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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
Table (5.5) Characteristics Factors by Decision Makers
on Productivity (SMI) 49
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Table # Title Page
Table (5.6) Characteristics Factors by Decision Makers
on Service Level (SMI) 58
Table (5.7) Normalization of the Measuring Means of
the three Decision Makers of Matrices (SMI) 51
Table (5.8) The feedback data input of the five
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
Table (5.11) Characteristics Factors by Decision Makers
on Quality (AEC) 57
Table (5.12) Characteristics Factors by Decision Makers
on Productivity (AEC) 58
Table (5.13) Characteristics Factors by Decision Makers
on Service Level (AEC) 59
Table (5.14) Normalization of the measuring Means of
the three Decision Makers of Matrices (AEC) 60
Table (5.15) The feedback data input of the five
measuring factors (SLC) 62
Table (5.16) Characteristics Factors by Decision Makers on
Lead Time (SLC) 63
Table (5.17) Characteristics Factors by Decision Makers
on Cost (SLC) 64
Table (5.18) Characteristics Factors by Decision Makers
on Quality (SLC) 65
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Table # Title Page
Table (5.19) Characteristics Factors by Decision Makers
on Productivity (SLC) 66
Table (5.20) Characteristics Factors by Decision Makers
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
Characteristics FactorsLeanAgileLeagileConsistency
Over Production0.4580.1260.4160.01
Inventory Transportation Waiting0.1690.4430.3870.02
Knowledge Misconnection0.5400.1630.2970.01
Manufacturing Flexibility0.2000.4000.4000
Delivery Flexibility0.1960.4930.3110.05
Source Flexibility0.1260.4580.4160.01
Electronic Data Interchange0.1000.4330.4660.01
Mean of Information0.3870.1690.4430.02
Data and Knowledge Base0.2380.1360.6250.02
Delivery Speed0.1260.4580.4160.01
New Product introduction0.3870.1690.4430.02
Customer Responsiveness0.1960.3110.4930.05
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]
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Table (3.4) Characteristics Factors Rating for the Quality
Characteristics FactorsLeanAgileLeagileConsistency
Over Production0.4740.1490.3760.05
Inventory Transportation Waiting0.3870.1690.4430.02
Knowledge Misconnection0.4580.1260.4160.01
Manufacturing Flexibility0.2600.3270.4130.02
Delivery Flexibility0.1960.3110.4930.05
Source Flexibility0.1490.4740.3760.05
Electronic Data Interchange0.1490.4740.3760.05
Mean of Information0.3110.1960.4930.05
Data and Knowledge Base0.3270.2600.4130.05
Delivery Speed0.2600.4130.3270.05
New Product introduction0.4430.1690.3870.02
Customer Responsiveness0.1630.2970.5400.01
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
Characteristics FactorsLeanAgileLeagileConsistency
Over Production0.5580.1220.3200.02
Inventory Transportation Waiting0.5280.1400.3330.05
Knowledge Misconnection0.6270.0940.2800.08
Manufacturing Flexibility0.3200.1220.5880.02
Delivery Flexibility0.3330.1400.5280.05
Source Flexibility0.3270.4130.2600.05
Electronic Data Interchange0.1740.1920.6340.01
Mean of Information0.3330.3330.3330
Data and Knowledge Base0.1690.4330.3870.02
Delivery Speed0.1690.3870.4430.02
New Product introduction0.4740.1490.3760.05
Customer Responsiveness0.2400.2100.5500.02
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].
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Table (3.6) Characteristics Factors Rating for the Service Level
Characteristics FactorsLeanAgileLeagileConsistency
Over Production0.1490.4740.3760.05
Inventory Transportation Waiting0.1220.5580.3200.02
Knowledge Misconnection0.1690.4430.3870.02
Manufacturing Flexibility0.1430.5710.2860
Delivery Flexibility0.1140.4810.4050.03
Source Flexibility0.1260.4580.4160.01
Electronic Data Interchange0.1050.4990.3960.05
Mean of Information0.1170.6140.2680.02
Data and Knowledge Base0.2600.4130.3270.05
Delivery Speed0.2000.4000.4000
New Product introduction0.1690.3870.4430.02
Customer Responsiveness0.1690.3870.4430.02
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 (3.7Characteristics 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
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
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
Characteristics FactorsD1D2D3
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
Characteristics FactorsD1D2D3
Over ProductionGoodFairGood
Inventory Transportation
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
Characteristics FactorsD1D2D3
Over ProductionFairGoodFair
Inventory Transportation
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
=
LeagileAgileLeanTraditional
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
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)
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.
Measuring FactorsD1D2D3Mean
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)
Measuring FactorsCharacteristics
FactorsD1D2D3
Over ProductionGoodV. GoodV. Good
Inventory Transportation
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
Delivery SpeedGoodGoodGood
New Product introductionV. GoodV. GoodV. Good
Customer ResponsivenessV. 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
Characteristics FactorsD1D2D3
Over ProductionGoodGoodGood
Inventory Transportation
WaitingV. GoodV. GoodV. Good
Knowledge MisconnectionV. GoodV. GoodV. Good
Manufacturing FlexibilityGoodGoodGood
Delivery FlexibilityGoodV. GoodGood
Source FlexibilityV. GoodGoodGood
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
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
Characteristics FactorsD1D2D3
Over ProductionV. GoodV. GoodV. Good
Inventory Transportation
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
New Product introductionV. GoodV. GoodV. 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
Characteristics FactorsD1D2D3
Over ProductionV. GoodV. GoodV. Good
Inventory Transportation
WaitingV. GoodGoodV. Good
Knowledge MisconnectionGoodV. GoodGood
Manufacturing FlexibilityV. GoodV. GoodV. Good
Delivery FlexibilityGoodV. GoodGood
Source FlexibilityV. GoodGoodGood
Electronic Data InterchangeV. GoodV. GoodGood
Mean of InformationV. GoodGoodV. Good
Data and Knowledge BaseGoodV. GoodV. Good
Delivery SpeedV. GoodGoodGood
New Product introductionV. GoodGoodV. Good
Customer ResponsivenessV. GoodV. GoodV. 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)
Measuring FactorsCharacteristics
FactorsD1D2D3
Over ProductionGoodGoodFair
Inventory Transportation
WaitingGoodGoodFair
Knowledge
MisconnectionGoodGoodFair
Manufacturing FlexibilityGoodFairGood
Delivery FlexibilityFairFairGood
Source FlexibilityGoodFairFair
Electronic Data
InterchangeFairFairGood
Mean of InformationGoodGoodFair
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
The result of the multiplication is
18.052.3
18.084.1
= 0.50 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
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
LeagileAgileLeanTraditional
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
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)
Measuring FactorsD1D2D3Mean
Lead timeFairGoodFair
( 0.17,0.37,0.57)
CostFairFairFair
( 0.1,0.3,0.5)
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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)
Measuring FactorsCharacteristics
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
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
Characteristics FactorsD1D2D3
Over ProductionGoodFairGood
Inventory Transportation
WaitingGoodFairGood
Knowledge MisconnectionGoodFairGood
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
Characteristics FactorsD1D2D3
Over ProductionFairFairFair
Inventory Transportation
WaitingFairGoodGood
Knowledge MisconnectionGoodFairGood
Manufacturing FlexibilityGoodGoodGood
Delivery FlexibilityFairFairFair
Source FlexibilityFairFairGood
Electronic Data InterchangeGoodGoodGood
Mean of InformationGoodGoodFair
Data and Knowledge BaseFairGoodFair
Delivery SpeedGoodGoodGood
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)
Measuring FactorsCharacteristics
FactorsD1D2D3
Over ProductionFairGoodGood
Inventory
Transportation
Waiting
FairGoodFair
Knowledge
MisconnectionFairGoodFair
Manufacturing
FlexibilityFairFairFair
Delivery FlexibilityGoodGoodGood
Source FlexibilityGoodFairGood
Electronic Data
InterchangeGoodGoodGood
Mean of InformationGoodGoodFair
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
Characteristics FactorsD1D2D3
Over ProductionFair
Good
Good
Inventory Transportation
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
The result of the multiplication is
18.052.3
18.084.0
= 0.20 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
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
LeagileAgileLeanTraditional
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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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