vinod thesis.pdf

i

VENDOR SELECTION AND QUOTA

LINEAR PROGRAMMING

A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE

REQUIREMENTS

FOR THE AWARD OF DEGREE OF

MASTER OF ENGINEERING

IN

PRODUCTION ENGINEERING

SUBMITTED BY

VINOD BASHER

UNIVERSITY ROLL NO. 8531

COLLEGE ROLL NO. 13/ PRO/08

UNDER THE SUPERVISION OF

DR. R.K. SINGH

AND

PROF. PARVIN KUMAR

DEPARTMENT OF MECHANICAL AND PRODUCTION ENGINEERING

DELHI COLLEGE OF ENGINEERING

UNIVERSITY OF DELHI, DELHI-110042

2008-2010

ALLOCATION USING FUZZY TOPSIS AND

ii

Candidate’s Declaration

I hereby certify that the work which is being presented in the dissertation entitled “Vendor

Selection and Quota Allocation Using Fuzzy TOPSIS and Linear Programming ”,

submitted in partial fulfillment of the requirements for the award of the degree of Master of

Engineering in Production Engineering, Delhi College of Engineering, Delhi is an authentic

record of my own work carried under the supervision of DR. R. K. SINGH and PROFESSOR

PARVIN KUMAR, Department of Mechanical and Production Engineering, Delhi College of

Engineering, Delhi.

I have not submitted the matter embodied in this major project report for the award of any other

degree.

VINOD BASHER

University Roll no. 8531

College Roll No. 13/ PRO/ 08

iii

CERTIFICATE

This is to certify that dissertation entitled “Vendor Selection and Quota Allocation Using

Fuzzy TOPSIS and Linear Programming” being submitted by Mr. VINOD BASHER in the

partial fulfilment for the award of degree of “MASTER OF ENGINEERING” with

specialization in “PRODUCTION ENGINEERING” submitted to Delhi College of Engineering,

University of Delhi, is a bonafide project work carried out by him under our guidance and

supervision.

The matter in this dissertation has not been submitted to any other university or institute for the

award of any degree.

Dr. R. K. Singh Prof. Parvin Kumar

Department of Mechanical and Production Engineering

Delhi College of Engineering

Delhi-110042 (India)

iv

Acknowledgement

I wish to acknowledge my profound sense of gratitude to my project Guides Dr. R. K. Singh and

Prof. Pravin Kumar, Department of Mechanical and Production Engineering, Delhi College of

Engineering, Delhi, for their remarkable guidance and many valuable ideas during the

preparation of this project. Indeed it was a matter of great felicity and privilege for me to work

under his aegis was express my thankfulness to him for his dedicated inspiration, lively interest

and patience through my errors with which it would have being impossible to bring the project

near completion.

I express sincere gratitude to Prof. B.D. Pathak, Head of Department, Department of

Mechanical and Production Engineering and Prof. S.K. Garg, Department of Mechanical and

Production Engineering with whose blessing this project has been completed.

I wish to extend my hearty thanks to all the staff of the department who directly or

indirectly helped me in this project work.

VINOD BASHER

University Roll No. 8531

College Roll No. 13/PRO/08

v

ABSTRACT

Vendor selection is an important aspect of a supply chain. The objective of vendor selection is to

reduce purchasing risk, maximize overall value to the purchaser and build a long term, reliable

relationships between buyers and suppliers. Therefore selection of a good vendor is an important

decision making process for an organization. An inappropriate decision affects not only that

specific buyer but also the entire supply chain. In general, many quantitative and qualitative

factors such as quality, price, and flexibility and delivery performance must be considered to

determine suitable vendors. Vendor selection is a multi-criteria decision making problem. In the

real-world situations, due to incomplete and vague information, the required data (imprecise in

nature) for decision making often cannot be described deterministically. Therefore, in order to

make more realistic decisions, fuzzy sets theory can be applied in such cases. So we have used

Fuzzy TOPSIS method to deal with impreciseness in vendor selection data. In this work,

important criteria for the vendor evaluation are indentified based on literature. Then Fuzzy

TOPSIS methodology is applied to calculate fuzzy positive ideal situation and fuzzy negative

ideal situation for finding closeness coefficient. On the basis of closeness coefficient vendors are

evaluated and ranked.

In this work, our goal is to maximize the total value of purchase (TVP) by evaluating the vendors

and then appropriate quota have been allocated among the selected vendors using linear

programming. A high rated vendor rated may not complete all the demand of buyers. Therefore

multiple quota allocation has been suggested in this project. The linear programming model

based on closeness coefficients of vendors and capacity constraints is developed and order

quantity is allocated to each vendor. To solve linear programming problem LINGO 12.0

software is used. A case study is presented in this thesis, which shows the selected methodology

is appropriate for such decision making situation.

Key Words: Vendor selection, Supply chain, Fuzzy TOPSIS, Fuzzy Logic

vi

Table of content

Contents

Candidate’s declaration

Certificate

Acknowledgement

Abstract

Table of Contents

List of figures

List of tables

List of abbreviations

CHAPTER 1: INTRODUCTION

1.1 Introduction

1.2 Supply chain

1.3 Supply chain management (SCM)

1.4 Aim of SCM

1.5 Major drivers of supply Chain

1.6 Supply chain decision phases

1.7 Supply chain management problems

1.8 Vendor Selection Problem in SCM

1.9 Objective of project

1.10 Methodology

Pages

ii

iii

iv

v

vi

ix

x

xi

1-19

1

4

6

7

9

12

14

15

17

18

vii

1.11 Conclusion

CHAPTER 2 : LITERATURE REVIEW

2.1 Introduction

2.2 Supply chain management

2.3 Importance of supply chain management

2.4 Supply chain management approaches

2.5 Sourcing

2.6 Vendor selection in supply chain

2.7 Vendor selection criteria

2.8 Vendor selection methods

2.9 Fuzzy approach in vendor selection

2.10 Conclusion

CHAPTER 3: RESEARCH METHODOLOGY

3.1 Introduction

3.2 Fuzzy logic

3.3 Fuzzy set theory

3.4 Fuzzy Numbers

3.5 Algebraic operations on TFNs

3.6 TOPSIS

3.7 Fuzzy TOPSIS

3.8 The fuzzy TOPSIS methodology algorithm

3.9 Linear programming

3.10 General structure of linear programming

18

20-51

20

20

27

29

30

32

36

45

49

51

52-71

52

52

54

55

57

58

62

64

69

69

viii

3.11 General mathematical model of linear programming problem

3.12 Conclusion

CHAPTER 4: CASE ILUSTRATION

4.1 Introduction

4.2 Company profile

4.3 Case illustration

4.4 Linear programming and quota allocation

4.5 Formulation of LPP

4.6 Solution of LPP

4.7 Sensitivity analysis

4.8 Result and discussion

CHAPTER 5: CONCLUSION

REFRANCES

70

71

72-88

72

72

73

82

83

84

86

87

89-90

91-104

ix

List of figures

Figure No.

1.1

1.2

1.3

2.1

2.2

2.3

3.1

4.1

4.2

4.3

Title

Supply chain stages

Major drivers of supply chain

Major question to be addressed

Supply chain network

Types of channel relations and flows across a supply chain

Cost-Responsiveness Efficient Frontier and Zone of Strategic Fit

A triangular fuzzy number

Objective Max Z (Total Value of Purchase)

Total Value of Purchase Objective Solution

Quota allocations v/s % change in capacity

Page No.

5

10

11

24

26

30

56

84

85

87

x

List of Tables

Table No. Title Page no.

1.1 Quantified benefit of supply chain

8

2.1 Definitions of supply chain

21

2.2 Important vendor selection criteria used by various researchers

43

4.1 Linguistic variable for importance of weight of each criterion

75

4.2 Fuzzy rating of vendors

75

4.3 Fuzzy weight importance given by three decision makers

77

4.4 Aggregated fuzzy weights for criteria

77

4.5 Fuzzy rating of vendors given by decision makers

78

4.6 Aggregated rating of vendors (fuzzy decision matrix)

79

4.7 Normalized decision matrix

79

4.8 Weighted normalized fuzzy decision matrix

80

4.9 Distance of alternative from A+ and A

–

81

4.10 Closeness coefficient and ranking

81

4.11 Capacity constraint and performance coefficient of vendors

82

4.12 Percentage change in capacity

86

xi

LIST OF ABBREVIATIONS

Symbols/Notations used throughout the text of this thesis are explained as given below in

alphabetical order.

AHP Analytical Hierarchy Process

ANN Artificial Neural Network

ANP Analytical Network Processes

ART Adaptive Resonance Theory

CBR Case Based Reasoning

DC Distribution Center

DEA Data envelopment analysis

DMs Decision Makers

EDI Electronic Data Interchange

ERP Enterprise Resource Planning

FNIS Fuzzy Negative Ideal Solution

FPIS fuzzy positive ideal solution

FST Fuzzy Set Theory

ISA Intelligent Software Agents

ISM Interpretive Structural Modeling

LP Linear Programming

LPP Linear Programming Problem

MCDM Multi-Criteria Decision Making

MP Mathematical Programming

OLS Ordinary Least Square

xii

1

CHAPTER 1

INTRODUCTION

1.1 Introduction

Supply chain management is set of approaches utilize to efficiently integrate suppliers,

manufacturers, warehouses and retailers so, that merchandise produced and distributed in right

quantities, to right locations, in order to lower the system cost while satisfying the service

requirements needed. Companies spend a large amount of their sales revenue on purchasing of

raw material and components. So decision on selecting a competent supplier is important for

successful implementation of supply chain management. As customers and suppliers band

together in mutually beneficial partnerships, the need for better supply chain management

processes and systems is more evident and becomes a very high business priority. In typical

supply chain, raw materials are procured and items are produced at one or more factories,

shipped to warehouses for intermediate storage, and then shipped to retailers or customers.

Consequently, to reduce cost and improve service levels, effective supply chain strategies must

take in to account the interaction at the various levels in the supply chain. The supply chain,

which is also referred to as the logistics network, consist of suppliers, manufacturing centers,

warehouses, distribution centers, and retail outlets, as well as raw materials, work-in-process

inventory, and finished products that flow between the facilities.

Supply chain management involves the flows of material, information and finance in a network

consisting of customers, suppliers, manufacturers, and distributors. It begins with raw materials,

Continues through internal operations, ends with distribution of finished goods. The short-term

2

objective of SCM is primarily to increase productivity and reduce the entire inventory and the

total cycle time, while the long-term objective is to increase customer satisfaction, market share,

and profits for all organizations in the supply chain: suppliers, manufacturers, distribution centers

(DCs), and customers.

In supply chains, coordination between a manufacturer and suppliers is typically a difficult and

important link in the channel of distribution. Since suppliers are manufacturer‟s external

organizations, the coordination with the suppliers is not easy unless systems for cooperation and

information exchange are integrated. The coordination between a manufacturer and suppliers is

important because the failure of coordination results in excessive delays, and ultimately leads to

poor customer services. Consequently, inventories of incoming parts from suppliers or those of

finished goods at the manufacturer and distribution centers (DCs) may accumulate. Hence, the

total cost of the entire supply chains will rise. Manufacturers are able to assist their suppliers by

providing knowledge, skills, and experience, and to benefit in turn from suppliers‟ improved

delivery performance and from fewer production disruptions that are caused by poor quality

materials. The suppliers also can benefit by becoming more competitive than other suppliers as

performance improves and costs go down. Thus, supplier development is a vehicle that can be

used to increase the competitiveness of the entire supply chains.

Supplier selection is one of the most critical activities of purchasing management in a supply

chain, because of the key role of supplier‟s performance on cost, quality, delivery and service in

achieving the objectives of a supply chain. The cost of raw materials, component parts and

services purchased from external vendors or suppliers is significant for most manufacturing

firms. On average, manufacturers‟ purchases of goods and services constitute up to 70% of

product cost (Ghobadian et al., 1993) and in high technology firms, purchased materials and

3

services represent up to 80% of total product cost (Weber et al., 1991).Therefore vendor

selection is one of the most critical activities for many companies and selection of the wrong

vendor could be enough to upset the company‟s financial and operational position, while the

selection of an appropriate vendor may significantly reduce the purchasing cost and improve

competitiveness.

The vendor selection process has undergone significant changes during the past thirty years. In

today‟s competitive operating environment it is impossible to successfully-produce low cost,

high quality products without satisfactory vendors (Weber et al., 1991). Therefore, vendor

selection decisions are an important component of production and logistics management for

many firms (Weber et al., 1998). The analysis of criteria for selection and measuring the

performance of vendors has been the focus of many academicians and purchasing practitioners

since the 1960s (Weber et al., 1991). In a supply chain, vendor selection includes the selection of

the right vendors and their quota allocation which also needs to consider a variety of vendor

attributes such as price, quality, service, delivery performance. A vendor selection problem must

consider these various attributes because of their direct impact on final product dimensions such

as cost and quality. Vendor selection decisions play an important role in supply chain

management and have a significant impact on the competitiveness of a firm because purchases

from vendors account for a large percentage of the total cost for many firms. Vendor selection

has long been regarded as one of the most important functions performed by purchasing

departments.

In a real situation, many input information are not known precisely to select a supplier. In

decision making processes, many criteria and constraints are expressed in vague terms such as

„„very high in quality‟‟ or „„low in price‟‟. Deterministic models cannot easily take this

4

vagueness into account. In these cases the theory of fuzzy sets is one of the best tools for

handling uncertainty. Fuzzy set theories are employed due to the presence of vagueness and

imprecision of information in the supplier selection problem. Bellman and Zadeh (1970)

suggested a fuzzy programming model for decision-making in fuzzy environments.

Zimmermann (1978) first used the Bellman and Zadeh (1970) method to solve fuzzy

multiobjective linear programming problems. In his model the fuzzy goals and fuzzy constraints

are treated equivalently, which is why the model is called symmetric. It is very common in

business activities, such as supplier selection, that the goals importance or weights are different

for DMs. Thus, the symmetrical models may not be appropriate for the same multiobjective

decision-making problem, because the objectives may not be equally important.

1.2 Supply Chain

A supply chain consists of all parties involved, directly or indirectly, in fulfilling a customer

request. The supply chain includes not only the manufacturer and suppliers, but also transporters,

warehouses, retailers, and even customers themselves. Within each organization, such as a

manufacturer, the supply chain includes all functions involved in receiving and filling a customer

request. These function include, are not limited to, new product development, marketing,

operations, distribution, finance, and customer service (Chopra and Meindl, 2007).

A typical supply chain may involve a variety of stages as shown in fig 1.1. These supply chain

stages include:

Customers

Retailers

Wholesalers/distributors

5

Manufacturers

Component/raw material suppliers.

Source: Chopra and Meindl (2007)

Figure 1.1: Supply Chain Stages

A supply chain is a network of retailers, distributors, transporters, storage facilities and

suppliers that participate in the sale, delivery and production of a particular product. So, a

supply chain is product specific, not company specific. A supply chain is the process of

moving goods from the customer order through the raw materials stage, supply, production

and distribution of products to the customer. All organizations have supply chains of

verifying degrees, depending upon the size of the organization and the type of product

manufactured. These networks obtain supplies and components, change these materials into

finished products and then distribute them to the customer.

Each stage in a supply chain is connected through the flow of products, information and

funds. These flows often occur in both direction and may be managed by one of the stages or

an intermediary. Managing the chain of events in this process is what is known as supply

chain management. Effective management must take into account coordinating all the

Supplier Manufactur

er

Distributor Retailer Customer

Manufactur

e

Distributor Retailer Customer Supplier

Supplier Manufactur

er

Distributor Retailer Customer

6

different pieces of this chain as quickly as possible without losing any of the quality or

customer satisfaction, while still keeping cost down.

1.3 Supply Chain Management (SCM)

Supply chain management (SCM) is the oversight of materials, information, and finances as

they move in a process from supplier to manufacturer to wholesaler to retailer to consumer.

Supply chain management involves coordinating and integrating these flows both within and

among companies.

Most common and accepted definitions of Supply Chain Management are follows:

Supply chain management (SCM) is the management of a network of interconnected

businesses involved in the ultimate provision of product and service packages

required by end customers (Harland, 1996).

Global Supply Chain Forum - Supply Chain Management is the integration of key

business processes across the supply chain for the purpose of creating value for

customers and stakeholders (Lambert, 2005).

The supply chain refers to all those activities associated with the transformation and

flow of goods and services, including their attendant information flows, from the

sources of raw materials to end users. Management refers to the integration of all

these activities, both internal and external to the firm (Ballou et al.; 2000).

“Supply chain management is the management of eight key business processes:

customer relationship management, customer service management, demand

management, order fulfillment, manufacturing flow management, procurement,

product development and commercialization and returns”. These processes subsume

7

or include much of logistics, purchasing, marketing and operation management

(Stock and Lambert, 2001).

1.4 AIM of SCM

The main aim of SCM is to provide- right product, right quality, right cost, right time, to the

right customers. In order to minimize system wide costs while satisfying service-level

requirements and maximize value & lower waste. The objective of every supply chain should be

to maximize the overall value generated. The value a supply chain generates is the difference

between what the final product is worth to the customer and the costs the supply chain incurs in

filling the customer‟s request. For most commercial supply chains, value will be strongly

correlated with supply chain profitability (also known as supply chain surplus), the difference

between the revenue generated from the customer and the overall cost across the supply chain.

Supply chain success should be measured in terms of supply chain profitability and not in terms

of the profits at individual stages. The higher the supply chain profitability, the more successful

is the supply chain. The objectives of supply chain integration are to supply superior quality

goods faster, with more efficient processes and in essence be more responsive to the perceptions

of the marketplace and be able to change direction at will.

Some of the consequences of supply chain integration results in:

Reduced inventory at all sites of supply chain.

Reduced costs.

Faster processing speed.

Reduced lead times.

8

Reduced warehouse costs.

Reduced obsolescence.

Greater responsiveness to customer changes.

Electronic links to suppliers and customers.

Continuous flow of products and information.

Speeding up the development cycle.

The typical quantified benefits are highlighted in table 1.1. Relationships and predictable

performance become more important in an integrated supply chain (Mohanty and Deshmukh,

2005).

Table 1.1: Quantified Benefits of Supply Chain

Delivery Performance 15% to 30% improvement

Inventory Reduction 20% to 50% improvement

Fulfilment Cycle Time 30% to 60% improvement

Forecast Accuracy 20% to 50% improvement

Overall Productivity 10% to 25% improvement

Lower Supply - Chain Costs 20% to 50% improvement

9

Fill Rates 10% to 20% improvement

1.5 Major Drivers of Supply Chain

There are five major supply chain drivers as depicted in figure 1.2 (Mohanty and Deshmukh,

2005).

Production: This is typically related to issues on what to produce, how to produce (which

manufacturing process) and when to produce. These decisions have a big impact on the revenues,

costs and customer service levels of the firm. These decisions assume the existence of the

facilities, but determine the exact path(s) through which a product flows to and from these

facilities. Another critical issue is the capacity of the manufacturing facilities--and this largely

depends the degree of vertical integration within the firm. Operational decisions focus on

detailed production scheduling. These decisions include the construction of the master

production schedules, scheduling production on machines, and equipment maintenance.

Inventory: Here the decisions and issues may be concerned with how much to make and how

much to store as inventory and where to store these items (at the plant itself, warehouse, or at the

retailer etc.).

Location: A number of issues regarding location such as where to locate a plant, where to locate

a warehouse facility etc. may have significant bearing on the dynamics of the supply chain and in

turn may affect the overall costs. The location of facilities involves a commitment of resources to

a long-term plan. Once the size, number, and location of these are determined, so are the possible

10

paths by which the product flows through to the final customer. These decisions are of great

significance to a firm since they represent the basic strategy for accessing customer markets, and

will have a considerable impact on revenue, cost, and level of service.

Transportation: The issues may be related to how to move a product from one location to

another and by what mode of transportation. One needs to evaluate economies of scale on one

hand and the desired level of customer satisfaction on the other hand.

Information: Information is a binding force having critical implications for the supply chain.

Information acts as basis for making various decisions in the supply chain. It also acts as an

integrator. Unless information flows are handled properly, one may not be able to derive benefits

from the supply chain integration.

Source: ( Mohanty and Deshmukh 2005)

Figure 1.2 Major Derivers of Supply Chain

Production

What, How and When

to Produce

Transportation

How and when to

move product

Location

When the best to do

what activity

Inventory

How much to make

how much to store

INFORMATION

The basis for making these decisions and line of organization

11

A number of question need to be addressed while designing and operating an effective supply

chain. These questions are related to various agents in the chain such as supplier, manufacturer,

warehouse management and the customer. These questions are shown in figure 1.5 (Mohanty

and Deshmukh, 2005). Resolution of these questions requires:

(a) Intimate knowledge of various process taking place at these agents,

(b) A complete understanding of the dynamics of the chain and most importantly,

(c) A total system orientation of the supply chain.

Source: (Mohanty and Deshmukh, 2005)

Figure 1.3 Major Question to be addressed

Supplier

Manufacture

Manufacture

Warehouse

Warehouse

Customer

Customer

Customer

Customer

Where to acquire

material & components?

What market to serve?

What level of service?

What level of service cost?

How much to shop? , When to ship?

What mode of transportation?

What fleet size?

What vehicle routes?

What shipment routes

Where to produce & assemble goods?

How much to produce?

When to produce?

12

1.6 Supply chain decision phases

Successful supply chain management requires many decisions relating to the flow of

information, product, and funds. These decisions fall into three categories or phases, depending

on the frequency of each decision and the time frame over which a decision phase has an impact.

a. Supply chain design or strategy

b. Supply chain planning

c. Supply chain operation

Supply chain design or strategy

During this phase, given the marketing and pricing plans for a product, a company decides how

to structure the supply chain over the next several years. It decides what the chain‟s

configuration will be, how resources will be allocated, and what processes each stage will

perform. Strategic decisions made by companies include whether to outsource or perform a

supply chain function in-house, the location and capacities of production and warehousing

facilities, the products to be manufactured or stored at various location, the modes of

transportation to be made available along different shipping legs, and the type of information

system to be utilized. A firm must ensure that the supply chain configuration supports its

strategic objectives and increase the supply chain surplus during this phase.

Supply chain planning

For decision made during this phase, the time frame considered is a quarter to a year. Therefore,

the supply chain‟s configuration determined in the strategic phase is fixed. The configuration

13

establishes constraints within which planning must be done. The goal of planning is to maximize

the supply chain surplus that can be generated over the planning horizon given the constraints

established during the strategic or design phase. Companies start the planning phase with a

forecast for the coming year of demand in different markets. Planning includes making decision

regarding which market will be supplied from which locations, the subcontracting of

manufacturing, the inventory policies to be followed, and the timing and size of marketing and

price promotions. In planning phase, companies must include uncertainty in demand, exchange

rates, and competition over this time horizon in their decisions. Given a shorter time frame and

better forecasts than the design phase, companies in the planning phase try to incorporate any

flexibility built into the supply chain in design phase and exploit it to optimize performance. As a

result of the planning phase, companies define a set of operating policies that govern short term

operations.

Supply chain operation

The time horizon here is weekly or daily, and during this phase companies make decisions

regarding individual customer orders. At the operational level, supply chain configuration is

considered fixed and planning policies are already defined. The goal of supply chain operations

is to handle incoming customer orders in the best possible manner. During this phase, firm

allocate the inventory or production to individual orders, set a date that an order is to be filled,

generate pick lists at a warehouse, allocate an order to a particular shipping mode and shipment,

set delivery schedules of trucks, and place replenishment orders. Because operational level are

being made in the short term (minutes, hours, or days), there is less uncertainty about demand

information. Given the constraints established by the configuration and planning policies, the

14

goal during the operation phase is to exploit the reduction of uncertainty and optimize the

performance.

The design, planning, and operation of a supply chain have a strong impact on overall

profitability and success (Chopra and Meindl, 2007).

1.7 Supply chain management problems

Distribution Network Configuration: number, location and network missions of

suppliers, production facilities, distribution centers, warehouses, cross-docks and

customers.

Distribution Strategy: Centralized versus decentralized, direct shipment, Cross docking,

pull or push strategies, third party logistics.

Information: Integration of processes through the supply chain to share valuable

information, including demand signals, forecasts, inventory, transportation, potential

collaboration, etc.

Inventory Management: Quantity and location of inventory, including raw materials,

work-in-progress (WIP) and finished goods.

Cash-Flow: Arranging the payment terms and methodologies for exchanging funds

across entities within the supply chain.

Supply chain execution means managing and coordinating the movement of materials,

information and funds across the supply chain. The flow is bi-directional.

15

1.8 Vendor Selection Problem in SCM

Vendor selection problem (VSP) is an area of tremendous importance in the effective

management of a supply chain. This is due to the compelling need to evolve strategic alliances

with the vendors. The material and equipment supplied from the vendors play an important role

in the management of a supply chain. Many issues in the supply chain are influenced by the

proper selection of Vendors. In the logistics decisions of a firm, the location of vendors has a

great influence on the supply chain design in terms of transportation and distribution planning.

Hence, it is important to select the potential vendors so that different objectives of the supply

chain are achieved. Similarly, reliable vendors may lead to less number of vendors in a supply

chain, whereas the selection of a large number of vendors may be done to minimize the risk

associated with the purchase, the associated costs increase with this approach. Hence, the

optimization of vendor-base is needed to identify better performing vendors in a supply chain.

In the process of vendor selection, the most important issue is to determine a suitable decision-

making method and select the right vendor. Essentially the vendor selection problem is a multi-

criteria decision making problem under an uncertain environment. Fuzzy set theory best handles

these uncertainties. In decision-making, especially when a high degree of fuzziness and

uncertainties are involved, due to imperfections and complications of information processes the

theory of fuzzy sets is one of the best tools of systematically handling uncertainty in decision

parameters. VSP is complex in nature and invites strategic decision of long-term implications.

Much information at the decision process is not known with certainty. Due to this, the VSP

inherits the characteristics of impreciseness and fuzziness. Fuzzy set theories are employed due

to vagueness and imprecision in the VSP and are used to transform imprecise and vague

16

information of the objective(s) and constraint(s) into the fuzzy objective(s) and fuzzy

constraint(s).

Evaluation of the company‟s vendors is considered an effective tool for rectification of defects,

improving their ability to serve more satisfactorily and as a basis for making future purchasing

decisions. A Vendor selection problem typically consists of four phases namely: problem

definition (recognition of the need for a new dealer), formulation of criteria, qualification of

suitable suppliers and final selection of the ultimate suppliers (De Boer et al., 2001). The

evaluation of vendors is done on a periodic basis and includes written evaluation aspects relating

to quality, quantity, price, service etc. as obtained from the buyer, user and quality control and

other concerned staff.

There are four stages in the purchasing and supply literature, namely, defining the problem,

formulation of criteria, qualification, and final selection. Defining the problem in the decision-

making processes is the first step in the method that supports the DMs in carefully questioning

the need for a decision and identifying available alternatives. During the criteria formulation

stage, the main task for buying firms is assessing the key competitive factors in their industry

and translating these dimensions into supplier selection criteria. Strategic management decisions

influence the relative importance of the various criteria involved in the supplier selection process

(Weber et al., 2000). The choice and the number of criteria to be included in the supplier

selection process must be carefully determined to represent the competitive strategies of buying

firm (Sarkis & Talluri, 2002). No publication that treats the stages of problem definition and

criteria formulation can be found for supplier selection processes (De Boer et al., 2001). The

17

majority of supplier selection models in the existing literature ignore the fact that evaluation

criteria must be aligned with firm strategy.

The performance of the vendor is a key element in a company's success or failure. In order to

attain the goals of low cost, consistent high quality, flexibility and quick response, companies

have increasingly considered better vendor selection approaches. The overall goal of selection is

to identify high-potential vendors and their quota allocations. An effective and appropriate

vendor selection method is therefore very crucial to the competitiveness of companies.

The vendor selection problem (VSP) is associated with deciding how one vendor should be

selected from a number of potential alternatives (Dickson, 1966). Weber et al., (1998) believe

that vendor selection decisions entail the selection of individual vendors to employ, and the

determination of order quantities to be placed with the selected ones. Therefore vendor selection

is one of the most critical activities for many companies and selection of the wrong vendor could

be enough to upset the company‟s financial and operational position, while the selection of an

appropriate vendor may significantly reduce the purchasing cost and improve competitiveness.

1.9 Objective of the project

The objective of this research is to select and evaluate the vendors on the basis of important

criteria identified through literature review. Vendor plays important role in the supply chain

management. A good vendor increases the overall performance of organization and profit, but a

bad vendor choice may be disrupts the entire supply chain. Delays in components and material

supply may lead to long waiting time, which results in customer and profit loss both. So it is

necessary to evaluate vendors so that organization remains competitive. In this work we have

18

evaluated the selected vendors on the basis some important criteria and our purpose is to find

best suited vendors for particular organization.

1.10 Methodology

Vendor evaluation is a multiple criteria decision making problem, in which all the information

and data available is imprecise in nature. Under imprecise condition decision making process is

difficult. But to deal with this kind of impreciseness fuzzy set theory is used. Thus for vendor

evaluation we have used Fuzzy TOPSIS methodology.

In this methodology fuzzy positive ideal solution (FPIS), which is nearest to best possible

alternative and fuzzy negative ideal solution (FNIS), which is farthest from worst situation is

calculated. Then on the basis of FPIS and FNIS the closeness index is calculated. Then final

ranking is done, based on closeness coefficient.

To maximize the total value of purchase (TVP) and quota allocation to each vendor we have

used linear programming.

1.11 Conclusion

The supplier selection problem is of vital importance for operation of every firm because the

solution of this problem can directly and substantially affect costs and quality. Indeed, for many

organizations effective supplier evaluation and purchasing processes are critical success factors.

A great deal of research has been conducted to determine what criteria should be used to evaluate

suppliers. In practice, any set of criteria must be considered in light of real-life constraints,

19

making the supplier selection a complicated decision problem that involves balancing many

tradeoffs and satisfying conflicting desiderata.

20

CHAPTER 2

LITERATURE REVIEW

2.1 Introduction

Supplier selection is one of the most critical activities of purchasing management in a supply

chain, because of the key role of supplier’s performance on cost, quality, delivery and service in

achieving the objectives of a supply chain. Supplier selection is a multiple criteria decision-

making (MCDM) problem which is affected by several conflicting factors.

2.2 Supply Chain Management

SCM is management of material, money, men, and information within and across the supply

chain to maximize customer satisfaction and to get an edge over competitors. Customers want

products at the right place and at the right time. For this, there should be an excellent

synchronization between the manufacturer and the customers. As things started becoming

complicated, where one person had to reach many individuals for his needs, one of the

individuals started management of gathering the products from different people and supplying to

those who are in need and thus fulfilling his needs in return. This was the revolutionized form of

the Barter system and today it is known as the supply chain management (Chopra & Meindl,

2001). The supply chain encompasses all activities associated with the flow and transformation

of goods from the raw materials stage through to the end user, as well as the associated

21

information flows. Material and information flow both up and down the supply chain. Supply

chain management is the integration of these activities through improved supply chain

relationships to achieve a sustainable competitive advantage (Handfield and Nicols, 1999). The

great benefit of supply chain management is that when all of the channel members – including

suppliers, manufacturers, distributors, and customers – behave as if they are part of the same

company, they can enhance performance significantly across the board (Copacino, 1997).

Researchers found that the lack of commonly accepted definition of supply chain management

and the problems associated with supply chain activities makes the understanding of supply

chain management difficult. There are numerous definitions of SCM; few definitions

summarized in table no. 2.1

Table 2.1: Definitions of Supply chain

Author Definition

Govindan et al. (2009) Supply chain management (SCM) is the term used to describe the

management of the flow of materials, information, and funds across the

entire supply chain, from suppliers to component producers to final

assemblers to distribution (warehouses and retailers), and ultimately to

the consumer.

Simchi-Levi et al. (2003) SCM is a set of approaches utilized to efficiently integrate suppliers,

manufacturers warehouses and stores, so that merchandise is produced

22

and distributed at the right quantities, to the right locations, and at the

right time, in order to minimize system wide costs while satisfying

service level requirements

Stock & Lambert (2001) “Supply chain management is the management of eight key business

processes: customer relationship management, customer service

management, demand management, order fulfillment, manufacturing flow

management, procurement, product development and commercialization

and returns”. These processes subsume or include much of logistics,

purchasing, marketing and operation management.

Chopra and Meindl

(2001)

The processes which occur before manufacturing or production into a

deliverable product or service, typically processes dedicated to getting

raw materials from suppliers; and the processes which occur after

manufacturing or production dedicated to getting goods and services to

customers

Ballou et al. (2000) The supply chain refers to all those activities associated with the

transformation and flow of goods and services, including their attendant

information flows, from the sources of raw materials to end users.

Management refers to the integration of all these activities, both internal

and external to the firm.

23

Christopher (1998) SCM is the management of upstream and downstream relationships with

the suppliers and customers to deliver superior customer value at lesser

cost to the chain as a whole.

Leenders and Fearon

(1997)

SCM is a systems approach to managing the entire flow of information,

materials and services from raw materials suppliers through factories and

warehouses to the end customer.

SCM addresses the management of materials and information across the entire chain from

suppliers to producers, distributors, retailers, and customers. It helps the company to make an

optimal plan for the whole chain. Actions taken by one member of the chain can influence all

others in the chain (Chopra and Meindl, 2001).

The historical evolution of supply chain can be traced back to the development of quick response

(QR) programs in the textile and clothing industry (Lummus and Vokurka, 1999). According to

Fiorito et al; (1998), QR is a “strategy where the manufacturer strives to provide products and

services to its retail customers in exact quantities on a continuous basis with minimum lead

times, resulting in minimum inventory levels throughout the pipeline”. The focus of SCM is not

limited to improving the relationship and co-ordination between buyers and suppliers. Rather,

SCM requires all parties involved in producing and delivering a product to take a holistic

approach to manage and integrate key business functions in order to achieve a smooth flow of

information and product along a supply chain (Cooper et al.; 1997). Early efforts of SCM was

24

focused on the firm's internal processes and extended to supply chain partners that were most

immediately affected by the firm's products and services (Lancaster, 2006). Supply chain

strategy is no longer a single focus discipline, meant for one department, but a cross-functional

decision-making process. It is a means to compete in the marketplace and thus a factor in

corporate strategy (Subrahmani, 2004). Most significant paradigm shifts of modern business

management is that individual businesses no longer compete as independent entities, but rather

as supply chains. Now the competition becomes supply chain versus supply chain. The supply

chain is not a chain of businesses with one-to-one, but a network of multiple businesses and

relationships (Drucker, 1998).

According to Lambert et al.; (1998) managing from initial suppliers’ to end customer’s network

is an enormous undertaking. Managing the entire supply chain is a very difficult and challenging

task as illustrated in Figure 2.1

Source: Lambert et al.; (1998)

Figure 2.1: Supply Chain Network

25

It is necessary to identify who are the core members of the supply chain, because all types of

members may cause the total network to become highly complex. It is important to sort out some

basis for determining which members are critical to the success of the supply chain and, thus,

should be allocated managerial attention and resources. (Cooper et al.; 1997).

The same company can perform primary activities related to one process and supportive

activities related to another process in the supply chain. For example a manufacturer that buys

some critical and complex production equipment from a supplier and when the manufacturer

develops new products, it works very closely with the equipment supplier and becomes a

primary member of the manufacture’s product development process. However, once the

machinery is in place, the Supplier is a supportive, not a primary, member for the manufacturing

process. The point of origin of the supply chain occurs where no previous primary suppliers

exist. The point of consumption is where no further value is added, and the product or service is

consumed (Lambert et al; 1998).

The management of supply chains is characterized by high degrees of difficulty, recognized in

the multiple relationships and interactions between trading partners. These interactions are

complicated by their volume, variation in processes and the complexity inherent in the

dependencies between parties in time and space (Walker, 2001). It has a direct impact on the

ability of organizations to manage inventories, cash flows and service levels beyond the

enterprise. The information technologies become more sophisticated and accessible, so the

expectation for the technology to improve information flows has been high.

Modern supply chain deals with material flows and information flows across the entire chain,

from suppliers of raw material to final customers, it comprises at least two major fields: the

26

physical transformation field (mining, smelting, casting, alloying, machining, assembling; etc.),

and the goods distribution field (conveyance, storage, and transportation). Due to the

development of modern information technology, firms can coordinate all organizations and all

functions involved in the whole supply chain. (Xiaobo et al; 2006).

Supply chains can differ in size, complexity of relations between the members and distribution of

physical presence. In the Figure 2.2, two different types of channel relations can be seen: direct,

where the SC consists of one supplier and one customer of an organization, and extended, where

apart from the above, a supplier’s supplier, a customer’s customer, etc. are included. In general,

supply chains are dynamic, and involve the flow of information, products and funds between

different stages (Lee, 2000) as shown in the Figure 2.2.

Source: Lee, (2000)

Figure 2.2: Types of channel relations and flows across a supply chain

Supplier Customer Organization

Supplier’s

supplier

Supplier Customer’s

customer

Customers Organization

Direct Supply Chain

Extended Supply Chain

Product

Information

Fund

27

Supply chain management has the objective to have the right products in the right quantities at

the right time at minimal cost (Cutting et. al.; 2006) a situation that would guarantee optimal

service levels for the customer and optimal performance for the organizations as a whole and

separately. Therefore, SCM involves the management of flows between and among members of

supply, chain in order to maximize total supply chain profitability (Chopra and Meindl, 2003)

hence maximize the total value generated throughout the SC.

Bearing in mind the different dimensions of SCM, there are three types of SCM decisions:

strategic, tactical and operational (Narasimhan &Mahapatra, 2004). Strategic SCM decisions

involve the design and configuration of the supply chain, capacity planning and facility location;

tactical decisions include supplier selection and evaluation, bidding and contracts; operational

decisions include inventory management, production planning and scheduling, and

replenishment policy. The research concerning SCM decisions is wide, and it includes the five

illustrative decision models by Narasimhan et al. (2004), thus buyer-supplier behavior, sourcing,

integrated operations and marketing and logistics models.

2.3 Importance of Supply Chain Management

Supply chain management is becoming more and more important in today’s business world.

Enterprises are now competing through supply chains, Gattorna (2006) claims that “supply

chains are the business”. However, what is it that makes supply chains so important? To answer

this question one has to consider the modern business environment and Porter’s value chain

model (Porter , 1980).

Research conducted in the early 1990s in the SCM field centered on minimizing transaction costs

in the buyer/supplier interaction. Then companies changed their focus and their perspective to a

28

more relationship-oriented approach to SCM (Tanner, 1999). Companies are now stressing a

value delivery network that is based on strong alliances alongside significant vertical and

horizontal integration (McCutcheon and Stuart, 2000 and Corsten and Kumar, 2005).

Companies today are increasingly dealing with suppliers and buyers from all over the world. The

products they design, manufacture and sell are shipped globally. As a result, SCM seems to have

gained increasing importance with today's large multinational corporations (Lummus et al.;

2001). In fact, some academics have suggested that the competitive battle is now between supply

chain and supply chain rather than between firms (Lambert and Cooper 2000). This is because as

much as a product or service itself is important to a firm, an effective SCM strategy can assist a

company with an established and sustainable competitive advantage, if well executed (Martin

2000). With the onset of globalization, the value delivery network has increased its impact on

corporate planning. Complex global supply chains must extend beyond value delivery to include

traceability and safety due to numerous recalls of pet food, toys and drugs (Roth et al.; 2008).

In global supply chain manufacturers are graphically dispersed around the world. Each company

is involved in a wide variety of supply chain activities such as order fulfillment, international

procurement, acquisition of information technology, manufacturing, faster and reliable delivery

of products and customer service (McIvor, 2000).

SCM includes extensive research and data analysis to perform above activities efficiently. It is

also observed that whole supply network could improve its ability to meet expectation of

consumers in terms of quality through co-management of quality and supply chain practices

(Romano and Vinelli, 2001). Thus by using supply chain management principles, the company

will be able to reduce product defects and to improve relationships within supply chain (Sahay et

29

al.; 2006). In today’s ERP controlled uncertain manufacturing environments SCM ensures higher

availability of product to avoid lost sales / stock outages. It also stresses the importance of the

efficient consumer response process to achieve both customer satisfaction and business

efficiency (Romano and Vinelli, 2001).

2.4 Supply Chain Management Approaches

Given the increasing importance of SCM for business success, there is a growing interest in

SCM in both academia and business world. Different approaches are adopted in the analysis of

the phenomenon and the problems to seek the best choice of supply chain strategy.

One can tackle this problem through modelling – models of successful supply chains are

expected to give insight into SC theory and extend it. Beamon (1998) provides us with a focused

review of multi-stage supply chain modelling, differentiating four types of SC models:

deterministic analytical, stochastic analytical, economic models and simulation models. In the

same article an analysis is given on the different SC performance measures being used, divided

into qualitative and quantitative categories.

It is also common practice to examine and analyze important issues of supply chain

management. For example, it has been argued (Chopra and Meindl, 2003) that “competitiveness

and supply chain strategies must have the same goal”, in other words a company should achieve

a strategic fit by aligning its SC strategies with the customer priorities. Towards this, three steps

should be followed: understand the customer and supply chain uncertainty, understand the

supply chain capabilities and achieve the strategic fit. As far as the second step is concerned, one

should bear in mind the so-called “cost-responsiveness efficient frontier”, which can be seen in

the Figure 2.3.

30

Source: Chopra and Meindl (2003)

Figure 2.3: Cost-Responsiveness Efficient Frontier and Zone of Strategic Fit

Many researchers are also concerned with the strategic dimension of SCM. Cohen et al. (2005)

propose five disciplines for top performance: view your supply chain as a strategic asset, develop

an end-to-end process architecture, design your organization for performance, build the right

collaborative model and use metrics to drive business success.

2.5 Sourcing

Sourcing decisions have recently gained in strategic importance. As companies are under

constant pressure to outperform increasingly fierce competition, cost effectiveness, innovative

capability and quality consciousness in the supply chain offer opportunities for achieving

competitive advantage (Juttner et al., 2007). Strategic sourcing must thus incorporate capability

assessments of the supply partners and total cost of ownership considerations when comparing

different alternative partners (Talluri and Narasimhan, 2003) emphasizes the impact of sourcing

31

on a firm’s growth and profit and provides a short review of different global sourcing strategies

(multiple, single and hybrid network).

Most of the literature (Bhote, 1989; Schorr, 1992; Gadde and Hakansson, 2001) compares

multiple sourcing as a strategy with single sourcing. In multiple sourcing, the buying company is

splitting its orders for the same item among different available sources, whereas single sourcing

is an extreme form of source loyalty towards one single supplier within a range of acceptable

sources. Freeman and Cavinato (1990) and Stork (1999a, b) suggest that single sourcing is the

ultimate stage of full partnerships between buyers and sellers on industrial markets.

Other literature compares the theoretical advantages and inconveniencies of different sourcing

strategies (Zeng, 2000). In general, most authors attribute more advantages than disadvantages to

a single sourcing strategy (Sriram and Mummalaneni, 1990).

Single sourcing is often preferred to multiple sourcing because of an imminent cutting of costs.

Single supplier-buyer relationships offer different cost advantages. As volumes are not split

between different sources, the buyer has the opportunity of negotiating better purchasing

conditions (Ellram and Billington, 2001).

Less investment in warehousing is needed as delivery schedules do not have to be split and

deliveries can more easily be planned (Kelle and Miller, 2001). The administrative costs of

handling just one supplier are obviously lower ( Brierly, 2001). Buyer and supplier can finally

also achieve cost reductions in the logistics field (Lynch, 2001).

Moreover, improvements in quality are noticed (Sriram and Mummalaneni, 1990). This is due to

fact that the supplier is capable of managing operations more efficiently and acquiring more

32

expertise in developing solutions for technical, logistic and other problems (Clayton, 1998).

Improved products and better quality result from that. A strict prerequisite for realising this

benefit is that a lot of detailed attention is paid to the selection and evaluation of the suppliers’

performance. Certification is considered to be a very effective way in achieving this (Kulchitsky,

1998). Larson and Kulchitsky (1998) report both cost reductions and quality improvements

resulting from single sourcing. Automotive companies are therefore increasingly relying on

single sourcing to safeguard their global competitive position (Pfaffman and Stephan, 2001).

Dependency of both partners on one another is the one major drawback associated with a single

sourcing strategy. It may lead to higher switching costs (as suppliers will want to create captive

customers). Potentially less competitive cost structures (Haywood, 2001) might also result. As it

may become cumbersome and costly to change supply partnerships, the buyer might loose

market feeling. Knowledge of supply alternatives might fade. Thus the flexibility of the supplier

might shrink and cost and price competitiveness might be gradually reduced (Talluri and

Narasimhan, 2003).

2.6 Vendor selection in Supply Chain

In the recent years, supply chain management (SCM) has gained immense importance since

enterprises are now competing on supply chain rather than manufacturing or service operations.

Supply chain management involves the flows of material, information and finance in a network

consisting of customers, suppliers, manufacturers, and distributors. A well managed supply chain

leads to cost reduction by lead time reduction, timely delivery and low inventories. In supply

chain a good coordination between supplier and manufacturer are necessary. Since suppliers are

manufacturer’s external organizations, the coordination with the suppliers is not easy unless

33

systems for cooperation and information exchange are integrated. Selection of appropriate

suppliers is one of the fundamental strategies for enhancing the quality of output of any

organization, which has a direct influence on the company’s reputation. The importance of

supplier selection has been stressed in the literature (Weber et al., 1991). As pointed out by

Bhutta and Huq (2002), the supplier selection problem requires the consideration of multiple

objectives, and hence can be viewed as a multi-criteria decision-making (MCDM) problem.

In most industries the cost of raw materials and component parts constitutes the main cost of a

product, such that in some cases it can account for up to 70% (Ghodsypour & O’Brien, 1998).

Goffin et al. (1997) have stated that supplier management is one of the key issues of supply chain

management because the cost of raw materials and component parts constitutes the main cost of

a product and most of the firms have to spend considerable amount of their sales revenues on

purchasing. So it becomes important to have good vendors which lead to cost reduction and

maximize the profit. According to Ghodsypour and O’Brien, (2001) selecting a good supplier

significantly reduce the purchasing cost and improve corporate competitiveness. Krajeweski&

Ritzman (2006) reported for instance, that the percentage of sales revenues spent on materials

varies from more than 80 percent in the petroleum refining industry to 25 percent in the

pharmaceutical industry. Most firms have spent 45 to 65 percent of sales revenues on materials.

Moreover, the emphasis on quality and timely delivery in today's globally competitive

marketplace adds a new level of complexity to outsourcing and supplier selection decisions. The

importance of vendor selection comes from the fact that “it commits resources while

simultaneously impacting such activities as inventory management, production planning and

control, cash flow requirements and product quality”( Narasimhan, 1983).

34

Global competitive environment continues to force many companies to make strategic changes in

managing their business. Numerous manufacturers have been downsizing, concentrating on their

core competencies, moving away from vertical integration, and outsourcing more extensively

(Goffin, et al.; 1997). As outsourcing has increased, so has the importance of vendor selection

decisions (Ernst et al.; 2007). In today’s competitive operating environment, it is impossible to

successfully produce low-cost, high-quality products without satisfactory suppliers

(Golmohammadi et al.; 2009). Selection of appropriate vendors or suppliers is one of the

fundamental strategies for enhancing the quality of output in any organization, which has a direct

influence on the company (Shlrouyehzad et al.; 2009).

A variety of changes in the business environment including globalization, accelerated global

competition, decreased governmental regulation worldwide, intensified environmental concerns,

increased rates of technological change as well as increasingly demanding customers, fast

product development cycle time, short product life cycle, increased product complexity and

quality consciousness are leading firms towards development of long-term strategic partnerships

with a few competent and innovative suppliers and collaborate with them in non-core process

outsourcing to improve organizational performance and generate long-term competitive

advantage. This structured approach to the design of the supply chain will result in an

organization that is an appropriate mix of the company’s own capabilities with those of partners

or suppliers in a relationship that is consistent with the strategy of business. For this reason,

suppliers should be selected based on how their actions will impact all competitive elements of

the supply chain. This indicates that one of the competencies essential to supply chain success is

an effective purchasing function (Tracey & Tan, 2001). Purchase decision process of

organizational buyers has become increasingly a complex, multidimensional and multifunctional

35

activity as the traditional role of the purchasing has significantly changed over the past few years

as organizations increasingly globalize their sourcing activities. In today's highly competitive

and interrelated manufacturing environment, the effective selection of suppliers is very important

to the success of a manufacturing firm (Weber et al.; 2000). The involvement of a large number

of closely interrelated decisions regarding financing, negotiations, distribution, procurements and

product quality assurance at the source implies the significance and long-lasting impact of

suppliers’ selection on sourcing (Min, 1994). Companies in order to attain the goals of low cost,

consistent high quality, flexibility and quick response have increasingly considered better

supplier selection approaches. These approaches require cooperation in sharing costs, benefits,

expertise and in attempting to understand one another's strengths and weaknesses, which in turn

leads to single sourcing and long-term partnerships (Bhutta & Huq, 2002).

Several factors have been identified by Dzever et al. (2001) which impact supplier selection

decisions of organizational buyers. These factors (which are both of a firm-specific nature as

well as environmentally determined) include: the composition and functional specialization of

the members of the decision-making unit, patterns of buyer-seller interaction and relationships,

the role of intermediaries in the decision process and the impact of environmental factors such as

market structure, technology, economic and culture on these decisions. Moreover, purchase

decisions are also influenced by three dimensions of buyer behavior identified as technical,

commercial and social (Dzever et al.; 2001). It is thus by having a correct understanding of these

factors that one can fully appreciate the decision process of organizational buyers in a wider

perspective.

Since the supplier selection process encompasses different functions (such as purchasing,

quality, production, etc.) within the company, it is a multi-objective problem, encompassing

36

many tangible and intangible factors in a hierarchical manner (Bhutta and Huq, 2002). Supplier

selection process is inherently multi-objective in nature, because typically more than one

criterion (e.g. price, quality, delivery performance) needs to be considered and evaluated in

selecting suppliers and monitoring their performance (Talluri and Sarkis, 2002). When

evaluating sources, the single most important task for buyers is assessing the key competitive

factors in their industry and translating these dimensions into supplier evaluation criteria. An

evaluation of best-in-class performance in product and process technology, quality, delivery and

design flexibility is key determinants in this decision (Handfield, 1994). Therefore, a buyer

should analyze and evaluate the potential threats when selecting suitable supplier resulting from

a systematic selection process and its corresponding attributes. The source-selection decision is

highly complex and purchase’s most difficult responsibility. First, such a decision involves more

than one selection criterion when choosing among the available suppliers. Second, criteria

included in the supplier selection process may frequently contradict each other (lowest price

against poor quality). A third complication surrounding the supplier selection decision arises

from internal policy constraints and externally imposed system constraints placed on the buying

process. Fourth, as organizational requirements and market conditions change, the importance of

the analysis of tradeoffs among the selection criteria may be increased (Weber et al.; 2000).

2.7 Vendor Selection Criteria

The vendor selection process has undergone significant changes during the past thirty years. In

today’s competitive operating environment it is impossible to successfully-produce low cost,

high quality products without satisfactory vendors (Weber, 1991). Therefore, vendor selection

decisions are an important component of production and logistics management for many firms

37

(Weber, 1998). The analysis of criteria for selection and measuring the performance of vendors

has been the focus of many academicians and purchasing practitioners since the 1960s (Weber,

1991).

In his study, Dickson (1966) investigated 273 purchasing agents and managers selected from

membership list of National Association of Purchasing Managers, and indentified 23 vendor

evaluation criteria and their importance rating. He concluded that critical factor for supplier

selections are quality, on time delivery, supplier performance history and warranties and claims

got high ranking.

Weber et al. (1991) reviewed 74 supplier selection papers that has appeared since 1966, and

classified that criteria used in these paper based on Dickson’s 23 criteria. They found that most

frequently mentioned criteria net price, delivery, quality, production facilities, technical

capabilities, reputation, financial position, performance history, repair and attitude. They

concluded that quality was considered as the most important factor followed by delivery

performance and cost. Almost all the articles pay more attention to price, quality, capability,

delivery, especially JIT philosophy.

Wilson (1994) reviewed and compared the supplier selection criteria in 70ss and 90s, and found

that quality and services are becoming more important while price is becoming less important.

The reason for this shifting is implementation of supply chain management and JIT system. In

70s company had numerous suppliers, competition was solely based on price and contact was

usually short term. In 90s, single sourcing is becoming more common and competition was based

on quality, delivery, engineering and price, and contracts were increasingly long term.

38

Choi and Hartley (1996) explored the supplier selection practices in auto industry. Their

selection criteria are based on supplier selection criteria of Dickson (1966) and Weber et al.

(1991), and included others that some researcher had suggested as important. They indentified 26

criteria and grouped in to 8 main factors using principle component analysis. The 8 factors are

(1) Finances: financial condition, profitability of supplier, financial record disclosures and

performance awards. (2) Consistency: conformance quality, consistent delivery, quality

philosophy and prompt response. (3) Relationship: long term relationship, relation closeness,

communication openness and reputation. (4) Flexibility: product volume changes, short setup

time, short delivery lead time and conflict resolutions. (5) Technological capability: design

capability and technical capability. (6) Services: after sale support and sale representative

competences. (7) Reliability: incremental improvement and product reliability. (8) Price: low

initial price. They concluded that selecting supplier based on potential for cooperative, long term

relationship was important and price was least important criteria regardless of position in supply

chain.

Barbaresoglu and Yazagac (1997) proposed a general purpose model for Turkish industry to

evaluate suppliers and applied the A.H.P. to generate the weights for suppliers’ selection criteria.

The primary criteria used in the supplier selection model were performance assessment,

manufacturing capability assessment and quality system assessment. The weights for these

primary criteria were 0.625, 0.136, and 0.238 respectively. The second level criteria and there

weights under the performance assessment are: shipment quality (0.429), delivery (0.429) and

cost analysis (0.142).

Pearson and Ellram (1995) investigated the supplier selection and evaluation problem in small

and large electronics firms. The criteria they used for the supplier selection and evaluation

39

included quality, cost, current technology, design capabilities, speed to market, manufacturing

process, assessment of future technology, economic performance, management compatibility,

location and proximity, visitation to supplier facilities and organizational structure. They

concluded that both large and small firms were putting much greater emphasis on quality in

supplier selection. In addition, large firms used more formal methods to evaluate suppliers, while

small firms used informal methods.

Petroni and Braglia (2000) indentified that management capability, production capacity and

flexibility, design and technological capability, financial stability, experience and geographical

location are important criteria for selecting a suitable supplier.

Akarte et al. (2001) used 18 criteria to evaluate suppliers for automobile castings sector. These

criteria were groped in to 4 groups: product development capability, manufacturing capability,

quality capability, cost and delivery. They also used the A.H.P. to generate the criteria weights

and implemented this evaluation system in a web environment.

Wang et al. (2004) proposed that low cost and high quality should be considered for a lean

supply chain and that speed, flexibility, and quality should be considered for an agile supply

chain.

Thaver and Wilcock (2006) used 16 criteria for vendor selection which are used by textile and

apparel buyers to select overseas vendors in Canadian firms. They are offering competitive

prices, merchandise fashionability, quality of first samples, sound financial position, willingness

to negotiate prices, sufficient export quota, short lead times for delivery, long-term commitment

supplying quotations promptly, economic stability in country, effective communication system

registered to a quality program, low minimum quantities required, possessing EDI, technical

40

expertise and registration to ISO 9000. He concluded that Canadian buyers are not influenced by

ISO 9000 during overseas vendor selection.

Ho et al. (2010) reviewed papers published between 2000 to 2008 on multicriteria decision

making approaches for supplier selection and evaluation. They found that price or cost is not

most widely adopted criterion. Instead, the most popular criterion used for evaluating the

performance of suppliers is quality, followed by delivery, price or cost, manufacturing capability,

service, management, technology, research and development, finance, flexibility, reputation,

relationship, risk, and safety and environment. In table 2.2 important criteria used by researchers

are summarized.

There are many criteria discussed in literature for vendor selection some of important criteria are

mentioned below:

Cost: The purchasing price is very significant to decrease cost and to promote the competitive

capability of products for core enterprise. Therefore the purchasing price is a highlighted

consideration to the purchasing organization (Bei et al., 2006). Ho et al. (2010) reviewed

international journal published between (2000- 2008) and found price/ cost is third most

important criteria for vendor selection. Its related attributes include appropriateness of the

materials price to the market price, competitiveness of cost, cost reduction capability, cost

reduction effort, cost reduction performance, direct cost, fluctuation on costs, indirect-

coordination cost, logistics cost, manufacturing cost, unit cost, ordering cost, parts price, product

price, and total cost of shipments.

Quality: Quality is a critical concern for most enterprises. The need for high quality vendors has

always been an important issue for enterprises. The factors assessing quality include mainly

41

quality systems, equipment for manufacturing required products, process capability and rate of

certified product. In supplying products or services there are three fundamental parameters that

determine their sale ability. They are price, delivery, and quality. Customers require products and

services of a given quality to be delivered by, or be available by, a given time, and to be at a

price that reflects value for money. These are the needs of customers. An organization will

survive only if it creates and retains satisfied customers and this can only be achieved if the

products or services meet customer needs and expectations. While price is a function of cost,

profit margin, and market forces, and delivery is a function of the organization’s efficiency and

effectiveness, quality is determined by the extent to which a product or service successfully

serves the purpose of the user during usage (not just at the point of sale). Price and delivery are

transient features whereas the impact of quality is sustained long after the attraction or the pain

of price and delivery has subsided. Therefore, the fact that quality is on top of the list of critical

success factors for supplier selection should not be surprising. Ho et al. (2010) found that quality

is the topmost criteria for vendor selection.

Delivery: Along with quality, another factor that is considered a basic prerequisite for supplier

selection is delivery. As outsourcing has increased, so has the importance of vendor selection

decisions. In the selection process, delivery performance remains an important criterion (Ernst et

al 2007). Since Dickson’s (1966) study conforming to quality specifications and meeting

delivery deadlines remain the most important supplier selection criteria. In a fundamental sense,

these form the threshold criteria that buying firms apply to all suppliers before they can be

considered as strategic partners in the buyer-supplier relationship (Choi and Hartley, 1996). They

have emerged as order qualifiers to the extent that if suppliers cannot demonstrate acceptable

performance in these two areas, they will be dropped as potential candidates during the screening

42

phase itself. Ho et al. (2010) found that second most used criteria for supplier selection is

delivery.

Flexibility: Flexibility is one of the important objectives in operation strategy model (Schroeder,

2000) and is often seen as a reaction to environmental uncertainty (Suarey et al., 1991; Gerwin,

1993). Flexibility is described as the ability of a manufacturing system to cope with

environmental uncertainties (Barad and Sipper, 1988). There are many ways to characterize

supply chain flexibility, for example, manufacturing flexibility and marketing flexibility

(Vickery et al., 1999). In general, flexibility reflects an organization’s ability to effectively adapt

or respond to changes that add value in the customer’s eyes (Upton, 1995).

Financial Capability: A solid financial position helps ensure that performance standards can be

maintained and that products and services will continue to be available (Kahraman et al. 2003).

Both buyers and sellers are looking for partners that are viable, ongoing concerns that will

contribute to the relationship both for the present and in the future. A supplier on financially

unstable footing will have much more difficulty contributing to the partnership venture, as it

must focus its efforts on improving its financial soundness. Hence, both suppliers and buyers are

becoming more mindful of the financial position of their potential partners in their decision

making (Ellram, 1990).

Reputation: Reputation is defined as the perception of quality over time. There is definitely a

notion of quality, but it may be real today or may be perceived based on past quality or past

experience. The experience may be personal or may be secondary. Someone else who is trusted

may have expressed their perception of quality. A positive reputation can lead to trust. Achrol

(1997) notes that business decisions superficially based on trust may in reality judgment related

43

to party’s reputation. Wilson (1995) argues that “reputation for performance become a measure

of trust when partner is untested”. Similarly, Michel et al. (1998) suggest that reputation is an

element of trust because it affects cognitive perception of quality

Table 2.2: Important vendor selection criteria used by various researchers

Author Criteria Used

Ho et al. (2010) They found most popular criterion used for evaluating the performance

of suppliers is quality, followed by delivery, price or cost,

manufacturing capability, service, management, technology, research

and development, finance, flexibility, reputation, relationship, risk, and

safety and environment

Thaver and Wilcock

(2006)

They used offering competitive prices, merchandise fashionability,

quality of first samples, sound financial position, willingness to

negotiate prices, sufficient export quota, short lead times for delivery,

long-term commitment supplying quotations promptly, economic

stability in country, effective communication system registered to a

quality program, low minimum quantities required, possessing EDI,

technical expertise and registration to ISO 9000

Low cost and high quality should be considered for a lean supply chain

and that speed, flexibility, and quality should be considered for an

44

Wang et al. (2004) agile supply chain.

Akarte et al. (2001) Product development capability, manufacturing capability, quality

capability, cost and delivery

Petroni and Braglia (2000) Management capability, production capacity and flexibility, design and

technological capability, financial stability, experience and

geographical location

Pearson and Ellram (1995) Quality, cost, current technology, design capabilities, speed to market,

manufacturing process, assessment of future technology, economic

performance, management compatibility, location and proximity,

visitation to supplier facilities and organizational structure.

Barbaresoglu and Yazagac

(1997)

Performance assessment, manufacturing capability assessment and

quality system assessment.

Choi and Hartley (1996) Finances, Consistency, Relationship, Flexibility, Technological

capability, Services, Readability and Price

Wilson (1994) They found that quality and services are becoming more important

while price is becoming less important

45

Weber et al. (1991 They found that most frequently mentioned criteria net price, delivery,

quality, production facilities, technical capabilities, reputation,

financial position, performance history, repair and attitude.

Dickson (1966) Quality, delivery, performance history, warranties and claim

policies, production facilities, price, technical capability, financial

capability, bidding procedural compliance, communication

system, industry reputation and position, desire for business,

management and organization, operating controls, repair service,

altitude, impression, packaging ability, labour relations record,

geographical location, amount of past business, training aids,

Reciprocal arrangement.

2.8 Vendor Selection Methods

Boer et.al. (2001) presented a review of decision methods reported in the literature for

supporting the supplier selection process. They showed that several suitable Operations Research

methods such as data envelopment analysis, total cost approaches, linear programming, linear

weighting models, statistical methods, artificial intelligence.

46

In linear weighting models weights are given to the criteria, the biggest weight indicating the

highest importance. Ratings on the criteria are multiplied by their weights and summed in order

to obtain a single figure for each supplier. The supplier with the highest overall rating can then

be selected. This basic linear weighting model is described mostly in Purchasing textbooks, see

e.g. Zenz (1981) and in Timmerman (1986).

Nydick and Hill (1992) and Masella and Rangone (2000) propose the use of the analytic

hierarchy process (AHP) to deal with imprecision in supplier choice. In short, AHP circumvents

the difficulty of having to provide point estimates for criteria weights as well as performance

scores in the basic linear weighting model. Instead, using AHP the buyer is only required to give

verbal, qualitative statements regarding the relative importance of one criterion versus another

criterion and similarly regarding the relative preference for one supplier versus another on a

criterion. Tahriri et al (2008) applied AHP approach for supplier selection and evaluation in steel

manufacturing company.

Sarkis and Talluri (2000) propose the use of the analytical network process (ANP), a more

sophisticated version of AHP, for supplier selection. Mandal and Deshmukh (1994) applied

interpretive structural modeling(ISM) technique for the supplier selection.its main goal to

indentify and summarize the relationship among items and to form structural model of problem.

Min (1994) used multi attribute utility approach for international supplier selection.

Mathematical Programming (MP) allows the decision-maker to formulate the decision problem

in terms of a mathematical objective function that subsequently needs to be maximized (e.g.

profit) or minimized (e.g. Costs) by varying the values of the variables in the objective function.

Karpak et al. (1999) use goal programming to minimize costs and maximize quality and delivery

47

reliability when selecting suppliers and allocating orders between them. Weber and Desai (1996)

propose data envelopment analysis (DEA) for evaluation of vendors that were already selected.

Weber et al. (1998) combine MP and the DEA method to provide buyers with a tool for

negotiations with vendors that were not selected right away as well as to evaluate different

numbers of suppliers to use (Weber et al., 2000). Rosenthal et al. (1995) developed a mixed

integer linear program that would find the purchasing strategy for the buyer to minimize the total

purchase cost, and the computational results was also presented on a personal computer with

standard optimization software.

Ghodsypour and O’Brien (1998) used an integrated AHP and LP model for the vendor selection

and order allocation problem. Degraeve and Roodhooft (2000) develop a mathematical

programming model that minimises the total cost of ownership of the supplier choice and

inventory management policy using activity-based costing information. Degraeve et al. (2000)

extend this methodology to the service sector in developing an airline selection model for the

procurement of business travel.

Tempelmeier (2002) formulated a mixed integer linear optimization model for supplier selection

and purchase order sizing for a single item under dynamic demand conditions. Dahel (2003)

presented a multi-objective mixed integer programming approach to simultaneously determine

the number of vendors to employ and the order quantities to allocate to these vendors in a

multiple-product, multiple-supplier competitive sourcing environment.

Statistical models deal with the stochastic uncertainty related to the vendor choice. Ronen and

Trietsch (1988) develop a decision support system for supplier choice and ordering policy in the

context of a large one/of project where the order lead time is uncertain. Soukoup (1987)

48

introduces a simulation solution for unstable demand in his rating model. Tracey and Tan (2001)

employed confirmatory factor analysis and path analysis to examine empirically the relationships

among supplier selection criteria, supplier involvement on design teams and in continuous

improvement programs, customer satisfaction, and overall firm performance.

Khoo et al. (1998) discuss the potential use of an Internet-based technology called intelligent

software agents (ISAs). ISA's are generally used for automating the procurement of goods. The

authors suggest different types of agents - learning agents and shopping agents -that can be

applied to the supplier selection problem. The focus is on the development of a simple model to

demonstrate the electiveness of using intelligent software agents for electronic sourcing. Choy

and Lee (2003) presented an intelligent generic supplier management tool using the CBR

technique for outsourcing to suppliers and automating the decision making process when

selecting them.

Karpak et al. (2001) implemented the Visual Interactive Goal programming (VIG) in a multiple

replenishment purchasing problem. Bhutta and Huq (2002) presented two approaches related to

supplier selection decision, AHP and TCO and provided a comparison. Handfield et al. (2002)

proposed an AHP model that included relevant environmental criteria in supplier selection

decision.

Weber et al. (2000) presented data envelopment analysis method for selecting vendors and their

quota allocation. Wu et al. (2007) presented a so-called augmented imprecise DEA for supplier

selection. The proposed model was able to handle imprecise data (i.e., to rank the efficient

suppliers) and allow for increased discriminatory power (i.e., to discriminate efficient suppliers

from poor performing suppliers). A web-based system was developed to allow potential buyers

49

for supplier evaluation and selection. Ha and Krishnan (2008) applied an integrated approach in

an auto parts manufacturing company for supplier selection. Twelve evaluating criteria were

proposed for the selection problem. In the approach, AHP was used first to evaluate the

performance of suppliers with respect to five qualitative factors. Then, the remaining seven

quantitative criteria along with the scores for each supplier calculated by AHP were passed to

DEA and artificial neural network (ANN) to measure the performance efficiency of each

supplier. Both results were compiled into one efficiency index using a simple averaging method.

Ng (2008) developed a weighted linear programming model for the supplier selection problem,

with an objective of maximizing the supplier score. Similar to AHP, it involves the decision

makers in determining the relative importance weightings of criteria.

2.9 Fuzzy Approach in Vendor selection

Fuzzy set theory can provide a valuable tool to cope with three major problematic areas of

vendor selection: imprecision, randomness and ambiguity. As far as imprecision is concerned it

provides a powerful tool to weigh selection criteria importance. As far as randomness is

concerned, it is more effective than probabilistic approaches in that the selection problems

should not use prediction based on previous vents, since each selection case is not repeatable. As

far as ambiguity is concerned it copes better than other methods with the treatment of linguistic

variables. Fuzzy logic enables us to emulate the human reasoning process and make decisions

based on vague or imprecise data. There are few papers in order to handle imprecise information

and uncertainty in supplier selection models (Narasimhan, 1983, Soukup, 1987, Nydick and Hill,

1992). In these papers, for finding the best overall rating supplier, simple linear weighting

models have been adapted to deal with uncertainty from incomplete and qualitative data in

50

unstructured purchasing situations. Based on fuzzy logic approaches, Morlacchi (1997)

developed a model that combines the use of FST with AHP and implements it to evaluate small

suppliers in the engineering and machine sectors. Li et al. (1997) proposed a measure for

supplier performance evaluation. They used fuzzy bag method to score qualitative criteria and

then all scores for qualitative and quantitative criteria are combined in an intuitive sum of

weighted averages. Holt (1998) reviewed of contractor evaluation and selection modelling

methodologies including FST method. In these methods, binary decisions (e.g. the contractor

does, or does not, have a formal safety policy) can convert to linguistic variables (e.g. No,

Minimum, Strong and Maximum). Albino et al. (1998) used fuzzy logic system to support

vendor rating and compared it to a neural network in order to evaluate the different system

performances. Nassimbeig and Battain (2003) developed a vendor-rating tool based on fuzzy

logic, a neural application and ordinary least squares (OLS) regression. Erol and Ferrel (2003)

proposed a methodology that assists DMs to use qualitative and quantitative data in a

multiobjective mathematical programming model. In their method first, qualitative information

converts into quantitative format using fuzzy quality function deployment (QFD) and then

combines this data with other quantitative data to parameterize a multi-objective model. Kumar

et al. (2006) used a fuzzy programming approach for vendor selection problem in a supply chain

considering a fuzzy Multi-objective Integer Programming formulation and a fuzzy mixed integer

goal programming formulation. Chou et al. (2006) used a fuzzy factor rating system to evaluate

potential vendors based on a modified re-buy situation. Keskin et al. (2010) used Fuzzy Adaptive

Resonance Theory (ART)’s classification ability to the supplier evaluation and selection area.

The proposed selection method, using Fuzzy ART not only selects the most appropriate

supplier(s) and also clusters all of the vendors according to chosen criteria.

51

2.10 Conclusion

In this chapter, brief literature review of supply chain management is given. Then, vendor

selection criteria used by various researchers are discussed. Some important criteria are

indentified for vendor evaluation based on literature. Then various methods used so far for

vendor evaluation are discussed in brief.

In conclusion, focusing on selecting best vendor will make a major contribution to the

competitiveness of the entire organization. This main task requires careful evaluation, selection,

and continuous measurement of the suppliers that provide the goods and services that help satisfy

the needs of an organization’s final customers.

In other words, once a supplier is selected, the focus must shift from supplier evaluation to the

continuous measurement of supplier performance. An organization must have the tools to

measure, manage, and develop the performance of its supply base. Supplier performance

measurement includes the methods and systems to collect and provide information to measure,

rate, or rank supplier performance on a continuous basis. Supplier performance measurement

differs from the process used to initially evaluate and select supplier, given that is a continuous

process.

52

CHAPTER 3

RESEARCH METHODOLOGY

3.1 Introduction

This chapter contains methodology used in this thesis for evaluating and ranking vendors based

on selected criteria. The chapter consists of fuzzy logic introduction and then methodologies

TOPSIS, Fuzzy TOPSIS and Linear Programming. Firstly fuzzy logic is discussed, and then

TOPSIS and Fuzzy TOPSIS are discussed. Fuzzy TOPSIS is used for evaluating and ranking of

vendors and in the last Linear Programming is, discussed which is used for quota allocation in

this thesis.

3.2 Fuzzy Logic

Fuzzy logic is a form of multi-valued logic derived from fuzzy set theory to deal with reasoning

that is approximate rather than precise. Most real world decision problems take place in a

complex environment where conflicting systems of logic, uncertain and imprecise knowledge,

and possibly vague preferences have to be considered. To face such complexity, the use of

specific tools, techniques, and concepts which allow the available information to be represented

with the appropriate granularity is believed as crucial. Particularly, fuzzy set theory can ideally

cope with this kind of problems.

53

Verma (1997) applied fuzzy logic for construction of offender profiles. He proposed a fuzzy

logic based mathematical procedure for criminal’s justice fields. He found out that this is a

strong mathematical technique that can handle imprecise and fuzzy data is undoubtedly going to

strengthen the analytical capabilities of the social researchers. Above all, an exposure to the

concept of fuzzy variables and an understanding of the mathematical base of fuzzy logic could

initiate a new research process for police and criminal justice fields, for obviously this is only a

beginning.

Shore and Venkatachalam (2003) used fuzzy logic technique for evaluation of the information

sharing capabilities of supply chain partners. The methodology allows decision makers to

evaluate information sharing capability of suppliers in a natural way while preserving the

fuzziness of the measurement process and capturing data in linguistic terms. Fuzzy logic, used

extensively in engineering for control problems, seems potentially very useful in solving a range

of supply chain evaluation problems. While the purpose of this paper is to introduce the

methodology, the next step should be to apply this methodology to an actual problem and extend

the methodology to a wider range of evaluation problems.

Chena and Hsu (2004) presented a new method for forecasting the enrollments of the University

of Alabama using fuzzy time series. The proposed method belongs to the first order and time-

variant methods. The proposed method gets a higher forecasting accuracy rate for forecasting

enrollments than the existing methods.

Oke and Charles-Owaba (2006) used fuzzy logic control model for Gantt charting preventive

maintenance scheduling. The research has serious implication in terms of the ability to monitor

the Imprecision those were introduced in early work. He provide a more reliable framework for

54

researchers and practitioners interested in maintenance scheduling activities.

Malagoli and Magni (2007) used fuzzy logic and expert systems to provide a score for the

firm(s) under consideration, representing the firm value-creating power. They introduced a

system which was capable of dealing with both quantitative and qualitative variables and

integrates financial, managerial and strategic variables. The use of a fuzzy expert system for

ranking firms within a sector and pricing firms is a first attempt at an alternative way of

measuring performance and value. Ordoobadi (2008) proposed a tool for decision makers to

make more informed decisions regarding their investment in advanced technologies. He

proposed that addition of subjective perceptions to the purely quantitative approach provides a

more realistic evaluation process. He founded a procedure that would help practitioners with

their technology. The value of the paper is the inclusion of the decision maker’s judgment in the

evaluation process by use of fuzzy logic. Munoz et al. (2008) used fuzzy logic for evaluating

sustainability in organizations. His aim was to determine whether the organizations more

strategically committed to their stakeholders present better social and financial performance and,

based on this relationship, to determine the state of the art of the Spanish sectors’ approach to

sustainable development.

3.3 Fuzzy Set Theory

Fuzzy set theory introduced by Zadeh (1965) is used to represent the vagueness of human

thinking; it expands traditional logic to include instances of partial truth. In traditional set theory,

elements have either complete membership or complete non-membership in a given set. With

fuzzy set theory, intermediate degrees of membership are allowed. The coding of the degree of

membership to each of the elements in the set is defined as the membership function of the fuzzy

55

set. The membership function is commonly depicted as a membership curve. The membership

curve contains three main components: the horizontal axis consisting of domain elements

(usually real numbers) of the fuzzy set, the vertical axis consisting of the degree of membership

scale from 0 to1, and the surface of the set itself which relates the degree of membership to the

domain element. These membership curves can take on several shapes, but the triangular and

trapezoidal are the most frequently used. This type of methodology is very useful when the

model requires human perceptions as inputs where ambiguity and vagueness exists. In particular,

systems requiring linguistic descriptions are more easily modeled using fuzzy sets. There are two

main inputs to the evaluation process of data. The first is the decision maker’s perception

regarding the importance weight of the criteria of interest. The second input is how the decision-

maker rates each parameter with respect to objective. However, it is very difficult to obtain exact

assessments from the decision maker. The nature of these assessments is often subjective and

qualitative and thus forcing the decision makers to express their opinion in pure numeric scales

does not allow any room for subjectivity. Subjectivity of human assessments and beliefs can be

expressed by using linguistic terms such as “low importance” or “highly likely.” The fuzzy set

theory and fuzzy numbers allow such qualitative expressions. As a result, their use in modeling

of our proposed system seems a logical choice.

3.4 Fuzzy Numbers

Fuzzy numbers are the special classes of fuzzy quantities. A fuzzy number is a fuzzy quantity M

that represents a generalization of a real number r. intuitively; M(x) should be a measure of how

well M (x) “approximates” r. (Nguyen and Walker, 2000). A fuzzy number M is a convex

normalized fuzzy set. A fuzzy number is characterized by a given interval of real numbers, each

56

with a grade of membership between 0 and 1. (Deng, 1999) A triangular fuzzy number (TFN),

M is shown in Figure 1.

Figure 3.1: A Triangular fuzzy number, M

Triangular fuzzy numbers are defined by three real numbers, expressed as (l, m, u). The

parameters l, m, and u, respectively, indicate the smallest possible value, the most promising

value, and the largest possible value that describe a fuzzy event. Their membership functions are

described as;

,0

),()(

,)()(

,0

)~

/(muxu

lmlxMx

ux

uxm

mxl

lx

,

,

,

(3.1)

In applications it is convenient to work with TFNs because of their computational simplicity, and

they are useful in promoting representation and information processing in a fuzzy environment.

In this study TFNs in the Fuzzy TOPSIS is adopted.

1.0

0.0

l m u

u

M

57

3.5 Algebraic Operations on TFNs

Although we are familiar with algebraic operations with crisp numbers, when we want to use

fuzzy sets in applications, we have to deal with fuzzy numbers. We can define various operations

on TFNs. But in this section, important operations used in this study are illustrated. (Tang and

Beynon, 2005) If we define, two TFNs A and B by the triplets A= (la, ma, ua) and B= (lb, mb, ub).

Then

Multiplication:

A.B=(la, ma, ua).(lb, mb, ub)

= (lalb, mamb, uaub) (3.2)

Inverse:

(la, ma, ua)-1

=(1/ua, 1/ma, 1/la) (3.3)

Distance B/W Two triangular Fuzzy numbers:

Distance between two triangular fuzzy numbers a (la, ma, ua ) and b (l b, mb ,ub) can be calculated

as follows:

1 2 2 2( , ) [( ) ( ) ( )

3d a b l l m m u ua a ab b b

( , )d a b R

(3.4)

Distance between two fuzzy numbers is crisp in nature

58

3.6 TOPSIS

The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method was firstly

proposed by Hwang and Yoon (1981). It is very effective in multi-criteria decision-making

(MCDM). The basic concept of this method is that the chosen alternative should have the

shortest distance from the positive ideal solution and the farthest distance from negative ideal

solution. Positive ideal solution is a solution that maximizes the benefit criteria and minimizes

cost criteria, whereas the negative ideal solution maximizes the cost criteria and minimizes the

benefit criteria.

The TOPSIS method assumes that each criterion has a tendency of monotonically increasing or

decreasing utility. Therefore, it is easy to define the ideal and negative ideal solutions. The

Euclidean distance approach was proposed to evaluate the relative closeness of the alternatives to

the ideal solution. Thus, the preference order of the alternatives can be derived by a series of

comparison of these relative distances.

The TOPSIS method evaluates the following decision matrix (D) which refers to m alternatives

which are evaluated in terms of n criteria:

.11 12 1

.21 22 2

. . . .

. . . .

.1 2

x x xn

x x xn

D

x x xmnm m

59

Where ijx denotes the performance measure of the i-th alternative in terms of j-th criterion.

The steps of the method are as follows:

Step 1: Construct the Normalized Decision Matrix

TOPSIS converts the various criteria dimensions into non-dimensional criteria as done in the

ELECTRE method. An element ijr of the normalized decision matrix R is thus calculated as:

2

1

xijrij m

xkjk

(3.5)

Step2: Construct the Weighted Normalized Decision Matrix

A set of weights ( , , ,......, )1 2 3

W w w w wn , (where: 1wi ) defined by the decision maker

is used with the decision matrix to generate the weighted normalized decision matrix V as

follows:

.1 11 2 12 1

.1 21 2 22 2

. . . .

. . . .

.1 1 2 2

w r w r w rn n

w r w r w rn nv

w r w r w rn mnm m

60

Step 3: Determine the Ideal and Negative Ideal Solutions

The ideal denoted as *A , and the negative ideal denoted as A

, alternatives (solutions) are

defined as follows:

* {(max ),(min ), 1,2,3,. . . . , }A v j J v j J i mij ijii

{ , , . . . . . , }1* 2* *

v v vn

(3.6)

{(min ),(max ), 1,2,3,. . . . , }A v j J v j J i mij iji i

{ , , . . . . . , }1 2

v v vn (3.7)

Where, { 1,2,3,. . . . ,J j n and j is associated with the benefit criteria}

{ 1,2,3,. . . . ,J j n and j is associated with cost /lost criteria }

The previous two alternatives are fictitious. However, it is reasonable to assume here that for the

benefit criteria, the decision maker wants to have a maximum value among the alternatives. For

the cost criteria the decision maker wants to have a minimum value among the alternatives. From

the previous definition it follows that the alternative *A indicates the most preferable alternative

that is, ideal solution. Similarly Aindicates the least preferable alternative that is, negative-

ideal solution.

61

Step 4: Calculate the Separation Measure

The n- dimensional Euclidean distance method is applied to measure the separation distances of

each alternative from the ideal solution and negative-ideal solution. Thus, for the distances from

the ideal solution we have:

2( )* *1

nS v viji jj

, for 1,2,3,...,i m (3.8)

Where *Si is the distance of each alternative from the ideal solution.

Simillarly the distance from the negative-ideal solution can be calculated as:

2( )1

nS v vi ij j

j

, for 1,2,3,...,i m

(3.9)

Where Si is the distance of each alternative from the negative-ideal solution.

Step 5: Calculate the Relative Closeness to the Ideal Solution

The relative closenessof an alternative iA with respect to the ideal solution *A is defined as

follows:

**

SiCi S Sii

(3.10)

Where, 1 0*

Ci

and 1,2,3,...,i m

62

Apparently, 1

*C

i

, if *

A Ai and 0Ci , if

A Ai

Step 6: Rank the Preference Order

The best alternative can be decided according to the preference rank order of *iC . Therefore,

the best alternative is the one which have shortest distance to the ideal solution. The previous

defination can also be used to demonstrate that any alternative which has the shortest distance

from the ideal solution is also guranteed to have the longest distance from the negative-ideal

solution.

3.7 Fuzzy TOPSIS

In the classical TOPSIS method, the weights of the criteria and the ratings of alternatives are

known precisely and crisp values are used in the evaluation process. However, under many

conditions crisp data are inadequate to model real-life decision problems. Therefore, the fuzzy

TOPSIS method is proposed where the weights of criteria and ratings of alternatives are

evaluated by linguistic variables represented by fuzzy numbers to deal with the deficiency in the

traditional TOPSIS.

The TOPSIS method is a linear weighting technique, which was first proposed, in its crisp

version by Chen and Hwang (1992), with reference to Hwang and Yoon (1981). Since then, this

method has been widely adopted to solve MCGDM problems in many different fields, ranging

from robot design (Parkan and Wu, 1999) to materials selection (Jee and Kang, 2000), from the

evaluation of performance of competitive companies (Deng et al.; 2000), to the assessment of

service quality in airline industry (Tsaur et al.; 2002).

63

There are many applications of fuzzy TOPSIS in the literature. For instance, Triantaphyllou and

Lin (1996) developed a fuzzy version of the TOPSIS method based on fuzzy arithmetic

operations, which leads to a fuzzy relative closeness for each alternative. Chen (2000) extended

the TOPSIS to the fuzzy environment and gave a numerical example of system analysis engineer

selection for a software company. Chu (2002) presented a fuzzy TOPSIS model under group

decisions for solving the facility location selection problem. Chu and Lin (2003) proposed the

fuzzy TOPSIS method for robot selection. Yong (2006) used fuzzy TOPSIS method for plant

location selection. Wang and Elhag (2006) proposed a fuzzy TOPSIS method based on alpha

level sets and presented a nonlinear programming solution procedure for bridge risk assessment.

Jahanshahloo et al. (2006) extended the TOPSIS method to decision-making problems with

fuzzy data and they used the concept of α-cuts to normalize fuzzy numbers. Wang and Chang

(2007) developed an evaluation approach based on the fuzzy TOPSIS to help the Air Force

Academy in Taiwan to choose initial training aircraft. Benitez et al. (2007) presented a fuzzy

TOPSIS method for measuring quality of service in the hotel industry. Wang and Lee (2007)

generalized TOPSIS to fuzzy multiple-criteria group decision-making in a fuzzy environment.

They proposed two operators Up and Low that are employed to find ideal and negative ideal

solutions.

Semih et.al (2009) used a combined model of fuzzy AHP and fuzzy TOPSIS model for selecting

shopping centre site in Istanbul. Sun and Lin (2009) used fuzzy TOPSIS method for evaluating

the competitive advantage shopping websites and selecting the best alternate based on 12

different criteria.

64

3.8 The Fuzzy TOPSIS Methodology Algorithm

In this paper, the extension of TOPSIS method is considered which was proposed by Chen

(2000) and Chen et al. (2006) the algorithm of this method can be described as follows:

Step 1: First of all a team of decision-makers is formed. In a decision team that has K decision-

makers; fuzzy rating of each decision maker Dk =(k =1.2..k) can be represented by triangular

fuzzy number k

x =( k =1,2...k) with membership function µ῀k(X).

Step 2: Then evaluation criteria are determined.

Step 3: After that, appropriate linguistic variables are chosen for evaluating criteria and

alternatives.

Step 4: Then the weight of criteria and performance rating of each alternatives are aggregated as

shown below

1( .... )

,1 ,2 ,w w w wj j j j kk

1 21( .... )

kx x x xij ij ijij k

(3.11)

w j and xij are aggergated fuzzy weight of critieria and performance rating of each

alternative Ai.

65

Step 5: Construct the fuzzy decision matrix with m alternatives, n criteria and k decision makers

1 2

11 12 11

2 21 22 2

1 2

C C Cn

x x xA n

A x x xnD

Amx x xmnm m

, i=1, 2…………..m, j=1, 2…………n

1 21( .... )

kx x x xij ij ijij

k

, ,x l m uij x x xij ij ij

Where D represent fuzzy decision matrix with alternative Ai and criteria Cj , k

ijx triangular

fuzzy no. represent judgement by expert k and x ij (triangular fuzzy number) is the rating of the

alternatives Ai with respect to criteria Cj evaluated by experts.

Step 6: Normalize the decision matrix

The normalization of fuzzy decision matrix is denoted by shown as the following formula

= [rij] m*n , i = 1; 2; . . . ; m; j = 1; 2; . . . ; n (3.12)

66

=

1

1 1,1 1, 1,

, ,,1

,,,1

C C Cnj

A r r rj n

A r r ri i j i ni

A r r rm m nm jm

Where rij element of normalized decision matrix

The aim of normalization is twofold: on the one hand, normalization is necessary to compare

heterogeneous criteria, on the other, normalization ensures that triangular fuzzy numbers all

range within the interval [0, 1].In the normalization process, different equations have to be

applied to benefit criteria and to cost criteria. The following formulae are used respectively:

; ;l m uxij xij xij xij

r j Biju u u uj j j j

(3.13)

; ;l l l lj j j j

r j Ciju m lx x xij ij ijxij

(3.14)

max( ) 1,....,u u m Bxj ij i

min( ) 1,....,l l m Cxj ij i

Where B represents benefit criteria and C represents cost criteria

Step 7: Considering the different weight of each criterion, the weighted normalized decision

matrix is computed by multiplying the importance weights of evaluation criteria and the values

67

in the normalized fuzzy decision matrix. The weighted normalized decision matrix is defined

as:

= [vij]m*n i= 1,2......m; j= 1,2.......n (3.15)

V=

1

1 1,1 1, 1,

,1 , ,

,1 , ,

j n

j n

i i i j i n

m m m j m n

C C C

A v v v

A v v v

A r v v

, ,v r wi j i j j 1,..., ; 1,...,i m j n

In this matrix, each element vij is a fuzzy normalized number which ranges within the interval [0,

1].

Step 8: This step is aimed at determining the fuzzy positive ideal solution A+ and the fuzzy

negative ideal solution A-.We know that after normalization each element

,i jv of fuzzy weight

normalized matrix is in the rang [0,1]. Then we can define fuzzy positive ideal solution A+ and

the fuzzy negative ideal solution A- by following formula:-

A+

= (v1+; vj+........vn+) (3.16)

A- = (v1

- ;vj

-...........vn

-) (3.17)

Where jv=max of

ijv for B and jv min of

ijv for C

jv=min of

ijv for B and jv max of

ijv for C

68

(1,1,1)

(0, 0, 0)

j Bv j

j C

(0, 0, 0)

(1,1,1)

j Bv j

j C

Step 9: In this step, the n-dimensional separation distances of each alternative i =1,. . . m to the

fuzzy Positive Ideal Solution A+

and to the fuzzy Negative Ideal Solution A -are computed.

( ; )1

nd d v vi ij j

j

; i= 1, 2 ...m; j=1, 2 ...n (3.18)

( ; )1

nd d v vi ij j

j

; i= 1, 2 ...m; j=1, 2 ...n (3.19)

d Ri

d Ri

Where di+ and di- are the separation distances from fuzzy positive ideal situation and fuzzy

negative solution respectively

Step 10: In this step each alternatives closeness index is calculated by following formula

[0,1]

diCi d di i

Ci

(3.20)

Ci is closeness index

69

The optimal alternatives have value of closeness index closer to 1. According to the closeness

coefficient, the ranking of the alternatives can be determined. Closeness coefficient value much

nearer to 1 is ranked high, while closeness coefficient having value farthest from 1 is ranked

lowest.

3.9 Linear programming (LP)

Linear programming (LP) is a mathematical modeling technique useful for allocation of scarce

resources or limited resources such as labour, material, machine, time, warehouse space, capital,

energy etc., to several competing activities, such as product, service, job, new equipment etc., on

the basis of given criterion of optimality (Sharma, 1997).

Linear programming is a relatively young mathematical discipline, dating from the

invention of the simplex method by G. B. Dantzig in 1947. Historically, development in linear

programming is driven by its applications in economics and management. Dantzig initially

developed the simplex method to solve U.S. Air Force planning problems and planning and

scheduling problems still dominate the applications of linear programming. One reason that

linear programming is a relatively new field is that only the smallest linear programming

problems can be solved without a computer.

3.10 General structure of linear programming

The linear programming consists of three basic elements

Decision variable: In LPP model, the decision variables whose quantitative values are required

to be found so as to minimize or maximize the objective function.

70

Objective Function: Function which minimized or maximized is called objective function. In its

general form, it is represented as:

Z=c1x1+ c2x2 + ………… cnxn

Where Z is measure of performance variable, which is function of x1, x2,,….. xn. Quantities c1,

c2,……….cn are parameters that represent the contribution of a unit of the respective variable x1,

x2,,….. xn to measure the of performance of Z.

The Constraint: There are always certain limitations on the use of resources, e.g. labour,

machine, raw material etc., that limit the objective function can be achieved. Such constraint

must be expressed in linear equalities or inequalities in term of decision variable.

3.11 General mathematical model of linear programming problem

The general linear programming problem with n decision variable and m constraints can be

stated in following form:

Find the value of decision variable x1, x2,,….. xn so as to

Optimize (Max. or Min.) Z = 1

nC xj j

j (3.21)

Subject to linear to constraint

1

na xj j

j

(bi; i=1, 2,…….m ( Capacity constraints) (3.22)

71

xJ j = 1,2,………,n (Non- negativity conditions) (3.23)

Here xJ = j th decision variable about which the which decision maker is interested.

CJ = Unit contribution to the j th decision variable in the objective function.

aij = Exchange coefficient of j th variable in ith constraint set.

bi = Requirement or availability.

3.12 Conclusion

The chapter introduces to TOPSIS, Fuzzy TOPSIS and Linear Programming methods and from

literature of fuzzy TOPSIS, it is clear that fuzzy TOPSIS is a suitable tool for making multi-

criteria decisions in fuzzy environment as it’s applied in various other multi-criteria decision

problems in uncertain environments. Vendor selection is also a multi-criteria decision problem

so Fuzzy TOPSIS can suitable used, for evaluation and ranking of vendors.

72

CHAPTER 4

CASE ILLUSTRATION

4.1 Introduction

In this chapter a case study for vendor selection and evaluation is presented. The Fuzzy TOPSIS

methodology is used for the evaluation of vendor so that company may reduce lead time and

increase profit. After evaluation and raking of vendors linear programming has been used for the

quota allocation purpose. At the last sensitivity analysis is done and result obtained is discussed

in detail.

4.2 Company Profile

XYZ is the leading two wheeler manufacturer company in India. It is joint venture of Indian

company X and Japanese company Y come into exist in to 1984.Over the course of two and a

half decades, and three successive joint venture agreements later, both partners have fine-tuned

and perfected their roles as joint venture partners. What the two partners did was something quite

basic. They simply stuck to their respective strengths. As one of the world's technology leaders

in the automotive sector, Y has been able to consistently provide technical know-how, design

specifications and R&D innovations. This has led to the development of world class, value - for-

money motorcycles and scooters for the Indian market. On its part, the X has taken on the

singular and onerous responsibility of creating world-class manufacturing facilities with robust

processes, building the supply chain, setting up an extensive distribution networks and providing

insights into the mind of the Indian customer. Since both partners continue to focus on their

73

respective strengths, they have been able to complement each other. In the process, XYZ is

recognized today as one of the most successful joint ventures in the world. XYZ primarily

operates in India. It is headquartered in New Delhi, India and employs over 4,500 people. XYZ

currently spin out from its three globally benchmarked manufacturing units sited at Dharuhera

and Gurgaon in Haryana and Haridwar, Uttrakhand. These plants collectively are proficient of

producing out 4.4 million units per year. Over 20 million XYZ company’s two wheelers squash

Indian roads. Having reached an unassailable pole position in the Indian two wheeler market,

XYZ is constantly working towards consolidating its position in the market place.

The company recorded net sales and other income of INR125, 398.4 million during the financial

year ended march 2009, an increase of 19.2% over financial year 2008. The operating profit of

the company was INR17, 497.8 million during financial year 2009, an increase of 27.3% over

financial year 2008. The net profit was INR12, 817.6 in financial year 2009, an increase of

32.4% over financial year 2008.

The companies procure large amount of material and component from the vendor. It has more

than 250 vendors at present, which keep the flow in supply chain smooth by timely delivering.

4.3 Case illustration

The company located at Gurgaon Haryana needs to select vendor for the supply of disk brakes

for particular brand of two wheelers. There is large number of vendors who supplies disk brakes,

so company wants to evaluate best suited vendors for its operation. Company management

believes that consideration of some key factors in vendor evaluation will increase its efficiency

and reduce the cost.

74

But the capacity of each vendor is limited and a single vendor can’t fulfill the all the demand of

company so company wants to maximize the total value of purchase by evaluating the selected

vendors.

In order to demonstrate the use of fuzzy TOPSIS methodology in vendor selection an industrial

case is discussed in this chapter. After preliminary screening five vendors are selected for further

analysis .Now Company has faced the problem of effectively evaluating five alternatives. For

confidentiality we will name those companies as A1, A2, A3, A4 and A5.

Then, the selection phase was arranged. The panel of expert was instructed about the

fundamentals of approximate reasoning, fuzzy logic, and the TOPSIS methodology to be

adopted. Specifically, the panel acknowledge about the efficacy of the results provided by

TOPSIS in terms of relative distances from positive and negative ideal solutions. The avoidance

of complex pair-wise comparisons as in AHP and the opportunity to give direct linguistic

judgments to weights and ratings was largely appreciated since it made the selection process very

straightforward. .The project team agreed that the selection criteria to be used were those

illustrated in the “The vendor selection criteria” paragraph. First of all, linguistic scales were set

to assess both the relative importance of criteria and the performance of each candidate for each

criterion. The scales and related fuzzy triangular numbers are shown in table 4.1 and 4.2

respectively.

75

Table 4.1: Linguistic variable for importance of weight of each criterion

Linguistic variable Triangular fuzzy numbers

Very Low (VL) (0.0, 0.1, 0.2)

Low (L) (0.1, 0.2, 0.3)

Medium (M) (0.2, 0.3, 0.4)

High (H) (0.3, 0.4, 0.5)

Very High (VH) (0.4, 0.5, 0.5)

Table 4.2: Fuzzy rating of vendors

Linguistic variable triangular fuzzy numbers

Poor (P) (0.0, 0.1, 0.2)

Fair (F) (0.1, 0.2, 0.3)

Good (G) (0.2, 0.3, 0.4)

Very Good (VG) (0.3, 0.4, 0.5)

Excellent (E) (0.4, 0.5, 0.5)

The three decision makers were then separately asked to judge the importance of each selection

criterion. Weights given by each DM, together with pooled fuzzy values are summarized in

Table 4.4.

The same panel was separately asked to express verbal opinions about candidate performance for

each selection criterion. The results are shown in Table 4.6. Then aggregate of weight and

criteria rating is done and no candidate is considered better than other because every provider has

some strength and weakness. So proposed Fuzzy TOPSIS methodology applied to rank the 5

different vendors as shown below:

76

Step 1: In first step a panel of three experts from the case company is selected as per their

experience and role in management of company. They are we denoting here D1, D2 and D3

respectively.

Step 2: The important criteria used for vendor evaluation are indentified based on literature. The

six important criteria used are

Cost

Quality

Delivery

Flexibility

Financial capability

Reputation

They are denoted by C1, C2, C3, C4, C4, C5 and C6 respectively.

Step 3: Three decision makers asked to weight the criteria according to the provided linguistic

variable as per Table no.4.1. Similarly performance rating is also done on the basis of linguistic

scale given in Table no. 4.2, by the panel of same decision makers

Step 4: Then the weight of criteria and performance rating of each alternative is aggregated

using equation 3.11. The result is shown in Table no. 4.4 and Table no. 4.6 respectively.

77

Table 4.3: Fuzzy weight importance given by three decision makers

Criteria D1 D2 D3

C1 H H VH

C2 VH VH VH

C3 VH H VH

C4 H VH H

C5 H H H

C6 M H M

Table 4.4: Aggregated fuzzy weights for criteria

Criteria Aggregated weight

C1 (0.333,0.433,0.500)

C2 (0.400,0.500,0500)

C3 (0.367,0.467,0.500)

C4 (0.333,0.433,0.500)

C5 (0.300,0.400,0.500)

C6 (0.233,0.333,0.433)

78

Table4.5: Fuzzy rating of vendors given by decision makers

CRITERIA ALTERNATIVE D1 D2 D3 Aggregated Rating

C1 A1

A2

A3

A4

A5

E

VG

G

E

VG

VG

E

VG

VG

VG

VG

E

G

VG

VG

(0.333,0.433,0.500)

(0.367,0.467,0.500)

(0.233,0.333,0.433)

(0.333,0.433,0.500)

(0.300,0.400,0.500)

C2 A1

A2

A3

A4

A5

G

E

VG

VG

G

F

E

E

G

VG

F

E

E

VG

E

(0.133,0.233,0.333)

(0.400,0.500,0.500)

(0.367,0.467,0.500)

(0.267,0.367,0.467)

(0.300,0.400,0.467)

C3 A1

A2

A3

A4

A5

VG

E

G

E

VG

G

VG

VG

E

F

VG

E

VG

E

G

(0.267,0.367,0.467)

(0.367,0.467,0.500)

(0.267,0.367,0.467)

(0.400,0.500,0.500)

(0.200,0.300,0.400)

C4 A1

A2

A3

A4

A5

VG

E

VG

VG

F

E

VG

G

E

G

E

E

F

E

VG

(0.367,0.467,0.500)

(0.367,0.467,0.500)

(0.200,0.300,0.400)

(0.367,0.467,0.500)

(0.200,0.300,0.400)

C5 A1

A2

A3

A4

A5

VG

E

VG

VG

VG

VG

E

G

E

E

VG

E

VG

G

E

(0.300,0.400,0.500)

(0.400,0.500,0.500)

(0.267,0.367,0.467)

(0.300,0.400,0.467)

(0.367,0.467,0.500)

C6 A1

A2

A3

A4

A5

G

VG

E

G

E

VG

G

VG

VG

E

VG

VG

E

VG

G

(0.267,0.367,0.467)

(0.267,0.367,0.467)

(0.367,0.467,0.500)

(0.267,0.367,0.467)

(0.333,0.433,0.467)

79

Table 4.6: Aggregated rating of vendors (fuzzy decision matrix)

A1 A2 A3 A4 A5

C1 (0.333,0.433,0.500) (0.367,0.467,0.500) (0.233,0.333,0.433) (0.333,0.433,0.500) (0.300,0.400,0.500)

C2 (0.133,0.233,0.333) (0.400,0.500,0.500) (0.367,0.467,0.500) (0.267,0.367,0.467) (0.300,0.400,0.467)

C3 (0.267,0.367,0.467) (0.367,0.467,0.500) (0.267,0.367,0.467) (0.400,0.500,0.500) (0.200,0.300,0.400)

C4 (0.367,0.467,0.500) (0.367,0.467,0.500) (0.200,0.300,0.400) (0.367,0.467,0.500) (0.200,0.300,0.400)

C5 (0.300,0.400,0.500) (0.400,0.500,0.500) (0.267,0.367,0.467) (0.300,0.400,0.467) (0.367,0.467,0.500)

C6 (0.267,0.367,0.467) (0.267,0.367,0.467) (0.367,0.467,0.500) (0.267,0.367,0.467) (0.333,0.433,0.467)

Step 4: Normalize the fuzzy decision matrix according to equation (3.13) and (3.14) for benefit

and cost criteria respectively the normalized decision matrix is given in table no. 4.7.

Table 4.7: Normalized decision matrix

A1 A2 A3 A4 A5

C1 (0.466,0.538,0.700) (0.466,0.499,0.635) (0.538,0.700,1.000) (0.466,0.538,0.738) (0.466,0.583,0.777)

C2 (0.266,0.466,0.666) (0.800,1.000,1.000) (0.734,0.934,1.000) (0.534,0.734,0.934) (0.600,0.800,0.934)

C3 (0.534,0.734,0.934) (0.734,0.934,1.000) (0.534,0.734,0.934) (0.800,1.000,1.000) (0.400,0.600,0.800)

C4 (0.734,0.934,1.000) (0.734,0.934,1.000) (0.400,0.600,0.800) (0.734,0.934,1.000) (0.400,0.600,0.800)

C5 (0.600,0.800,1.000) (0.800,1.000,1.000) (0.534,0.734,0.934) (0.600,0.800,0.934) (0.734,0.934,1.000)

C6 (0.534,0.734,0.934) (0.534,0.734,0.934) (0.734,0.934,1.000) (0.534,0.734,0.934) (0.666,0.866,0.934)

Step 6: Considering the different weight of each criterion, the weighted normalized decision

matrix is computed by multiplying the importance weights of evaluation criteria and the values

in the normalized fuzzy decision matrix. This is shown in table no. 4.8.

80

Table 4.8: Weighted normalized fuzzy decision matrix

A1 A2 A3 A4 A5

C1 (0.155,0.233,0.350) (0.155,0.216,0.318) (0.179,0.303,0.500) (0.155,0.233,0.369) (0.155,0.233,0.389)

C2 (0.106,0.233,0.333) (0.320,0.500,0.500) (0.294,0.467,0.500) (0.214,0.367,0.467) (0.240,0.400,0.467)

C3 (0.196,0.343,0.467) (0.269,0.436,0.500) (0.196,0.343,0.467) (0.294,0.467,0.500) (0.147,0.280,0.400)

C4 (0.244,0.404,0.500) (0.244,0.404,0.500) (0.133,0.260,0.400) (0.244,0.404,0.500) (0.133,0.260,0.400)

C5 (0.180,0.320,0.500) (0.240,0.400,0.500) (0.160,0.294,0.467) (0.180,0.320,0.467) (0.220,0.374,0.500)

C6 (0.124,0.244,0.404) (0.124,0.244,0.404) (0.171,0.311,0.433) (0.124,0.244,0.404) (0.155,0.288,0.404)

Step 7: This step is aimed at determining the fuzzy positive ideal solution A+ and the fuzzy

negative ideal solution A-. The value of normalized fuzzy decision matrix is in the range within

the interval [0, 1].which is 1 for positive ideal situation and 0 for negative ideal situation. This is

A+= [(1, 1, 1)] for B, [(0, 0, 0)] for C

A- = [(0, 0, 0)] for B, [(1, 1, 1)] for C

Where B is benefit and C is cost criteria

Step 8: In this step, the n-dimensional separation distances of each alternative i =1, . . . , m to the

fuzzy Positive Ideal Solution di+ and to the fuzzy Negative Ideal Solution di- are computed

according to equation (3.16) and (3.17),as shown in Table no. 4.9.

81

Table 4.9: Distance of alternative from A+ and A

-

Alternative di+ di-

A1 3.774 2.390

A2 3.417 2.708

A3 3.765 2.410

A4 3.578 2.569

A5 3.762 2.390

Step 9: In this step each alternatives closeness index is calculated by following formula

Ci =

Ci 1]

The optimal alternative have value closeness index closer to 1. According to the closeness

coefficient, the ranking of the alternatives can be determined as shown in Table no. 5.10

Table 4.10: Closeness coefficient and ranking

di+ di- Ci Rank

A1 3.774 2.390 0.387 5

A2 3.417 2.708 0.442 1

A3 3.765 2.410 0.390 3

A4 3.578 2.569 0.418 2

A5 3.762 2.390 0.388 4

82

Now vendors are rated according to value of closeness coefficient. In table 5.10 vendor A2 has

value of closeness coefficient much nearer to 1, so it is ranked first while the value of closeness

coefficient for A1 is farthest from 1, so it is ranked last. In this way all other vendors also ranked

according to value of their closeness coefficients.

Now after ranking company found the difficulty that not a single vendor can fulfil all its demand.

So company wants to maximize Total Value of Purchase (TVP) to meets its demand so linear

programming is used to solve this problem.

4.4 LINEAR PROGRAMMING AND QUOTA ALLOCATION

Now company has total demand of 15,000 disk brakes and capacity of each vendor is shown in

table

Table 4.11: Capacity constraint and performance coefficient of vendors

Vendor Capacity Performance Coefficient

A1 6000 units 0.387

A2 3000 units 0.442

A3 4000 units 0.390

A4 3500 units 0.418

A5 2500 units 0.388

Now company wants to maximize the Total Value of Purchase (TVP), which is our objective

represented by Z and X1, X2, X3, X4, X4 and X5 is the number of units purchased from the ith-

83

vendor. Closeness coefficients are parameters that represent the contribution of a unit of the

respective variable x1, x2,,….. xn to measure the of performance of Z

4.5 Formulation of LPP

Max Z = 0.387X1+ 0.442X2+0.390X3+0.418X4+0.388X5 (TVP)

Subject to

X1+X2+X3+X4+X5= 15000 (Demand Constraints)

X1 6000 (Capacity Constraints)

X2 3000

X3 4000

X4 3500

X5 2500

X1, X2, X3, X4, X4, X5 0 (Non Negativity Condition)

X1, X2, X3, X4, and X5 are quota allocated to vendors A1, A2, A3, A4 and A5 respectively.

Where Z represents total value of purchase

84

4.6 SOLUTION of LPP

Now to solve this LPP we use LINGO 12.0 software which is popular for solving LPP

problems.

Figure 4.1 Objective Max Z (Total Value of Purchase)

85

Figure 4.2 Total Value of Purchase Objective Solution

So from the solution Figure 4.2 our objective function optimizes when company purchase from

five vendors below shown units:

X1= 2000

X2=3000

86

X3=4000

X4=3500

X5=2500

Total Value of Purchase (TVP) = 6093.

4.7 Sensitivity Analysis

The main goal of sensitivity analysis is to gain insight to find out which assumptions are critical,

i.e., which assumptions affect choice. The process involves various ways of changing input

values of the model to see the effect on the output value. In this case the value of the supply

capacity of each vendor have been decreased and increased in percentage as shown in Table no.

4.12.

Table 4.13: Percentage change in capacity

vendors Percentage decrease in capacity

-20% -15% -10% 5%

No

change

Percentage increase in capacity

5% 10% 15% 20%

A1 4600 3950 3300 2650 2000 1350 700 50 0

A2 2400 2550 2700 2850 3000 3150 3300 3450 3600

A3 3200 3400 3600 3800 4000 4200 4400 4600 4800

A4 2800 2975 3150 3325 3500 3675 3850 4025 4200

A5 2000 2125 2250 2375 2500 2625 2750 2875 2400

87

The graph is plotted between quota allocation and percentage change in vendor capacity. This is

shown below:

Figure 4.3 Quota allocations v/s % change in capacity

4.8 Result and discussion

Now from Table 4.10 we can see that vendor A2 is ranked 1st while vendor A1 is ranked 5th

. As

per fuzzy TOPSIS methodology the vendor having value of closeness coefficient nearest to 1 is

ranked 1 while vendor having closeness coefficient farthest from 1 is ranked last. In table 4.10

value of closeness coefficient of A2 is 0.442, which is highest, so vendor is ranked 1st while A1

have value 0.387 which is lowest, so it ranked last. In this way all other vendors also ranked

according to their closeness coefficient.

0

1000

2000

3000

4000

5000

6000

-20 -15 -10 -5 0 5 10 15 20

Qu

ota

Allo

cati

on

Quota allocation v/s % change in capacity

A1

A2

A3

A4

A5

% change in total Capacity

88

In quota allocation we found that vendor A1 having highest capacity though quota allocated to

A2 is lowest. This is all due to the poor ranking of the vendor while all other vendor used their

all capacity due to their better ranking in comparison to A1.

Now from the graph between quota allocation and capacity change we found that as capacity of

vendors is increasing the quota allocation to A1 is decreasing. At 20% increase in capacity

vendor A1 has not allocated any quantity. This is due to poor ranking of vendor A1. Also from

seeing graph it is clearly visible as the capacity of vendors increases the quantity allocated to

poor rated vendor is decreasing. As shown in graph quota allocation to vendor A5 also

decreasing as it ranked 4th

.

But when capacity of vendor is decreased quota allocation to A1 is increasing because capacity

of other vendor is not sufficient to fulfill the demand. So to meet the demand when capacity of

high rated vendor is decreased poor rated vendor also get more quota allocation.

89

CHAPTER 5

CONCLUSION

Many practitioners and researchers have presented the advantages of supply chain management.

In order to increase the competitive advantage, many companies consider that a well designed

and implemented supply chain system is an important tool. Under this condition, building on the

closeness and long-term relationships between buyers and suppliers is critical success factor to

establish the supply chain system. Therefore, vendor selection problem becomes the most

important issue to implement a successful supply chain system.

Vendor selection is a fuzzy multi criteria decision making problem in which all the information

and data available are not crisp and precise for decision making. So to make decision in

uncertain condition we used Fuzzy TOPSIS methodology to evaluate and rank the vendors. In a

decision making process, the use of linguistic variables in decision problems is highly beneficial

when performance values cannot be expressed by means of numerical values. In other words,

very often, in assessing of possible suppliers with respect to criteria and importance weights, it is

appropriate to use linguistic variables instead of numerical values.

In this work, some of the important criteria for vendor evaluation are indentified namely, cost,

quality, delivery, flexibility, financial capability and reputation. Fuzzy weights for criteria and

performance rating of alternative is done on the basis of linguistic variable as done in fuzzy

problems. Then fuzzy TOPSIS methodology is applied to select best alternative and rank the

90

various alternatives. For quota allocation, linear programming methodology has been used.

Which is appears a right decision to achieve the goal.

Fuzzy TOPSIS method is very flexible. The closeness coefficient, not only the ranks the

alternatives but also gives the assessment status of all the possible suppliers. Significantly, the

proposed method provides objective information for supplier selection and evaluation in a supply

chain system.

Future scope

The systematic framework for supplier selection in a fuzzy environment presented in this work

can be easily extended to the analysis of other management decision problems. However,

improving the approach for solving supplier selection problems more efficiently and developing

a group decision support system in a fuzzy environment can be considered as a topic for future

research.

91

REFERENCES

1. Achrol, R.S. (1997), “Change in the theory of inter-organisational relation in market toward

network paradigm”, Journal of Academy of Marketing Science, Vol. 25, No. 1, pp. 56-71.

2. Akarte, M. M., Surendera, N. V., Ravi, B., and Rangraj, N., “Web based casting supplier

evaluation using analytical hierarchy process”, Journal of Operational Research Society,

Vol. 52, No. 5, pp. 511-522.

3. Albino, V., Garavelli, A.C., Gorgoglione, M., (1998), “Fuzzy logic in vendor rating: A

comparison between a fuzzy logic system and a neural Network”, Fuzzy Economic Review,

Vol. 32, pp. 25-47.

4. Ballou, R.H., Gilbert, S.M., and Mukherjee, A. (2000), “New managerial challenges from

supply chain opportunities”, Industrial Marketing Management, Vol. 29, pp. 7-18.

5. Barad, M. and Sipper, D. (1988), “Flexibility in manufacturing systems: definitions and

perti-net modeling”, International Journal of Production Research, Vol. 26, No. 2, pp. 237-

48.

6. Barbarosoglu, G., and Yazgac, T. (1997), “An application of analytical hierarchy process to

the supplier problem”, Production and Inventory Management Journal”, Vol. 31, pp. 14-21.

7. Beamon, B. M. (1998). "Supply chain design and analysis: Models and methods."

International Journal of Production Economics, Vol. 55, No. 3, pp. 281-294.

8. Bei, W., Wang, S. and Hu, J. (2006), An analysis of Supplier Selection in Manufacturing

Supply Chain Management.

9. Bellman, R.G., Zadeh, L.A., (1970), “Decision Making a fuzzy environment”, Management

Sciences, Vol.17, pp. B141–B164.

10. Benitez, JM, Martin, JC and Roman, C. (2007), “Using fuzzy number for measuring quality

of service in the hotel industry”, Tour Management, Vol. 28, pp. 544–555.

11. Bhote, K.R. (1989), Strategic Supply Management, Amacom, New York, NY.

12. Bhutta, K.S. and Huq, F. (2002) “Supplier selection problem: a comparison of the total cost

of ownership and analytic hierarchy process approaches” Supply Chain Management, Vol.

7, No. 3, pp. 126-35.

92

13. Boer, L. d., Labro, Eva, and Morlacchi, P. (2001), “A review of methods supporting

supplier selection”, European Journal of Purchasing & Supply Management, Vol. 7, pp. 75-

79.

14. Brierly, S. (2001), “Beyond the buzzword: single sourcing”, Intercom, Vol. 49, No. 1, pp.

15-18.

15. Chen C.T., Lin C.T., Huang S.F. (2006),“A fuzzy approach for supplier evaluation and

selection in supply chain management”, International Journal of Production Economics,

Vol.102, No.2, pp. 289–301.

16. Chen, C.T. (2000), “Extensions of the TOPSIS for group decision-making under fuzzy

environment”, Fuzzy Sets and Systems, Vol. 114, pp. 1-9.

17. Chen, S. M. and Hsu, C.C. (2004), “A New Method to Forecast Enrollments Using Fuzzy

Time Series”, International Journal of Applied Science and Engineering, Vol. 2, pp. 234-

244.

18. Chen, S.J. and Hwang, C.L. (1992), Fuzzy Multiple Attribute Decision Making: Methods

and Applications, Springer-Verlag, Berlin.

19. Choi, T. Y. and Hartley, J. L. (1996), “An exploration of supplier selection practices across

the supply chain”, Journal of Operations Management, Vol. 14, No. 4, pp. 333-343.

20. Chopra, S., and Meindl, P., “Supply chain management, strategy, planning & operation”,

2007- edition, PHI, Delhi.

21. Chopra, S., Meindl, P. (2003), “Supply Chain Management: Strategy, Planning and

Operation” Prentice Hall.

22. Chopra,S., Meindl.P., (2001), Supply Chain management .Strategy, Planning and Operation,

Pearson Education Asia.

23. Chou, S.Y., Shen, C.Y., and Chang, Y.H., (2006), “Vendor selection in a modified re-buy

situation using a strategy aligned fuzzy approach”, International Journal of Production

Research, Vol.21, pp. 1-21.

24. Choy, K. L. and Lee, W. B. (2003), “A generic supplier management tool for outsourced

manufacturing”, Supply Chain Management, Vol. 8, No. 2, pp.140-154.

25. Christopher, M., (1998), “Logistics and supply chain management”, 2nd edition, Prentice

hall; London.

93

26. Chu TC (2002), “Selecting plant location via a fuzzy TOPSIS approach”, International

Journal of Advance Manufacturing Technology, Vol. 20, pp. 859–864.

27. Chu, T.C. and Lin,Y.C.(2003), “A Fuzzy TOPSIS Method for Robot Selection” Journal of

Advance Manufacturing Technology, Vol. 21, pp. 284–290.

28. Clayton, R. (1998), “Insource or outsource?”, Electronic Publishing, Vol. 22, No. 5, pp. 53-

4.

29. Cohen, S., Roussel, J. (2005), Strategic Supply Chain Management, McGraw-Hill.

30. Cooper, M. C., Lambert, D. M., and Pagh, J. D. (1997), “SCM: More Than a New Name for

Logistics”, The International Journal of Logistics Management, Vol. 8, No.1, pp.1–13.

31. Copacino, W. C. (1997), “Supply chain management: The basics and beyond”, Boca Raton:

St. Lucie Press.

32. Cutting-Decelle, A., Das, B., Young, R., Rahimifard, S., Anumba, C., Bouchlaghem, N.

(2006). "Building Supply Chain Communication Systems: A Review of Methods and

Techniques", Data Science Journal, Vol. 5, pp. 1-23.

33. Dahel, N. E. (2003), “Vendor selection and order quantity allocation in volume discount

environments”, Supply Chain Management: An International Journal, Vol. 8, No. 4, pp.

335-342.

34. De Boer, L., Labro, E., & Morlacchi, P. (2001), “A review of methods supporting supplier

selection”, European Journal of Purchasing and Supply Management, Vol. 7, pp. 75–89.

35. Degraeve, Labro and Roodhooft (2001), “Constructing a Total Cost of Ownership

Methodology based on Activity Based Costing and Mathematical Programming”, The 10th

International Annual IPSERA Conference, pp. 267-285.

36. Degraeve, Z., Labro, E., Roodhooft, F., (2000), “An evaluation of supplier selection

methods from a Total Cost of Ownership perspective”, European Journal of Operational

Research, Vol. 125, No. 1, pp. 34-59.

37. Degraeve, Z., Roodhooft, F., (2000), “A mathematical programming approach for

procurement using activity based costing”, Journal of Business Finance and Accounting,

Vol. 27, pp. 69-98.

38. Deng, H. (1999), “Multi-criteria analysis with fuzzy pair-wise comparison”, International

Journal of Approximate Reasoning, Vol. 21, pp. 215-231.

94

39. Deng, H., Yeh, C. and Willis, R.J. (2000),“Inter-company comparison using modified

TOPSIS with objective weights”, Computers & Operations Research, Vol. 27, pp. 963-73.

40. Dzever, S., Merdji, M. & Saives, A. (2001), “Purchase decision making and buyer-seller

relationship development in the French food processing industry”, Supply Chain

Management: An International Journal, Vol. 6, No. 5, pp. 216-229.

41. Dickson, G, (1966), “An analysis of vendor selection systems and decisions”, Journal of

purchasing, Vol.12, No.2, pp. 28-41.

42. Dickson, G.W. (1966), “An analysis of vendor selection systems and decisions”, Journal of

Purchasing, Vol. 2, No.1, pp. 5–17.

43. Drucker, P. F. (1998): “Management‟s New Paradigms.” Forbes Magazine, pp. 152–177.

44. Ellram, L. and Billington, C. (2001), “Purchasing leverage considerations in the outsourcing

decision”, European Journal Of Purchasing and Supply Management, Vol. 7, No. 1, pp. 15-

28.

45. Ellram, L.M. (1990), “ The supplier selection decision in strategic partnerships”, Journal of

Purching and Material Manaement, Vol. 26, No. 4, pp. 8-14.

46. Ernst, R., Kamrad, B. and Ord, K. (2007), “Delivery performance in vendor selection

decisions”, European Journal of Operational Research, Vol. 176, pp. 534-541.

47. Erol, I., William, G., Ferrell Jr., W.G., (2003), “A methodology for selection problems with

multiple, conflicting objectives and both qualitative and quantitative criteria”, International

Journal of Production Economics, Vol. 86, pp. 187–199.

48. Fiorito, S.S., Giunipero, L.C., Yan, H. (1998), "Retail buyers‟ perceptions of quick response

systems", International Journal of Retail & Distribution Management, Vol. 26, No.6, p.237-

46.

49. Freeman, V.T. and Cavinato, J.L. (1990), “Fitting purchasing to the strategic firm:

frameworks, processes and values”, Journal of Purchasing and Materials Management,

Vol. 26, No. 4, pp. 6-10.

50. Gadde, L.E. and Hakansson, H. (2001), Supply Network Strategies, John Wiley & Sons,

Chichester.

51. Gattorna, J. (2006). "Supply Chains are the Business", Supply Chain Management Review.

52. Gerwin, D. (1993), “Manufacturing flexibility: a strategic perspective”, Management

Science, Vol. 39, No. 4, pp. 395-410.

95

53. Ghobadian, A., Stainer, A., & Kiss, T. (1993), “A computerized vendor rating system”, In

Proceedings of the First International Symposium on Logistics”, (pp. 321–328),

Nottingham, UK: The University of Nottingham.

54. Ghodsypour, S.H. and O‟Brien, C. (2001), “The total cost of logistics in supplier selection,

under conditions of multiple sourcing, multiple criteria and capacity constraints”,

International Journal of Production Economics, Vol. 73, pp. 15-27.

55. Ghodyspour, S.H. and O`Brien, C., (1998), “A decision support system for supplier

selection using an integrated analytic hierarchy process and linear programming”,

International Journal of Production Economics, Vol. 56-57, pp. 199-212.

56. Goffin, K., Szwejczewski, M. and New, C. (1997), “Managing suppliers: when fewer can

mean more”, International Journal of Physical Distribution & Logistics Management, Vol.

27, No. 7, pp. 422-36.

57. Golmohammadi, D., Creese, C.R., Valian, H. and Kolassa, J. (2009), “Supplier selection

based on neural network model using genetic algorithm”, IEEE Transactions on neural

network, Vol. 20, No. 9, pp. 1504-1519.

58. Govindan, K., Kanan, D. and Haq A.N. (2009), “Analyzing supplier development criteria

for an automobile industry”, Industrial Management & Data Systems, Vol. 110, No. 1, pp.

43-62.

59. Ha, S.H., Krishnan, R..(2008), “A hybrid approach to supplier selection for the maintenance

of a competitive supply chain”, Expert Systems with Applications, Vol.34, No. 2, pp.1303–

1311.

60. Handfield, R. B. and Nicolas, E. L. (1999), “Introduction to supply chain management”,

Upper Saddle River, N.J: Prentice Hall.

61. Handfield, R. B., Walton, Steven V., Sroufe, Robert and Melynyk, Steven A. (2002)

Applying environmental criteria to supplier assessment: a study in the application of the

analytical hierarchy process, European Journal of Operational Research, Vol. 141, pp. 70-

87.

62. Handfield, R. B. (1994), “US global sourcing patterns of development”, International

Journal of Operation and Production Management, Vol. 14, pp. 40-51.

63. Harland, C.M. (1996), “Supply chain management: relationships chains and networks”,

British Journal of Management, Vol. 7, pp. S63-S80.

96

64. Haywood, J.B. (2001), The Outsourcing Dilemma: The Search for Competitiveness,

Pearson Education, Upper Saddle River, NJ.

65. Ho, W., Xu, Xiaowei and Parshanta, K.D. (2010), “Multi-criteria decision making

approaches for supplier evaluation and selection: A literature review”, European Journal of

Operational Research, Vol. 202, pp. 16-24.

66. Holt, G.D., (1998), “Which contractor selection methodology?”, International Journal of

project management, Vol. 21, No.16, pp. 153-164.

67. Hwang CL, Yoon K (1981) Multiple attributes decision making methods and applications.

Springer, Berlin.

68. Jahanshahloo GR, Hosseinzadeh LF, Izadikhah M. (2006), “Extension of the TOPSIS

method for decision-making problems with fuzzy data”, Applied Mathematics Computer

science, Vol. 181, pp. 1544–1551.

69. Jee, D. and Kang, K. (2000), “A method for optimal material selection aided with decision

making theory”, Materials and Design, Vol. 21, pp. 199-206.

70. Juttner, U., Christopher, M. and Baker, S. (2007), “Demand chain management integrating

marketing and supply chain management”, Industrial Marketing Management, Vol. 36 No.

3, pp. 377-92.

71. Kahraman, C., Cebeci, U. and Ulukan, Z. (2003), “Multi-criteria supplier selection using

fuzzy AHP”, Logistics Information Management , Vol. 16, No. 6, pp. 382-394.

72. Karpak, B., Kumcu, E., Kasuganti, R., (1999), “An application of visual interactive goal

programming: a case in vendor selection decisions”, Journal of Multi-Criteria Decision

Analysis, Vol. 8, pp. 93-105.

73. Karpak, B., Kumcu, E., Kasuganti, R.R., (2001), “Purchasing materials in the supply chain:

Managing a multi-objective task”, European Journal of Purchasing and Supply

Management, Vol. 7, No. 3, pp. 209–216.

74. Kelle, P. and Miller, P.A. (2001), “Stockout risk and order splittin”, International Journal of

Production Economics, Vol. 71, Nos 1-3, pp. 407-16.

75. Khoo, L.P., Tor, S.B., Lee, S.S.G., (1998), “The potential of intelligent software agents in

the World Wide Web in the automated part procurement”, International Journal of

Purchasing and Materials Management, Vol. 34, No. 1, pp. 46-52.

97

76. Kieskin, G. A., IIhan, S. and Ozkan, C. (2010), “The Fuzzy ART algorithm: A

categorization method for supplier evaluation and selection”, Expert system with

applications, Vol. 37, pp. 1235- 1240.

77. Krajeweski, L. & Ritzman, L. (2006), Operations Management-Strategy and Analysis.

Reading, MA: Addison Wesley.

78. Kulchitsky, T.D. (1998), “Single sourcing and supplier certification: performance and

relationship implications”, Industrial Marketing Management, Vol. 27, No. 9, pp. 73-82.

79. Kumar, M., Vrat, P., and Shankar, R., (2006), “A fuzzy programming approach for vendor

selection problem in a supply chain, International Journal of Production Economics”, Vol.

101, No. 2, pp. 273-285.

80. Lambert Douglas M. (2005) “ An Evaluation of Process-oriented Supply Chain

Management Frameworks”, The International Journal of Business Logistic, Vol.26, No.1,

pp. 25-45.

81. Lambert, D. M., Cooper, M. C., and Pagh, J. D.(1998 ), “SCM: Implementation Issues and

Research Opportunities”, The International Journal of Logistics Management, Vol. 9, No.2,

pp.1–19.

82. Lambert, D.M. and Cooper M.C. (2000), "Issues in Supply Chain Management," Industrial

Marketing Management, Vol. 29, No. 1, pp. 65-83.

83. Lancaster, S., Yen, D., and Yuan K. C. (2006), “Information Management, Computer

Security”, Vol.14, No. 2, pp. 167-184.

84. Larson, P.D. and Kulchitsky, T.D. (1998), “Single sourcing and supplier certification:

performance and relationship implications”, Industrial Marketing Management, Vol. 27,

No. 1, pp. 71-84.

85. Lee, H. L. (2000). "Creating Value through Supply Chain Integration". Supply Chain

Management Review, Vol. 4, No 4, pp. 30-36.

86. Leenders, M. L., Fearon, H. E. (1997), “Purchasing and Supply Management”, 11th edition

New York: McGraw-Hill.

87. Li, C.C., Fun, Y.P., Hung, J.S., (1997), “A new measure for supplier performance

evaluation”, IIE Transactions on Operations Engineering, Vol. 29, pp. 753–758.

98

88. Lummus, R.R., D.W. Krumwiede and R.J. Vokurka (2001), "The Relationship of Logistics

to Supply Chain Management: Developing a Common Industry Definition," Industrial

Management Data Systems, Vol. 101, pp. 426-431.

89. Lummus, R.R., Vokurka, R.J. (1999), "Defining SCM: a historical perspective and practical

guidelines", Industrial Management & Data Systems, Vol. 99, No.1, pp.11-17.

90. Lynch, C. (2001), “Developing a strategy for outsourcing”, Logistic Manangement and

Distribution, Vol. 40, No. 6, pp. T3-T7.

91. Malagoli, S. and Magni, C.A. (2007) "The use of fuzzy logic and expert systems for rating

and pricing firms: A new perspective on valuation", Managerial Finance, Vol. 33, No. 11,

pp.836 – 852.

92. Mandal, A., Deshmukh, S.G., (1994), “Vendor selection using Interpretive Structural

Modelling (ISM)”, International Journal of Operations and Production Management, Vol.

14, No. 8, pp. 52-59.

93. Martin, C.(2000), "The Agile Supply Chain: Competing in Volatile Markets," Industrial

Marketing Management, Vol. 29, No. 1, pp. 37-44.

94. Masella, C. and Rangone, A. (2000), “A contingent approach to the design of vendor

selection systems for different types of co-operative customer/supplier relationship”,

International Journal of Operations & Production Management, Vol. 20, No.1, pp. 70-84.

95. McCutcheon, D. and F.I. Stuart (2000) ", Issues in the Choice of Supplier Alliance

Partners," Journal of Operations Management, Vol. 18, No. 3, pp. 279-301.

96. McIvor,R. (2000), “A practical framework for understanding the outsourcing process”

Supply Chain Management: An International Journal, Vol.5, No. 1, pp.22 – 36.

97. Michel, P., Reast, J. and Lynch, J. (1998), “Exploring the foundation of trust”, Journal of

marketing management, Vol. 14, No. 1-3, pp. 159-72.

98. Min, H., (1994), “International supplier selection: a multi-attribute utility approach”,

International Journal of Physical Distribution & Logistics Management, Vol. 24, pp. 24-33.

99. Mohanty, R.P., and Deshmukh, S.G. (2005), “Supply chain management: theories and

practices”, Biztantra Publication, Delhi.

100. Morlacchi, P., (1997), “Small and medium enterprises in supply chain: a supplier

evaluation model and some empirical results”, In: Proceedings of IFPMM Summer

School, August, Saltsburg.

99

101. Muñoz, M.J., Rivera, J.M. and Moneva, J.M. (2008) "Evaluating sustainability in

organisations with a fuzzy logic approach", Industrial Management & Data Systems, Vol.

108, No. 6, pp.829 - 841.

102. Narasimhan, R., (1983), “An analytic approach to supplier selection”, Journal of

Purchasing and Materials Management Winter, Vol. 21, pp. 27–32.

103. Narasimhan, R., Mahapatra, S. (2004). "Decision models in global supply chain

management", Industrial Marketing Management, Vol. 33, No.1, pp. 21-27.

104. Nassimbeig, G., Battain, F., (2003), “Evaluation of supplier contribution to product

development:fuzzy and neuro-fuzzy based approaches”, International Journal of

Production Research, Vol. 41, No. 13, pp. 2933-2956.

105. Ng, W.L. (2008), “An efficient and simple model for multiple criteria supplier selection

problem”, European Journal of Operational Research, Vol. 186, No.3, pp. 1059–1067.

106. Nguyen, H. T. and Walker, E. A. (2000), A first course in fuzzy logic, Chapman &

Hall/CRC Florida.

107. Nydick and Hill (1992), “Using the Analytic Hierarchy Process to Structure the Supplier

Selection Procedure”, International Journal of Purchasing and Materials Management,

Vol. 28, No. 2, pp. 31-36.

108. Oke, S.A. and Charles-Owaba, O.E. (2006) "Application of fuzzy logic control model to

Gantt charting preventive maintenance scheduling", International Journal of Quality &

Reliability Management, Vol. 23, pp.441 – 459.

109. Ordoobadi, S.M. (2008) "Fuzzy logic and evaluation of advanced technologies", Industrial

Management & Data Systems, Vol. 108, No. 7, pp.928 – 946.

110. Parkan, C. and Wu, M. (1999),“Decision-making and performance measurement models

with applications to robot selection”, Computers & Industrial Engineering, Vol. 36, pp.

503-23.

111. Pearson, J. N. and Ellram, L. M. (1995), “Supplier selection and evaluation in small versus

large electronics firms”, Journal of Small Business Management, Vol. 9, pp.53-65.

112. Petroni and Braglia (2000), “Vendor Selection Using Principal Component Analysis”, The

Journal of Supply Chain Management: A Global Review of Purchasing and Supply, Vol.

23, pp. 63-69.

100

113. Petroni, A. & Braglia, M. (2000), „Vendor selection using principal component analysis‟,

Journal of Supply Chain Management, Vol. 36, No. 2, pp. 63-69.

114. Pfaffman, E. and Stephan, M. (2001), “How Germany wins out in the battle for foreign

direct investment: strategies of multinational suppliers in the car industry”, Long Range

Planning, Vol. 34, No. 3, pp. 336-55.

115. Porter, M. (1980). Competitive Strategy, New York, Free Press.

116. Romano, P., Vinelli, A. (2001), “Quality management in a supply chain perspective:

Strategic and operative choices in a textile-apparel network”, Operations & Production

Management, Vol.21, No.4, pp. 446- 460.

117. Ronen, B., Trietsch, D., (1988), “A decision support system for purchasing management

of large projects”, Operations Research, Vol. 36, pp. 882-890.

118. Rosenthal, E.C., Zydiak, J.L., Chaudhry, S.S., (1995), “Vendor selection with bundling”,

Decision Sciences, Vol. 26, No. 1, pp. 35-48.

119. Roth, A.V.,Tsay AA., Pullman M.E. and Gray J.V. (2008), "Unraveling the Food Supply

Chain: Strategic Insights from China and the 2007 Recalls," The Journal of Supply Chain

Management, Vol. 44, No.1, pp. 22-39.

120. Sahay, B.S., Jatinder N.D. Gupta, Mohan,R., ; 2006. Managing supply chains for

competitiveness: the Indian scenario. Supply Chain Management: An International

Journal, Vol. 11, No. 1, pp. 15-24.

121. Sarkis, J., & Talluri, S. (2002), “A model for strategic supplier selection”, Journal of

Supply Chain Management, Vol. 38, No.1, pp. 18–28.

122. Sarkis, J., Talluri, S., (2000), “A model for strategic supplier selection”, In: Leenders, M.

(Ed.), Proceedings of the 9th international IPSERA Conference. Richard Ivey Business

School, London, Ontario, pp. 652-661.

123. Schorr, J.E. (1992), Purchasing in the 21st Century, Oliver Wight Publications,

Brattleboro, VT.

124. Schroeder, R.G. (2000), Operations Management, Contemporary Concepts and Cases,

McGraw-Hill, Irwin, New York, NY.

125. Semih, O., Efendigil, T. and Kara, S.S. (2009), “A combined fuzzy MCDM approach for

selecting shopping center site: An example from Istanbul, Turkey” Expert Systems with

Applications, Vol. 37, pp. 1973–1980.

101

126. Sharma, J.K. (1997), “Operations research theory and applications”, Macmillan India Ltd.

127. Shlrouyehzad, H., Hossieinzadeh, F. and Dabestani, R. (2009), “A date envelopment

analysis approach based on service quality concept for vendor selection”, Vol. 978, No. 1,

pp. 426-430.

128. Shore, B., Venkatachalam, A.R. (2003) "Evaluating the information sharing capabilities of

supply chain partners: A fuzzy logic model", International Journal of Physical

Distribution & Logistics Management, Vol. 33, No.9, pp.804 – 824.

129. Simchi-Levi, D., Kaminsky, P. & Simchi-Levi, E. (2003), “Designing and Managing the

Supply Chain: Concepts, Strategies, and Case Studies”, McGraw-Hill, New York.

130. Soukup, W.R., (1987), “Supplier selection strategies”, Journal of Purchasing and

Materials Management, Vol. 23, No.3, pp. 7-12.

131. Sriram, V. and Mummalaneni, V. (1990), “Determinants of source loyalty in buyer-seller

relationships”, Journal of Purchasing and Materials Management, Vol. 26, No. 4, pp. 21-

6.

132. Stock, J.R., and Lambert, D.M. (2001), “Strategic logistics management”, 4th

edition,

McGraw-Hill Irwin; Boston; MA.

133. Stork, K. (1999a), “A best practice that should be used more: single sourcing”,

Purchasing, Vol. 127, No. 4, pp. 27-8.

134. Suarey, F., Cusumano, M.A. and Fine, C.H. (1991), Flexibility and Performance: A

Literature Critique and Strategic Framework, Sloan School, MIT, Cambridge, MA.

135. Subramani, M. (2004), “How Do Suppliers Benefit From Information Technology Use In

supply chain Relationships?”, MIS Quarterly, Vol. 23, No. 1, pp. 45-73.

136. Sun,Chia-Chi and Lin,T.R.,Grace (2009), “Using fuzzy TOPSIS method for evaluating the

competitive advantages of shopping websites”, Expert Systems with Applications, Vol.36,

pp.11764–11771.

137. Tahriri, F., Osman, M.R. and Ali A. (2008) “AHP approach for supplier evaluation and

selection in a steel manufacturing company”, Journal of industrial engineering and

management, Vol. 1, No. 3, pp. 54-76.

138. Talluri, S. and Narasimhan, R. (2003), “A methodology for strategic sourcing”, European

Journal of Operations Research, Vol. 154, pp. 236-50.

102

139. Talluri, S. and Sarkis, J. (2002), “ A model for performance monitoring of supplier”,

International journal of production research, Vol. 40 No. 16, pp. 4257-4259.

140. Tanner, J.F. (1999), "Organizational Buying Theories: a Bridge to Relationships Theory,"

Industrial Marketing Management, Vol. 28 No. 3, pp. 245-255.

141. Tempelmeier, H. (2002), “A simple heuristic for dynamic order sizing and supplier

selection with time-varying data”, Production and Operation Management, Vol.11 No. 4,

pp. 499-515.

142. Thaver, I. and Wilock, A. (2006), “Identification of overseas vendor selection criteria

used by Canadian apparel buyers Is ISO 9000 relevant?” Journal of Fashion Marketing

and Management, Vol. 10 No. 1, pp. 56-70.

143. Timmerman, M. (1986), “An approach to vendor performance evaluation,” J. Purchasing

Mater. Manag., vol. 22, No. 4, pp. 2–8.

144. Tracey, M. and Tan, C. L. (2001), “Empirical analysis of supplier selection and

involvement, customer satisfaction, and firm performance”, Supply Chain Management:

An International Journal, Vol. 6, No. 4, pp.174-188.

145. Triantaphyllou, E. and Lin, C.T. (1996), “Development and evaluation of five fuzzy

multiattribute decision making methods”, International Journal of Approximate

Reasoning, Vol. 14, pp. 281-310.

146. Tsaur, S.H., Chang, T.Y. and Chang-Hua Yen, C. (2002) “The evaluation of airline

service quality by fuzzy MCDM”, Tourism Management, Vol. 23, pp. 107-15.

147. Upton, D.M. (1995), “What really makes factories flexible?”, Harvard Business Review,

July/August, pp. 74-84.

148. Verma, A. (1997) "Construction of offender profiles using fuzzy logic", Policing: An

International Journal of Police Strategies & Management, Vol. 20, No. 2, pp .408 – 418.

149. Vickery, S., Calantone, R. and Droge, C. (1999), “Supply chain flexibility: an empirical

study”, The Journal of Supply Chain Management, pp. 16-25.

150. Walker, W.T. (2001) “Synchronizing supply chain operations”, The Journal of Enterprise

Resource Management, First Quarter, Vol. 5, No. 1, pp. 15–21.

151. Wang TC, Chang TH (2007), “Application of TOPSIS in evaluating initial training

aircraft under fuzzy environment”, Expert System Application, Vol. 33, No. 4, pp. 870–

880.

103

152. Wang, G., Huang, S. H. e Dismukes, J. P. (2004), “Product-driven supply chain selection

using integrated multi-criteria decision-making methodology”,International Journal of

Production Economics, Vol. 91, pp. 1-15.

153. Wang, Y.J., Lee H.S. (2007), “Generalizing TOPSIS for fuzzy multi-criteria group

decision making”, Computer and Math Applications, Vol. 53, pp. 1762–1772.

154. Wang, Y.-M., & Elhag, T. M. S. (2006). “Fuzzy TOPSIS method based on alpha level sets

with an application to bridge risk assessment”, Expert Systems with Applications, Vol. 31,

No. 2, pp. 309–319.

155. Weber C.A., C.R. John and Desai A. (1998) , “Non-cooperative negotiation strategies for

vendor selection”, European Journal of Operational Research, Vol.108, No.1, pp208-223.

156. Weber, C. A., Current, J. R., & Benton, W. C. (1991), “Vendor selection criteria and

methods”, European Journal of Operational Research, Vol. 50, No.1, pp. 2–18.

157. Weber, C. A., Current, J. R., & Desai, A. (2000), “An optimization approach to

determining the number of vendors to employ, Supply Chain Management”, An

International Journal, Vol.5, No.2, pp. 90–98.

158. Weber, C. A., Current, J. R., Desai, A. (1998), “Non-cooperative negotiation strategies for

vendor selection”, European Journal of Operational Research, Vol.108, pp. 208–223.

159. Weber, C.A., Desai, A., (1996), “Determination of paths to vendor market e$ciency using

parallel co-ordinates representation: a negotiation tool for buyers”, European Journal of

Operational Research, Vol. 90, pp. 142-155.

160. Weber, C.A., J.R. Current, and Desai A. (2000), “An optimization approach to

determining the number of vendors to employ”, Supply Chain Management: An

International Journal, Vol.2, No.5, pp. 90-98.

161. Wilson, D.T., (1995), “An integrated model of buyer-supplier relationship”, Journal of

academy of marketing science, Vol. 23, No. 4, pp. 335-45.

162. Wilson, E.J. (1994), “The relative importance of supplier selection criteria: A review and

update”, International journal of purchasing and material management, Vol. 30, No.3, pp.

35-45.

163. Wu, T., Shunk, D., Blackhurst, J., Appalla, R. (2007), “AIDEA: A methodology for

supplier evaluation and selection in a supplier-based manufacturing environment”,

International Journal of Manufacturing Technology and Management, Vol. 11, No2, pp.

174–192.

104

164. Xiaobo Zhao, Deju X., Hanqin Z. and Qi-Ming H.,(2006), “Modeling and analysis of a

supply–assembly–store chain”, European Journal of Operational Research, Vol. 176, No.

1, pp.275-294.

165. Yong D (2006), “Plant location selection based on fuzzy TOPSIS” Int J Adv Manuf

Technology, Vol. 28, pp. 839–844.

166. Zadeh LA (1965), “ The concepts of linguistic variable and its application to approximate

reasoning”, Inf Sci, Vol.8, No.1, pp.199–249.

167. Zeng, A. Z. (2000), “A synthetic study of sourcing strategies”, Industrial Management &

Data Systems, Vol. 100, Nos 5/6, pp. 219-26.

168. Zenz, G., 1981. Purchasing and the Management of Materials. Wiley, New York.

169. Zimmermann, H. J. (1978), “Fuzzy programming and linear programming with several

objective functions”, Fuzzy Sets and Systems, Vol.1, pp. 45–55.