Total average WIP level for design 2 is slightly higher than design 1 with a difference of only 0.51. Average waiting time for design 2 is lower than design 1. Lastly the total time spent in the system for design 2 is also lower than design 1 which is 83.14 minutes.
For coaxial waveguide adapter, design 2 is recommended as it yields higher output, shorter travel distance, travel cost and travel time. The average resource utilization is also higher. Moreover, no extra space is needed from other departments.
For E-cal, the total travel time for design 2 is lesser by 3.5 minutes per product. The total travel distance is also shorter. For travel cost, design 2 is lesser by 34 % compared to design 1.Outputs for both layout designs are the same. The resource utilization for design 2 is higher by 0.01% compared to design 1. As for the total average WIP level, design 1 is lesser than design 2 by 8 units. However, this would not effect the total travel time and output, which are the important value added parameters. There is basically no average waiting time in the system. Last but not least, total time spent in the system is lesser for design 2 compared to design 1.
From the experimentation, it can be seen that layout design 2 is a much sought after choice compared to layout design 1. This is because no negotiation is needed to be done with other department on the extra space needed. There is also less costs for re-layout incurred as the microcircuit assembly station remains at the same location.
138 7.6
Conclusion
The experimentation for layout design 1 and layout design 2 has been discussed. Based on the results generated the alternative which has the most significant improvement in performance measures is selected. Layout design 2 is proposed to management because besides saving greater travel cost and having more output, it does not required additional floor space.
139
CHAPTER VIII
CONCLUSIONS AND RECOMMENDATIONS
8.1
Introduction
The conclusions and recommendations are discussed in this chapter. This chapter includes the project summary, findings of the project and further recommendations for future improvement.
8.2
Project Summary
This case study is conducted in MTA department of Agilent Technologies, Inc., an electronics industry located in Bayan Lepas, Penang. The objectives of this study are to improve the production floor layout at MTA department and to suggest improvement alternatives using Systematic Layout Planning. Last but not least is to evaluate this alternatives using ARENA simulation software.
140 The opportunity for improvement is high cross-over found between building 5 and building 6 for both coaxial waveguide adapter and e-cal. This is due to the processes which have high interdependency are located at different departments. The travelling distance and travelling cost are high as well. It has been identified that the layout of the production floor is the main cause of the high cross-over, long travelling time and high travelling cost.
Layout improvement alternatives have been proposed using Systematic Layout Planning (SLP). There are altogether 11 steps which have been discussed previously in chapter 5. Two layout alternatives are generated. Simulation is used as it is most cost effective. The layout alternative with the best results generated by simulation is selected and proposed to management.
8.3
Findings
After completing the simulation, it is found that layout design 2 yields the best results for coaxial waveguide adapter and e-cal in terms of total travel distance, total travel time, travelling cost, cross-over, output, average utilization ,average waiting time and total time spent in the system.
On top of that no extra space is needed during the re-layout. This will enable smooth process of station transfers to building 6. The cost of the re-layout will be less.
141 8.4
Further Recommendation
After improving layout of the production, some performance measures such as resource utilization and total average WIP level could be reduced, As we can see from the results in chapter 7 that both resource utilization for coaxial waveguide adapter and e-cal is less than 60%. Operators and machines are under utilized. This can be done running production in a single shift instead of 2 shifts or combining stations. Headcount would be reduced and the line would operate more efficiently.
As for improvement in total average WIP level, factors such as low yield rate, machine breakdowns can be studied.
In order to enhance the features of the simulation animation, 3D graphics could be used. However, the student version of ARENA does not have this feature.
8.5
Conclusions
To conclude this project, based on the results generated from the simulation, the objectives of this case study have been achieved. The problem faced by this company has been identified and improvement alternatives proposed using SLP. ARENA modules have been created to build the simulation model. The alternatives have been chosen based the significant improvement in performance measures. The best layout alternative which is design 2 is recommended to the company and further improvements for future works have been proposed.
142
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146 Appendix A
(i)
Process Flow for E-Cal
147 (ii)
Process Flow for Coaxial Waveguide Adapter
148 Appendix B
(i)
Cross-Over Quantity for E-Cal
149 (ii)
Cross-Over Quantity for Coaxial Waveguide Adapter
150 Appendix C
(i) Travelling cost for E-Cal
(ii) Travelling cost for E-Cal
151 Appendix D: Department Distance Calculation Based On Current Layout
152 Appendix E: Data and sample size for cycle time (10 observations)
Coaxial Waveguide Adapter
Assembly
Transport
Take Epoxy
Transport
153 Assembly
Setup
Transport
Pin Depth Gauging
154 Transport
Setup
Testing
Transport
155 Packaging
E-Cal
Microcircuit Assembly
Transport
156 Setup
Transport
Laser Welding
Assembly
157 Transport
Transport
Take Epoxy
Assembly
158 Transport
Testing
Setup
Transport
159 Setup
Packaging
Transport
160 APPENDIX F: Additional process cycle time (40 observations) Coaxial Waveguide Adapter
Assembly
Take Epoxy
161 Assembly2
Setup1
Pin Depth Gauging
162 Setup2
Testing
Packaging
163 E-Cal
Microcircuit Assembly
Setup3
Laser Welding
164 Assembly3
Take Epoxy
Assembly4
165 Setup4
Testing2
Setup5
166 Packaging2
167 APPENIX G: ARENA Input Analyzer Analysis for E-Cal Microcircuit Assembly
Transport5
168 Setup3
Laser Welding
169 Transport6
Assembly3
170
Transport7
Take Epoxy
171 Transport8
Assembly4
172 Setup4
Testing
173 Setup5
Testing
174 Transport12
Packaging2
175 APPENDIX H : ARENA Input Analyzer Analysis for Coaxial Waveguide Adapter Coaxial Assembly
Transport1
176 Take epoxy
Transport2
177 Assembly2
Transport3
178 Setup1
Pin depth gauging
179 Setup2
Testing
180 Transport4
Packaging
181 APPENDIX I: DATA COLLECTION FOR WALKING TIME DESIGN 1
Coaxial Waveguide Adapter Tranport9
Transport10
182 Transport11
Transport12
183 Transport13
184
E-Cal Transport1
Transport2
185 Transport3
Transport4
186 Transport5
Transprot6
187 Transport7
188 APPENDIX J: INPUT ANALYZER DISTRBUTION FOR WALKING TIME DESIGN 1 Walking Time for coaxial waveguide adapter ( Design 1) Transport 9
Transport10
189
Transport11
Transport12
190 Transport13
191 APPENDIX K:DATA COLLECTION FOR WALKING TIME DESIGN 2
Transport9
Transport10
192
Transport11
Transport12
193 E-Cal
Transport1
Transport2
194 Transport3
Tranport4
195
Transport5
Transport6
196 Transport7
197 APPENDIX L: NEW PROCESS CYCLE TIME DESIGN 1
Coaxial Waveguide Adapter
E-Cal
198 APPENDIX M : NEW PROCESS CYCLE TIME DESIGN 2
Coaxial Waveguide Adapter
E-Cal
