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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 theCollege of Engineering, King Saud University

Riyadh

December 2007

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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

RiyadhOctober 2007

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Acknowledgment

I wish to acknowledge the support of my advisor Dr. Abdulaziz Al-

Tamimi 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

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Contents

Acknowledgement 3

List of Tables 6

List of Figures 9

Abstract 10

Chapter 1: Introduction 11

1.1.1 Overview 11

1.2 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

1.3 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

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Chapter 4: Decision Support System (DSS) 39

4.1 Building a Decision Support System Using Visual Basic 39

Chapter 5: Case Studies 44

5.1 Saudi Mechanical Industries Company (SMI) 44

5.1.1 SMI Study 45

5.2 Advanced Electronics Company (AEC) 53

5.2.1 AEC Study 54

5.3 Saudi Lighting Company (SLC) 62

5.3.1 SLC Study 62

Chapter 6: Discussion and Conclusion 70

References 72

Appendix A 75

Appendix B 107

Appendix C 113

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List of Tables

Table # Title Page

Table (1.1) Comparison of Lean and Agile Manufacturing 18

Table (2.1) Summary of related references for lean and agile

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 38

Table (5.1) The feedback data input of the five

measuring factors (SMI) 45

Table (5.2) Characteristics Factorsby Decision Makers on

Lead Time (SMI) 46

Table (5.3) Characteristics Factors by Decision Makers on Cost

(SMI) 47

Table (5.4) Characteristics Factors by Decision Makers

on Quality (SMI) 48


on Productivity (SMI) 49

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Table # Title Page


on Service Level (SMI) 58

Table (5.7) Normalization of the Measuring Means of

the three Decision Makers of Matrices (SMI) 51


measuring factors (AEC) 54

Table (5.9) Characteristics Factors by Decision Makers on

Lead Time (AEC) 55Table (5.10) Characteristics Factors by Decision Makers

on Cost (AEC) 56


on Quality (AEC) 57


on Productivity (AEC) 58


on Service Level (AEC) 59

Table (5.14) Normalization of the measuring Means of

the three Decision Makers of Matrices (AEC) 60


measuring factors (SLC) 62

Table (5.16) Characteristics Factors by Decision Makers on

Lead Time (SLC) 63


on Cost (SLC) 64


on Quality (SLC) 65

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Table # Title Page


on Productivity (SLC) 66


on Service Level (SLC) 67

Table (5.21) Normalization of the Measuring Means

of the three Decision Makers of Matrices (SLC) 67

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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 28

Figure (3.4) Characteristics of Manufacturing and

Related Methods 30

Figure (4.1) Selection of the Manufacturing System 39

Figure (4.2) Triangular Fuzzy 40

Figure (4.3) -Cut of the Triangular Fuzzy Number 41

Figure (4.4) The Manufacturing System Strategy 43

Figure (4.3) The -Cut of the Example 43

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Abstract

The objective of this research is to develop a methodology forevaluating 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).

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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 non-

added 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

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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].

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 1990when 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

Organizationalissues)

Agile Manufacturing

Technical /Operational issues

Lean Manufacturing

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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 Partnerships 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

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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 byreducing 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 machinedowntime.

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]:

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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.

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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].

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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 non-

added 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.

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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

Table (1.1) Comparison of Lean and Agile Manufacturing

Lean ManufacturingAgile Manufacturing

Customer drivenMarket driven

Orders based on customersOrders based on changing the market

Checking samples on the line by workersChecking samples on the line by workers

Flexible production for product varietyGreater flexibility for customized products

Focused on factory operationsFocused on enterprise-wide operations

Emphasis on supplier managementEmphasis on virtual enterprises

Emphasis on efficient use of resourcesEmphasis on thriving in a market environment

Predictable market demandUnpredictable market demand

Low product varietyHigh product variety

Low profit marginHigh profit margin

Physical dominant costMarketability dominant cost

Highly desirable enrichmentObligatory enrichment

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(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.

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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.

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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 complexenvironment. His method utilizes a users 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.

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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

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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 multi-

criteria 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 discussthe forms of fuzzy control rules and inference methods.

Arnold Kaufmann et al [23] present a comprehensive and self-

contained 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

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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 agilemanufacturing

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.

Ref.lead timecostqualityservice levelflexibilityproductivityelimination

of waste

Market

sensitivity

Information

technology

1111111100

2111010000

3111100000

4111011000

5111111111

6110011100

7111111100

8111111111

11111100000

12111111110

13111000111

14111100000

18111100000

19111101000

21111101000

Total1515141189743

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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 multi-

decision 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:

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(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.

(2) The elements of any level are subjected to a series of paired

comparisons on the Saatys scale (ranging from 1/9 to 9/9) and a paired

comparison matrixis built.

Table (3.1) AHP comparison scale

Intensity of relative

importance

Definition Intensity relative importance

1Factor i and j are of equal importance

3Factor i is weakly more important than j

5Factor i is strongly more important than j

7Factor i is more strongly more important than j

9Factor i is absolutely more important than j

2,4,6 and 8intermediate

Criterion 1 Criterion 2 Criterion 3

Objective

Alternative 1 Alternative 2

Criterion 2-1 Criterion 2-2

Figure (3.1) Hierarchal Approach of AHP

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(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.

)

321

( AAA ++ )

321

( BBB ++ )

321

( CCC ++

(5) 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:

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

333

222

111

CBA

CBA

CBA

=++++++

=++++++

=++++++

3

321

3

321

2

321

2

2321

3

321

2

321

2

1321

1

321

1

321

1

AvrgCCC

C

BBB

B

AAA

A

AvrgCCC

C

BBB

B

AAA

A

AvrgCCC

C

BBB

B

AAA

A

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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.

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

o Electronic data interchange;o Means of information and

data accuracyo Data and knowledgebases

Lean Manufacturing Agile Manufacturing

o Over-productiono Inventory

transportation waitingo Knowledge Misconec

o Delivery speedo New product introductiono Customer

responsiveness

(sub

Characteristic

Lead time Cost Quality Productivity Service

EliminationOf waste

Flexibility InformationTechnolo

MarketSensitivit

o Manufacturing

flexibility,o Delivery flexibilityo Source flexibility

measures(Objective

(Manufacturin

Leagile ManufacturingManufacturing System

strategies

Measuring

factors

Characteristics

factors

ub

characteristics

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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

products properties or characteristics [8].

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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]

a)Elimination of waste

Thisis 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 andagile manufacturing Flexibility is also considered to be the ease with which a

The characteristics

EliminationOf Waste

Flexibility InformationTechnology

MarketSensitivity

Figure (3.4) Characteristics of Manufacturing and Related Methods

o Over-productiono Inventory

transportation waitingo Knowledge

misconnection

o Manufacturingflexibility,

o Delivery flexibilityo Source flexibility

o Electronic data interchange;o Means of information and

data accuracyo Databases and Knowledge

Bases

o Delivery speedo New product introductiono Customer responsiveness

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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: ANPbased 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: ANPbased approach MMLA). Market sensitivity

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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.

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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

Characteristics FactorsLeanAgileLeagileConsistency

Over Production0.1360.2380.6250.02

Inventory Transportation Waiting0.1050.2580.6370.04

Knowledge Misconnection0.250.250.50

Manufacturing Flexibility0.1090.3090.5820

Delivery Flexibility0.1110.2220.6670

Source Flexibility0.1090.3450.5470.05

Electronic Data Interchange0.1960.3110. 4930.05

Mean of Information0.1090.3450.5470.05

Data and Knowledge Base0.1690.3870.4430.02

Delivery Speed0.1050.3960.4990.05

New Product introduction0.1630.2970.540.01

Customer Responsiveness0.210.240.550.02

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].

a28

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Table (3.3) Characteristics Factors Rating for the Cost




Knowledge Misconnection0.5400.1630.2970.01


Delivery Flexibility0.1960.4930.3110.05


Electronic Data Interchange0.1000.4330.4660.01



Delivery Speed0.1260.4580.4160.01



Table 3.4 expresses the manufacturing performance for the quality

with respect to lean, agile and leagile manufacturing strategy. a consistency


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]

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Table (3.4) Characteristics Factors Rating for the Quality





Manufacturing Flexibility0.2600.3270.4130.02






Delivery Speed0.2600.4130.3270.05



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].

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Table (3.5) Characteristics Factors Rating for the Productivity





Manufacturing Flexibility0.3200.1220.5880.02




Mean of Information0.3330.3330.3330


Delivery Speed0.1690.3870.4430.02



Table 3.6 expresses the manufacturing performance for the service

level with respect to lean, agile and leagile manufacturing strategy. a


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].

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Table (3.6) Characteristics Factors Rating for the Service Level











Delivery Speed0.2000.4000.4000



The above results are summarized for measuring factors of lead time,

cost, quality, productivity and service level as shown in table 3.7. a


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 (3.7Characteristics Factors Rating for the Measures Factors

Measuring FactorsLeanAgileLeagileconsistency

Lead Time0.1400.3110.5490.07

Cost0.2620.330. 4080.08

Quality0.3320.2480.4210.08

Productivity0.3960.2150.3900.07

Service Level0.1490.4850.3660.08

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Hence the experts opinions rating is shown in table 3.8 . a consistency


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

Expert OpinionsLeanAgileLeagileconsistency

Overall Rating0.2580.3190.4230.09

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

Expert OpinionsLeanAgileLeagileconsistency

Overall Rating0.168 to 0.3480.229 to 0.4090.333 to 0.5130.09

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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

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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 ),,(~

111 cbaA = and it is defined as shown in figure 4.2.

Poor Fair Good V. Good Excellent

1

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1 3 5 7 9

Let A~

and B~

be two fuzzy numbers represented by the triplet ),,( 321 aaa

and ),,( 321 bbb , respectively, then the operations of triangular fuzzy numbers are

expressed as [24]:

A~

(+) B~

= ),,( 321 aaa + ),,( 321 bbb = ),,( 332211 bababa +++

A~

(-) B~

= ),,( 321 aaa - ),,( 321 bbb = ),,( 332211 bababa

A~

(x) B~

= ),,( 321 aaa x ),,( 321 bbb = ),,( 332211 bababa

A~

( ) B~

= ),,( 321 aaa ),,( 321 bbb = ),,( 332211 bababa .

(A~

+ B~

)/n = (/ ),,( 321 bbb ) = ( ),,( 332211 bababa )/ 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 -cut, [0,1] as shown in

figure 4.3. Moreover, (the -cut of the triangular fuzzy number)

can be calculated as

T

),,( 321 aaa

Figure 4.2 Triangular Fuzzy

X

1

)))((),)(((),,(_ 323112321 aaaaaaaaacut ++==

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3) comparing the existing manufacturing rating by the expert opinions

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.

1a 2a 3a

-cutFigure 4.3 -cut of the triangular fuzzy number

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0 0.258 0.319 0.423 1

Supposing 0.31 is existing

manufacturing rating with

existing manufacturing rating = 0.312 Manufacturing System Strategy = Lean

Figure 4.4 The Manufacturing System Strategy

Suppose , and , Hence

-cut = ((2-1.5)0.31+1.5,(-2.5+2)0.31+2.5 =(1.65,2.4)

`````

0.31

1.5 2 2.5

-cut

5.11 =a 22 =a 5.23 =a

1

1.64 2.4

LeagileAgileLeanTraditional

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Figure 4.5 the -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 Riyadhs 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 SMIs new plant for Continuous Cast Bronze bars and

bronze centrifugal casting came online. This focused approach to growth hasyielded a company that today stands as a pre-eminent world producer of

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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.

Measuring FactorsD1D2D3Mean

Lead timeGoodFairGood( 0.23,0.43,0.63)

CostGoodFairGood( 0.23,0.43,0.63)

QualityGoodGoodFair( 0.23,0.43,0.63)

ProductivityFairGoodFair( 0.17,0.37,0.57)

Table 5.1 The feedback data input of the five measuring factors

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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

Characteristics FactorsD1D2D3

Over ProductionFairV. GoodV. Good

Inventory Transportation

WaitingFairGoodGood

Knowledge MisconnectionFairGoodV. Good

Manufacturing FlexibilityFairFairGood

Delivery FlexibilityGoodFairFair

Source FlexibilityV. GoodGoodGood

Electronic Data InterchangeGoodGoodFair

Mean of InformationGoodGoodGood

Data and Knowledge BaseFairV. GoodFair

Delivery SpeedGoodV. GoodFair

New Product introductionV. GoodFairV. Good

Customer ResponsivenessFairFairFair

Lead time

mean( 2.33,4.33,6.33)( 2.83,4.83,6.83)( 2.67,4.67,6.67)

Service LevelFairGoodGood( 0.23,0.43,0.63)

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2. Cost

Moreover, Table 5.3 demonstrates the characteristics factors for the measuringfactor of cost.

Table (5.3 ) Characteristics Factors by Decision Makers on Cost (SMI)Measuring Factor


Over ProductionGoodV. GoodGood

Inventory Transportation WaitingGoodGoodGood

Knowledge MisconnectionGoodGoodFair

Manufacturing FlexibilityFairFairFair

Delivery FlexibilityFairV. GoodFair

Source FlexibilityGoodV. GoodGood

Electronic Data InterchangeFairGoodGood

Mean of InformationGoodFairFair

Data and Knowledge BaseV. GoodFairGood

Delivery SpeedV. GoodGoodFair

New Product introductionV. GoodFairGood

Customer ResponsivenessV. GoodGoodFair

Cost

mean( 3.17,5.17,7.17)( 2.83,4.83,6.83)( 2,4,6)

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3. Quality

Furthermore, Table 5.4 shows the characteristics factors for the measuringfactor of Quality.

Table (5.4) ) Characteristics Factors by Decision Makers on Quality (SMI)

Measuring Factors


Over ProductionGoodFairGood


WaitingGoodGoodGood

Knowledge MisconnectionGoodFairGood

Manufacturing FlexibilityV. GoodFairFair

Delivery FlexibilityV. GoodFairGood

Source FlexibilityFairFairFair

Electronic Data InterchangeFairGoodGood

Mean of InformationFairGoodFair

Data and Knowledge BaseGoodV. GoodGood

Delivery SpeedGoodV. GoodFair

New Product introductionFairFairGood

Customer ResponsivenessGoodGoodFair

Quality

mean

(

2.67,4.67,6.67

)

( 2.33,4.33,6.33)( 2.17,4.17,6.17)

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4. Productivity

in addition, Table 5.5 shows the characteristics factors for the measuringfactor of productivity.

Table (5.5) ) Characteristics Factors by Decision Makers on Productivity (SMI)

Measuring FactorsCharacteristics

FactorsD1D2D3

Over ProductionV. GoodGoodV. Good

Inventory TransportationWaiting

V. GoodFairV. Good

Knowledge

MisconnectionV. GoodFairV. Good

Manufacturing FlexibilityV. GoodFairGood

Delivery FlexibilityGoodGoodFair

Source FlexibilityGoodGoodGood

Electronic Data

InterchangeFairGoodFair

Mean of InformationGoodFairGood

Data and Knowledge

BaseGoodGoodFair

Delivery SpeedV. GoodGoodGood

New Product introductionV. GoodV. GoodV. Good

Customer ResponsivenessV. GoodV. GoodV. Good

Productivity

mean( 4,6,8)( 2.67,4.67,6.67)( 3.33,5.33,7.33)

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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


Over ProductionFairGoodFair


WaitingFairGoodGood

Knowledge MisconnectionFairFairFair

Manufacturing FlexibilityGoodFairFair

Delivery FlexibilityV. GoodFairFair

Source FlexibilityV. GoodFairFair

Electronic Data InterchangeV. GoodFairFair

Mean of InformationGoodGoodFair

Data and Knowledge BaseGoodFairFair

Delivery SpeedGoodGoodGood

New Product introductionGoodGoodGood

Customer ResponsivenessV. GoodFairGood

Service Level

mean( 3.17,5.17,7.17)( 3.5,5.5,7.5)( 1.67,3.67,5.67)

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]

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Table (5.7) Normalization of the Measures Factors Means of the three Decision Makers (SMI)

Lead TimeCostQualityProductivityService LevelD1( 0.23,0.43,0.63)(0.32,0.52,0.72)( 0.27,0.47,0.67)( 0.4,0.6,0.8)( 0.32,0.52,0.72)

D2( 0.28,0.48,0.68)( 0.28,0.48,0.68)( 0.23,0.43,0.63)( 0.27,0.47,0.67)( 0.35,0.55,0.75)

D3( 0.27,0.47,0.67)( 0.2,0.4,0.6)(0. 22,0.42,0.62)( 0.33,0.53,0.73)( 0.17,0.37,0.57)

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

The result of the multiplication is

Then applying the equation

18.052.3

18.0097.1

= 0.28 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.

0.57)0.17,0.37,(0.73)0.33,0.53,(0.62)0.22,0.42,(0.60)0.20,0.40,(067)0.27,0.47,(0.75)0.35,0.55,(0.67)0.27,0.47,(0.63)0.23,0.43,(0.68)0.28,0.48,(0.68)0.28,0.48,(

0.72)0.32,0.52,(8)0.4,0.6,0.(0.67)0.27,0.47,(0.72)0.32,0.52,(0.63)0.23,0.43,(

,0.63)(0.23,0.43

.57)0.170.37,0(

,0.63)(0.23,0.43

,0.63))(0.23,0.43

,0.63)(0.23,0.43

,1.97)(0.25,0.91

,2)(0.27,0.94

,2.18)(0.33,1.06

Average = 1.19

= 1.07 Average 1.097

= 1.04

=


0.196

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0 0.258 0.319 0.423 1

Thus SMIs system is at present that of traditional manufacturing.

In order to facilitate its evolution to lean manufacturing, SMI shouldimplement the following tools (described earlier in Section 1.2.2):

Cellular Manufacturing Total Quality Management Value Stream Mapping 5-S, Pokayoke Kaizen

Takt Time

-cut = ((1.07-1.04)0.196+1.04,(-1.19+1.07)0.196+1.19 = (1.05, 1.16)

0.196

1.04 1.07 1.19

-cut

1

1.05 1.16

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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.

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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





Table (5.8) The feedback data input of the five measuring factors (AEC)






Lead timeGoodV. GoodGood( 0.37,0.57,0.77)

CostGoodV. GoodGood( 0.37,0.57,0.77)

QualityGoodV. GoodV. Good( 0.43,0.63,0.83)

ProductivityV. GoodGoodGood( 0.37,0.57,0.77)

Service LevelGoodGoodV. Good( 0.37,0.57,0.77)

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1. Lead time

Table (5.9) Characteristics Factors by Decision Makers on Lead Time (AEC)


FactorsD1D2D3

Over ProductionGoodV. GoodV. Good


WaitingV. GoodGoodV. Good

Knowledge

MisconnectionV. GoodV. GoodV. Good

Manufacturing FlexibilityV. GoodV. GoodGood

Delivery FlexibilityGoodV. GoodV. Good

Source FlexibilityGoodV. GoodV. Good

Electronic Data

InterchangeV. GoodV. GoodV. Good

Mean of InformationGoodV. GoodV. Good

Data and Knowledge

BaseV. GoodV. GoodV. Good




Lead time

mean( 4.17,6.17,8.17)( 4.67,6.67,8.67)( 4.67,6.67,8.67)

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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


Over ProductionGoodGoodGood


WaitingV. GoodV. GoodV. Good

Knowledge MisconnectionV. GoodV. GoodV. Good

Manufacturing FlexibilityGoodGoodGood

Delivery FlexibilityGoodV. GoodGood


Electronic Data InterchangeV. GoodV. GoodV. Good

Mean of InformationV. GoodGoodGood

Data and Knowledge BaseV. GoodV. GoodV. Good

Delivery SpeedGoodV. GoodGood

New Product introductionGoodGoodV. Good

Customer ResponsivenessV. GoodGoodGood

Cost

mean( 4.17,6.17,8.17)( 4,6,8)( 3.83,5.83,7.83)

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3. Quality


factor of quality.

Table (5.11) Characteristics Factors by Decision Makers on Quality (AEC)

Measuring Factors


Over ProductionV. GoodV. GoodV. Good


WaitingV. GoodGoodExcellent

Knowledge MisconnectionGoodGoodExcellent

Manufacturing FlexibilityV. GoodGoodV. Good

Delivery FlexibilityV. GoodV. GoodV. Good

Source FlexibilityGoodGoodV. Good

Electronic Data InterchangeGoodGoodV. Good

Mean of InformationGoodV. GoodV. Good

Data and Knowledge BaseV. GoodV. GoodV. Good

Delivery SpeedGoodGoodV. Good


Customer ResponsivenessGoodV. GoodV. Good

Quality

mean( 4,6,8)( 4,6,8)( 5.33,7.33,9)

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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


Over ProductionV. GoodV. GoodV. Good


WaitingV. GoodGoodV. Good

Knowledge MisconnectionGoodV. GoodGood

Manufacturing FlexibilityV. GoodV. GoodV. Good

Delivery FlexibilityGoodV. GoodGood


Electronic Data InterchangeV. GoodV. GoodGood

Mean of InformationV. GoodGoodV. Good

Data and Knowledge BaseGoodV. GoodV. Good

Delivery SpeedV. GoodGoodGood

New Product introductionV. GoodGoodV. Good


Productivity

mean( 4.5,6.5,8.5)( 4.17,6.17,8.17)( 4.33,6.33,8.17)

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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)


FactorsD1D2D3

Over ProductionGoodGoodFair


WaitingGoodGoodFair

Knowledge

MisconnectionGoodGoodFair

Manufacturing FlexibilityGoodFairGood

Delivery FlexibilityFairFairGood

Source FlexibilityGoodFairFair

Electronic Data

InterchangeFairFairGood


Data and Knowledge

BaseFairGoodGood

Delivery SpeedGoodFairFair

New Product introductionGoodFairGood

Customer ResponsivenessFairGoodFair

Service Level

mean( 2.33,4.33,6.33)( 2,4,6)( 1.83,3.83,5.83)

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]

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Table (5.14) Normalization of the Measuring Means of the three Decision Makers

Lead TimeCostQualityProductivityService Level

F( 0.42,0.62,0.82)( 0.42,0.62,0.82)( 0.40,0.60,0.80)( 0.45,0.65,0.85)( 0.23,0.43,0.63)

AZ( 0.47,0.67,0.87)( 0.40,0.60,0.80)( 0.40,0.60,0.80)( 0.42,0.62,0.82)( 0.20,0.40,0.60)

AB( 0.47,0.67,0.87)( 0.38,0.58,0.78)( 0.53,0.73,0.90)( 0.43,0.63,0.82)( 0.18,0.38,0.58)

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


18.052.3

18.084.1


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

0.58)0.18,0.38,(.82)043,0.63,0(0.90)0.53,0.73,(0.78)0.38,0.58,(0.87)0.47,0.67,(

0.60)0.20,0.40,(0.82)0.42,0.62,(0.80)0.40,0.60,(0.80)0.40,0.60,(0.87)0.47,0.67,(

0.63)0.23,0.43,(0.85)0.45,0.65,(0.80)0.40,0.60,(0.82)0.42,0.62,(0.82)0.42,0.62,(

0.77)0.37,0.57,(

0.77)0.37,0.57,(

0.83)0.43,0.63,(

0.77)0.37,0.57,(

0.77)0.37,0.57,(

,3.1)(0.76,1.74

,3.04)(0.72,1.68

3.07)(0.73,1.7,

Average = 1.83

= 1.82 Average 1.84

= 1.87

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0 0.258 0.319 0.423 1

Thus AECs 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).

-cut = ((1.83-1.82)0.350+1.82,(-1.87+1.83)0.35+1.87 = (1.825, 1.86)

0.350

1.82 1.83 1.87

-cut


1

1.825 1.86

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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





Table (5.15 The feedback data input of the five measuring factors (SLC)


Lead timeFairGoodFair

( 0.17,0.37,0.57)

CostFairFairFair

( 0.1,0.3,0.5)

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1. Lead time

Table (5.16) Characteristics Factors by Decision Makers on Lead Time (SLC)


FactorsD1D2D3

Over ProductionFairGoodFair

Inventory

Transportation WaitingFairFairFair

Knowledge

MisconnectionGoodFairFair

Manufacturing

FlexibilityGoodFairFair

Delivery FlexibilityGoodFairGood

Source FlexibilityGoodGoodGood

Electronic Data

InterchangeGoodGoodGood

Mean of InformationFairGoodGood

Data and Knowledge

BaseGoodGoodFair

Delivery SpeedFairGoodFair

Lead time

New Product

introductionFairFairFair

QualityFairFairFair

( 0.1,0.3,0.5)

ProductivityGoodFair

Good(0.23,0.43,0.63)

Service LevelGoodFair

Good(0.23,0.43,0.63)

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Customer

ResponsivenessFairFairGood

mean( 2,4,6)( 2,4,6)( 1.83,3.83,5.83)

2. Cost


factor of cost.

Table (5.17) Characteristics Factors by Decision Makers on Cost (SLC)

Measuring Factors


Over ProductionGoodFairGood


WaitingGoodFairGood


Manufacturing FlexibilityFairGoodGood

Delivery FlexibilityFairGoodFair

Source FlexibilityFairGoodFair

Electronic Data

InterchangeGoodGoodFair

Mean of InformationFairFairFair

Data and Knowledge BaseGoodGoodGood

Delivery SpeedFairFairGood

New Product introductionGoodFairGood

Customer ResponsivenessFairGoodFair

Cost

mean( 2,4,6)( 2,4,6)( 2.17,4.17,6.17)

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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


Over ProductionFairFairFair


WaitingFairGoodGood


Manufacturing FlexibilityGoodGoodGood

Delivery FlexibilityFairFairFair

Source FlexibilityFairFairGood

Electronic Data InterchangeGoodGoodGood


Data and Knowledge BaseFairGoodFair


New Product introductionFairFairGood

Customer ResponsivenessGoodFairGood

Quality

mean( 2,4,6)( 2,4,6)( 2.33,4.33,6.33)

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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)


FactorsD1D2D3

Over ProductionFairGoodGood

Inventory

Transportation

Waiting

FairGoodFair

Knowledge

MisconnectionFairGoodFair

Manufacturing

FlexibilityFairFairFair

Delivery FlexibilityGoodGoodGood

Source FlexibilityGoodFairGood

Electronic Data

InterchangeGoodGoodGood


Data and Knowledge

BaseFairFairFair

Delivery SpeedFairFairFair

New Product

introductionFairFairFair

Productivity

Customer

ResponsivenessFairGoodGood

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mean( 1.67,3.67,5.67)( 2.17,4.17,6.17)( 1.83,3.83,5.83)

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


Over ProductionFair

Good

Good


Waiting

FairPoorPoor

Knowledge MisconnectionFairPoor

Good

Manufacturing FlexibilityGoodPoorGood

Delivery FlexibilityPoorGood

Poor

Source FlexibilityGoodGood

Good

Electronic Data InterchangePoorFair

Good

Mean of InformationGoodPoor

Good

Data and Knowledge BasePoor

PoorGood

Delivery SpeedGood

Good

Good

New Product introductionPoorGood

Good

Customer ResponsivenessGoodPoor

Fair

Service Level

mean( 1.83,3.17,5.17)( 1.83,2.83,4.83)( 2.5,4.17,6.17)

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

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Lead TimeCostQualityProductivityService Level

N( 0.20,0.40,0.60)( 0.20,0.40,0.60)( 0.20,0.40,0.60)( 0.17,0.37,0.57)( 0.18,0.32,0.52)

KH( 0.20,0.40,0.60)( 0.20,0.40,0.60)( 0.20,0.40,0.60)( 0.22,0.42,0.62)( 0.18,0.28,0.48)

F( 018,0.38,0.58)( 0.22,0.42,0.62)( 0.23,0.43,0.63)( 0.18,0.38,0.58)( 0.25,0.42,0.62)

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


18.052.3

18.084.0


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

0 0.258 0.319 0.423 1

0.62)0.25,0.42,(0.58)0.18,0.38,(063)0.23,0.43,(0.62)0.22,0.42,(0.58)0.18,0.38,(

0.48)0.18,0.28,(062)0.22,0.42,(0.60)0.20,0.40,(0.60)0.20,0.40,(0.60)0.20,0.40,(

0.52)0.18,0.32,(0.57)0.17,0.37,(0.60)0.20,0.40,(0.60)0.20,0.40,(0.60)0.20,0.40,(

0.63)0.23,0.43,(

0.63)0.23,0.43,(

5)0.1,0.3,0.(

5)0.1,0.3,0.(

0.57)0.17,0.37,(

,1.71)(0.17,0.74

,1.64)(0.17,0.69

,1.63)(0.15,0.68

Average = 0.82

= 0.83 Average 0.84

= 0.88


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Thus SLCs 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

-cut = ((0.83-0.82)0.140+0.83,(-0.88+0.83)0.140+0.88 = (083, 0.99)

0.82 0.83

-cut

1

0.821 0.99

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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.

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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.

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References:

1) J. Ben Naylor, Mohammad M Naim, Danny Berry, (1999),

Leagility: Integration the lean and agile manufacturing paradigms in

the total supply chain, Production Economics, Vol 62.pp 107-118.

2) Brown, S (1998), "New Evidence on Quality in Management Plants: A

Challenge to lean Production", Inventory Management, Vol. 39 No.1,

pp. 24-29.

3) Richard Lee Stroch and Sanggyu Lim, (1999), Improving flow to

achieve lean manufacturing in shipbuilding", Planning control, Vol. 10

No.2, pp. 127-137.

4) Ruth Banomyong, Nucharee Supatan (2000), "Comparing lean and

agile logistics strategies: a case study", Production Economics, Vol 62.

pp 111-134

5) Adhijith Mukunda, Apratim N Dixit, (2001), "An agile enterprise

Prototype for the Indian Electronics Industry", Technology

Management, Vol 8, pp 161-171.

6) Ian Christian, Hossam Ismail, Jim Mooney, Snowden Simon, (1997),

'Agile Manufacturing transitional Strategies, University of Liverpool.

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7) Abraham Kandel (1999), Fuzzy Expert Systems, Florida State

University. Library of Congress Cataloging in - Publication Data.

8) Ashish Agarwal, Ravi Shanker, (2005),"Modeling the Metrics of lean,

agile and leagile Supply chain: An ANP-based approach", Operational

Research, Vol.1, pp.1- 15.

9) Saaty, T.L., (1980), "The Analytic Hierarchy Process:, New York,

N.Y., McGraw Hill, reprinted by RWS Publication, Pittsburgh.

10) Naim, Naylor and Barlow, (1999), Developing lean and agile supply

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