Munich Personal RePEc Archive
Supplier selection by F-compromise
method: a case study of cement industry
of NE India
Mukherjee, Krishnendu and Sarkar, Bijon and
Bhattacharyya, Ardhendu
Department of Mechanical Engineering,Budge Budge Institute of
Technology, Department of Production Engineering, Jadavpur
University, Department of Mechanical Engineering, JIS College of
Engineering
2012
Online at https://mpra.ub.uni-muenchen.de/57786/
MPRA Paper No. 57786, posted 06 Aug 2014 17:47 UTC
162 Int. J. Computational Systems Engineering, Vol. 1, No. 3, 2013
Copyright © 2013 Inderscience Enterprises Ltd.
Supplier selection by F-compromise method: a case
study of cement industry of NE India
Krishnendu Mukherjee*
Department of Mechanical Engineering,
Budge Budge Institute of Technology,
Kolkata-700137, India
E-mail: gopal.mech2010@gmail.com
*Corresponding author
Bijon Sarkar
Department of Production Engineering,
Jadavpur University,
Kolkata-700032, India
E-mail: bijon_sarkar@email.com
Ardhendu Bhattacharyya
Department of Mechanical Engineering,
JIS College of Engineering,
Kalyani, Nadia, West Bengal-741235, India
E-mail: dharam2004@gmail.com
Abstract: In this paper, we initially conducted a brief review of supplier selection methods to
find most cited multi-criteria decision making method, present trend of supplier selection and
most cited criteria for supplier selection. Our study reveals that irrespective of several limitations
of analytic hierarchy process (AHP), AHP and its integrated model is most preferred supplier
selection method. Present research trend of supplier selection gives more emphasises on multiple
sourcing instead of single sourcing. Based on our initial study, a suitable integrated model is
proposed. In this integrated model, fuzzy analytic hierarchy process (FAHP) and VIKOR
(VlseKriterijumska Optimizacija I Kompromisno Resenje; in Serbian) is used to select suppliers
from a predefined supplier base. Multi-objective genetic algorithm (MOGA) is used further to
allocate order among suppliers. Finally, a case study is discussed to use proposed method.
Keywords: supplier selection; fuzzy AHP; VIKOR; multi-objective genetic algorithm; MOGA;
India.
Reference to this paper should be made as follows: Mukherjee, K., Sarkar, B. and
Bhattacharyya, A. (2013) ‘Supplier selection by F-compromise method: a case study of cement
industry of NE India’, Int. J. Computational Systems Engineering, Vol. 1, No. 3, pp.162–174.
Biographical notes: Krishnendu Mukherjee received his first class Bachelor of Engineering
degree in Mechanical Engineering in 1998 from Jadavpur University and a Masters degree in
Mechanical Engineering in 2002 from Birla Institute of Science and Technology, Pilani, India.
He is pursuing his PhD from Jadavpur University, Kolkata, India. He has ten years teaching
experience in India and abroad. He also worked as a reviewer of Journal of Operational
Research Society, UK, PalGrave Macmillan publication. Currently, he is an Assistant Professor
with the Department of Mechanical Engineering, Budge Budge Institute of Technology,
West Bengal, India. He has published papers in Computers and Industrial Engineering, Elsevier,
International Journal of Applied Engineering Research, IEEE, etc. His main research areas
include supplier selection, green supply chain, decision engineering and mass customisation.
Bijon Sarkar is Professor and former Head of Production Engineering Department, Jadavpur
University, Kolkata, India. He has received Outstanding Paper Award at Emerald Literati
Network for Excellences 2006, UK. He had also received the Best Paper Awards from ‘Indian
Institute of Industrial Engineering (IIIE), Mumbai’ in 2002 and 2003. He was also awarded
certificates of merit by the ‘Institution of Engineers India’ during 2001–2002 and 2002–2003. He
is the co-author of the book on Production Management published by AICTE, CEP, New Delhi.
He has published more than 150 papers in the national/international journals and proceedings. He
is the reviewer of IJPR, EJOR and IE(I). His fields of interests include tribology, reliability
engineering, AI, soft-computing applications and decision engineering.
Supplier selection by F-compromise method 163
Ardhendu Bhattacharyya is former Professor and Head of Mechanical Engineering Department,
Jadavpur University, Kolkata, India. His 40 years of experience includes under graduate and post
graduate teaching, research and consultation on many academic and industrial projects at Indian
Institute of Technology, Kharagpur and Jadavpur University, Kolkata, India. He has published 59
papers in the national/international journals and proceedings. Currently, he is working as the
Dean in JIS College of Engineering, Kalyani, Nadia, West Bengal, India.
1 Introduction
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 60%. In such cases the
procurement department can play an important role in cost
reduction, and supplier selection is one of the most
important functions of procurement department. Moreover,
judicious selection of supplier could reduce various
upstream supply chain risks and thereby develop a resilient
supply chain. Among various approaches, multi-criteria
decision making (MCDM) approach is one of the most
discussed aids in conflict management situation and widely
used for single as well as multiple sourcing process to
trade-off tangible and intangible criteria. In this paper, we
initially conducted a brief review to select most cited
MCDM processes and their integrated approach. Our study
further tries to reveal the present trend of supplier selection.
Based on our initial study, we proposed a suitable MCDM
method. Rest of the paper is organised as follows – first
it discusses about fuzzy VIKOR (VlseKriterijumska
Optimizacija I Kompromisno Resenje; in Serbian) method
for supplier selection. Second it gives a brief introduction to
multi-objective optimisation by MATLAB. Third it depicts
multi-objective optimisation and the integrated model of
fuzzy VIKOR and multi-objective genetic algorithm
(MOGA). Finally, the proposed model is further explained
with the case study of cement industry of NE India.
Figure 1 Different supplier selection methods
164 K. Mukherjee et al.
Table 1 A partial list of supplier selection methods and criteria
Year Author/s Criteria
1998 S.H. Ghodsypour and C. O’Brien Cost, quality, service
2001
Eon-Kyung Lee, Sungdo Ha and
Sheung-Kown Kim
Quality, cost, delivery, service
2001
Maggie C.Y. Tam and
V.M. Rao Tummala
Cost, quality
2002
Robert Handfield, Steven V. Walton,
Robert Sroufe and Steven A. Melnyk
Environmental issues
2005 Ozden Bayazit and Birsen Karpak Logistical performance, commercial structure, production
2006 Huan-Jyh Shyur and Hsu-Shih Shih
On-time delivery, product quality, price/cost, facility and technology, responsiveness
to customer needs, professionalism of salesperson, quality of relationship with vendor
2007 Fu Yao and Liu Hongli Cost, quality, project, certification and delivery performance
2007
Felix T.S. Chan and Niraj Kumar Overall cost of the product, quality of the product, service performance of supplier,
supplier’s profile, risk factor
2007 Weijun Xia and Zhiming Wu Price, quality, service
2007 Min Wu
Quality, price, delivery, service, management and culture, technology, financial
situation, etc.
2007
Sanjay Jharkharia and Ravi Shankar Compatibility, cost, reputation, quality
2007 Cevriye Gencer and Didem Gürpinar Business structure, manufacturing capability, quality system
2007 Ezgi Aktar Demirtas and Ozden Ustun
Cost, quality, service, customer complaint, order delay, inability to meet further
requirement, consistency, support to design process, mutual trust and ease of
communication
2008 Ali Kokangul and Zeynep Susuz Price performance, delivery performance, collaboration and developing performance
2008 Reuven R. Levary Supplier reliability; country risk; transportation reliability; reliability of the supplier’s
suppliers
2008 Jing-Rung Yu and Chao-Chia Tsai Cost, quality, delivery, service, environment
2008 Ozan Cakir and Mustafa S. Canbolat Cost, annual demand, blockade effect, availability, lead time, common use
2008 Sung Ho Ha and Ramayya Krishnan
Product facilities, quality management intention, organisational control, business
plans, and customer communication
2008 Eleonora Bottani and Antonio Rizzi
customer satisfaction, technical and organisational capabilities, supplier willingness,
firm’s interest
2008 Ozden Ustun and Ezgi Aktar Demirtas
Cost, quality, service, customer complaint, order delay, inability to meet further
requirement, consistency, support to design process, mutual trust and ease of
communication
2009 Amy H.I. Lee Delivery, cost, quality, flexibility, product/process technology
2009
Semih Önüt, Selin Soner Kara and
Elif Işik
Cost, quality, delivery time, execution time, etc.
2009
Jia-Wen Wang, Ching-Hsue Cheng and
Huang Kun-Cheng
Cost, quality, service
2009 Rong-Ho Lin Quality, technique, price, delivery
2009 Wann-Yih Wu et al. Quality, cost
2009 Chia-Wei Hsu and Allen H. Hu Procurement, R&D, process, incoming quality, management system
1.1 Supplier selection methods revisited
In today’s highly competitive environment, an effective
supplier selection process is very important to the success of
any manufacturing organisation. In this context, supplier
selection represents one of the most important functions to
be performed by the purchasing department. Supplier
selection is a multi-criterion problem which includes both
qualitative and quantitative factors (criteria). A trade-off
between these tangible and intangible factors is essential in
selecting the best supplier. A number of models and
techniques have been developed to deal with the selection
and evaluation of suppliers. In this paper, a brief review is
conducted to identify different selection methods
concerning supplier selection. In this regard, 30 papers are
randomly selected from reputed peer reviewed journal from
1998–2009 to find recent trend of supplier selection.
Figure 1 shows a partial list of existing methods for both
single sourcing as well as multiple sourcing supplier
selection.
Supplier selection by F-compromise method 165
However, our study reveals that out of all
methods-analytic hierarchy process (AHP), ANP and their
integrated model is mostly used by various researchers.
Figures 2 to 4 clearly indicate that. Irrespective of several
limitations, AHP and the integrated method of AHP and
other tools is most cited method for supplier selection.
Moreover, our study reveals that present research trend on
supplier selection gives more emphasises on multiple
suppliers selection instead of single supplier selection. It is
shown in Figure 4. We further extended our study to find
different criteria used for supplier selection methods. This
can be seen from Table 1.
Figure 2 Distribution of review papers on the use of AHP, ANP
and their integrated approach
Figure 3 Use of AHP, ANP and their integrated approach
Figure 4 Distribution of review papers for single and multiple
sourcing
Among several criteria-cost, quality and service are mostly
cited by various researchers. However, different researchers
used same criterion with different terminology, ex. delivery
time, on-time delivery, delivery reliability, etc.
2 Fuzzy numbers and linguistic variables
In this section, some basic definition of fuzzy sets, fuzzy
numbers and linguistic variables are given. Throughout this
paper these basic definitions and notations will be used until
otherwise stated.
Definition 1: A fuzzy set à in universe of discourse X is
defined as the set of ordered pairs: Ã = {(x, µ(x) | x ∈ X)}
where µ(x) is called the membership function (MF). MF
maps each element of X in the interval [0, 1] (Jang et al.,
2004).
Definition 2: The core of a fuzzy set à is the set of all points
in X such that µ(x) =1. A fuzzy set is normal if its core is
non-empty (Jang et al., 2004).
Definition 3: A fuzzy set à is convex if and only if for any
x
1
and x
2
∈ X and any λ ∈ [0, 1], µ
Ã
(λ x
1
+ (1 – λ) x
2
)
≥ min{µ
Ã
(x
1
), µ
Ã
(x
2
)}.
Definition 4: A fuzzy number ñ is a fuzzy subset in the
universe of discourse X that is both convex and normal. A
matrix is called fuzzy matrix if at least one of its member is
fuzzy number (Chen et al., 2006).
Definition 5: A positive triangular fuzzy number (TFN) ñ
can be specified by three parameters (a, b, c), shown in
Figure 5. The MF µ
Ã
is defined as
0,
,
,
0,
Ã
xa
xa
axb
ba
cx
bxc
cb
cx
μ
≤
⎧
⎪
−
⎪
≤
≤
⎪
−
=
⎨
−
⎪
≤
≤
⎪
−
⎪
≤
⎩
