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Selection
Selection of third-party reverse of 3PRLP
logistics provider using fuzzy
extent analysis
149
Kannan Govindan
Department of Business and Economics,
University of Southern Denmark, Odense, Denmark, and
P. Murugesan
Department of Mechanical Engineering,
Odaiyappa College of Engineering and Technology, Theni, India
Abstract
Purpose – The purpose of this paper is to propose a structured model for the selection of a third-party
reverse logistics provider (3PRLP) under fuzzy environment for the battery industry, which establishes
the relative weights for attributes and sub-attributes.
Design/methodology/approach – This paper uses fuzzy extent analysis to solve the 3PRLP
selection problem.
Findings – Owing to growing environmental legislations, reverse logistics (RL) has attained more
importance among practitioners and academicians. The important decision related to RL is whether the
company should maintain the separate RL system or whether it can be outsourced. RL takes 12 times as
many steps to process returns as it does to manage outbound logistics (Accenture supply chain
management practice), consequently many companies decided to outsource the RL activities or
functions through 3PRLPs. The paper proposes a way of selecting the best 3PRLP using fuzzy extent
analysis.
Research limitations/implications – Fuzzy extent analysis is a highly multi-faceted methodology
which requires more numerical calculations and increases the time to take a decision. A limitation of
this work is that in this study only fuzzy extent analysis has been concentrated on and other
multi-criteria decision-making methods such as VIKOR, TOPSIS and ELECTRE can be applied in a
fuzzy environment for solving such problems.
Originality/value – In this research, seven attributes and 34 sub-attributes are considered and the
interpretation of RL attributes in terms of their pair-wise comparisons has been carried out. Those
attributes possessing lower priorities in the fuzzy extent analysis need to be taken care of on a
selection of the best 3PRLP.
Keywords Supply chain management, Fuzzy analysis, Operations management
Paper type Research paper
1. Introduction
In the recent years, the importance of reverse logistics (RL) has increased the
responsibility of organizations. Because of changes in legislation, both for environmental
protection and for economic and service reasons, an increasing number of companies
now take into account reverse flows, going backwards from customers to point of origin Benchmarking: An International
or recovery centers, within their logistics systems (Rogers and Tibben-Lembke, 1998; Journal
Vol. 18 No. 1, 2011
Fleischmann et al., 2000). pp. 149-167
Given that RL is not the firm’s core activity, one of the most important decisions to be q Emerald Group Publishing Limited
1463-5771
taken by any producer is whether or not to outsource such functions to a third-party DOI 10.1108/14635771111109869
2. BIJ reverse logistics provider (3PRLP). This typically is an irreversible decision, because the
18,1 chosen strategy, once adopted, will not be changed frequently. The management of returns
is complicated by the substantial uncertainties associated with their timing, volume and
condition (Serrato et al., 2007). Cardinali (2001a, b) examines a number of environments in
which strategic planning models, sometimes with associated regulatory intervention, have
been suggested to counter the growing problem of waste and its effects on the environment.
150 It is not a new industrial practice but has recently received growing attention, as more
companies are using it as a strategic tool to get more profit, to avoid any waste and even
benefit the customer relationship (Andel, 1997; Klausner and Hendrickson, 2000).
RL mainly concern with returned products. RL can be defined as the reverse process of
logistics (Luttwak, 1971). Traditionally, RL has been viewed primarily as the process of
recycling products. Recently, definitions vary depending on what company or segment
of industry is attempting to define it. Retailers see RL as a way to get product that has
been returned by a consumer back to the vendor (Buxbaum, 1998). Manufacturers tend
to view RL as the process of receiving defective products or reusable containers back
from the user. Council of Logistics Management (CLM, 1998) defines RL as:
The process of planning, implementing, and controlling the efficient, cost effective flow of
raw materials, in-process inventory, finished goods and related information from the point of
consumption to the point of origin for the purpose of recapturing value or proper disposal
(Rogers and Tibben-Lembke, 1998).
RL has become an important entity and plays an important role in company’s
competitive advantage and making the pursuit of their function a strategic decision
(Schwartz, 2000). It does not include disposition management, administration time and
cost of converting unproductive returns into productive assets. This also gives rise to the
situation for outsourcing. Hence, RL is necessary for handling and disposition of
returned products and information. The general RL chain is shown in Figure 1.
Outsourcing to a 3PRLP has been identified as one of the most important management
strategies for RL networks in the recent years. While considering outsourcing decisions
for RL, the fundamental factor to be considered is whether there is a reliable 3PRLP for
the type of RL network required. Even though there are several 3PRLPs in some of the
scenarios such as pharmaceuticals, container reuse, cellular telephone reuse, electronic
goods, automobiles and computers, etc. one of the most important issues in RL systems
is that some of them are not really prepared to effectively address the service needs
Distribution Production Supply
Use
Collection Selection Re-processing Re-distribution Re-use
Figure 1.
The RL chain Disposal
3. due to the lack of knowledge in RL networks. Outsourcing makes the firm to concentrate Selection
on RL activities in order to earn the customers reputation through immediate response. of 3PRLP
Owing to the complexity of the process, RL activities can effectively be accomplished by
involving a 3PRLP, which specializes in these activities and can take advantage of the
economies of scale to convert RL functions in a profit-creating activity into the
closed-loop chain.
This paper presents a structured model for evaluating the 3PRLP selection for 151
battery industry located in southern part of India using fuzzy extent analysis. In this
research, after introduction of the literature review is given in Section 2. Section 3
describes the problem and 3PRLP model. Section 4 describes the solution methodology.
In Section 5, the application model and result analysis are discussed and conclusion of
the paper is discussed in Section 6.
2. Literature review
The literature review is mainly aimed at identifying the attributes and sub-attributes
that need to be considered in 3PRLP selection process, which has the impact on the
organizations strategic goal. Various attributes and sub-attributes used in this study has
been found in literature and finalized through discussions made with the
organization-outsourcing group.
Table I shows the attributes of selection criteria, relevance in 3PRLP selection
process and the references in detail. This table is adopted from the study done by
Kannan (2009) in his earlier work using analytical hierarchy process (AHP).
Based on the above literature, this paper presents a structured model for evaluating
the third-party RL selection using fuzzy extent analysis proposed by Chang (1996).
3. Problem description
The company chosen for this study is to build a partner who is a third party for its RL
network. RL systems can be given to the 3PRLPs. An analysis should be made such that
the third party provider should be partnered or make alliance in RL systems to achieve
optimal result and multiple organizations might be involved in the RL function. The
attributes and sub-attributes have to be most prevalent and important in the third-party
selection process. Choosing the possible criteria for the third-party selection involves a
decision-making process which includes experts from various functional activities of the
organization. The attributes and sub-attributes involved in the third-party selection have
been chosen by conducting a survey. The selected attributes and sub-attributes are given
in Table I. The objective is to select a set of third parties, evaluate and rank them
according to pre-defined attributes. Figure 2 is comprised of four levels for selecting the
best third party. Level 1 represents the goal, i.e. selection of best third party. Level 2
represents the seven attributes such as third-party logistics (3PL) services, organizational
performance criteria, organizational role, user satisfaction, IT application, impact of
use of 3PL and RL functions. Level 3 represents 34 sub-attributes and Level 4 represents
the number of third parties (alternatives).
4. Solution methodology
In this work, fuzzy extent analysis is adopted to solve the problem of selection of 3PRLP.
The outline of the fuzzy extent analysis method (Chang, 1996) can be summarized
below.
4. BIJ
Attributes Sub-attributes
18,1
Third party logistics Inventory replenishment (3PLS1), Dowlatshahi (2000), van den Berg and
services (3PLS) warehouse management (3PLS2), Zijm (1999), Kleinsorge et al. (1991),
shipment consolidation (3PLS3), Gunasekaran et al. (2001), Davis and
carrier selection (3PLS4) and Gaither (1985), Gupta and Bagchi (1987),
152 direct transportation services Khoo and Mitsuru (2006) and Holguin-
(3PLS5) Veras (2002)
Reverse logistics Collection (RLF1), packing Schwartz (2000), Dowlatshahi (2000),
functions (RLF) (RLF2), storage (RLF3), sorting Jeffery and Ramanujam (2006),
(RLF4), transitional processing Kaliampakos et al. (2002), Schwartz (2000),
(RLF5) and delivery (RLF6) Jules (1990) and Stock (1990)
Organizational role Reclaim (OR1), recycle (OR2), Meade and Sarkis (2002), Dowlatshahi
(OR) remanufacture (OR3), re use (2000), Demir and Orhan (2003) and
(OR4) and disposal (OR5) Schwartz (2000)
User satisfaction (US) Effective communication (US1), Mohr and Spekman (1994), Bensaou
service improvement (US2), (1993), Monczka et al. (1993), Gunipero
cost saving (US3) and overall (1990), Lynch (2000), Boyson et al. (1999),
working relations (US4) Langley et al. (2002), Andersson and
Norrman (2002) and Boyson et al. (1999)
Impact of use of 3PL Customer satisfaction (IU3PL1), Razzaque and Sheng (1998), Hendrik et al.
(IU3PL) frequently updating (IU3PL2), (2006), Lynch (2000), Boyson et al. (1999)
profitability (IU3PL3) and and Mohrman and Von Glinow (1990)
employee morale (IU3PL4)
Organizational Quality (OPC1), cost (OPC2), time Kim et al. (2004), Kwang et al. (2007),
performance criteria (OPC3), flexibility (OPC4) and Andersson and Norrman (2002), Lynch
(OPC) customer satisfaction (OPC5) (2000), Boyson et al. (1999), Lynch (2000),
Langley et al. (2002), Boyson et al. (1999),
Stock et al. (1998), Kleindorfer and Partovi
(1990) and Stank and Daugherty (1997)
IT applications (IT) Warehouse management (IT1), Dowlatshahi (2000), van den Berg and
order management (IT2), supply Zijm (1999), Jing et al. (2006), Scalle and
Table I. chain planning (IT3), shipment Cotteleer (1999), Khoo and Mitsuru (2006),
Attributes and and tracking (IT4) and freight Holguin-Veras (2002) and Jeffery and
sub-attributes payment (IT5) Ramanujam (2006)
Let X ¼ {x1, x2, . . . , xn} be an object set, and U ¼ {u1, u2, . . . , um} be a goal set. As per
Chang (1992, 1996), each object is taken and analysis for each goal, gi, is performed,
respectively. Therefore, m extent analysis values for each object can be obtained, as
under:
M 1i ; M 2 i ; . . . ; M mi ; i ¼ 1; 2; . . . ; n
g g g ð1Þ
where all the M jgi ð j ¼ 1; 2; . . . ; mÞ are triangular fuzzy numbers whose parameters
are, depicting least, most and largest possible values, respectively, and represented as
(a, b, c).
The following steps depicted by Kahraman et al. (2004) for Chang’s extent analysis:
.
Step 1. The value of fuzzy synthetic extent with respect to ith object is defined as:
" #21
Xm XX
n m
Si ¼ M jgi ^ M jgi ð2Þ
j¼1 i¼1 j¼1
5. Level I
Selecting the best third party
IT 3PL Level II
Impact Organizational services
applications
of use role
of 3pl
Reverse Organizational
User performance
logistics satisfaction
functions criteria
OPC1
IU3PLI RLF1 OR1 US1 IT1
OPC2 3PLS1 Level III
IU3PL2 RLF2 OR2 US2 IT2
OPC3 3PLS2
IU3PL3 RLF3 OR3 US3 IT3
OPC4 3PLS3
IU3PL4 RLF4 OR4 US4 IT4
3PLS4
IT5 OPC5
RLF5 OR5 3PLS5
RLF6
Level IV
....... .. .
3PRLP 1 3PRLP 2 3PRLP n
of 3PRLP
Selection
analysis model
Proposed fuzzy extent
Figure 2.
153
6. P
BIJ To obtain m M jgi , perform the fuzzy addition operation of m extent analysis
j¼1
values for a particular matrix such that:
18,1 !
Xm X X X
m m m
j
M gi ¼ aj ; bj ; cj ; ð3Þ
j¼1 j¼1 j¼1 j¼1
hP i21
n Pm j
154 And to obtain i¼1 j¼1 M gi , perform the fuzzy addition operation of
M jgi ð j ¼ 1; 2; . . . ; mÞ values such that:
!
XX
n m X X X
n n n
M jgi ¼ ai ; bi ; ci ð4Þ
i¼1 j¼1 i¼1 i¼1 i¼1
And then compute the inverse of the vector in the above equation such that:
" #21
XX
n m
1 1 1
j
M gi ¼ Pn ; Pn ; Pn ð5Þ
i¼1 j¼1 i¼1 ci i¼1 bi i¼1 ai
.
Step 2. The degree of possibility of M 2 ¼ ða2 ; b2 ; c2 Þ $ M 1 ¼ ða1 ; b1 ; c1 Þ is
defined as:
V ðM 2 $ M 1 Þ ¼ sup½minðmM 1 ðxÞ; mM 2 ðxÞÞŠ ð6Þ
And can be equivalently expressed as follows:
V ðM 2 $ M 1 Þ ¼ hgtðM 1 M 2 Þ ¼ mM 2 ðd Þ
8
1; if b2 $ b1
¼ 0; if a1 $ c2 ð7Þ
a1 2c2
: ðb2 2c2 Þ2ðb1 2a1 Þ ; otherwise
where d is the ordinate of the highest intersection point D between mM 1 and mM 2
as shown in Figure 3.
To compare M1 and M2, both the values of V (M1 $ M2) and V (M2 $ M1).
.
Step 3. The degree of possibility for a convex fuzzy number to be greater than k
convex fuzzy numbers Mi (I ¼ 1, 2, . . . , k) can be defined by:
V ðM $ M 1 ; M 2 ; . . . ; M k Þ
 Ã
¼ V ðM $ M 1 Þ and ðM $ M 2 Þ and. . .ðM $ M k Þ ð8Þ
¼ min V ðM $ M i Þ; ði ¼ 1; 2; 3; . . . ; kÞ:
Assuming that:
d0 ðAi Þ ¼ min V ðS i $ S k Þ ð9Þ
For k ¼ 1, 2, . . . , n; k – i. Then the weight vector is given by:
W 0 ¼ ðd0 ðA1 Þ; d0 ðA2 Þ; . . . ; d 0 ðAn ÞÞT ð10Þ
where Ai ¼ (1, 2, . . . , n) are n elements.
7. .
Step 4. By normalizing, the normalized weight vectors are: Selection
W ¼ ðdðA1 Þ; dðA2 Þ; . . . ; dðAn ÞÞT ð11Þ of 3PRLP
where W is a non-fuzzy number.
5. Application of the model and result analysis
An objective of this section is to illustrate how to choose the best 3PRPL’s using this 155
model and the model was applied to a battery company which is located in the
southern part of India.
The first step in the fuzzy extent analysis is creating a pair-wise comparison matrix.
In order to perform a pair-wise comparison among the attributes and sub-attributes,
the linguistic scale for the triangular numbers and fuzzy conversion scales given in
Table II are used in the proposed model.
First, the pair-wise comparison matrix is constructed with the help of expert team
and the same is shown in Table III.
M2 M1
1
V (M2 ≥ M1)
D Figure 3.
The intersection between
0 M1 and M2
a2 b2 a1 d c2 b1 c1
Linguistics scale for importance Triangular fuzzy scale Triangular fuzzy reciprocal scale
Just equal ( JE) (1, 1, 1) (1, 1, 1)
Equally important (EI) (1/2, 1, 3/2) (2/3, 1, 2)
Weakly important (WI) (1, 3/2, 2) (1/2, 2/3, 1)
Strongly more important (SMI) (3/2, 2, 5/2) (2/5, 1/2, 2/3)
Very strongly more important (VSMI) (2, 5/2, 3) (1/3, 2/5, 1/2)
Absolutely more important (AMI) (5/2, 3, 7/2) (2/7, 1/3, 2/5) Table II.
Triangular fuzzy
Source: Modified from Percin (2008)
¸ conversion scale
3PLS RLF OPC OR IT US IU3PL
3PLS (1, 1, 1) (5/2, 3, 7/2) (5/2, 3, 7/2) (2, 5/2, 3) (1/2, 1, 3/2) (1/2, 1, 3/2) (1/2, 1, 3/2)
RLF (2/7, 1/3, 2/5) (1, 1, 1) (1/2, 1, 3/2) (1, 3/2, 2) (1, 3/2, 2) (1, 3/2, 2) (1, 3/2, 2)
OPC (2/7, 1/3, 2/5) (2/3, 1, 2) (1, 1, 1) (1/2, 1, 3/2) (1, 3/2, 2) (2, 5/2, 3) (2, 5/2, 3)
OR (1/3, 2/5, 1/2) (1/2, 2/3, 1) (2/3, 1, 2) (1, 1, 1) (1, 1, 1) (1, 3/2, 2) (1, 3/2, 2)
IT (2/3, 1, 2) (1/2, 2/3, 1) (1/2, 2/3, 1) (1, 1, 1) (1, 1, 1) (1, 3/2, 2) (1, 1, 1)
US (2/3, 1, 2) (1/2, 2/3, 1) (1/3, 2/5, 1/2) (1/2, 2/3, 1) (1/2, 2/3, 1) (1, 1, 1) (1/2, 1, 3/2) Table III.
IU3PL (2/3, 1, 2) (1/2, 2/3, 1) (1/3, 2/5, 1/2) (1/2, 2/3, 1) (1, 1, 1) (2/3, 1, 2) (1, 1, 1) Fuzzy evaluation matrix
8. BIJ By applying formula (2) given in Section 4:
18,1 S 3PLS ¼ ð9:50; 12:50; 15:50Þ^ð1=74:30; 55:70; 42:57Þ ¼ ð0:13; 0:22; 0:36Þ
S RLF ¼ ð5:79; 8:33; 10:90Þ^ð1=74:30; 55:70; 42:57Þ ¼ ð0:08; 0:15; 0:26Þ
S OPC ¼ ð7:45; 9:83; 12:90Þ^ð1=74:30; 55:70; 42:57Þ ¼ ð0:10; 0:18; 0:30Þ
S OR ¼ ð5:50; 7:07; 9:50Þ^ð1=74:30; 55:70; 42:57Þ ¼ ð0:07; 0:13; 0:22Þ
156 S IT ¼ ð5:67; 6:83; 9:00Þ^ð1=74:30; 55:70; 42:57Þ ¼ ð0:08; 0:12; 0:21Þ
S US ¼ ð4:00; 5:40; 8:00Þ^ð1=74:30; 55:70; 42:57Þ ¼ ð0:05; 0:10; 0:19Þ
S IU 3PL ¼ ð4:67; 5:73; 8:50Þ^ð1=74:30; 55:70; 42:57Þ ¼ ð0:06; 0:10; 0:20Þ
With the help of equations (7), (9) and (10) the minimum degree of possibility of
superiority of each criterion over another is obtained. This further decides the weight
vectors of the criteria. Therefore, the weight vector is given as:
W 0 ¼ ð1; 0:632; 0:785; 0:494; 0:451; 0:32; 0:372ÞT
The normalized value of this vector decides the priority weights of each criterion over
another. The normalized weight vectors are calculated using the equation (11) and the
same is given follow:
W ¼ ð0:24669; 0:15593; 0:1939; 0:12202; 0:11133; 0:07909; 0:09173Þ
Further the weights of sub-attributes and weights of alternatives with respect to each
sub-attribute are found using the similar procedure. The results are shown in
Tables IV and VI.
In this part, the result obtained through fuzzy extent analysis (Tables IV and VI) is
compared with solution obtained through AHP (Table V; Kannan, 2009). From Table VI,
it can be concluded that the calculated importance level of attributes for the case is in
the following order. 3PL service, organizational performance criteria, RL functions,
organizational role, IT applications, impact of use of 3PL and user satisfaction. This
result is compared with the previous study done by Kannan (2009) using AHP and it
shows that the top priority remains the same with little changes in the other attributes
priorities. Table VI gives the local priority vectors for the alternatives with respect to
attributes and sub-attributes.
The total weighted score is shown in the Table VI for the each alternative
(3PRLP1-3PRLP7) and it was obtained by multiplying the local priority vectors of
alternatives, priority vectors of attributes and sub-attributes. Based on the global
priority weight, the 3PRLP is selected when it has the highest overall priority. From
Table VI, it can be seen that 3PRLP1 is preferred which has the highest weight of (0.2176)
among seven third parties. Third party 2 is at the second choice (0.17779).
6. Conclusion
RL service provider problem becomes more important for most manufacturing
companies in today’s complex environment. The selection process in the RL service
provider involves both types of attributes like quantitative and qualitative attributes to
select the best possible provider.
However, the top-level management and managers are often uncertain about how to
share the key information to enhance the selection process. Fuzzy extent analysis
15. approach seems to be particularly effective in reducing the uncertainty in the Selection
determination of the relative weight given to the different attribute and in finding the of 3PRLP
impact of each alternative with respect to the attributes and sub-attributes which
are involved in the selection process.
This study utilizes a fuzzy extent analysis framework to select the best 3PRLP for a
battery manufacturing industry in India. While fuzzy AHP requires weighty computations,
it is a more systematic method than the others, and it is more capable of capturing a 163
human’s appraisal of ambiguity when complex multi-attribute decision-making problems
are considered. This is true because pair-wise comparisons provide a flexible and realistic
way to accommodate real-life data (Tolga et al., 2005).
For future research multi-criteria decision-making methods such as VIKOR, TOPSIS
and ELECTRE can be applied in a fuzzy environment for solving such a problem.
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Corresponding author
Kannan Govindan can be contacted at: gov@sam.sdu.dk
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