College of Engineering Department of Industrial Engineering
Decision Support System for Lean, Agile and Leagile Manufacturing
By: Eng. Hesham Al-Masoud
Supervised By: Dr. Abdulaziz Al-Tamimi
Submitted in partial fulfillment of the requirements for the degree of Master of Science in the Industrial Engineering Department with the College of Engineering, King Saud University
Riyadh December 2007
We hereby approve the Master of Science Thesis report entitled:
"Decision Support System for Lean, Agile and Leagile Manufacturing" Prepared by: Eng. Hesham Al-Masoud
COMMITTEE MEMBERS:
SUPERVISOR
Signature: ________________ Dr. Abdulaziz Al-Tamimi
EXAMINER
Signature: ________________ Dr. Abdulrahman Al-Ahmari
EXAMINER
Signature: ________________ Dr. Mohammed Ramadan
Riyadh October 2007
1
2
Acknowledgment
I wish to acknowledge the support of my advisor Dr. Abdulaziz AlTamimi for providing me with the opportunity to gain the host of goals and practices acquired through this thesis. I would also like to thank him for his patient guidance, collaboration in designing my internship experience. Furthermore, I am thankful to Dr. Abdulrahman Al-Ahmari and Mohammed Ramadan for their assistance on reviewing my thesis writing. .
Eng. Hesham Al-Masoud December 2007
3
Contents
Acknowledgement List of Tables
3 6
List of Figures
9
Abstract
10
Chapter 1: Introduction
11
1.2
1.3
1.1.1 Overview
11
Lean and Agile Manufacturing Concepts
12
1.2.1 Lean Manufacturing
12
1.2.2 Lean Manufacturing Tools
14
1.2.3 Agile Manufacturing
16
1.2.4 Agile Manufacturing Tools
17
1.2.5 Comparison of Lean and Agile manufacturing
17
Research Objective
18
Chapter 2: Literature Review
20
2.1
Previous Work
20
2.2
Literature Conclusion
23
Chapter 3: Modeling of Lean, Agile and Leagile Manufacturing
25
3.1
Analytical Hierarchy Process (AHP)
25
3.2
Modeling the Manufacturing Strategies Using AHP
27
3.3
Developing the Expert Opinions Rating
32 4
Chapter 4: Decision Support System (DSS)
39
4.1
39
Building a Decision Support System Using Visual Basic
Chapter 5: Case Studies
44
5.1
Saudi Mechanical Industries Company (SMI)
44
5.1.1 SMI Study
45
Advanced Electronics Company (AEC)
53
5.2.1 AEC Study
54
Saudi Lighting Company (SLC)
62
5.3.1 SLC Study
62
5.2 5.3
Chapter 6: Discussion and Conclusion
70
References
72
Appendix A
75
Appendix B
107
Appendix C
113
5
List of Tables Table #
Title
Table (1.1)
Comparison of Lean and Agile Manufacturing
Table (2.1)
Summary of related references for lean and agile
Page 18
manufacturing
24
Table (3.1)
AHP Comparison Scale
26
Table (3.2)
Characteristics Factors for the lead time
33
Table (3.3)
Characteristics Factors for the cost
34
Table (3.4)
Characteristics Factors for the quality
35
Table (3.5)
Characteristics Factors for the productivity
36
Table (3.6)
Characteristics Factors for the service level
37
Table (3.7)
Characteristics Factors for the Measures
37
Table (3.8)
Relative Impact with respect to Experts’ Opinions rating
Table (5.1)
The feedback data input of the five measuring factors (SMI)
Table (5.2)
47
Characteristics Factors by Decision Makers on Quality (SMI)
Table (5.5)
46
Characteristics Factors by Decision Makers on Cost (SMI)
Table (5.4)
45
Characteristics Factors by Decision Makers on Lead Time (SMI)
Table (5.3)
38
48
Characteristics Factors by Decision Makers on Productivity (SMI)
49
6
Table # Table (5.6)
Title Characteristics Factors by Decision Makers on Service Level (SMI)
Table (5.7)
63
Characteristics Factors by Decision Makers on Cost (SLC)
Table (5.18)
62
Characteristics Factors by Decision Makers on Lead Time (SLC)
Table (5.17)
60
The feedback data input of the five measuring factors (SLC)
Table (5.16)
59
Normalization of the measuring Means of the three Decision Makers of Matrices (AEC)
Table (5.15)
58
Characteristics Factors by Decision Makers on Service Level (AEC)
Table (5.14)
57
Characteristics Factors by Decision Makers on Productivity (AEC)
Table (5.13)
56
Characteristics Factors by Decision Makers on Quality (AEC)
Table (5.12)
55
Characteristics Factors by Decision Makers on Cost (AEC)
Table (5.11)
54
Characteristics Factors by Decision Makers on Lead Time (AEC)
Table (5.10)
51
The feedback data input of the five measuring factors (AEC)
Table (5.9)
58
Normalization of the Measuring Means of the three Decision Makers of Matrices (SMI)
Table (5.8)
Page
64
Characteristics Factors by Decision Makers on Quality (SLC)
65
7
Table # Table (5.19)
Title Characteristics Factors by Decision Makers on Productivity (SLC)
Table (5.20)
66
Characteristics Factors by Decision Makers on Service Level (SLC)
Table (5.21)
Page
67
Normalization of the Measuring Means of the three Decision Makers of Matrices (SLC)
67
8
List of Figures Figure #
Title
Page
Figure (1.1)
Technical vs Organizational (Lean vs Agile)
12
Figure (3.1)
Hierarchical Approach of AHP
26
Figure (3.2)
Model for Lean, Agile and Leagile Manufacturing 28
Figure (3.3)
Measures of Manufacturing Strategies
Figure (3.4)
Characteristics of Manufacturing and
28
Related Methods
30
Figure (4.1)
Selection of the Manufacturing System
39
Figure (4.2)
Triangular Fuzzy
40
Figure (4.3)
α-Cut of the Triangular Fuzzy Number
41
Figure (4.4)
The Manufacturing System Strategy
43
Figure (4.3)
The α-Cut of the Example
43
9
Abstract
The objective of this research is to develop a methodology for evaluating whether an existing manufacturing system operates under traditional, lean, agile or leagile manufacturing. The research is carried out as follows: Measuring factors and characteristics factors should be defines from the literature to built the model by Analytical Hierarchy Process (AHP). More after, a questionnaire was built to distribute it to internal and external experts according to their qualifications. The composed data is adjusted using Expert Choice (EC) software to get the Expert Opinions Ratings. Other questionnaire was developed to dispense to plants for getting their response. a Decision Support System (DSS) using a Visual Basic was developed to come with an Existing Evaluating Rating of plant. Finally, the Experts Opinion Rating and Existing Evaluating Rating were compared to conclude that either the manufacturing system strategy is traditional, lean, agile or leagile manufacturing.. To resolve the manufacturing system in order to become lean, agile or leagile; a lot of tools will help in becoming lean like Cellular Manufacturing, Total Quality Management, ,Pokayoke, Kaizen , Value Stream Mapping, 5 S, Takt Time, address issues within its supply chain management, increase its focus on customer service and improve the quality of its IT applications. and so on. Three case studies have been carried out with reference to the three companies which are Saudi Mechanical Industries
(SMI) Company,
Advanced Electronics Company (AEC) and Saudi Light Company (SLC).
10
Chapter 1 Introduction
1.1 Overview Over the past two decades a powerful drive by enterprises and academic institutions has boosted the development and adoption of new manufacturing initiatives to enhance business in an increasingly competitive market. Several studies have discussed the concepts of lean and agile manufacturing and their tools as a means of improving the efficiency and performance of organizations, which leads to improvement in the success of said organizations. Lean manufacturing focuses on cost reduction by eliminating nonadded activities so that several advantages can be obtained such as minimization/elimination of waste, increased business opportunities and more competitive organizations. Agile manufacturing focuses on the introduction of new products into rapidly changing markets, achieving the ability of expected short market life, pricing by customer value, and high profit margins. The tools and techniques of lean manufacturing have been widely used in the industry, starting with the introduction of the original Toyota Production Systems and more recently including total productive maintenance and better utilization of labours and setup reduction. The tools of agile manufacturing include short life cycle and flexibility. The concepts of lean and agile manufacturing in industry can be summarised
by
Figure
(1.1).
Lean
manufacturing
deals
with
technical/operational issues inside the factory such as minimizing or 11
eliminating waste, improving the work environment and the organization of teams. Agile manufacturing is concerned with organizational issues outside the industry such as supply chain strategy and the strength or weakness of the market [1]. Lean Manufacturing Agile Manufacturing
Technical / Operational issues
Organizational issues)
Figure (1.1) Technical vs Organizational Issues (lean vs agile)
1.2 Lean & Agile Manufacturing Concepts 1.2.1 Lean Manufacturing The term ‘lean manufacturing’, which first appeared in 1990 when it was used to refer to the elimination of waste in the production process, has been heralded as the production system of the 21st century. Historically the concept of lean manufacturing originated with Toyota Production Systems (TPS) and has been implemented gradually throughout Toyota's operations since the 1950s. By the 1980s, Toyota had increasingly become known for its effectiveness in implementing Just-In-Time (JIT) manufacturing systems, and today Toyota is often considered one of the most efficient manufacturing companies in the world and the company that sets the standard for best practices in lean manufacturing. This started when Mr. Ohno led the development of the lean manufacturing concept. He recognized that keeping the production system running at maximum production
12
efficiency at all costs to minimize the cost of parts and cars lead to: (a) extensive intermediate inventory and (b) defects built into the cars as they passed down the line. He stated the importance of eliminating the waste rather than running at maximum efficiency because increasing the line speed could add waste if variability was injected into the flow of work. Zero time delivery of a car meeting customer requirements with nothing in inventory required tight coordination between the progress of each car down the line and the arrival of parts from supply chains [1]. Lean manufacturing can now be understood as a new way to design and make things different from mass and craft forms of production by the objectives and techniques applied on the shop floor, both in design and along supply chains. Lean manufacturing aims to optimize performance of the production system against a standard of perfection to meet unique customer requirements. [2] The National Institute of Standards and Technology (NIST) Manufacturing Extension Partnership’s Lean Network offers the following definition of lean manufacturing: “A systematic approach to identifying and eliminating waste through continuous improvement of the flow of the product at the pull of the customer in pursuit of perfection.” [3]. The main benefits of lean manufacturing are lower production costs, increased output and shorter production lead times. More specifically are the following factors: [4]
1) Defects and waste – reduction of defects and unnecessary physical waste, including excess use of raw material inputs, preventable defects
13
and costs associated with reprocessing defective items and unnecessary product characteristics which are not required by customers. 2) Cycle Times – reduction of manufacturing lead times and production cycle times by reducing waiting times between processing stages, as well as process preparation times and product/model conversion times. 3) Inventory levels – minimization of inventory levels at all stages of production, particularly works-in-progress (WIP) between production stages. 4) Labor productivity – improvement of labour productivity, both by reducing the idle time of workers and ensuring that when workers are working, they are using their effort as productively as possible (including not doing unnecessary tasks or unnecessary motions). 5) Utilization of equipment and space – utilization of equipment and manufacturing space more efficiently by maximizing the rate of production though existing equipment, while minimizing machine downtime. 6) Flexibility – acquisition of the ability to produce a more flexible range of products with minimum changeover costs and changeover time. 7) Output – reduction of cycle times, increase in labour productivity. Companies can generally significantly increase output from their existing facilities.
1.2.2 Lean Manufacturing Tools Based on the definition of lean manufacturing, it is apparent that it is a set of tools and methodologies aiming for continuous elimination of waste in manufacturing processes. Lean Manufacturing Tools include [4]:
14
• Cellular manufacturing: organization off the entire process for similar products into a group of team members including all the necessary equipment. • Total Quality Management: a management philosophy committed to a focus on continuous improvement of products and services with the involvement of the entire workforce. Continuous improvement minimizes product defects. • Rapid Setup (SMED): a method for a reduction of tool changeover times to facilitate increased capacity, smaller batch sizes, lower inventory and reduced lead times • Kanban: a finished goods and components management system whereby the manufacturer keeps safety stock on hand at all times for each stage in the manufacturing process. • Value Stream Mapping: a technique used in lean manufacturing that maps the flow of material/data and associated time requirements from initial supplier to end customer for a given business process. • 5S: five terms beginning with 'S' utilized to create a workplace suited to visual control and lean production: 1- SORT: eliminate everything not required for the current work, keeping only the bare essentials. 2- STRAIGHTEN: arrange items in a way that makes them easily visible and accessible. 3- SHINE: clean everything and find ways to keep it clean; make cleaning a part of everyday work. 4- STANDARDIZE: create rules by which the first 3 Ss are maintained.
15
5- SUSTAIN: Keep 5S activities from unraveling. • Pokayoke: supports problem solving and decision making in the context of any manufacturing organization that adopts lean production. • Total
Productive
Maintenance:
activity
that
targets
zero
machinery/equipment downtime, zero defects and zero accidents by the proactive identification of potential problems. • Standard Work: specification of tasks to indicate the best way to get the job done in the amount of time available while ensuring the job is done within a suitable timeframe. • Takt Time: named after the German word for 'beat', this represents the pace at which the customer requires the product. Takt Time is the rate at which parts have to be produced to match the customer requirements. • Kaizen: a Japanese word defined as the constant effort to eliminate waste, reduce response time, simplify the design of both products and processes and improve quality and customer service.
1.2.3 Agile Manufacturing The term ‘Agile Manufacturing’ appeared at the beginning of the 90s. In 1991 the Iacocca Institute released its now famous document outlining their vision of manufacturing in the 21st century. Agile manufacturing is essentially the utilization of market knowledge and virtual corporation to exploit profitable opportunities in a volatile marketplace [5].
16
Agile manufacturing is a flexible manufacturing model that enables manufacturers to build and deliver a wider mix of customized products, faster and more cost effectively [5]. Agile manufacturing is the ability to respond to and create new windows of opportunity in a turbulent market environment, driven by the individualization of customer requirements cost effectively, rapidly and continuously. Essentially the customer, and more importantly the product requirements that they represent, is central to manufacturing profitability. These requirements must be met at the right price, to the right quality, and at the right time. However, due to changes in the business environment, the ability to fulfill these requirements is under permanent pressure from environmental turbulence. Agile Manufacturing sets out to identify and apply practical tools, methodologies and best practices that enable companies to achieve manufacturing agility within a turbulent business environment. [5]
1.2.4 Agile Manufacturing Tools Agile manufacturing allows a company to make rapid changes in a volatile marketplace. As a result of this, the essential tools of such a manufacturing concept will be: Customer Value Focus, IT Systems and Supply Chain Management[6].
1.2.5 Comparison of Lean and Agile manufacturing Lean manufacturing focuses on cost reduction by elimination of nonadded value, while agile manufacturing focuses on cost reduction by efficient response to a volatile market environment. Table 2.1 shows a comparison between lean and agile manufacturing.
17
Table (1.1) Comparison of Lean and Agile Manufacturing Agile Manufacturing
Lean Manufacturing
Market driven
Customer driven
Orders based on changing the market
Orders based on customers
Checking samples on the line by workers
Checking samples on the line by workers
Greater flexibility for customized products
Flexible production for product variety
Focused on enterprise-wide operations
Focused on factory operations
Emphasis on virtual enterprises
Emphasis on supplier management
Emphasis on thriving in a market environment
Emphasis on efficient use of resources
Unpredictable market demand
Predictable market demand
High product variety
Low product variety
High profit margin
Low profit margin
Marketability dominant cost
Physical dominant cost
Obligatory enrichment
Highly desirable enrichment
1.3
Research Objective The objective of this research is to develop a methodology for
choosing whether the system exist for lean, agile or leagile manufacturing by the development of a Decision Support System using Visual Basic. This research will proceed in the following manner: a)
The literature previously published to provide and define methods of lean, agile and leagile manufacturing, and Decision Support System (DSS).
b)
Applying the Analytical Hierarchy Process (AHP) developed by [21] is prepared to help in getting a reference rating (Experts Opinion Rating) to compare it with the rating that comes from the Decision Support System
18
(DSS) (Manufacturing Strategy Rating) to discover the Manufacturing System of the industries which were evaluated. . c)
Built a Visual Basic: computerized Decision Support System (DSS) to help in assessing manufacturing system lean, agile and leagile manufacturing.
d)
Use of developed DSS in case studies.
19
Chapter 2 Literature Review This literature review covers previous work that has been carried out on the related subjects of lean and agile manufacturing and decision support systems. It represents the current accepted thinking for these manufacturing strategies and their applications in industry.
2.1 Previous Work Naylor et al [1] discuss both approaches, focusing on the aggregate output of the total value related to service, quality, cost and lead-time. They show the appropriate application according to product variety and demand variability requirements. In addition, a case study is given and they conclude that there are advantages in considering both approaches. Brown [2] surveys the application of quality and manufacturing strategies and their relations to lean manufacturing. He concludes that lean manufacturing combines all quality principles and manufacturing strategies. Storch et al. [3] describe the concepts of lean thinking and lean manufacturing by exploring the use of the flow principle of lean manufacturing in the shipbuilding industry. They propose an approach to move the industry closer to lean manufacturing in terms of flow by offering a metric to determine the value of closeness to ideal flow. Banamyong and Supatan [4] compares the effects of lean and agile strategies on the process of aquarium manufacture. He also discusses the benefits of lean and agile manufacturing in enhancing competitiveness.
20
Mukunda and Dixit [5] discuss the problems associated with the Indian Electronics Industry, and suggest how agile manufacturing can provide a solution to these problems. Christian et al [6] provide an overview of the framework and tools developed in agile manufacturing. The framework is based on four main pillars: auditing of the company, auditing of the operating environment, benchmarking and learning from best practice. Abraham Kandel [7] explains the specific area of fuzzy expert systems. He identifies the basic features of the evaluation of expert systems and fuzzy expert systems and describes the uncertainty in said systems. Ashish Agarwal al et [8] discuss the relationship among lead time, cost, quality and service level. This paper concludes that there is justification for a framework which represents the effect of market winning criteria and market qualifying criteria on the three types of manufacturing state Saaty [9] introduces a new method of making decisions in a complex environment. His method utilizes a user’s experience, along with judgments supported by explanations, to ensure a sense of realism and broad perspective. He describes how to structure a complex situation and identify its criteria and factors. Niam et al [10] present the concept of leagility as opposed to leanness and agility. They describe the similarities and differences between these three concepts and the application of each one. They also describe the application of leagility in various issues such as house building Zadeh [11] introduces the theory of fuzzy numbers as a means to represent uncertainty. He also describes fuzzy events and fuzzy statistics, fuzzy relation and fuzzy logic.
21
Groover
[12]
compares
both
approaches
(lean
and
agile
manufacturing) and concludes that lean deals more with technical and operational issues while agile deals with organizational issues. Hence lean manufacturing applies to the factory while agile manufacturing applies to the enterprise. Der Gaag and Helsper [13] discuss how knowledge can be represented using production rules and frames. They claim that knowledge-based systems are used to solve real-life problems which are typically not predefined. Chiadamrong and Brien [14] present a decision support tool to assist decision makers in choosing the best alternative in manufacturing a production system in a given situation. Quarterman [15] discusses the implementation of lean manufacturing. He states that every factory is different. These differences require unique approaches and sequences of implementation, and many other details differ from factory to factory. David Ashall et al [16] suggest that companies within a turbulent market environment will need to operate in a more responsive manner and adopt an agile philosophy. The authors’ opinion is that both lean and agile philosophies will be able to operate within differing types of supply chain and areas of business. Yanchun Luo and Zhou [17] present a mathematically sound model for the design and optimization of a supply chain in terms of performance indices such as cost, cycle time, quality and environmental impact. They also state that agile manufacturing can produce the desired products with minimum environmental impact over their life cycle. Moore [18] discusses the necessary issues of agility (such as product uniqueness, volume, quality, speed of delivery and cost) with respect to their
22
benefits and restrictions. He states the possibility of providing a solution for creating lean and agile operations within the same organization to focus on differing operational needs. Cellura et al [19] present and define a mathematical model to assess the whole environmental performance of urban systems and to control the developing trends towards sustainability as a result of differing human management scenarios. They develop a user-friendly software programme as a decision support system for policy makers during the process of multicriteria selection among differing planning and management options. Mekong [20] describes the introduction of lean manufacturing. He also explains the tools, methodology and implementation of lean manufacturing. Knuf [21] investigates the use of benchmarking in the transformation of a conventional organization into a lean enterprise. Toshiro Terano et al [22] introduce the practical application of fuzzy theory. They describe the concept of fuzzy linear programming and discuss the forms of fuzzy control rules and inference methods. Arnold Kaufmann et al [23] present a comprehensive and selfcontained theory of fuzzy numbers and their application. They claim that fuzzy numbers are a broad tool for dealing with uncertainty. 2.2 Literature Conclusion A survey of twenty-seven references has been made above, with nineteen references focusing on lean, agile and leagile manufacturing, three on a Decision Support System (DSS), four on Fuzzy Logic and one on an Analytic Hierarchy Process (AHP). Table (2.1) summarizes the nineteen lean, agile and leagile references which provide measures and criteria for manufacturing strategies. The table 2.1 shows that all fifteen of the related references discuss lead time and cost, fourteen of the fifteen discuss quality, eleven of the fifteen 23
discuss service level, nine of the fifteen discuss productivity and so on. Hence, it can be concluded that lead time, cost, quality, service level and productivity are main measures. Thus, they represent the objectives to be achieved by manufacturing strategies.
Table (2.1) Summary of related references for lean and agile manufacturing Information
Market
elimination
technology
sensitivity
of waste
0
0
0
productivity
flexibility
service level
quality
cost
lead time
Ref.
1
1
1
1
1
1
1
1
0
0
0
1
0
1
1
1
2
0
0
0
0
0
1
1
1
1
3
0
0
0
1
1
0
1
1
1
4
1
1
1
1
1
1
1
1
1
5
0
0
1
1
1
0
0
1
1
6
0
0
1
1
1
1
1
1
1
7
1
1
1
1
1
1
1
1
1
8
0
0
0
0
0
1
1
1
1
11
0
1
1
1
1
1
1
1
1
12
1
1
1
0
0
0
1
1
1
13
0
0
0
0
0
1
1
1
1
14
0
0
0
0
0
1
1
1
1
18
0
0
0
1
0
1
1
1
1
19
0
0
0
1
0
1
1
1
1
21
3
4
7
9
8
11
14
15
15
Total
The other criteria flexibility, elimination of waste, market sensitivity and information technology represent the characteristics of manufacturing system which affect the objective criteria. these characteristics should be considered when identifying the manufacturing strategies.
24
Chapter 3 Modeling of Lean, Agile and Leagile Manufacturing
The achievements of the manufacturing strategies (Lean, agile, or leagile) depend on several factors which are composed of complex multidecision variables. They are defined as changing factors in a model that is determined by decision makers. These variables are composed of the criteria and strategies through which alternative solutions can be found. One of the main methods used is the Analytical Hierarchy Process (AHP) method [7]. This technique is used to identify the experts’ opinions - which are the objective of this section - for selecting one of the strategies. In the following sections a brief description of the method and the developed model will be given.
3.1
Analytical Hierarchy Process (AHP)
Analytical Hierarchy Process (AHP) is a method used in management and economics for the ranking of a set of strategies and the selection of the most suitable one. AHP allows improved understanding of complex decisions by breaking down the problem into a hierarchically-structured design. AHP can be thought of as answering the questions: “Which one do we choose?” or “Which one is the best?” by selecting the best alternative that matches all of the decision makers’ criteria. The implementation of the AHP method involves the following steps:
25
(1)
The problem is reduced to a hierarchy of levels as shown in Figure
(3.1). The highest level corresponds to the overall objective. The lowest level is formed by a set of strategies by which objective can be achieved. The intermediary levels are composed of hierarchical criteria levels which measure the objective achievement. Objective
Criterion 1
Criterion 2
Criterion 2-1
Alternative 1
Criterion 3
Criterion 2-2
Alternative 2
Figure (3.1) Hierarchal Approach of AHP
(2)
The elements of any level are subjected to a series of paired
comparisons on the Saaty’s scale (ranging from 1/9 to 9/9) and a paired comparison matrix is built. Table (3.1) AHP comparison scale Definition Intensity relative importance
Intensity of relative importance
Factor i and j are of equal importance
1
Factor i is weakly more important than j
3
Factor i is strongly more important than j
5
Factor i is more strongly more important than j
7
Factor i is absolutely more important than j
9
intermediate
2,4,6 and 8
26
(3) All required judgments are obtained.
There are n(n-1)/2 paired
comparisons to be obtained for each matrix developed.
(4) The sum of the values in each column is calculated.
⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣
A 1 A 2 A 3 ( A + A2 + A3) 1
(5)
B 1 B 2 B 3 (B + B2 + B3) 1
⎤ ⎥ C 1 ⎥⎥ ⎥ C 2 ⎥⎥⎥ ⎥ C 3 ⎥⎦ (C + C2 + C3) 1
The values in each column are divided by the corresponding column
sums (note that the sum of the values in each column is 1). Then the average of each row is calculated:
⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣⎢
A
A + A + A 1 2 3 A
B
1
B
1
+ B B
2
A + A + A 1 2 3
+ B B
2
A + A + A 1 2 3 A
B
1
B
1
+ B
1 2 2 2 2 2
C + B
3
C
1
+C C
+ B
3
C
1
+C C
+ B
3
C
1
+C
1 2 3 2 3 2
+C
+C
+C
3
= Avrg 1
= Avrg
2
= Avrg
3
3
3
⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦⎥
3.2 Modeling the Manufacturing Strategies Using AHP Since several strategies can structure a particular manufacturing system which in turn provides certain strategies (lean, agile, or leagile manufacturing), a value should be obtained based on measuring factors and
27
characteristics factors for this particular manufacturing system in order to identify the strategies. Therefore for a proper decision to be made, these factors modeling using AHP as shown in figure 3.2 according to survey given in chapter 2 section 2.2. Measuring measures factors (Objective
Characteristics factorsCharacteristic
Sub (sub .characteristics characteristic
Manufacturing System (Manufacturin strategies St t
i
Cost
Lead time
Quality
Service
Productivity
Elimination Of waste
Flexibility
Information Technology
Market Sensitivity
o Over-production o Inventory transportation waiting o Knowledge Misconec
o Manufacturing flexibility, o Delivery flexibility o Source flexibility
o Electronic data interchange; o Means of information and data accuracy o Data and knowledge bases
o Delivery speed o New product introduction o Customer responsiveness
Lean Manufacturing
Agile Manufacturing
Leagile Manufacturing
)
Figure (3.2) Model for Lean, Agile and Leagile Manufacturing
The model is obtained from combined measure factors, characteristics factors and Manufacturing strategies which are descried as follows. A)
The Measuring Factors
The main measuring factors for lean, agile and leagile strategies are depended on five measures (lead time, cost, quality, productivity, service level) shown in Figure (3.3)
The Measures
Lead time
Cost
Quality
Productivity
Service Level
Figure (3.3) Measures of Manufacturing Strategies
28
a.
Lead Time: indicates the ability of the manufacturing firm to execute a
particular job - from the date of ordering to the date of delivery - quickly and as soon as the order is placed. Lead-time needs to be minimized in lean manufacturing as by definition excess time is waste, and leanness calls for the elimination of all waste. Lead-time also has to be minimized to enable agility, as demand is highly volatile and thus difficult to forecast. The essence of the difference between leanness and agility in terms of the total value provided to the customer is that service is the critical factor calling for agility, whilst cost, and hence the sales price, is clearly linked to leanness. [8] b.
Cost: indicates the extent to which the minimization of expenses is
manifested in company operations (the cost of capital, overhead and any recorded cost of production and distribution). This is an essential factor to be minimized in lean and agile manufacturing in order to maximize the profit of factory.[8] c.
Productivity: indicates how well resources are used to produce
marketable goods (i.e. the amount of output per unit of labour input, equipment, and capital). Productivity needs to be maximized in leanness in the form of zero non-value-added-production while at the same time covering the market requirement.[8] d.
Service Level: indicates the extent to which customer orders can be
executed with market-acceptable standards of delivery. [8] e.
Quality: indicates the standard of the finished product, and needs to be
maximized in lean and agile manufacturing in the form of minimal defects and maximal reliability, thus satisfying customers with the desirability of the product’s properties or characteristics [8].
29
B)
The Characteristic Factors
A characteristic can be defined as the feature of the property which is obtained by considering several parameters. Hence the manufacturing system in a described state performs under closely specified conditions that produce a metric value. Figure (3.3) shows four key characteristics for lean and agile manufacturing with their related parameters. These characteristics are taken from [9]
The characteristics
Elimination Of Waste
o Over-production o Inventory transportation waiting o Knowledge misconnection
Flexibility
o Manufacturing flexibility, o Delivery flexibility o Source flexibility
Information Technology
Market Sensitivity
o Electronic data interchange; o Means of information and data accuracy o Databases and Knowledge Bases
o Delivery speed o New product introduction o Customer responsiveness
Figure (3.4) Characteristics of Manufacturing and Related Methods
a) Elimination of waste This is common sense, yet it continues to be a problem for many companies in every sector and activity. The various kinds of waste include: process waste (things that manufacturers do as a function of their production system design), business waste (things all businesses do as a function of their business process design) and pure waste (things we all do because they are more convenient than changing our habits). [9] b) Flexibility: The ability to respond quickly to changes in market environment by adapting with little penalty in time, effort, cost or performance [lean production and agile manufacturing Flexibility is also considered to be the ease with which a 30
system or component can be modified for use in applications or environments other than those for which it was specifically designed. System flexibility leads to lead time compression and higher service level [lean production system control. Flexibility can be obtained by several methods such as: manufacturing flexibility, delivery flexibility and source flexibility.[9]
d) Information technology: Information is a term with many meanings depending on context, but as a rule it is closely related to such concepts as meaning, knowledge, instruction, communication, representation and mental stimulus [modeling the metric of lean, agile and leagile supply chain: ANP–based approach MMLA]. Depending on the type offered, every product should include some aspect of information. In addition a company must achieve cost development of the new product. Information technology is obtained by several methods such as electronic data interchange, means of information and data accuracy - which enable the firms to manufacture in accordance with real time demand - and databases and knowledge bases.[9] d) Market sensitivity: A market is a mechanism which allows people to trade, normally governed by the theory of supply and demand. Both general and specialized markets, where only one commodity is traded, exist. Markets work by placing many interested sellers in one place, thus making them easier to find for prospective buyers. Sensitivity is the awareness and understanding of facts, truths or information gained in the form of experience or learning. It involves issues related to quick response to real-time demand, so it has to improve quality, lead time comparison and service level (modeling the metric of lean, agile and leagile supply chain: ANP–based approach MMLA). Market sensitivity
31
is characterized by methods such as delivery speed, delivery, new product introduction and customer responsiveness. [9] Hence, Based on the AHP technique, a model for lean, agile and leagile manufacturing strategies has been developed to assist in making decisions regarding the defining of the degree to which to apply the strategies of lean, agile, and/or leagile manufacturing in accordance with the criteria. 3.3 Developing the Expert Opinions Rating To find a reference measurement rating for manufacturing strategies a questionnaire was designed as shown in Appendix A to seek expert opinion about the requires rating for implementing lean, agile and leagile manufacturing strategies in industries. The opinions provide the necessary data which are captured from internal and external experts according to their qualifications. The composed data is adjusted using Expert Choice (EC) software which is a multi-objective decision support tool based on the Analytic Hierarchy Process (AHP). Expert Choice is designed for the analysis, synthesis and justification of complex decisions and evaluations for use in individual or group settings. It can be for a variety of applications such as resource
allocation,
source
selection,
HR
management,
employee
performance evaluation, salary decisions, selecting strategies and customer feedback [10]. After running the software, the experts opinion rating was founded in the following tables as the characteristics factor rating . Appendix A shows in detail the program. The output of the program are shown in table 3.2 to table 3.8.
32
Table 3.2 explains the characteristics factors for the lead time with respect to lean, agile and leagile manufacturing strategy. a consistency ratio was calculated by the software to check the applicability of the paired comparisons The value consistency ratio should be 10 percent or less. Therefore, all the consistency ratio of the below table is less than 10 % [9].
Table (3.2) Characteristics Factors Rating for the Lead Time Consistency
Leagile
Agile
Lean
Characteristics Factors
0.02
0.625
0.238
0.136
Over Production
0.04
0.637
0.258
0.105
Inventory Transportation Waiting
0
0.5
0.25
0.25
Knowledge Misconnection
0
0.582
0.309
0.109
Manufacturing Flexibility
0
0.667
0.222
0.111
Delivery Flexibility
0.05
0.547
0.345
0.109
Source Flexibility
0.05
0. 493
0.311
0.196
Electronic Data Interchange
0.05
0.547
0.345
0.109
Mean of Information
0.02
0.443
0.387
0.169
Data and Knowledge Base
0.05
0.499
0.396
0.105
Delivery Speed
0.01
0.54
0.297
0.163
New Product introduction
0.02
0.55
0.24
0.21
Customer Responsiveness
Table 3.3 demonstrates the manufacturing performance for the cost with respect to lean, agile and leagile manufacturing strategy. a consistency ratio was calculated by the software to check the applicability of the paired comparisons The value consistency ratio should be 10 percent or less. Therefore, all the consistency ratio of the below table is less than 10 % [9].
33
Table (3.3) Characteristics Factors Rating for the Cost Consistency
Leagile
Agile
Lean
Characteristics Factors
0.01
0.416
0.126
0.458
Over Production
0.02
0.387
0.443
0.169
Inventory Transportation Waiting
0.01
0.297
0.163
0.540
Knowledge Misconnection
0
0.400
0.400
0.200
Manufacturing Flexibility
0.05
0.311
0.493
0.196
Delivery Flexibility
0.01
0.416
0.458
0.126
Source Flexibility
0.01
0.466
0.433
0.100
Electronic Data Interchange
0.02
0.443
0.169
0.387
Mean of Information
0.02
0.625
0.136
0.238
Data and Knowledge Base
0.01
0.416
0.458
0.126
Delivery Speed
0.02
0.443
0.169
0.387
New Product introduction
0.05
0.493
0.311
0.196
Customer Responsiveness
Table 3.4 expresses the manufacturing performance for the quality with respect to lean, agile and leagile manufacturing strategy. a consistency ratio was calculated by the software to check the applicability of the paired comparisons. The value consistency ratio should be 10 percent or less. Therefore, all the consistency ratio of the below table is less than 10 % [9]
34
Table (3.4) Characteristics Factors Rating for the Quality Consistency
Leagile
Agile
Lean
Characteristics Factors
0.05
0.376
0.149
0.474
Over Production
0.02
0.443
0.169
0.387
Inventory Transportation Waiting
0.01
0.416
0.126
0.458
Knowledge Misconnection
0.02
0.413
0.327
0.260
Manufacturing Flexibility
0.05
0.493
0.311
0.196
Delivery Flexibility
0.05
0.376
0.474
0.149
Source Flexibility
0.05
0.376
0.474
0.149
Electronic Data Interchange
0.05
0.493
0.196
0.311
Mean of Information
0.05
0.413
0.260
0.327
Data and Knowledge Base
0.05
0.327
0.413
0.260
Delivery Speed
0.02
0.387
0.169
0.443
New Product introduction
0.01
0.540
0.297
0.163
Customer Responsiveness
Table 3.5 expresses the manufacturing performance for the productivity with respect to lean, agile and leagile manufacturing strategy. a consistency ratio was calculated by the software to check the applicability of the paired comparisons The value consistency ratio should be 10 percent or less. Therefore, all the consistency ratio of the below table is less than 10 % [9].
35
Table (3.5) Characteristics Factors Rating for the Productivity Consistency
Leagile
Agile
Lean
Characteristics Factors
0.02
0.320
0.122
0.558
Over Production
0.05
0.333
0.140
0.528
Inventory Transportation Waiting
0.08
0.280
0.094
0.627
Knowledge Misconnection
0.02
0.588
0.122
0.320
Manufacturing Flexibility
0.05
0.528
0.140
0.333
Delivery Flexibility
0.05
0.260
0.413
0.327
Source Flexibility
0.01
0.634
0.192
0.174
Electronic Data Interchange
0
0.333
0.333
0.333
Mean of Information
0.02
0.387
0.433
0.169
Data and Knowledge Base
0.02
0.443
0.387
0.169
Delivery Speed
0.05
0.376
0.149
0.474
New Product introduction
0.02
0.550
0.210
0.240
Customer Responsiveness
Table 3.6 expresses the manufacturing performance for the service level with respect to lean, agile and leagile manufacturing strategy. a consistency ratio was calculated by the software to check the applicability of the paired comparisons The value consistency ratio should be 10 percent or less. Therefore, all the consistency ratio of the below table is less than 10 %. [9].
36
Table (3.6) Characteristics Factors Rating for the Service Level Consistency
Leagile
Agile
Lean
Characteristics Factors
0.05
0.376
0.474
0.149
Over Production
0.02
0.320
0.558
0.122
Inventory Transportation Waiting
0.02
0.387
0.443
0.169
Knowledge Misconnection
0
0.286
0.571
0.143
Manufacturing Flexibility
0.03
0.405
0.481
0.114
Delivery Flexibility
0.01
0.416
0.458
0.126
Source Flexibility
0.05
0.396
0.499
0.105
Electronic Data Interchange
0.02
0.268
0.614
0.117
Mean of Information
0.05
0.327
0.413
0.260
Data and Knowledge Base
0
0.400
0.400
0.200
Delivery Speed
0.02
0.443
0.387
0.169
New Product introduction
0.02
0.443
0.387
0.169
Customer Responsiveness
The above results are summarized for measuring factors of lead time, cost, quality, productivity and service level as shown in table 3.7. a consistency ratio was calculated by the software to check the applicability of the paired comparisons. The value consistency ratio should be 10 percent or less. Therefore, all the consistency ratio of the below table is less than 10 %. [9]. Table (3.7 Characteristics Factors Rating for the Measures Factors consistency
Leagile
Agile
Lean
Measuring Factors
0.07
0.549
0.311
0.140
Lead Time
0.08
0. 408
0.33
0.262
Cost
0.08
0.421
0.248
0.332
Quality
0.07
0.390
0.215
0.396
Productivity
0.08
0.366
0.485
0.149
Service Level
37
Hence the experts opinions rating is shown in table 3.8 . a consistency ratio was calculated by the software to check the applicability of the paired comparisons. a consistency ratio was calculated by the software to check the applicability of the paired comparisons The value consistency ratio should be 10 percent or less. Therefore, all the consistency ratio of the below table is less than 10 %. [9].
Table (3.8) Relative Impact with respect to Experts’ Opinions rating consistency
Leagile
Agile
Lean
Expert Opinions
0.09
0.423
0.319
0.258
Overall Rating
To vary the above Experts Opinions Ratings to fuzzy numbers , these ratings should be added and subtracted from their constancy. Accordingly, table 3.9 shows the fuzzy numbers of Experts Opinions Ratings fuzzy numbers.
Table (3.9) Relative Impact with respect to Experts’ Opinions rating fuzzy numbers consistency
Leagile
Agile
Lean
0.09
0.333 to 0.513
0.229 to 0.409
0.168 to 0.348
Expert Opinions Overall Rating
38
Chapter 4 Decision Support System (DSS)
4.1 Building a Decision Support System Using Visual Basic A decision support system (DSS) is built using visual basic (VB) to acquire an existing manufacturing rating based on the illustration shown in figure 4.1. This is described as follows:
Figure 4.1 Selection of the Manufacturing System 1- Finding the input data of an existing manufacturing system in plant by evaluating their measuring factors and characteristics factors. Appendix B shows the questionnaire that were given to
39
plants to get their feedback data of measuring and characteristics factors. 2- Analyzing the given factors by fuzzy system to get the existing manufacturing rating. the data from the questionnaire was entered into the visual basic program as an input data. afterward, the visual basic program analyze these data by the fuzzy method to obtain the manufacturing strategy rating. Then, the experts opinion rating was acquired. Zadeh [11] introduced fuzzy system theory to solve problems involving the uncertain absence of criteria. A fuzzy system is a quantity whose value is imprecise, rather than exact (single-valued) numbers. There are types of fuzzy numbers like triangular fuzzy numbers, trapezoidal fuzzy number and normal fuzzy number. (The triangular fuzzy number ) T is very popular in fuzzy applications to get the manufacturing strategy rating . A triangular fuzzy number ~ can define as a triplet A = (a1 , b1 , c1 ) and it is defined as shown in figure 4.2.
Poor
Fair
Good
V. Good
Excellent
1
40
1
3
5
7
9
X
~ Triangular ~ Fuzzy Figure 4.2
(a1 , a 2 , a 3 )
Let A and B be two fuzzy numbers represented by the triplet
and (b1 , b2 , b3 ) , respectively, then the operations of triangular fuzzy numbers are expressed as [24]:
~ ~ A (+) B = (a1 , a 2 , a 3 ) + (b1 , b2 , b3 ) = (a1 + b1 , a 2 + b2 , a 3 + b3 ) ~ ~ A (-) B = (a1 , a 2 , a 3 ) - (b1 , b2 , b3 ) = (a1 − b1 , a 2 − b2 , a 3 − b3 ) ~ ~ A (x) B = (a1 , a 2 , a 3 ) x (b1 , b2 , b3 ) = (a1b1 , a 2 b2 , a 3 b3 ) ~ ~ A ( ÷ ) B = (a1 , a 2 , a 3 ) ÷ (b1 , b2 , b3 ) = (a1 ÷ b1 , a 2 ÷ b2 , a 3 ÷ b3 ) . ~
~
( A + B )/n = ( a1 , a 2 , a 3 ) (/ (b1 , b2 , b3 ) ) = ( (a1 ÷ b1 , a 2 ÷ b2 , a 3 ÷ b3 ) )/ n For example; The triplet good is (3,5,7), the triplet excellent is (7,9,9) and so on. The mean of triplet good and triplet excellent is (3+7,5+9,7+9) divided by three to get (3.33,4.67,5.33). these ways were the questionnaire was filled. An important concepts of fuzzy system is the α-cut, α Є [0,1] as shown in figure 4.3. Moreover, (the α-cut of the triangular fuzzy number)
T
α
can be calculated as
α _ cut = (a α 1 , a α 2 , a α 3 ) = (((a 2 − a1 )α + a1 ), (−(a3 − a 2 )α + a3 )) 1
α
41
a1
a2
a3
α-cut 3)Figure comparing the of existing manufacturing rating by the expert opinions 4.3 α-cut the triangular fuzzy number rating which is given in chapter 3. if the manufacturing strategy rating lies beneath lean manufacturing rating; then the manufacturing system is traditional manufacturing. Moreover, if the manufacturing strategy
rating lies between lean manufacturing rating and agile
manufacturing rating ; then the manufacturing system is lean manufacturing. Furthermore, if the manufacturing strategy rating lies between Agile manufacturing rating and leagile manufacturing rating ; then the manufacturing system is Agile manufacturing. Finally, if the manufacturing strategy rating lies beyond leagile manufacturing rating and; then the manufacturing system is leagile manufacturing.
4) finding the manufacturing strategy system of the plant. To find the manufacturing system strategy, the existing manufacturing rating should be obtained. However, after getting the existing manufacturing rating, this rating should be vary to fuzzy number according to consistency ratio to compare with the experts opinions ratings which is covered into chapter 3. For
more calcification figure 4.3 shows the
comparison to evaluate the manufacturing system strategy.
42
Traditional 0
Lean
Agile
0.258
0.319
Leagile 0.423
1
Supposing 0.31 is existing manufacturing rating α with existing manufacturing rating α = 0.312 Figure 4.4
Suppose
Manufacturing System Strategy = Lean
The Manufacturing System Strategy
a 1 = 1 .5
,
a 2 = 2 and a 3 = 2 .5 , Hence
α-cut = ((2-1.5)0.31+1.5,(-2.5+2)0.31+2.5 =(1.65,2.4)
1
`````
0.31
1.5
1.64
2
2.4
2.5
α-cut 43
Figure 4.5 the α-cut of the example
Chapter 5 Case Studies A questionnaire was built as shown in appendix B to seek manufacturing system of the plant. The questionnaires were distributed to three companies: Saudi Mechanical Industries Company (SMI), Advanced Electronics Company (AEC) and Saudi Light Company (SLC). Three employees from each company were met with to discuss their feedback on the questionnaire. 5.1) Saudi Mechanical Industries Company (SMI) Saudi Mechanical Industries Co. (SMI), located in Riyadh’s Second Industrial City, is an integrated entity for the manufacture of mechanically engineered products serving the domestic market of Saudi Arabia as well as the international markets of the Middle East, Europe and the USA.
SMI was founded in 1982 as a manufacturer of pipes, tubes, and shafts along with other related parts of the Vertical Turbine Pumps. The 1990s witnessed a thrust of growth for SMI with increased production of advanced manufacturing equipment, the manufacture of Right Angle Gear Drives, the setting up of the Round Steel Bar operation and the completion of a fully integrated Quality Control System. And in the years that followed came a yet greater increase in manufacturing capability and capacity, particularly with the advent of Computer Numerical Controlled (CNC) manufacturing equipment. In 2002 SMI’s new plant for Continuous Cast Bronze bars and bronze centrifugal casting came online. This focused approach to growth has yielded a company that today stands as a pre-eminent world producer of 44
quality engineered products and components. Since 1982 SMI has specialized in the manufacture of Electric Submersible pumps under license from the National Pump Company (USA) to cater for various commercial, industrial, residential, municipal and agricultural requirements. The continued growth of SMI can be attributed to its focus on customer service, its attention to quality, its ongoing product development and its increasing product range. The company currently has nine offices in Saudi Arabia. SMI can be described as the only company in the Middle East with the proven capabilities that have gained it a leading position in its field. The combination of high quality raw materials, precision manufacturing processes and top-level quality control procedures ensures a product of reliability and high performance. The company was awarded ISO 9002 certification in October 1999 and since then has fully implemented the documented Quality Management System, which conforms to the requirements of ISO9001/2000. 5.1.1 SMI Study
A committee of three Decision Makers (D1, D2 and D3) was formed to evaluate the existing manufacturing rating. The feedback data input of the five measuring factors that were filled out in the questionnaire is shown in table 5.1. Appendix C shows the relevant screenshots from Visual Basic Windows. Table 5.1 The feedback data input of the five measuring factors Mean
D3
D2
D1
Measuring Factors
( 0.23,0.43,0.63)
Good
Fair
Good
Lead time
( 0.23,0.43,0.63)
Good
Fair
Good
Cost
( 0.23,0.43,0.63)
Fair
Good
Good
Quality
( 0.17,0.37,0.57)
Fair
Good
Fair
Productivity
45
( 0.23,0.43,0.63)
Good
Good
Fair
Service Level
Then, the feedback data input of the characteristics factors are shown in tables 5.2,5.3,5.4,5.5,5.6. These tables show the characteristic factors for each decision D1, D2, D3. Table 5.2 demonstrates the characteristics factors for the measuring factor of lead time.
1. Lead time
Table (5.2) Characteristics Factors by Decision Makers on Lead Time (SMI) Measuring Factors D3
D2
D1
Characteristics Factors
V. Good
V. Good
Fair
Over Production
Good
Good
Fair
V. Good
Good
Fair
Knowledge Misconnection
Good
Fair
Fair
Manufacturing Flexibility
Fair
Fair
Good
Delivery Flexibility
Good
Good
V. Good
Source Flexibility
Fair
Good
Good
Electronic Data Interchange
Good
Good
Good
Mean of Information
Fair
V. Good
Fair
Data and Knowledge Base
Fair
V. Good
Good
Delivery Speed
V. Good
Fair
V. Good
New Product introduction
Fair
Fair
Fair
Customer Responsiveness
( 2.67,4.67,6.67)
( 2.83,4.83,6.83)
( 2.33,4.33,6.33)
mean
Inventory Transportation Waiting
Lead time
46
2. Cost
Moreover, Table 5.3 demonstrates the characteristics factors for the measuring factor of cost. Table (5.3 ) Characteristics Factors by Decision Makers on Cost (SMI) Measuring Factors D3
D2
D1
Characteristics Factors
Good
V. Good
Good
Over Production
Good
Good
Good
Inventory Transportation Waiting
Fair
Good
Good
Knowledge Misconnection
Fair
Fair
Fair
Manufacturing Flexibility
Fair
V. Good
Fair
Delivery Flexibility
Good
V. Good
Good
Source Flexibility
Good
Good
Fair
Electronic Data Interchange
Fair
Fair
Good
Mean of Information
Good
Fair
V. Good
Data and Knowledge Base
Fair
Good
V. Good
Delivery Speed
Good
Fair
V. Good
New Product introduction
Fair
Good
V. Good
Customer Responsiveness
( 2,4,6)
( 2.83,4.83,6.83)
( 3.17,5.17,7.17)
mean
Cost
47
3. Quality
Furthermore, Table 5.4 shows the characteristics factors for the measuring factor of Quality.
Table (5.4) ) Characteristics Factors by Decision Makers on Quality (SMI) Measuring Factors D3
D2
D1
Characteristics Factors
Good
Fair
Good
Over Production
Good
Good
Good
Good
Fair
Good
Knowledge Misconnection
Fair
Fair
V. Good
Manufacturing Flexibility
Good
Fair
V. Good
Delivery Flexibility
Fair
Fair
Fair
Source Flexibility
Good
Good
Fair
Electronic Data Interchange
Fair
Good
Fair
Mean of Information
Good
V. Good
Good
Data and Knowledge Base
Fair
V. Good
Good
Delivery Speed
Good
Fair
Fair
New Product introduction
Fair
Good
Good
Customer Responsiveness
( 2.17,4.17,6.17)
( 2.33,4.33,6.33)
( 2.67,4.67,6.67 )
mean
Inventory Transportation Waiting
Quality
48
4. Productivity
in addition, Table 5.5 shows the characteristics factors for the measuring factor of productivity.
Table (5.5) ) Characteristics Factors by Decision Makers on Productivity (SMI) D3
D2
D1
Characteristics Factors
V. Good
Good
V. Good
Over Production
V. Good
Fair
V. Good
V. Good
Fair
V. Good
Good
Fair
V. Good
Manufacturing Flexibility
Fair
Good
Good
Delivery Flexibility
Good
Good
Good
Source Flexibility
Measuring Factors
Inventory Transportation Waiting Knowledge Misconnection
Electronic Data
Fair
Good
Fair
Good
Fair
Good
Fair
Good
Good
Good
Good
V. Good
Delivery Speed
V. Good
V. Good
V. Good
New Product introduction
V. Good
V. Good
V. Good
Customer Responsiveness
( 3.33,5.33,7.33)
( 2.67,4.67,6.67)
( 4,6,8)
mean
Productivity
Interchange Mean of Information Data and Knowledge Base
49
5. Service Level
as well, Table 5.6 shows the characteristics factors of the measuring factor for service level.
Table (5.6) ) Characteristics Factors by Decision Makers on Service Level (SMI) Measuring Factors D3
D2
D1
Characteristics Factors
Fair
Good
Fair
Over Production
Good
Good
Fair
Fair
Fair
Fair
Knowledge Misconnection
Fair
Fair
Good
Manufacturing Flexibility
Fair
Fair
V. Good
Delivery Flexibility
Fair
Fair
V. Good
Source Flexibility
Inventory Transportation Waiting
Service Level Fair
Fair
V. Good
Electronic Data Interchange
Fair
Good
Good
Mean of Information
Fair
Fair
Good
Data and Knowledge Base
Good
Good
Good
Delivery Speed
Good
Good
Good
New Product introduction
Good
Fair
V. Good
Customer Responsiveness
( 1.67,3.67,5.67)
( 3.5,5.5,7.5)
( 3.17,5.17,7.17)
mean
After entering the input, the data output of the program is shown in table 5.7, Normalization all the above means of characteristics factors by dividing by 10. [9]
50
Table (5.7) Normalization of the Measures Factors Means of the three Decision Makers (SMI) Service Level Productivity Quality Cost Lead Time ( 0.32,0.52,0.72) ( 0.4,0.6,0.8) ( 0.27,0.47,0.67) (0.32,0.52,0.72) ( 0.23,0.43,0.63) ( 0.35,0.55,0.75) ( 0.27,0.47,0.67) ( 0.23,0.43,0.63) ( 0.28,0.48,0.68) ( 0.28,0.48,0.68) ( 0.17,0.37,0.57) ( 0.33,0.53,0.73) (0. 22,0.42,0.62) ( 0.2,0.4,0.6) ( 0.27,0.47,0.67)
The normalized means of table 5.7 is multiplied by The means of feedback data input of the five measuring factors table 5.1 to obtain the following ⎡( 0.23,0.43,0.63) ⎢( 0.28,0.48,0.68) ⎢ ⎢⎣( 0.27,0.47,067)
( 0.32,0.52,0.72) ( 0.28,0.48,0.68) ( 0.20,0.40,0.60)
( 0.27,0.47,0.67) ( 0.23,0.43,0.63) ( 0.22,0.42,0.62)
( 0.4,0.6,0.8) ( 0.32,0.52,0.72) ⎤ ( 0.27,0.47,0.67) ( 0.35,0.55,0.75)⎥⎥ ( 0.33,0.53,0.73) ( 0.17,0.37,0.57) ⎥⎦
⎡(0.23,0.43 ,0.63) ⎤ ⎢(0.23,0.43 ,0.63)) ⎥ ⎥ ⎢ ⎢(0.23,0.43 ,0.63) ⎥ ⎥ ⎢ ⎢( 0.170.37,0 .57) ⎥ ⎢⎣(0.23,0.43 ,0.63) ⎥⎦
The result of the multiplication is ⎡ (0.33,1.06 ,2.18) ⎤ ⎢ (0.27,0.94 ,2) ⎥ ⎢ ⎥ ⎢⎣ (0.25,0.91 ,1.97) ⎥⎦
Average
= 1.19 = 1.07 = 1.04
Average
1.097
Then applying the equation
α=
1 . 097 − 0 . 18 = 0.28 3 . 52 − 0 . 18
where 1.097 is the result of multiplication
and 0.18 and 3.52 are constant. The resulting 0.28 is multiplied by the certainty constant (0.70) to get 0.196. Figure (4.4) is consulted to conclude that SMI is below the 0.258 which represents the ‘lean’ baseline. 0.196 Traditional
Lean
Agile
Leagile
51
D1 D2 D3
0
0.258
0.319
0.423
1
Thus SMI’s system is at present that of ‘traditional’ manufacturing. In order to facilitate its evolution to ‘lean’ manufacturing, SMI should implement the following tools (described earlier in Section 1.2.2): • • • • • • •
Cellular Manufacturing Total Quality Management Value Stream Mapping 5-S, Pokayoke Kaizen Takt Time
α-cut = ((1.07-1.04)0.196+1.04,(-1.19+1.07)0.196+1.19 = (1.05, 1.16)
1
0.196
1.04 1.05
1.07
1.16
1.19
α-cut
52
5.2 Advanced Electronics Company (AEC)
AEC was established in 1988 with a paid-up capital of SR 110.5M, under a directive of the Saudi Government to create local capabilities in strategic
areas
such
as
advanced
manufacturing
technologies,
communications systems and product support. AEC's efforts have been directed towards developing national capabilities in strategic areas, thereby enhancing the Kingdom's self-sufficiency and improving the operational readiness of advanced systems through local maintenance. AEC has been able to acquire considerable technological knowledge and has developed substantial design, manufacturing and TPS design/build capabilities. It has become the leading electronics company in the region, capable of manufacturing sophisticated military and commercial electronic products, and exceeding the most demanding military and commercial standards. AEC, including its R&D operations, is currently certified to various military standards and ISO9001. The company continues to invest in expanding its capabilities in the fields of R&D, manufacturing, test process and manpower development. AEC plans to diversify its activities and product base in the military and commercial fields to encompass manufacturing, support and systems integration. It expects to work with leading, quality-orientated international companies which are seeking dependable and world-renowned partners in the Kingdom.
53
Major AEC customers include the Saudi Armed Forces, the Saudi Presidency of Civil Aviation, the Ministry of the Interior, Saudi Electricity Company (SEC), United Defense (FMC), Boeing and Ericsson. 5.2.1 AEC Study
A committee of three Decision Makers (D1, D2 and D3) was formed to evaluate the existing manufacturing rating. The feedback data input of the five measuring factors that were filled out in the questionnaire is shown in table 5.8. Appendix C shows the relevant screenshots from Visual Basic Windows. Table (5.8) The feedback data input of the five measuring factors (AEC) Mean
D3
D2
D1
Measuring Factors
( 0.37,0.57,0.77)
Good
V. Good
Good
Lead time
( 0.37,0.57,0.77)
Good
V. Good
Good
Cost
( 0.43,0.63,0.83)
V. Good
V. Good
Good
Quality
( 0.37,0.57,0.77)
Good
Good
V. Good
Productivity
( 0.37,0.57,0.77)
V. Good
Good
Good
Service Level
Then, the feedback data input of the characteristics factors are shown in tables 5.9,5.10,5.11,5.12,5.13. These tables show the characteristic factors for each decision D1, D2, D3. Table 5.9 demonstrates the characteristics factors for the measuring factor of lead time.
54
1. Lead time Table (5.9) Characteristics Factors by Decision Makers on Lead Time (AEC) D3
D2
D1
Characteristics Factors
V. Good
V. Good
Good
Over Production
V. Good
Good
V. Good
V. Good
V. Good
V. Good
Good
V. Good
V. Good
Manufacturing Flexibility
V. Good
V. Good
Good
Delivery Flexibility
V. Good
V. Good
Good
Source Flexibility
Measuring Factors
Inventory Transportation Waiting Knowledge Misconnection
Electronic Data
V. Good
V. Good
V. Good
V. Good
V. Good
Good
V. Good
V. Good
V. Good
Good
Good
Good
Delivery Speed
V. Good
V. Good
V. Good
New Product introduction
V. Good
V. Good
V. Good
Customer Responsiveness
( 4.67,6.67,8.67)
( 4.67,6.67,8.67)
( 4.17,6.17,8.17)
mean
Lead time
Interchange Mean of Information Data and Knowledge Base
55
2. Cost
Moreover, Table 5.10 demonstrates the characteristics factors for the measuring factor of cost.
Table (5.10) Characteristics Factors by Decision Makers on Cost (AEC) Measuring Factors D3
D2
D1
Characteristics Factors
Good
Good
Good
Over Production
V. Good
V. Good
V. Good
V. Good
V. Good
V. Good
Knowledge Misconnection
Good
Good
Good
Manufacturing Flexibility
Good
V. Good
Good
Delivery Flexibility
Good
Good
V. Good
Source Flexibility
V. Good
V. Good
V. Good
Electronic Data Interchange
Good
Good
V. Good
Mean of Information
V. Good
V. Good
V. Good
Data and Knowledge Base
Good
V. Good
Good
Delivery Speed
V. Good
Good
Good
New Product introduction
Good
Good
V. Good
Customer Responsiveness
( 3.83,5.83,7.83)
( 4,6,8)
( 4.17,6.17,8.17)
mean
Inventory Transportation Waiting
Cost
56
3. Quality
Moreover, Table 5.11 demonstrates the characteristics factors for the measuring factor of quality.
Table (5.11) Characteristics Factors by Decision Makers on Quality (AEC) Measuring Factors D3
D2
D1
Characteristics Factors
V. Good
V. Good
V. Good
Over Production
Excellent
Good
V. Good
Excellent
Good
Good
Knowledge Misconnection
V. Good
Good
V. Good
Manufacturing Flexibility
V. Good
V. Good
V. Good
Delivery Flexibility
V. Good
Good
Good
Source Flexibility
V. Good
Good
Good
Electronic Data Interchange
V. Good
V. Good
Good
Mean of Information
V. Good
V. Good
V. Good
Data and Knowledge Base
V. Good
Good
Good
Delivery Speed
V. Good
V. Good
V. Good
New Product introduction
V. Good
V. Good
Good
Customer Responsiveness
( 5.33,7.33,9)
( 4,6,8)
( 4,6,8)
mean
Inventory Transportation Waiting
Quality
57
4. Productivity
in addition, Table 5.12 shows the characteristics factors for the measuring factor of productivity.
Table (5.12) Characteristics Factors by Decision Makers on Productivity (AEC) Measuring Factors D3
D2
D1
Characteristics Factors
V. Good
V. Good
V. Good
Over Production
V. Good
Good
V. Good
Good
V. Good
Good
Knowledge Misconnection
V. Good
V. Good
V. Good
Manufacturing Flexibility
Good
V. Good
Good
Delivery Flexibility
Good
Good
V. Good
Source Flexibility
Good
V. Good
V. Good
Electronic Data Interchange
V. Good
Good
V. Good
Mean of Information
V. Good
V. Good
Good
Data and Knowledge Base
Good
Good
V. Good
Delivery Speed
V. Good
Good
V. Good
New Product introduction
V. Good
V. Good
V. Good
Customer Responsiveness
( 4.33,6.33,8.17)
( 4.17,6.17,8.17)
( 4.5,6.5,8.5)
mean
Inventory Transportation Waiting
Productivity
58
5. Service Level
in addition, Table 5.13 shows the characteristics factors for the measuring factor of service level.
Table (5.13) Characteristics Factors by Decision Makers on Service Level (AEC) D3
D2
D1
Characteristics Factors
Fair
Good
Good
Over Production
Fair
Good
Good
Fair
Good
Good
Good
Fair
Good
Manufacturing Flexibility
Good
Fair
Fair
Delivery Flexibility
Fair
Fair
Good
Source Flexibility
Good
Fair
Fair
Fair
Good
Good
Good
Good
Fair
Fair
Fair
Good
Delivery Speed
Good
Fair
Good
New Product introduction
Fair
Good
Fair
Customer Responsiveness
( 1.83,3.83,5.83)
( 2,4,6)
( 2.33,4.33,6.33)
mean
Measuring Factors
Inventory Transportation Waiting Knowledge Misconnection
Electronic Data
Service Level
Interchange Mean of Information Data and Knowledge Base
After entering the input, the data output of the program is shown in table 5.14, Normalization all the above means of characteristics factors by dividing by 10. [9]
59
Table (5.14) Normalization of the Measuring Means of the three Decision Makers Service Level Productivity Quality Cost Lead Time ( 0.23,0.43,0.63) ( 0.45,0.65,0.85) ( 0.40,0.60,0.80) ( 0.42,0.62,0.82) ( 0.42,0.62,0.82) ( 0.20,0.40,0.60) ( 0.42,0.62,0.82) ( 0.40,0.60,0.80) ( 0.40,0.60,0.80) ( 0.47,0.67,0.87) ( 0.18,0.38,0.58) ( 0.43,0.63,0.82) ( 0.53,0.73,0.90) ( 0.38,0.58,0.78) ( 0.47,0.67,0.87)
The normalized means of table 5.14 is multiplied by The means of feedback data input of the five measuring factors table 5.8 to obtain the following ⎡( 0.42,0.62,0.82) ⎢( 0.47,0.67,0.87) ⎢ ⎢⎣( 0.47,0.67,0.87)
( 0.42,0.62,0.82) ( 0.40,0.60,0.80) ( 0.38,0.58,0.78)
( 0.40,0.60,0.80) ( 0.40,0.60,0.80) ( 0.53,0.73,0.90)
( 0.45,0.65,0.85) ( 0.42,0.62,0.82) ( 043,0.63,0.82)
( 0.23,0.43,0.63)⎤ ( 0.20,0.40,0.60)⎥⎥ ( 0.18,0.38,0.58) ⎥⎦
⎡( 0.37,0.57, 0.77) ⎤ ⎢( 0.37,0.57, 0.77) ⎥ ⎥ ⎢ ⎢( 0.43,0.63, 0.83) ⎥ ⎥ ⎢ ⎢( 0.37,0.57, 0.77) ⎥ ⎢⎣( 0.37,0.57, 0.77) ⎥⎦
The result of the multiplication is ⎡ (0.73,1.7, 3.07) ⎤ ⎢ (0.72,1.68 ,3.04) ⎥ ⎢ ⎥ ⎢⎣ (0.76,1.74 ,3.1) ⎥⎦
1 . 84 − 0 . 18 = 0.50 3 . 52 − 0 . 18
Average
= 1.83 = 1.82 = 1.87
Average
1.84
where 1.84 is the result of multiplication and 0.18 and 3.52 are constant.
The resulting 0.50 is multiplied by the certainty constant (0.70) to get 0.350. Figure (4.3) is consulted to conclude that AEC falls within the ‘agile’ boundaries of 0.319 and 0.423. 0.35
60
F AZ AB
Traditional 0
Lean 0.258
Agile 0.319
Leagile 0.423
1
Thus AEC’s system is at present that of ‘agile’ manufacturing. There is no need to implement any of the lean manufacturing tools (described earlier in Section 1.2.2).
α-cut = ((1.83-1.82)0.350+1.82,(-1.87+1.83)0.35+1.87 = (1.825, 1.86)
1
0.350
1.82
1.83
1.87
α-cut 1.825
1.86
61
5.3 Saudi Lighting Company (SLC)
Saudi Lighting Company has continuously expanded and developed its products and manufacturing capability in response to rapid economic development and a changing market environment. In 1978 SLC began the production of outdoor lighting fixtures in a joint venture with Asia Swedish Company. In 1989 the company merged with Arabian Lighting Company and began expanding its product base with the manufacture of indoor lighting fixtures. Since this merger, SLC has grown to become the leading manufacturer of lighting fixtures in the Middle East and has continuously met ever-increasing customer demand for its products.
5.3.1 SLC Study
A committee of three Decision Makers (D1, D2 and D3) was formed to evaluate the existing manufacturing rating. The feedback data input of the five measuring factors that were filled out in the questionnaire is shown in table 5.15. Appendix C shows the relevant screenshots from Visual Basic Windows.
Table (5.15 The feedback data input of the five measuring factors (SLC) Mean ( 0.17,0.37,0.57) ( 0.1,0.3,0.5)
D3 Fair Fair
D2
D1
Measuring Factors
Good
Fair
Lead time
Fair
Fair
Cost
62
Fair
( 0.1,0.3,0.5) (0.23,0.43,0.63)
Fair
Fair
Good
(0.23,0.43,0.63)
Fair
Fair
Good
Quality
Good
Productivity
Good
Service Level
Then, the feedback data input of the characteristics factors are shown in tables 5.16,5.17,5.18,5.19,5.20. These tables show the characteristic factors for each decision D1, D2, D3. Table 5.16 demonstrates the characteristics factors for the measuring factor of lead time. 1. Lead time Table (5.16) Characteristics Factors by Decision Makers on Lead Time (SLC) D3
D2
D1
Characteristics Factors
Fair
Good
Fair
Over Production
Fair
Fair
Fair
Fair
Fair
Good
Fair
Fair
Good
Good
Fair
Good
Delivery Flexibility
Good
Good
Good
Source Flexibility
Good
Good
Good
Good
Good
Fair
Fair
Good
Good
Fair
Good
Fair
Fair
Fair
Fair
Measuring Factors Lead time
Inventory Transportation Waiting Knowledge Misconnection Manufacturing Flexibility
Electronic Data Interchange Mean of Information Data and Knowledge Base Delivery Speed New Product introduction
63
Good
Fair
Fair
( 1.83,3.83,5.83)
( 2,4,6)
( 2,4,6)
Customer Responsiveness mean
2. Cost
Moreover, Table 5.17 demonstrates the characteristics factors for the measuring factor of cost.
Table (5.17) Characteristics Factors by Decision Makers on Cost (SLC) Measuring Factors D3
D2
D1
Characteristics Factors
Good
Fair
Good
Over Production
Good
Fair
Good
Good
Fair
Good
Knowledge Misconnection
Good
Good
Fair
Manufacturing Flexibility
Fair
Good
Fair
Delivery Flexibility
Fair
Good
Fair
Source Flexibility
Inventory Transportation Waiting
Electronic Data
Fair
Good
Good
Fair
Fair
Fair
Mean of Information
Good
Good
Good
Data and Knowledge Base
Good
Fair
Fair
Delivery Speed
Good
Fair
Good
New Product introduction
Fair
Good
Fair
Customer Responsiveness
( 2.17,4.17,6.17)
( 2,4,6)
( 2,4,6)
mean
Cost
Interchange
64
3. Quality
furthermore, Table 5.18 demonstrates the characteristics factors for the measuring factor of quality. Table (5.18) Characteristics Factors by Decision Makers on Quality (SLC) Measuring Factors D3
D2
D1
Characteristics Factors
Fair
Fair
Fair
Over Production
Good
Good
Fair
Good
Fair
Good
Knowledge Misconnection
Good
Good
Good
Manufacturing Flexibility
Fair
Fair
Fair
Delivery Flexibility
Good
Fair
Fair
Source Flexibility
Good
Good
Good
Electronic Data Interchange
Fair
Good
Good
Mean of Information
Fair
Good
Fair
Data and Knowledge Base
Good
Good
Good
Delivery Speed
Good
Fair
Fair
New Product introduction
Good
Fair
Good
Customer Responsiveness
( 2.33,4.33,6.33)
( 2,4,6)
( 2,4,6)
mean
Inventory Transportation Waiting
Quality
65
4. Productivity
In addition,, Table 5.19 shows the characteristics factors for the measuring factor of productivity.
Table (5.19) Characteristics Factors by Decision Makers on Productivity (SLC) D2
D1
Characteristics Factors
Measuring Factors
D3 Good
Good
Fair
Over Production
Productivity
Inventory Fair
Good
Fair
Transportation Waiting Knowledge
Fair
Good
Fair
Fair
Fair
Fair
Good
Good
Good
Delivery Flexibility
Good
Fair
Good
Source Flexibility
Good
Good
Good
Fair
Good
Good
Fair
Fair
Fair
Fair
Fair
Fair
Fair
Fair
Fair
Good
Good
Fair
Misconnection Manufacturing Flexibility
Electronic Data Interchange Mean of Information Data and Knowledge Base Delivery Speed New Product introduction Customer Responsiveness
66
( 1.83,3.83,5.83)
( 2.17,4.17,6.17)
( 1.67,3.67,5.67)
mean
5. Service Level
As well,, Table 5.120 shows the characteristics factors for the measuring factor of service level. Table (5.20) Characteristics Factors by Decision Makers on Service Level (SLC) Measuring Factors D3
D2
Good Poor
D1 Fair
Good Poor
Fair
Characteristics Factors Over Production Inventory Transportation Waiting
Good Good
Poor
Fair
Poor
Good
Good
Poor
Good
Good
Fair
Poor
Poor Poor
Good
Poor Good Good Good Good
Poor Good Good Good
Good Good
Poor
Poor
Good
( 1.83,2.83,4.83)
( 1.83,3.17,5.17)
Fair ( 2.5,4.17,6.17)
Knowledge Misconnection Manufacturing Flexibility Delivery Flexibility Source Flexibility
Service Level
Electronic Data Interchange Mean of Information Data and Knowledge Base Delivery Speed New Product introduction Customer Responsiveness mean
After entering the input, the data output of the program is shown in table 5.21, Normalization all the above means of characteristics factors by dividing by 10. [24] Table (5.21) Normalization of the Measuring Means of the three Decision Makers
67
Service Level ( 0.18,0.32,0.52) ( 0.18,0.28,0.48) ( 0.25,0.42,0.62)
Productivity ( 0.17,0.37,0.57) ( 0.22,0.42,0.62) ( 0.18,0.38,0.58)
Quality ( 0.20,0.40,0.60) ( 0.20,0.40,0.60) ( 0.23,0.43,0.63)
Cost ( 0.20,0.40,0.60) ( 0.20,0.40,0.60) ( 0.22,0.42,0.62)
Lead Time ( 0.20,0.40,0.60) ( 0.20,0.40,0.60) ( 018,0.38,0.58)
The normalized means of table 5.14 is multiplied by The means of feedback data input of the five measuring factors table 5.8 to obtain the following ⎡( 0.20,0.40,0.60) ⎢( 0.20,0.40,0.60) ⎢ ⎢⎣( 0.18,0.38,0.58)
( 0.20,0.40,0.60) ( 0.20,0.40,0.60) ( 0.22,0.42,0.62)
( 0.20,0.40,0.60) ( 0.20,0.40,0.60) ( 0.23,0.43,063)
( 0.18,0.32,0.52)⎤ ( 0.18,0.28,0.48) ⎥⎥ ( 0.25,0.42,0.62) ⎥⎦
( 0.17,0.37,0.57) ( 0.22,0.42,062) ( 0.18,0.38,0.58)
⎡( 0.17,0.37, 0.57) ⎤ ⎥ ⎢( 0.1,0.3,0. 5) ⎥ ⎢ ⎥ ⎢( 0.1,0.3,0. 5) ⎥ ⎢ ⎢( 0.23,0.43, 0.63) ⎥ ⎢⎣( 0.23,0.43, 0.63) ⎥⎦
The result of the multiplication is ⎡ (0.15,0.68 ,1.63) ⎤ ⎢ (0.17,0.69 ,1.64) ⎥ ⎢ ⎥ ⎣⎢ (0.17,0.74 ,1.71) ⎥⎦
Average
0 . 84 − 0 . 18 = 0.20 3 . 52 − 0 . 18
= 0.82 = 0.83 = 0.88
Average
0.84
where 0.84 is the result of multiplication and 0.18 and 3.52 are constant.
The resulting 0.20 is multiplied by the certainty constant (0.70) to get 0.140. Figure (4.2) is consulted to conclude that SLC is below the 0.258 which represents the ‘lean’ baseline. 0.140 Traditional 0
Lean 0.258
Agile 0.319
Leagile 0.423
1
68
N KH F
Thus SLC’s system is at present that of ‘traditional’ manufacturing. In order to facilitate its evolution to ‘lean’ manufacturing, SLC should implement the following tools (described earlier in Section 1.2.2): • • • • • • •
Cellular Manufacturing Total Quality Management Value Stream Mapping 5-S, Pokayoke Kaizen Takt Time
α-cut = ((0.83-0.82)0.140+0.83,(-0.88+0.83)0.140+0.88 = (083, 0.99)
1
0.82 0.83 α-cut 0.821
0.99
69
Chapter 6 Discussion and Conclusion The significant of the research is to evaluate the manufacturing system strategy of plants which become either traditional, lean, agile or leagile manufacturing. To evaluate manufacturing system strategy, several steps should be occurred. First, defining the measuring factors ( lead time, cost , quality, productivity and service level) and the characteristics factors (over production, inventory transportation waiting, knowledge misconnections, manufacturing flexibility, delivery flexibility, source flexibility, electronics data interchange, Mean of Information, Data and Knowledge Base, Delivery Speed, New Product introduction and Customer Responsiveness)
which
come from the literature. Then, a questionnaire was designed to distributed into experts and take their feedback. Furthermore, the feedback entered in Expert Choice software to obtain the experts opinion rating. As well, other questionnaire was developed to obtain the feedback of plants. This feedback was collected to find existing evaluating rating by developing Decision Support System using Visual Basic. These two ratings were comprised to evaluate wither the manufacturing system strategy is traditional, lean, agile or leagile manufacturing.
70
Three case studies were implemented on Saudi Mechanical Industries (SMI) Company, Advanced Electronics Company (AEC) and Saudi Light Company (SLC) . In the end of the study. the manufacturing system strategy of SMI company is traditional manufacturing, the manufacturing system strategy of AEC company is Agile manufacturing and the manufacturing system strategy of SLC company is traditional manufacturing. To resolve the manufacturing system in order to become lean, agile or leagile; a lot of tools will help in becoming lean like Cellular Manufacturing, Total Quality Management,Pokayoke, Kaizen , Value Stream Mapping, 5 S, address issues within its supply chain management, increase its focus on customer service and improve the quality of its IT applications. and so on. Also, some tools will help in becoming agile like Customer Value Focus, IT Systems and Supply Chain Management.
71
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2)
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3)
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the
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L.C. van der Gaag and E.M. Helsper, (2000), “Introduction to Knowledge-Based
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74
Appendix A An Experts Opinions Questionnaire Introduction The purpose of this questionnaire is to seek expert opinion and views concerning the benefits and merits of implementing lean, agile and leagile manufacturing strategies in manufacturing industries, and the tools and techniques said experts think should be used to implement said strategies. Your valuable opinions will provide the necessary data to build a proposed methodological model for assessment of the implementation of these strategies. Therefore please consider this questionnaire as top priority and state your opinion carefully and frankly. Feel free to make any remarks or add any information you feel is important to improve the quality of data collected. We are grateful for your time and cooperation. Part I: General Information o Company manufacturing industry sector type:-----------------------o Capital of the company:----------------------------------------------------o Workforce in the company:-----------------------------------------------o The basic strategy/ philosophy of manufacturing in your company: Mass production
Batch production
Lean
Agile
Other
Part II: Company Practice 1- The main company manufacturing policies to achieve the strategy are: Table 1: Company Policies Implementation Remarks None Randomly Slightly Moderately
Fully
Policy
No.
Best quality
1
75
Lowest cost
2
Shortest lead time
3
Shortening total manufacturing cycle time Customer satisfaction
4
5
2- Techniques and tools used to implement these policies and strategies. Table 2: Company Technique/Tools * Implementation Remarks None Randomly Slightly Moderately
Fully
Technique/Tool
No
Cellular Manufacturing Total Quality Teams Rapid Set Up (SMED) Kanban Value Stream Mapping (VSM) Process Mapping Line Balancing 5-S Pokayoke Elimination of Waste Total Productive Maintenance Value Management Takt Time Kaizen Continuous improvement OEE
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Others: 123-
18
76
•
definition of these terms are given in the Appendix.
Part III: Merit value of your opinion Please give your opinion on achieving manufacturing performance through various strategies. Three tables follow which represent: Table (1): Metrics (measures) Table (2): Characteristics affecting the metrics Table (3): General parameters affecting each characteristic
Please state your opinions according to a merit value you give based on the following scale: AHP comparison scale Definition Intensity relative importance
Factor i and j are equal importance Factor i is weakly more important than j Factor i is strongly more important than j Factor i is more strongly more important than j Factor i is absolutely more important than j intermediate
Eg.
in the first element, if you choose
important than variable Y); then
a yx =
a xy =
Intensity of relative importance 1 3 5 7 9 2,4,6 and 8
5 (variable x is strongly more
1 5
C 1 3
B
A
5
1
A
2
1
1 5
B
1
1 2
3
C
77
Preferences
E
D
C
B
A 1
1 1 1 1
A B C D E
Preferences
E
D
C
B
A 1
1 1 1 1
A B C D E
Preferences
E
D
C
B 1
1
A 1
A B C
Metric statements (A) What is the relative impact of the response of lead time on lean performance with respect to other metrics? (B) What is the relative impact of the response of cost on lean performance with respect to other metrics? (C) What is the relative impact of the response of quality on lean performance with respect to other metrics? (D) What is the relative impact of the response of productivity cost on lean performance with respect to other metrics? (E) What is the relative impact of the response of service level cost on lean performance with respect to other metrics? Metric statements (A) What is the relative impact of the response of lead time on agile performance with respect to other metrics? (B) What is the relative impact of the response of cost on agile performance with respect to other metrics? (C) What is the relative impact of the response of quality on agile performance with respect to other metrics? (D) What is the relative impact of the response of productivity on agile performance with respect to other metrics? (E) What is the relative impact of the response of service level on agile performance with respect to other metrics? Metric statements (A) What is the relative impact of the response of lead time on leagile performance with respect to other metrics? (B) What is the relative impact of the response of cost on leagile performance with respect to other metrics? (C) What is the relative impact of the response of quality
Paradigm Lead Time Cost Quality
Lean Manufacturing
Productivity Service Level Paradigm Lead Time Cost Quality
Agile Manufacturing
Productivity Service Level Paradigm Lead Time
Leagile Manufacturing
Cost Quality
78
1
D E
1
on leagile performance with respect to other metrics? (D) What is the relative impact of the response of productivity on leagile performance with respect to other metrics? (E) What is the relative impact of the response of service level on leagile performance with respect to other metrics?
Preference (A)
D
C
B
A 1
1 1 1
(B) A B C D
(C)
(D)
(A) D
C
B
A 1
1 1 1
(B) A B C D
(C)
(D)
D
C
B 1
1
A 1
(A) A B C
(B)
Statements What is the relative impact of elimination of waste overproduction on lead time with respect to other characteristics? What is the relative impact of flexibility on lead time with respect to other characteristics? What is the relative impact of information technology on lead time with respect to other characteristics? What is the relative impact of market sensitivity on lead time with respect to other characteristics? What is the relative impact of elimination of waste overproduction on cost with respect to other characteristics? What is the relative impact of flexibility on cost with respect to other characteristics? What is the relative impact of information technology on cost with respect to other characteristics? What is the relative impact of on market sensitivity on cost with respect to other characteristics? What is the relative impact of elimination of waste overproduction on quality with respect to other characteristics? What is the relative impact of flexibility on
Productivity Service Level Characteristics Elimination Waste
Metrics
of
Flexibility Lead Time
Information Technology Market Sensitivity Elimination Waste
of
Flexibility Cost
Information Technology Market Sensitivity Elimination Waste
of
Quality
Flexibility
79
1
D (C)
(D)
Preference (A)
D
C
B
A 1
1 1 1
(B) A B C D
(C)
(D)
(A)
D
C
B 1
1 1
A 1
(B) A B C D
(C)
(D)
quality with respect to other characteristics? What is the relative impact of information technology on quality with respect to other characteristics? What is the relative impact of market sensitivity on quality with respect to other characteristics?
Statements What is the relative impact of elimination of waste overproduction on productivity with respect to other characteristics? What is the relative impact of flexibility on with respect to other productivity characteristics? What is the relative impact of information technology on productivity with respect to other characteristics? What is the relative impact of market sensitivity on productivity with respect to other characteristics? What is the relative impact of elimination of waste overproduction service level with respect to other characteristics? What is the relative impact of flexibility on with respect to other service level characteristics? What is the relative impact of information technology on service level with respect to other characteristics? What is the relative impact of market sensitivity on service level with respect to other characteristics?
Information Technology Market Sensitivity
Characteristics
Metrics
Elimination Of waste Flexibility Productivity Information Technology Market Sensitivity Elimination Of waste Flexibility Information Technology
Service Level
Market Sensitivity
80
Preference
C
B
A 1
1 1
C
B
A 1
1 1
C
B
A 1
1 1
C
B 1
1
A 1
A B C
A B C
A B C
A B C
Statements (A) What is the relative impact of overproduction on elimination of waste with respect to other parameters? (B) What is the relative impact of Inventory transportation waiting on elimination of waste with respect to other parameters? (C) What is the relative impact of Knowledge Misconnection on elimination of waste with respect to other parameters? (A) What is the relative impact of manufacturing flexibility on flexibility with respect to other parameters? (B) What is the relative impact of delivery flexibility on flexibility with respect to other parameters? (C) What is the relative impact of source flexibility on flexibility with respect to other parameters? (A) What is the relative impact of electronics interchange on information technology with respect to other parameters? (B) What is the relative impact of means of information on information technology with respect to other parameters? (C) What is the relative impact of data accuracy on information technology with respect to other parameters? (A) What is the relative impact of delivery speed on Market Sensitivity with respect to other parameters? (B) What is the relative impact of new product customer on Market Sensitivity with respect to other parameters? (C) What is the relative impact of customer responsiveness on Market Sensitivity with respect to other parameters?
Parameters
Characteristics
Overproduction Inventory transportation Waiting
Elimination Of waste
Knowledge Misconnection Manufacturing flexibility Delivery flexibility.
Flexibility
Source flexibility Electronics interchange Means of information
Information Technology
Data accuracy
Delivery speed New
product customer
Market Sensitivity
Customer responsiveness
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Page 1 of 25 Model Name: choosing Lean,agile leagile try Treeview
Goal: choosing Lean, Agile, Leagile or Traditional Lead time (L: .181) Over production (L: .126) Inventory transportation Waiting) (L: .057) Knowledge Misconnection (L: .046) Manufacturing Flexibilty (L: .139) Delivery Flexibilty (L: .044) Source Flexibilty (L: .212) Electronics interchange (L: .129) Means of information (L: .093) Data accuracy (L: .057) Delivery speed (L: .039) New product customer (L: .034) Customer responsiveness (L: .023) Cost (L: .077) Electronics interchange (L: .037) Over production (L: .124) Inventory transportaion waiting (L: .194) Knowledge misconnection (L: .112) Manufacturing Flexibilty (L: .181) Delivery Flexibilty (L: .058) Source Flexibilty (L: .076) Means of Information (L: .053) Data accuracy (L: .043) Delivery speed (L: .038) New product customer (L: .030) Customer responsiveness (L: .054) Quality (L: .365) Over Production (L: .157) Inventory transportaion waiting (L: .167) Knowledge misconnection (L: .134) Manufacturing Flexibilty (L: .053) Delivery Flexibilty (L: .131)
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Page 2 of 25
Source Flexibilty (L: .046) Electronics interchange (L: .093) Means of Information (L: .038) Data accuracy (L: .086) Delivery speed (L: .024) New product customer (L: .039) customer responsiveness (L: .031) Productivity (L: .130) Over production (L: .190) Inventory transportaion waiting (L: .120) Knowledge misconnection (L: .119) Manufacturing Flexibilty (L: .085) Delivery Flexibilty (L: .101) Source Flexibity (L: .076) Electronics interchange (L: .062) Means of Information (L: .067) Data accuracy (L: .062) Delivery speed (L: .037) New product customer (L: .034) Customer responsiveness (L: .047) Service Level (L: .247) Over production (L: .064) Inventory transportaion waitiong (L: .063) Knowledge misconnection (L: .182) Manufacturing flexibilty (L: .169) Delivery flexibilty (L: .107) Source felxibilty (L: .054) Electronics interchange (L: .105) Means of Information (L: .091) Data accuracy (L: .051) Delivery speed (L: .032) New product customer (L: .052) Customer responsiveness (L: .029)
* Ideal mode
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Page 3 of 25 Priority Graphs
Priorities with respect to: Goal: choosing Lean, Agile, Lea...
Lead time Cost Quality Productivity Service Level Inconsistency = 0.09 with 0 missing judgments.
.181 .077 .365 .130 .247
Priorities with respect to: Goal: choosing Lean, Agile, Leagile o >Lead time
Over production Inventory transportation Waiti Knowledge Misconnection Manufacturing Flexibilty Delivery Flexibilty Source Flexibilty Electronics interchange Means of information Data accuracy Delivery speed New product customer Customer responsiveness Inconsistency = 0.07 with 0 missing judgments.
.126 .057 .046 .139 .044 .212 .129 .093 .057 .039 .034 .023
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Page 4 of 25
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Lead time >Over production
lean agile leagile Inconsistency = 0.02 with 0 missing judgments.
.136 .238 .625
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Lead time >Inventory transportatio...
lean agile leagile Inconsistency = 0.04 with 0 missing judgments.
.105 .258 .637
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Lead time >Knowledge Misconnection
lean agile leagile Inconsistency = 0.00 with 0 missing judgments.
.250 .250 .500
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Page 5 of 25
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Lead time >Manufacturing Flexibilty
lean agile leagile Inconsistency = 0.00 with 0 missing judgments.
.109 .309 .582
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Lead time >Delivery Flexibilty
lean agile leagile Inconsistency = 0.00 with 0 missing judgments.
.111 .222 .667
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Lead time >Source Flexibilty
lean agile leagile Inconsistency = 0.05 with 0 missing judgments.
.109 .345 .547
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Page 6 of 25
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Lead time >Electronics interchange
lean agile leagile Inconsistency = 0.05 with 0 missing judgments.
.196 .311 .493
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Lead time >Means of information
lean agile leagile Inconsistency = 0.05 with 0 missing judgments.
.109 .345 .547
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Lead time >Data accuracy
lean agile leagile Inconsistency = 0.02 with 0 missing judgments.
.169 .387 .443
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Page 7 of 25
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Lead time >Delivery speed
lean agile leagile Inconsistency = 0.05 with 0 missing judgments.
.105 .396 .499
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Lead time >New product customer
lean agile leagile Inconsistency = 0.01 with 0 missing judgments.
.163 .297 .540
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Lead time >Customer responsiveness
lean agile leagile Inconsistency = 0.02 with 0 missing judgments.
.210 .240 .550
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Page 8 of 25
Priorities with respect to: Goal: choosing Lean, Agile, Leagile o >Cost
Electronics interchange Over production Inventory transportaion waitin Knowledge misconnection Manufacturing Flexibilty Delivery Flexibilty Source Flexibilty Means of Information Data accuracy Delivery speed New product customer Customer responsiveness Inconsistency = 0.08 with 0 missing judgments.
.037 .124 .194 .112 .181 .058 .076 .053 .043 .038 .030 .054
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Cost >Electronics interchange
lean agile leagile Inconsistency = 0.01 with 0 missing judgments.
.100 .433 .466
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Page 9 of 25
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Cost >Over production
lean agile leagile Inconsistency = 0.01 with 0 missing judgments.
.458 .126 .416
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Cost >Inventory transportaion ...
lean agile leagile Inconsistency = 0.02 with 0 missing judgments.
.169 .443 .387
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Cost >Knowledge misconnection
lean agile leagile Inconsistency = 0.05 with 0 missing judgments.
.528 .140 .333
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Page 10 of 25
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Cost >Manufacturing Flexibilty
lean agile leagile Inconsistency = 0.00 with 0 missing judgments.
.200 .400 .400
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Cost >Delivery Flexibilty
lean agile leagile Inconsistency = 0.05 with 0 missing judgments.
.196 .493 .311
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Cost >Source Flexibilty
lean agile leagile Inconsistency = 0.01 with 0 missing judgments.
.126 .458 .416
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Page 11 of 25
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Cost >Means of Information
lean agile leagile Inconsistency = 0.02 with 0 missing judgments.
.387 .169 .443
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Cost >Data accuracy
lean agile leagile Inconsistency = 0.02 with 0 missing judgments.
.238 .136 .625
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Cost >Delivery speed
lean agile leagile Inconsistency = 0.01 with 0 missing judgments.
.126 .458 .416
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Page 12 of 25
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Cost >New product customer
lean agile leagile Inconsistency = 0.02 with 0 missing judgments.
.387 .169 .443
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Cost >Customer responsiveness
lean agile leagile Inconsistency = 0.05 with 0 missing judgments.
.196 .311 .493
Priorities with respect to: Goal: choosing Lean, Agile, Leagile o >Quality
Over Production Inventory transportaion waitin Knowledge misconnection Manufacturing Flexibilty Delivery Flexibilty Source Flexibilty Electronics interchange Means of Information Data accuracy Delivery speed New product customer customer responsiveness Inconsistency = 0.08 with 0 missing judgments.
.157 .167 .134 .053 .131 .046 .093 .038 .086 .024 .039 .031
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Page 13 of 25
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Quality >Over Production
lean agile leagile Inconsistency = 0.05 with 0 missing judgments.
.474 .149 .376
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Quality >Inventory transportaion ...
lean agile leagile Inconsistency = 0.02 with 0 missing judgments.
.387 .169 .443
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Quality >Knowledge misconnection
lean agile leagile Inconsistency = 0.01 with 0 missing judgments.
.458 .126 .416
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Page 14 of 25
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Quality >Manufacturing Flexibilty
lean agile leagile Inconsistency = 0.21 with 0 missing judgments.
.260 .327 .413
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Quality >Delivery Flexibilty
lean agile leagile Inconsistency = 0.05 with 0 missing judgments.
.196 .311 .493
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Quality >Source Flexibilty
lean agile leagile Inconsistency = 0.05 with 0 missing judgments.
.149 .474 .376
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Page 15 of 25
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Quality >Electronics interchange
lean agile leagile Inconsistency = 0.05 with 0 missing judgments.
.149 .474 .376
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Quality >Means of Information
lean agile leagile Inconsistency = 0.05 with 0 missing judgments.
.311 .196 .493
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Quality >Data accuracy
lean agile leagile Inconsistency = 0.05 with 0 missing judgments.
.327 .260 .413
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Page 16 of 25
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Quality >Delivery speed
lean agile leagile Inconsistency = 0.05 with 0 missing judgments.
.260 .413 .327
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Quality >New product customer
lean agile leagile Inconsistency = 0.02 with 0 missing judgments.
.443 .169 .387
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Quality >customer responsiveness
lean agile leagile Inconsistency = 0.01 with 0 missing judgments.
.163 .297 .540
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Page 17 of 25
Priorities with respect to: Goal: choosing Lean, Agile, Leagile o >Productivity
Over production Inventory transportaion waitin Knowledge misconnection Manufacturing Flexibilty Delivery Flexibilty Source Flexibity Electronics interchange Means of Information Data accuracy Delivery speed New product customer Customer responsiveness Inconsistency = 0.07 with 0 missing judgments.
.190 .120 .119 .085 .101 .076 .062 .067 .062 .037 .034 .047
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Productivity >Over production
lean agile leagile Inconsistency = 0.02 with 0 missing judgments.
.558 .122 .320
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Page 18 of 25
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Productivity >Inventory transportaion ...
lean agile leagile Inconsistency = 0.05 with 0 missing judgments.
.528 .140 .333
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Productivity >Knowledge misconnection
lean agile leagile Inconsistency = 0.08 with 0 missing judgments.
.627 .094 .280
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Productivity >Manufacturing Flexibilty
lean agile leagile Inconsistency = 0.02 with 0 missing judgments.
.320 .122 .558
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Page 19 of 25
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Productivity >Delivery Flexibilty
lean agile leagile Inconsistency = 0.05 with 0 missing judgments.
.333 .140 .528
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Productivity >Source Flexibity
lean agile leagile Inconsistency = 0.05 with 0 missing judgments.
.327 .413 .260
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Productivity >Electronics interchange
lean agile leagile Inconsistency = 0.01 with 0 missing judgments.
.174 .192 .634
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Page 20 of 25
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Productivity >Means of Information
lean agile leagile Inconsistency = 0.00 with 0 missing judgments.
.333 .333 .333
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Productivity >Data accuracy
lean agile leagile Inconsistency = 0.02 with 0 missing judgments.
.169 .443 .387
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Productivity >Delivery speed
lean agile leagile Inconsistency = 0.02 with 0 missing judgments.
.169 .387 .443
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Page 21 of 25
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Productivity >New product customer
lean agile leagile Inconsistency = 0.05 with 0 missing judgments.
.474 .149 .376
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Productivity >Customer responsiveness
lean agile leagile Inconsistency = 0.02 with 0 missing judgments.
.240 .210 .550
Priorities with respect to: Goal: choosing Lean, Agile, Leagile o >Service Level
Over production Inventory transportaion waitio Knowledge misconnection Manufacturing flexibilty Delivery flexibilty Source felxibilty Electronics interchange Means of Information Data accuracy Delivery speed New product customer Customer responsiveness Inconsistency = 0.08 with 0 missing judgments.
.064 .063 .182 .169 .107 .054 .105 .091 .051 .032 .052 .029
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Page 22 of 25
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Service Level >Over production
lean agile leagile Inconsistency = 0.05 with 0 missing judgments.
.149 .474 .376
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Service Level >Inventory transportaion ...
lean agile leagile Inconsistency = 0.02 with 0 missing judgments.
.122 .558 .320
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Service Level >Knowledge misconnection
lean agile leagile Inconsistency = 0.02 with 0 missing judgments.
.169 .443 .387
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Page 23 of 25
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Service Level >Manufacturing flexibilty
lean agile leagile Inconsistency = 0.00 with 0 missing judgments.
.143 .571 .286
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Service Level >Delivery flexibilty
lean agile leagile Inconsistency = 0.03 with 0 missing judgments.
.114 .481 .405
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Service Level >Source felxibilty
lean agile leagile Inconsistency = 0.01 with 0 missing judgments.
.126 .458 .416
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Page 24 of 25
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Service Level >Electronics interchange
lean agile leagile Inconsistency = 0.05 with 0 missing judgments.
.105 .499 .396
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Service Level >Means of Information
lean agile leagile Inconsistency = 0.02 with 0 missing judgments.
.117 .614 .268
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Service Level >Data accuracy
lean agile leagile Inconsistency = 0.05 with 0 missing judgments.
.260 .413 .327
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Page 25 of 25
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Service Level >Delivery speed
lean agile leagile Inconsistency = 0.00 with 0 missing judgments.
.200 .400 .400
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Service Level >New product customer
lean agile leagile Inconsistency = 0.02 with 0 missing judgments.
.169 .387 .443
Priorities with respect to: Goal: choosing Lean, Agile, Leagil >Service Level >Customer responsiveness
lean agile leagile Inconsistency = 0.02 with 0 missing judgments.
.169 .387 .443
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Appendix B Plants Questionnaire Introduction The purpose of this questionnaire is to seek expert opinion and views concerning the benefits and merits of implementing lean, agile and leagile manufacturing strategies in manufacturing industries, and the tools and techniques said experts think should be used to implement said strategies. Your valuable opinions will provide the necessary data to build a proposed methodological model for assessment of the implementation of these strategies. Therefore please consider this questionnaire as top priority and state your opinion carefully and frankly. Feel free to make any remarks or add any information you feel is important to improve the quality of data collected. We are grateful for your time and cooperation.
Part I: General Information o Company manufacturing industry sector type:-----------------------o Capital of the company:----------------------------------------------------o Work force in the company:-----------------------------------------------o The basic strategy/ philosophy of manufacturing in your company: Mass production
Batch production
Lean
Agile
Other
Part II: Company Practice 3- The main company manufacturing policies to achieve the strategy are: Table 1: Company Policies Remarks
None
Implementation Randomly Slightly Moderately
Fully
Policy
No.
Best quality
1
Lowest cost
2
Shortest lead time Shortening total manufacturing cycle time satisfy customer
3
4
5
107
4- Techniques and tools used to implement these policies and strategy. Table 2: Company Technique/ Tools * Remarks
None
Implementation Randomly Slightly Moderately
Technique/Tool
Fully
Cellular Manufacturing Total quality Teams Rabid Set Up (SMED) Kanban Value Stream Mapping (VSM) Process Mapping Line Balancing 5-S Pokayoke Elimination of Waste Total productive Maintenance Value Management Takt Time Kaizen Continuous improvement OEE
No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Others: 123-
18
Part III: Merit value of your opinion Please give your opinion on achieving manufacturing performance through various strategies. Use Table A to judge Table 1 of your industry:
108
Table A: Variables of the Importance Weight Poor
(0.1, 0.1, 0.3)
Fair
(0.1, 0.3, 0.5)
Good
(0.3, 0.5, 0.7)
V. Good
(0.5, 0.7, 0.9)
Excellent
(0.7, 0.9, 0.9)
Table 1
Average
Decision Maker3
Decision Maker2
Decision Maker1
……………………….
…………………….
……………………
Measures/ Decision Maker Lead Time Cost Quality Productivity Service Level
Use the rating variables in Table B to evaluate the parameters (refer to the acronyms at the end of this questionnaire) of all measures in Tables 2,3,4,5 and 6: Table B: Rating Variables Poor
(1, 1, 3)
Fair
(1, 3, 5)
Good
(3, 5, 7)
V. Good
(5, 7, 9)
Excellent
(7, 9, 9) 109
Table 2: Evaluation Parameters by Decision Makers D3
D2
D1
Parameters
Measure
OP ITW KM MF DF SF EDI
Lead time
MIADA DKB DS NPC CR mean
Table 3: Evaluation Parameters by Decision Makers D3
D2
D1
Parameters
Measure
OP ITW KM MF DF SF EDI
Cost
MIADA DKB DS NPC CR mean
110
Table 4: Evaluation Parameters by Decision Makers D3
D2
D1
Parameters
Measure OP
ITW KM MF DF SF EDI
Quality
MIADA DKB DS NPC CR mean
Table 5: Evaluation Parameters by Decision Makers D3
D2
D1
Parameters
Metrics
OP ITW KM MF DF SF EDI
Productivity
MIADA DKB DS NPC CR mean
111
Table 6: Evaluation Parameters by Decision Makers D3
D2
D1
Parameters
Metrics
OP ITW KM MF DF SF EDI
Service Level
MIADA DKB DS NPC CR mean
Definitions: OP: ITW: KM: MF: DF: SF: EDI: MIADA: DKB: DS: NPC: CR:
Overproduction Inventory Transportation Waiting Knowledge Misconnection Manufacturing Flexibility Delivery Flexibility Source Flexibility Electronics Data Interchange Mean of Information and Data Accuracy Data and Knowledge Base Delivery Speed New Product Customer Customer Responsiveness
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Appendix C Visual Basic (VB) Flowchart for choosing one of the Manufacturing Strategies using Visual Basic
. Start
Input1 = Input Lead Time, Cost, Quality, Productivity, Service Level by three persons in each company to get
Input2 = Input OP, ITW, KM, MF, DF, SF, EDI, MIADA, DKB, DS, NPI, CR for each of Lead Time, Cost, Quality,
Normalize to get Equations 4, 5, 6 (Matrix 2) Multiply Matrix 2 by Matrix 1to get Matrix 3 Add each row in Matrix 3, then take the overall average of column which = x
Last Value =
x − 0 . 18 3 52 0 18
Last Value
