A Review Paper on “Multicriteria Decision Making Approaches and Criterion” Used in Supplier Selection and Evaluation

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A Review Paper on “Multicriteria Decision Making Approaches and Criterion” Used in Supplier Selection and Evaluation

Abhishek P. Mohitea , Tushar R. Mohiteb , Ketan M. Mathakaric , Sachin B. Khotd

Students, Mechanical Engineering Dept., A.D.C.E.T., Ashtaa,b,c Assistant Professor, Mechanical Engineering Dept., A.D.C.E.T., Ashtad

Abstract

The objective of this paper is review of all developed appropriate methods and tools that deal with decision making problems in supplier selection. Supplier selection has become an important part of supply chain management and hence selecting and evaluating suppliers is complicated task due to the fact that various criterion must be considered in the decision making process. An extensive range of decision making methods have been suggested to handle the supplier selection problem by a large number of authors in this area. Review of international journal articles published between 2000 and 2013 have been surveyed for this purpose. The articles are observed and studied to summarize the existing methods and the repeatedly used most popular method is identified and presented in this paper. Finally, suggestions for future researches are proposed for the decision makers.

  1. Introduction

    Selecting and evaluating suppliers is complicated task due to the fact that various criterion must be considered in the decision making process. Supplier selection is one of the strategic elements in managing purchases, as the ability of a company to satisfy its clients, as well as its own continuity, depends to a large extent on its suppliers. The researchers in supplier selection field have been applied multi-criteria decision making methods, such as Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), Artificial Neural Network(ANN), Data Envelopment Analysis(DEA), fuzzy set theory, mathematical programming. The process involves different types of criteria with these approaches.

    There are at least six journal articles reviewing the literature regarding supplier evaluation and selection models (Weber et al. 1991; Holt 1998; Degraeve et al. 2000; de Boer et al. 2001; Ho et al. 2010; Amindoust et al. 2012). This paper presents a comprehensive review

    of literature to identify the existing supplier selection methods and determine the most popular ones.

  2. Approaches of Supplier Selection

    Most common reviewed methods that are used in decision making are briefly discussed below:

    1. Analytic Hierarchy Process (AHP):

      A AHP method was first introduced by Saaty. In AHP, the problem is constructed as a hierarchy breaking down the decision top to bottom. The goal is at the top level, criteria and sub-criteria are in middle levels, and the alternatives are at the bottom layer of the hierarchy.

    2. Analytic Network Process (ANP) :

      The ANP methodology is a general form of the AHP, both were introduced by Saaty . Although AHP is easy to use and apply, its unidirectional relationship characteristic cannot handle the complexity of many problems. ANP, however, deals with the problem as a network of complex relationships between alternatives and criteria where all the elements can be connected.

    3. Technique for order preference by similarity to ideal solutions (TOPSIS):

      The basic concept of this method is that the selected alternative is the one that has the best value for all criteria, i.e. has the shortest distance from the negative ideal solution.

    4. Multi-attribute utility theory (MAUT):

      This is one of the most popular MSDM methods. The theory takes into consideration the decision makers preferences in the form of the utility function which is defined over a set of attributes, where

      the utility of each attribute or criterion doesnt have to be linear.

    5. Simple Additive Weighting (SAW):

      APPROACH

      YEAR

      AUTHOR

      Data

      1997

      1.Baker and

      Envelopment

      Talluri

      Analysis(DEA).

      2000

      2. Braglia and

      2000

      Petroni

      3. Liu et al

      2001

      4.Forker and

      Mendez

      2001

      5.Narasimhan et al

      2001

      6. Narasimhan et

      al.

      2002

      7. Talluri and

      Baker

      2002

      8. Talluri and

      Sarkis

      2004

      9. Talluri and

      Narasimhan

      2006

      10. Garfamy

      2006

      11. Ross et al.

      2006

      12. Saen

      2006

      13.Seydel

      2006

      14.Talluri et al.

      2007

      15. Saen

      2007

      16.Wu et al.

      Linear

      2003

      1. Talluri and

      Programming

      Narasimhan

      (LP).

      2005

      2. Talluri and

      Narasimha

      2008

      3 .Ng

      Integer Linear

      2002

      1. Talluri

      Programming

      2005

      2. Hong et al.

      Integer Non- Linear

      Programming

      2001

      Ghodsypour OBrien

      Goal

      Programming

      2001

      Karpak et al

      Multi-objective

      2006

      1.Narasimhan et

      programming

      al.

      2007

      2. Wadhwa and

      Ravindran

      Analytic

      2001

      1. Akarte et al

      Hierarchy

      2002

      2. Muralidharan et

      Process(AHP)

      al.

      2004

      3. Chan and

      Chan

      2005

      4. Liu and Hai

      2007

      5. Chan et al.

      2007

      6. Hou and Su

      It is probably the most used MCDA method. It is intuitive and easy. Simple Additive Weighting (SAW) which is also known as weighted linear combination or scoring methods is a simple and most often used multi attribute decision technique. The method is based on the weighted average. An evaluation score is calculated for each alternative by multiplying the scaled value given to the alternative of that attribute with the weights of relative importance directly assigned by decision maker followed by summing of the products for all criteria. The advantage of this method is that it is a proportional linear transformation of the raw data which means that the relative order of magniude ofthe standardized scores remains equal.

    6. Artificial Neutral Network :

      The human brain provides proof of the existence of massive neural networks that can succeed at those cognitive, perceptual, and control tasks in which humans are successful. The brain is capable of computationally demanding perceptual acts (e.g. recognition off aces, speech) and control activities (e.g. body movements and body functions). The advantage of the brain is its effective use of massive parallelism, the highly parallel computing structure and the imprecise information-processing capability. Hence the student stress is dealing with the biological factor ANN is the best method to validate problems associated with it. Artificial neutral networks (ANN) have been developed as generalizations of mathematical models of biological nervous systems.

    7. Data Envelopment Analysis:

      Data envelopment analysis (DEA) is a mathematical programming method to provide a relative efficiency evaluation for a group of decision making units (DMU) with multiple numbers of inputs and outputs. It is proposed by Charnes, Cooper and Rhoders in 1978 . To allow for applications to a wide variety of activities, it uses the term DMU to refer to any entity that it to be evaluated in terms of its abilities to covert inputs into outputs. It assumes that there are n DMUs to be evaluated.

  3. Individual and Integrated Approaches Reviewed From The Papers During Year 2000-2013

Analytic

2002

1.Sarkis and

Network Process

Talluri

(ANP)

2006

2.Bayazit

2007

3.Gencer and

Gürpinar

Fuzzy Set

2006

1. Chen et al.

Theory

2006

2. Sarkar and

2007

3.Mohapatra

Florez-Lopez

Simple Multi-

2003

1.Barla

Attribute Rating

2.Huang and

Technique

Keska (2007)

(SMART)

Genetic

Algorithm (GA)

2005

Ding et al.

Integrated AHP And Bi-

Negotiation

2007

Chen and Huang

Integrated AHP

2007

1.Ramanathan

And DEA

2007

2.Saen

2007

3.Sevkli et al

Integrated AHP, DEA And Artificial Neural

Network

2008

Ha and Krishnan

Integrated AHP

2003

1.Çebi and

And GP

Bayraktar

2004,2005

2.Wang et al

2006

3.Perçin

2008

4.Kull and

Talluri

5. Mendoza et al.

Integrated AHP

2008

.Mendoza and

And Mixed

Ventura

Integer Non-

Linear

Programming

Integrated AHP And Multi-

Objective Programming

2007

.Xia and Wu

Integrated Fuzzy

2003

1. Kahraman et al.

And AHP

2.Chan and

Kumar

2007

Integrated Fuzzy, AHP And Cluster

Analysis

2008

Bottani and Rizzi

Integrated Fuzzy

And GA

2004

Jain et al.

Integrated Fuzzy

And Multi-

2006

Amid et al.

Objective

Integrated Fuzzy And Quality Function

Deployment

2006

Bevilacqua et al.

Integrated Fuzzy And SMART

2002

2008

  1. Kwong et al

  2. Cho u and Chang

Integrated ANN

And CBR

2003, 2004

Choy et al.

Integrated ANN

And GA

2006

Lau et al.

Integrated ANP And Multi- Objective

Programming

2008

Demirtas and Üstün

Integrated ANP

And GP

2009

Demirtas and

Üstün

Integrated DEA And Multi-

Objective Programming

2000

2008

  1. Weber et al.

  2. Talluri et al.

Integrated DEA

And SMART

2005

Seydel

Integrated GA And Multi- Objective

Programming

2007

Liao and Rittscher

Fuzzy logic

2009

Gulcin

Buyukozkan et al.

Fuzzy Technique for Order of Preference by Similarity to Ideal Solution

(TOPSIS).

2009

Fatih Emre Boran et al.

Analytic Hierarchy

Process (AHP)

2011

Katica Simunovic et al.

Fuzzy Analytic Hierarchy

Process

2011

Adnan Aktepe et al.

Technique for Order of Preference by Similarity to Ideal Solution

(TOPSIS).

2011

Mohammad Saeed Zaeri et al.

Fuzzy Analytic Network Process

(ANP)

2011

He-Yau Kang et al.

Data

Envelopment

2011

Mohsen Jafari

Songhori et al.

Analysis (DEA)

An integrated approach of Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution

(TOPSIS).

2012

Bahar Sennaroglu et al.

Technique for Order of Preference by Similarity to Ideal Solution

(TOPSIS).

2012

Ajit Pal Singh et al.

An integrated approach of Analytic Network Process (ANP)

and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution

(TOPSIS).

2012

Ali A. Yahya Tabar et al.

Analytic Hierarchy

Process (AHP)

2012

David Asamoah et al.

An integrated approach of Analytic Network Process (ANP)

and Technique for Order of Preference by Similarity to Ideal Solution

(TOPSIS).

2012

K. Shahroudi et al.

Measuring Attractiveness By a Categorical- Based Evaluation Technique

(MACBETH)

2012

Prasad Karandea et al.

Individual

Analytic

2013

Emrah Onder et

al.

Hierarchy Process (AHP) Individual Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)

An integrated approach of Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to

Ideal Solution (TOPSIS)

Individual Grey relational analysis (GRA) Individual Analytic Hierarchy Process (AHP) An integrated approach of Grey relational analysis (GRA) and Analytic

Hierarchy Process (AHP)

2013

Pandian Pitchipoo et al.

Fuzzy Decision- Making Trial and Evaluation Laboratory (DEMATEL)

Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)

Analytic Network Process

(ANP)

2013

Ozer Uygun et al.

Fuzzy Analytic Hierarchy

Process

2013

Mustafa Batuhan et al.

Technique for Order of Preference by

Similarity to

2013

Ashish H. et al.

Ideal Solution

(TOPSIS).

An integrated

2013

Massoud Kassaee

approach of

et al.

Fuzzy

Analytic

Network Process

(ANP)

and Fuzzy

Technique for

Order of

Preference by

Similarity to

Ideal Solution

(TOPSIS).

Green Supplier

2013

Malihe Dehghani

selection Fuzzy

et al.

Analytic

Network Process

(ANP)

  1. Most Popular Criterion Observed In These Review Papers

    The most popular criterion used for evaluating the performance of suppliers is quality, price/cost, performance, service, management, technology, production and development, finance, flexibility, reputation, relationship, risk, and safety and environment.

  2. Future Work

    Since in the proposed methodology all the inputs are ordinary or single-value numbers. The review it has been found that individual approaches were used more than the integral approaches in earlier days and Environmental criterion not precisely focused in many articles. Further study can be based on the integrated approaches along with the green supplier selection. Some criteria may be impractical to evaluate, information may be difficult to obtain, complex to analyze, or there may not be sufficient time to perform such evaluations. When the performance of alternative suppliers can only be approximated. The proposed model can be implemented to reduce the number of criteria to most important ones in some other problems, to which MCDM approaches can be applied. Among the numerous methods that have been proposed for assessing the supplier, loss functions such as Taguchi loss function without any range are considered one of the most effective techniques for identifying quality parts. Quality loss functions are more reliable and precise functions in order to assess the quality. Also integrating Taguchi loss function with other methods can be applied.

  3. Conclusion

This paper review the multi criteria decision making approaches for supplier evaluation and selection on literature from 2000 to 2013 and it has been found that many individual and integrated approaches were proposed for supplier selection. The supplier selection process is a technique for evaluating suitable companies to meet a particular need, and in order to narrow the field for such a selection, some evaluative criteria are needed. Even with the large number of available MCDA methods, none of them is considered the best for all kinds of decision-making situations. Different methods often produce different results even when applied to the same problem using same data. There is no better or worse method but only a technique that fits better in a certain situation. The most prevalent individual approach used earlier is DEA and now a days the TOPSIS method is used whereas the most popular integrated approach is AHP Mathematical Programming. The most popular criterion used for evaluating the performance of suppliers is quality, price/cost, performance, service, management, technology, production and development, finance, flexibility, reputation, relationship, risk, and safety and environment. Recently also the Environmental criteria are widely used in supplier selection systems called green supplier selection method along with integrated approaches.

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