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Resources, Conservation and Recycling 108 (2016) 63–81
Contents lists available at ScienceDirect
Resources, Conservation and Recycling
j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / r e s c o n r e c
n analysis of integrated robust hybrid model for third-party reverse ogistics partner selection under fuzzy environment
handra Prakash ∗, M.K. Barua epartment of Management Studies, IIT Roorkee, India
r t i c l e i n f o
rticle history: eceived 23 June 2015 eceived in revised form 4 December 2015 ccepted 25 December 2015 vailable online 23 January 2016
eywords: CDM
everse logistics (RL) uzzy AHP uzzy TOPSIS ensitivity analysis
a b s t r a c t
Owing to environmental and waste disposal issues, enforced legislation and corporate social concern; companies are focusing on reverse logistics (RL) practices, especially in the present scenario dominated by intense competition, demanding customer and fast changing technologies. These practices are widely adopted by industries through reverse logistics partners. However, the evaluation and selection of the reverse logistics partner is a matter of concern which needs a very grave decision, involving complexity due to presence of numerous associated factors. In addition, it is hypothesized that the decision mak- ers might be inconsistent to some extent in their views and preferences that affect other dominant constituents. Consequently, incomplete and inadequate sort of information may occur among various selection criteria, which is termed ‘multi-criteria decision making’ (MCDM) problem. The goal of the present study is to discuss an integrated model based on fuzzy analytic hierarchy process (FAHP) for evaluation and prioritization of selection criteria and fuzzy technique for order performance by similar- ity to ideal solution (FTOPSIS) for the selection and development of reverse logistics partner. This study is an attempt to present a genuine concern of Indian electronics industry using an integrated approach
to demonstrate the application of the proposed framework as well. In this study two stage sensitivity analyses are performed to get further insight of evaluation and selection of RL partner and verification of robustness of the model. This study aims to provide a significant contribution to electronics organizations in evaluation and selection of third party RL partner while achieving efficiency and effectiveness in RL practices.
© 2016 Elsevier B.V. All rights reserved.
. Introduction
To start with reverse logistics, it is equally important to know hat reverse logistics is and how it functions. Council of Logis-
ics Management describes ‘reverse logistics’ as “To achieve the urpose of recycle value and proper disposal, a process from the oint of consumption to the starting point in efficient and eco- omical way that plans, implements and controls raw materials, emi-finished inventory, finished goods and related information.” n my point of view, it is a process that disseminates various roducts/goods and where the movement is from consump- ion/distribution or use-point to origin with related information.
his process is further sub-divided into two heads namely, waste ogistics and recover logistics. In today’s modern era, due to ris- ng customer requirements, fast changing technologies, shorter
∗ Corresponding author. E-mail addresses: cpgarg86@gmail.com (C. Prakash), baruafdm@iitr.ac.in
M.K. Barua).
ttp://dx.doi.org/10.1016/j.resconrec.2015.12.011 921-3449/© 2016 Elsevier B.V. All rights reserved.
product life cycle and increasing waste; there have been a great emphasis on sustainability, to maintain the availability of resources for long time. An efficient reverse logistics (hereafter RL) pro- gram can support companies to make effective utilization of resources and retain equilibrium between environment and econ- omy (Xiangru, 2008). Moreover, RL practices have been seen as a part of sustainable business practices (Prakash and Barua, 2015a,b). The advent of online shopping and post-sale service has increased the reverse flow of the products. And organiza- tions have started to concentrate on recovery of used products because of growing ecological & waste removal concerns, imposed regulations and corporate citizenship (Fleischmann et al., 2000; Rogers and Tibben-Lembke, 2001). It has been observed that the product returns rate is high in case of electronics items, computers, cameras, mobile-phones, automobile, chemical and medical items. For some industries, product returns rate is very
high and for some 50% of the sales (Senthil et al., 2014). Cus- tomers are expecting immediate resolution in case of defective products. Extended producer responsibilities and waste regula- tions are imposing pressures on manufacturers to take back and
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4 C. Prakash, M.K. Barua / Resources, Co
ispose products properly after useful life (Prahinski and ocabasoglu, 2006). Managing reverse logistics operations are dif- cult and composite because of uncertain timing, quantities and he quality of returned products (Fleischmann et al., 1997). And ach return requires various treatments depending on the condi- ion of returned product apart from recovery options and proper isposal. Reverse logistics activities are more complex than tra- itional supply chain operations therefore; organizations require exibility to deal with the operational issues. Effective return man- gement practices can increase customer satisfaction and service evel. However efficient return needs specialized infrastructure equirements, advance IT and tracking system, dedicated equip- ent for the processing of returns, and trained manpower and
ther value-added services. Therefore, many organizations those ack in resources and capabilities outsource their reverse logistics perations requirements to 3rd party logistics (Krumwiede and heu, 2002). Moreover if RL operations are not the part of orga- ization core functions, RL activities might be outsourced to 3rd arty. This 3rd party reverse logistics partner (3PRLP) provides ben- fits in terms of reduction in costs, improvement in performance, perational efficiency, organizational competitiveness and better orporate image. The 3PRL partner has to deal with flexible capacity f return products along with various activities like product acqui- ition/gate keeping, collection, inspection & sorting, storage, and rocessing. These activities require state-of-the-art infrastructure, arehousing facilities, dedicated transportation, material handling
quipment’s, skilled labor and customized information systems to onitor shipments, and data mining & support. Co-ordination and
eliability of partner is very important aspect while outsourcing RL perations. Therefore, 3PRLP selection process has become a key trategic decision.
.1. Research motives
Since previous studies show that many researchers have rec- gnized the importance of the selection of 3PRLPs but very few tudies suggested empirical analysis for the selection of the part- er. Moreover, there is a limited literature on 3PRLP selection in eveloping countries context. Selection of 3PRLP needs flexible ecision system that can elect the best alternative from multiple ualitative as well as quantitative factors approved by industrial xperts. The presence of multiple criteria and the views from the xperts will increase the complexity in the selection of the appro- riate alternatives. Further, there are many gaps related to 3PRLP election still to be explored; and particularly, it becomes impor- ant when firms are looking toward implementation of RL practices Agrawal et al., 2015; Prakash and Barua, 2015a). Since partner election is the first stage of the return management, it is a critical ecision process affecting the consecutive stages toward achieve- ent of efficient RL practices. Hence, the need arises to evaluate
he 3rd party RL partner selection in order to ensure effective RL mplementation in the business.
.2. Research goals
This research work helps in achieving the goals, as follows:
To identify, finalize and evaluate the 3rd party RL partner selec- tion criteria. To support in selecting the best 3PRLP among available alternatives. To analyze the selected 3rd party RL partners.
Selection of the partner while considering different criteria is CDM problem and involvement of fuzziness in decision makes
ecision structure flexible and handle the uncertainty associated
tion and Recycling 108 (2016) 63–81
with decision making process. And this fuzzy based flexible sys- tematic decision support tool provides flexibility in the selection of the reverse logistics partner. This study used hybrid fuzzy analytic hierarchy process (FAHP) for evaluation of the selection criteria. The benefits of using FAHP over other methods (VIKOR, DEMATEL, PROMETHEE, etc.) can be understand by this, its shows the per- formance of a partner with respect to each sub-criteria and main criteria through structural dependency and as compared to the analytic network process (ANP), AHP is a linear assessment type of method (Harputlugil et al., 2011), Thus, it fosters the partner status on each criterion. Also, mathematically and philosophically, FAHP provides an easily understandable and defensible approach to practitioners. It allows practitioners to be involved in the analysis and actually to guide the decision more effectively. This managerial transparency and lack of complexity allow for greater acceptance by both researchers and practitioners. The application of FAHP has been seen in many MCDM problems. However integrating one hybrid model with other decision support system would improve decision making process.
The integrated hybrid approach with multi-faceted decision support systems would evolve which will mitigate the complexity of a real world decision process and it will provide a more practical, lucid, simple and effective solution in making decision as well. This study integrates FAHP with FTOPSIS hybrid model to build an intel- ligent decision support model and select the right RL partner. The integration of FAHP with other techniques may also be tied to its easy to understand mathematical basis, ease of use, and flexibility. The other reason for integration is that the individual techniques possess some unique advantages that allow for complementary contribution to the FAHP approach (Govindan et al., 2015).
Considering the highlighted significance of proposed integrated decision support framework, we opted to select and discuss a prag- matic example of electronics industry in Indian context. The case industry is seeking to analyze the 3PRLP selection criteria, and has a desire to build a structural model to select the best partner among alternatives for adoption of effective RL practices in busi- ness. Finally robustness of proposed integrated model is tested by performing extensive sensitivity analysis. For this, two stage sensitivity analyses are performed. In stage I; sensitiveness of the selection criteria has been checked. Second stage sensitivity anal- ysis shows the variation in final ranking of 3PRL partners. This is done through exchanging the weightage of the highest weightage criterion among other criteria. And effect of this change has been seen on selection of 3PRLP. The unique contribution of this study can be understood by that, this kind of sensitivity analysis has not seen in any past studies.
The rest of this paper is planned as follows. Section 2 highlights the background of the research. Section 3 deals with the problem. Section 4 describes the methodology and application of the model for selection of the partner is given in Section 5. The results and dis- cussions are presented in Section 6. Two stage sensitivity analyses are reported in Section 7. Managerial implications and concluding remarks with unique contribution of the study are given in Sections 8 and 9 respectively.
2. Background of the research
This section contains the literature on reverse logistics & supplier selection and modeling techniques used in RL supplier selection.
2.1. Reverse logistics & supplier selection
The studies on reverse logistics area got attention recently. Reverse logistics operations are also important like forward
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evalu
l s t n f e p
Fig. 1. Research framework for
ogistics. Organizations should focus on this also; to achieve uccess. Most of the reverse logistics literature focused on produc- ion & distribution planning, inventory control, reverse logistics
etwork design, and reverse supply chain issues. Integration of
orward supply chain with RL process can provide ecological and conomic advantages, and it is also helpful in improvement of cor- orate identity of the organizations and adheres with government
ation and selection of 3PRLPs.
regulations (Carter and Ellram, 1998). Furthermore, RL adoption is essential because it can outspread products life span and return back the products to the cycle, this could be great support in
abatement of environmental pollution and resolving the problem of scarcity of resources (González-Torre et al., 2010). The potential benefits by implementing RL practices were reduced inventory of products (raw materials and work-in progress) by utilizing
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eturned goods that have significant cost advantages (Silva et al., 013). Many countries have imposed environmental regulations,
aws and directives on the manufacturers of electronics, auto- obiles, battery etc. to take responsibilities after end-of life of
roducts (Fleischmann et al., 2000). Recently the study done on eveloping country (Brazil) by Jabbour et al. (2014); stated that ovt. has made compulsory regulations to implement RL operations n supply chain. Such regulations may enforce producers to include L functions in their business process. Additionally, the rules were ased on the principle of shared responsibility; which indicates ll the members of supply chain should work together into waste anagement practices. Such directives may bring together to initi-
tes private and public partnership through integrated approach in chievement of sustainable development. Many researchers stud- ed environmental attention among companies, ecological friendly
anufacturing practices and problems in recycling & disassembly erived through govt. regulation and customer awareness (Prakash nd Barua, 2015a; Govindan et al., 2013; Moyer and Gupta, 1997).
Many studies proposed RL operations were more complex han forward operations (Rosen, 2001; Rogers and Tibben-Lembke, 001; Amini et al., 2005). Chouinard et al. (2005) analyzed problems
n implementing reverse logistics operations. Ravi et al. (2005) pro- osed BSC & ANP approach to evaluate alternatives of reverse logis- ics operations for end-of-life computers. A fuzzy AHP is used to valuate recovery facilities and to determine the most economical roduct in a reverse supply chain by Pochampally and Gupta (2008). ue to uncertain demand pattern, inconsistent timing and quality f return products; flexible capacity and transportation service are ecessary (Amini et al., 2005; Blumberg, 1999). Therefore, many rganizations with limited resources and capabilities outsource heir RL activities to 3rd parties (Krumwiede and Sheu, 2002). Addi- ionally, the infrastructure, warehousing and aided service facilities or RL operations management can be effectively managed through hird party providers (Bai and Sarkis, 2013). And these 3rd parties’ ervice providers are well equipped to monitor and maintain the nvironmental legislation and local waste rules (Anttonen et al., 013). 3PRL service provider could be helpful in the achievement f sustainable business practices through implementing environ- ental/green practices in supply chain activities (Colicchia et al.,
013). 3PRL providers must be experts in management of the RL unctions and deliver vital services, such as disposal, refurbishing nd remanufacturing (Rogers and Tibben-Lembke, 1998). Moreover hese suppliers were able to reduce pollution and waste gener- ted through their operations (Thresh Kumar et al., 2014). Decision aking regarding selection of 3PL provider depends on overall
ost of the contract and service performance (Almeida, 2007). cCarthy et al. (2013) suggested that 3rd party outsourcing deci-
ion of a company as strategic decision to attain state-of-the-art echnologies, increase presence in the market and obtain gov- rnment persuasion. Moreover, Govindan et al. (2013) proposed dditional benefits of outsourcing RL tasks to 3rd party supplier part from aforementioned benefits under environmental, social, nd economic dimensions in order to achieve sustainable prac- ices in supply chain. Anttonen et al. (2013) stated that, to provide co-friendly services in RL functions, i.e. recycling, refurbish and isposal; organizations could outsource RL operations to special-
zed service providers. Evaluation of suppliers by Delphi technique s presented by Lee et al. (2009) and Liu and Wang (2009). Wagner nd Sutter (2012) investigated; how innovation projects would elp third-party logistics providers in achieving higher customer atisfaction, good relationship and good performances. Akamp and üller (2013) studied supplier management strategies based on
eveloping countries and identifies relationship of supplier per- ormance with supplier management and customer satisfaction. his study utilized structural equation modeling approach with artial least squares method and used data of 137 firms and
tion and Recycling 108 (2016) 63–81
identifies that supplier performance directly affected by supplier development and integration. Aguezzoul (2014) presented litera- ture review on supplier selection criteria and various approaches used in selection of 3PRLPs. This paper identified 11 criteria, i.e. cost, relationship, services, quality, information & equipment system, flexibility, delivery, professionalism, financial position, location and reputation and 4 evaluation methods namely MCDM, statis- tical, mathematical programming and soft computing. Guarnieri et al. (2014) proposed challenges in evaluation and selection of 3PRLPs and found six important criteria for selection of 3PRLPs. They are logistics, financial, capacity/infrastructure, value added services to customers, alliances with suppliers and environmen- tal. Wagner Trapp and Sarkis (2014) suggested robust strategies for sustainable supplier development and selection and used opti- mization technique to select best supplier among alternatives.
2.2. Modeling in RL supplier selection
RL studies suggested that several techniques have been pro- posed by many researchers such as mathematical programming, single and multi-objective programming approaches for selection of suppliers/partners (Dulmin and Mininno, 2003; Ghodsypour and O’Brien, 1998; Liu et al., 2000; Talluri and Narasimhan, 2003). Meade and Sarkis (2002) suggested ANP method for the selec- tion of 3PRLPs. Efendigil et al. (2008) applied two phase artificial neural network and fuzzy AHP for selection of 3rd Party logis- tics provider. Cheng and Lee (2010) used the analytical network process (ANP) to investigate and selection of 3PRLPs. Liou and Chuang (2010) presented hybrid decision model based on VIKOR, ANP and DEMATEL by considering four selection criteria namely compatibility, quality, cost and risk for selection of outsourcing provider. Sasikumar and Haq (2010) proposed integrated closed supply chain model for network design and utilized fuzzy VIKOR approach to select 3PRLP for the battery industry. Amin et al. (2011) applied SWOT analysis with linear programming for sup- plier selection under the consideration of strategic perspective and uncertainty. A fuzzy based integrated hybrid approach in evalu- ation of green suppliers was proposed by Büyüközkan and Ç ifç i (2012) and offered combined Fuzzy ANP, Fuzzy DEMATEL and Fuzzy TOPSIS framework for green suppliers’ selection for auto- mobile company. Ho et al. (2012) utilized QFD and Fuzzy AHP approaches in evaluation and selection of 3PL service providers and presented the case of hardware manufacturer of Hong-Kong. Kuo and Lin (2012) presented supplier selection framework using ANP and DEA and considered four dimensions such as organization structure and manufacturing capability, supplier’s implementation capability, quality system and environmental issues for high-tech industry supplier selection. Liou (2013) analyzed the selection of outsourcing provider by DEMATEL, ANP and gray relation theory. Kannan et al. (2013) presented integrated approach based on Fuzzy MAUT and MOLP for selection of automobile suppliers. Chai and Ngai (2014) used soft computing technique under fuzzy environ- ment for selection of strategic supplier for manufacturing company of electronics, computer and communication products by involv- ing multiple stakeholders and perspectives. Senthil et al. (2014) proposed robust MCDM approach for evaluation and selection of 3PRLPs for plastic industry and used AHP and FTOPSIS method. Apart from this, in this paper sensitivity analysis was also carried out. Recently Rajesh and Ravi (2015) utilized gray relational analy- sis with AHP and ANP under imprecise and limited information for selection of electronics resilient suppliers. Ayhan and Kilic (2015)
presented suppliers selection model for multi items and consid- ered four criteria, i.e. price, quality, delivery and after sales service under quantity discount and used Fuzzy AHP and multi integer linear programming approach.
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Since the selection criteria is subjective in nature and depend n the particular industry, but it can be well represented by ombinations of the various factors. Available literature on selec- ion of the 3PRLPs shows that few studies have utilized Fuzzy AHP nd Fuzzy TOPSIS as a combined approach for selection of the artners for electronics industry. And no study available in the lit- rature, which have such exhausted sensitivity analysis to check obustness of the model. Hence there is a need for intelligent, flexi- le, robust and rational scientific method for decision maker to take
decision and analyze simultaneously.
. Problem definition
Understanding the intricacies of selection criteria and their mplications on customers which will lead to provide an efficient esource recovery from return products, it is indeed desirable to nitiate RL partner. This 3rd party RL partner can be used to avoid artial information, incomplete stricture, and because of competi- ive market, producers are more focusing and shifting toward core ompetencies and outsourcing RL functions to a third party. Since roduct returns are uncertain, high variation in asset recovery and conomies of scale are difficult to achieve; 3rd party RL partner an play crucial role in adoption and effective implementation L operations. Moreover 3PRLP is employing the state-of-the-art
nfrastructure, resource and technology; it offers efficient resource ecovery from return products. In this paper case of electronic ndustry has been taken; which wishes to select a third party logis- ics partner (RLP). But seeking such partner from n alternatives, hose structure, culture and strategy should align with organi-
ation, is difficult and involvement of various conflicting criteria ncreases complexity. Identification of selection criteria and their valuation should consider operational environment and cater the eographical need. Therefore, it is important that selection criteria hould focus on cooperation, capability, commitment and compat- bility of the partners. This paper proposed selection criteria for he RL partners, which is not merely based on outsourcing decision elated to collection and transportation activities but perform com- lete RL activities such as the product acquisition, reuse, recycle, epair or remanufacturing and disposal activities; which is sup- osed to be multi-criteria decision making problem. Evaluation riteria and sub-criteria have been shown in Table 2.
Due to the dynamic nature of the electronics industry all around he world today, selection criteria have been identified through eveloping a team of experts in this area and relevant literature hrough brain storming sessions. This team has members from all unctional areas of the organizations, industry experts and industry ssociates, etc. This team has finalized and validated seven major riteria and various sub-criteria under them (see Table 1). This esearch develops a framework to evaluate the 3PRLP selection and tilize combined Fuzzy AHP and Fuzzy TOPSIS approach to select he most efficient partner among alternatives. To measure the obustness of the proposed integrated model; exhaustive sensitiv- ty analysis is performed and consideration of fuzzy environment ignifies, involvement of uncertainty and vagueness of the decision akers which makes this approach flexible and robust.
. Research methodology
In this paper three phase methodology have been applied for valuation and selection of the RL partners. In phase-I, decision roup has formed. This group has identified selection criteria for
artner through extensive discussion and with the support of exist-
ng literature. In phase-II, after finalization of criteria, decision roup has given rating to calculate the weights of the criteria by sing FAHP and in phase-III, again decision group has assigned final
tion and Recycling 108 (2016) 63–81 67
pre-defined rating for selection of RL partners by using FTOPSIS. The data of rating is collected from decision making group through questionnaire. This paper used fuzzy AHP to get the weights of crite- ria and prioritize them and Fuzzy TOPSIS is utilized to select partner. Although single MCDM method is sufficient for decision making but integrating one hybrid method with another can improve decision process. It is also expected that the execution of fuzzy framework will shun and substantially diminish impreciseness and uncertainty to a large extent. Thus the appropriateness of this approach in a complex multi-criteria decision environment leaves a scope for selecting this integrated methodology. Fig. 1 represents a schematic diagram of the research methodology.
4.1. Phase I selection criteria
The selection criteria should consider firm’s requirements and operating strategy that is why decision making group is formed including experts, industry associates and some senior managers of different departments. Identification of selection criteria are done by decision making group through literature review and validation is done by experts who have proficiency in this area. After brain- storming sessions and with the help of past research in this area, team has finalized 7 evaluation criteria along with various sub- criteria. This evaluation system is given in Table 1.
4.2. Phase II fuzzy analytical hierarchy process
In phase-II, after finalization of selection criteria, decision group has assigned rating to determine the weights of the criteria by using FAHP. The data of rating is collected from decision making group through questionnaire. AHP approach is pioneered by Saaty (1980); it was a numerical approach of MCDM. The use of this has some restriction due to applicability of AHP in certain environment, judgmental scale is unstable, and nonexistence of impreciseness and subjective nature. Since, human judgment based factors always entails subjectivity and ambiguity, in this situation; AHP method is not a suitable selection Garg, 2016; Bhatti et al., 2010). To manage this issue, it is suggested; to integrate the AHP method with fuzzy set theory (Mangla et al., 2015; Jakhar and Barua, 2014; Prakash et al., 2014, 2015a,b). The fuzzy AHP approach includes uncertainty and vagueness of expert’s judgments in linguistic variables. Uncer- tainty in decision making can be reduced by using fuzzy approach (Zadeh, 1965).
FAHP process is explained by Chang’s extent analysis (1996); according to this approach, extent analysis for each criterion gi; is done. The values of extent method for each criterion are obtained by using following notation.
M1gi , M 2 gi
, M3gi . . .. . .., M m gi
(i = 1, 2, 3, 4, 5, . . ., n and j = 1, 2, 3, 4, 5, . . ., m) are TFNs. The steps of Chang’s analysis can be given as in the following:
Step 1: The fuzzy synthetic extent value (Ei) with respect to the ith criterion is defined as,
Ei = m∑
j=1 M
j gi
×
⎡ ⎣ n∑
i=1
m∑ j=1
M j gi
⎤ ⎦
−1
(4.1)
m∑ j=1
M j gi
=
⎛ ⎝ m∑
j=1 aij ,
m∑ j=1
bij ,
m∑ j=1
cij
⎞ ⎠
⎡ n∑ m∑ ⎤−1 ( )
⎣
i=1 j=1 M
j gi ⎦ = 1∑n
i=1 ∑m
j=1aij ,
1∑n i=1 ∑m
j=1bij ,
1∑n i=1 ∑m
j=1cij
where (a, b, c) (lower value, most likely value, upper value).
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Table 1 Combined modeling techniques used in 3PL providers reported in the recent literature.
S. no. Researcher (Year) Modeling techniques used Issues covered Application, Country
1 Thakkar et al. (2005) ISM and ANP Selection of third party logistics Agriculture and Food industry, India
2 Chow et al. (2005) Data Mining and CBR Design of third party logistics system Freight forwarding and Logistics company, Hong-Kong
3 Hou and Su (2006) AHP and Web-based systems Evaluation of supplier selection system Manufacturing industry, UK 4 Bevilacqua et al. (2006) QFD and Fuzzy systems Supplier selection Automobile components, Italy 5 Iş ıklar et al. (2007) CBR/RBR and Fuzzy Compromise
programming Selection of 3PL providers logistics sector, Turkey
6 Almeida (2007) MAUT and ELECTRE Vendor selection – 7 Efendigil et al. (2008) Fuzzy AHP and ANN Selection of third party logistics providers – 8 Büyüközkan et al. (2008) Fuzzy AHP and Fuzzy TOPSIS Evaluation of e-logistics-based strategic
alliance partners Electronics industry, Turkish logistics sector
9 Li and Zhao (2009) Gray Correlational Analysis and AHP Supplier selection under environmental dimension
Vehicle manufacturing sector
10 Kannan et al. (2009) ISM and Fuzzy TOPSIS Selection of third-party reverse logistics providers
Battery manufacturing industry, India
11 Yan (2009) AHP and Genetic Algorithm Supplier selection in green supply chain – 12 Liu and Wang (2009) Fuzzy Delphi, Fuzzy interface and
Fuzzy linear assignment 3PL providers evaluation and selection Electronics, Power and Chemical
industry, Taiwan 13 Liou and Chuang (2010) DEMATEL, ANP and VIKOR Outsourcing partner selection Taiwanese airline, Taiwan 14 Kuo et al. (2010) DEA, ANP and ANN Green supplier selection Electronics industry, Taiwan 15 Wen and Chi (2010) DEA, AHP and ANP Evaluation and selection of green suppliers – 16 Sasikumar and Haq (2010) Fuzzy VIKOR and MILP Third-party reverse logistics provider
selection Battery manufacturing industry, India
17 Amin et al. (2011) Fuzzy SWOT and Fuzzy LP Supplier selection Auto-mobile company, Iran 18 Ho et al. (2012) QFD and Fuzzy AHP 3rd party logistics service provider
selection Hardware components, Hong Kong
19 Kuo and Lin (2012) ANP and DEA Evaluation and supplier selection Camera manufacturer, Taiwan 20 Falsini et al. (2012) AHP, DEA and linear programming 3rd party logistics service provider
selection Industry and defense Perishable products, Consumer goods, Italy
21 Li et al. (2012) Fuzzy based compound quantification values
3PL supplier selection Air conditioner manufacturers
22 Liou (2013) DEMATEL, ANP and Gray relation theory
Selection of outsourcing partner Taiwanese airline, Taiwan
23 Perç in and Min (2013) QFD and Fuzzy Linear regression Selection of 3PL service providers Automobile manufacturing company, Turkey
24 Kannan et al. (2013) Fuzzy AHP, Fuzzy TOPSIS and MOLP Supplier selection for green supply chain Automobile manufacturing company, Iran
25 Akman (2015) Fuzzy c means and VIKOR Evaluation of green supplier development programs
Automobile company, Turkey
26 Bai and Sarkis (2014) Rough set theory and Data envelopment analysis
Sustainability performance evaluation for suppliers
–
27 Jayant et al. (2014) TOPSIS and AHP 3PRL providers selection Mobile phones industry, India 28 Sivakumar et al. (2015) AHP and Taguchi loss functions Vendor evaluation model Mining industry, India 29 Dobos and Vörösmarty (2015) DEA and Composite indicators Evaluation and selection of green suppliers – 30 Senthil et al. (2014) AHP and Fuzzy TOPSIS 3PRL contractor evaluation and selection Plastic industry, India 31 Karsak and Dursun (2015) QFD and Fuzzy 2-tuple linguistic Supplier evaluation and selection Health sector, Istanbul
S S
s S
i
V
w t
V
w a
V
32 Ayhan and Kilic (2015) Fuzzy AHP and MILP 33 Igoulalene et al. (2015) Fuzzy TOPSIS and Goal Programming 34 Kar (2015) Fuzzy AHP and Fuzzy Neural network
Step 2: The possibility degree of E2 = (a2, b2, c2) ≥ E1 = (a1, b1, c1) s determined using
(E2 = E1) = supy≥x[min(�E1 (x), �E2 (y)]
here x and y are the membership values of each criterion. Above erm can be expressed through Eq. (4.2):
(E2 ≥ E1) =
⎧⎪⎪⎨ ⎪⎪⎩
1 if b2 ≥ b1 0 if a1 ≥ c2
a1 − c2 (b2 − c2) − (b1 − a1)
= �d otherwise (4.2)
here �d represents the highest intersection point between �E1 nd �E2 (see Fig. 2).
For comparison in E1 and E2, we required both V(E1 ≥ E2) and (E2 ≥ E1).
upplier selection Gear motor manufacturers, Turkey trategic supplier selection in supply chain – upplier selection Steel industry, India
Step 3: The possibility degree of a convex fuzzy number E to be greater than convex fuzzy numbers k, Ei (i = 1, 2, . . ., k) can be expressed by
V (E = E1, E2, . . ., Ek) = V [(E = E1) and (E = E2) and . . . and (E = Ek)] = min V (E = Ei), i = 1, 2, . . ., k
Assume that d′(Si) = min V (Ei ≥ Ek)
(4.3)
For k = 1, 2, . . ., n, k /= i, the weight vectors are expressed by Eq. (4.4) as,
W ′ = (d′(S1), d′(S2), . . ., d′(Sm))T (4.4)
Step 4: The normalized weight vectors are expressed by Eq. (4.5) as,
W = (d(S1), d(S2), . . ., d(Sm))T (4.5)
C. Prakash, M.K. Barua / Resources, Conservation and Recycling 108 (2016) 63–81 69
Table 2 Evaluation criteria and sub-criteria for 3PRLPs.
S. no. Criteria Sub-criteria References
1. Capacity Criteria (CC) Time, Flexible Capacity, Govt. clearance, Convenience, Facility of Warehousing & Repair centers and their capacity
Spencer et al. (1994), Chen (2011), Efendigil et al. (2008), Gunasekaran et al. (2004), Liu and Wang (2009), Büyüközkan and Ç ifç i (2012), Ha and Krishnan (2008), Liou et al. (2011), Gunasekaran et al. (2009), Chang and Hung (2010), Kwang et al. (2007), Boran et al. (2009). Senthil et al. (2014), Aguezzoul (2014), Experts team
2. Financial Ability (FA) Facility, processing, other associated logistics cost, Investment capacity, advanced components & equipment
Ayhan and Kilic (2015), Senthil et al. (2014), Aguezzoul (2014), Xiangru (2008), Darvish et al. (2009), Liu and Wang (2009), Chen (2011), Amin and Razmi (2009), Chen and Chao (2012), Lee et al. (2009)
3. IT System (IT) Integrated System, Separate & Shared communication, RFID/EDI enabled system, Information security system
Senthil et al. (2014), Aguezzoul (2014), Liu and Wang (2009), Chen (2011), Khaleie et al. (2012), Wong et al. (2009)
4. Service Quality (SQ) Service level, customized service, Problems resolution ability, customization, Skills
Ayhan and Kilic (2015), Aguezzoul (2014), Xiangru (2008), Liou et al. (2011), Liu and Wang (2009), Zouggari and Benyoucef (2012), Ha and Krishnan (2008), Chen (2011), Boran et al. (2009), Chamodrakos et al. (2010), Liou and Chuang (2010)
5. RL Activities (RA) Collection, Sorting, Warehousing, Intermediate process, Transportation, Repair, Recycle, Remanufacturing, Disposal
Senthil et al. (2014), Aguezzoul (2014), Meade and Sarkis (2002), Ha and Krishnan (2008), Dowlatshahi (2000), Schwartz (2000), Boran et al. (2009)
6. Geographical Location (GL) Destination & Market Coverage, Shipment, Distribution & delivery
Senthil et al. (2014), Aguezzoul (2014), Darvish et al. (2009), Liu and Wang (2009), Ha and Krishnan (2008), Amin and Razmi (2009)
re, ation.
4
c t i t c F p t S 2
b
g
(
t
e
a
f
∨ B
∨ p
∨ p
7. Partner Image & Experience (PE)
Shared benefits & risks, structure, cultu competency, compatibility and cooper
.3. Phase III Fuzzy TOPSIS
Hwang and Yoon (1981) introduced another hybrid method alled TOPSIS. It works on the principle of distances and propose hat; the selective element has minimum distance from positive deal solution and maximum distance from the negative ideal solu- ion. TOPSIS method provides better results if it would be able to onsider uncertainty and impreciseness rather than crisp values. uzzy theory allows; to incorporate uncertainty in decision making rocess, that is why, the fuzzy TOPSIS method is quite appropriate ool for the solution of real life problems. (Prakash et al., 2014; enthil et al., 2014; Prakash and Barua, 2015a,c; Patil and Kant, 013).
The description of step-wise Fuzzy TOPSIS methodology is given elow:
Step 1: Allocate TFNs rating to selective attributes using scale iven in Table 4 and construct matrix for alternatives.
Step 2: Determine aggregate TFNs ratings for the alternatives.
If TFNs rating of the Nth decision maker is ∨ X abN =
labN , pabN , uabN ) where a = 1, 2, 3, . . ., m, b = 1, 2, 3, . . ., n then
he aggregated TFNs ratings (∨
X ab
) of alternatives with respect to
ach criteria is given by ∨ X ab(lab, pab, uab), where
= min N
{labN }, b = 1 N
N∑ N=1
pabN , c = max N
{uabN } (4.6)
Step 3: Construct normalized fuzzy decision matrix. The linear transformation scale is utilized to get normalized
uzzy decision matrix and it is expressed by ∨ B where:
= [pij ]mxn where i = 1, 2, 3, . . .m and j = 1, 2, 3, . . ., n
ij = (
aij c∗
, bij c∗
, cij c∗
) and c∗
j = max cij (benefit criteria) (4.7)
j j j
ij = (
a− j
cij ,
a− j
bij ,
a− j
aij
) and a−
j = min aij (cost criteria) (4.8)
Liou (2013), Senthil et al. (2014), Aguezzoul (2014), Darvish et al. (2009), Liu and Wang (2009), Chen (2011), Amin and Razmi (2009), Ha and Krishnan (2008), Amin and Zhang (2012), Zhi-Hong and Qiang (2009)
Step 4: Construct the weighted normalized matrix by using the given equation:
∨ V = [
∨ vij ]m×n where i = 1, 2, 3, . . ., m and j = 1, 2, 3, . . ., n
where ∨ vij =
∨ vij ⊗ wj (4.9)
Step 5: Compute the fuzzy negative ideal solution (FNIS) and fuzzy positive ideal solution (FPIS) by using the given equations:
A+ = {v+1 , . . ., v + n } where v+j = {max(vij )
if j ∈ J; min(vij ) if j ∈ J′}, j = 1, . . ., n (4.10)
A− = {v−1 , . . ., v − n } where v−j = {min(vij )
if j ∈ J; max(vij ) if j ∈ J′}, j = 1, . . ., n (4.11)
Step 6: Determine the distance of each alternative from FNIS and FPIS is calculated as follows:
d+ i
=
⎧⎨ ⎩
n∑ j=1
( vij − v+ij
)2⎫⎬ ⎭
1/2
, i = 1, . . ., m
d− i
=
⎧⎨ ⎩
n∑ j=1
( vij − v−ij
)2⎫⎬ ⎭
1/2
, i = 1, . . ., m
(4.12)
Step 7: Calculate closeness coefficient (CCi) of each alternative using equation
CCi = d−
i
d− i
+ d+ i
i = 1, . . .m, CCi ∈ (0, 1) (4.13)
Step 8: Obtain final rank of the alternatives by arranging CCi values in descending order.
5. Applicability of the proposed framework to case analysis
To exhibit the application of the proposed integrated approach, an empirical case of Indian electronics industry is carried out. India
70 C. Prakash, M.K. Barua / Resources, Conservation and Recycling 108 (2016) 63–81
Fig. 2. The intersection of fuzzy numbers.
i t e 3 4 2 c T h f T 5 w A e (
f e b a a fi i a p b d T m
Table 3 Triangular fuzzy number (TFN) of linguistic rating.
Linguistic rating Allocated TFN
Equal (1, 1, 1) Very low (1, 2, 3) Low (2, 3, 4) Medium (3, 4, 5) High (4, 5, 6) Very high (5, 6, 7) Excellent (7, 8, 9)
Table 4 Linguistics ratings along with TFNs.
Linguistic rating Allocated TFN
Very low (.2, .25, .33) Low (.25, .33, .5) Medium (.33, .5, 1)
T T
Fig. 3. Hierarchy model.
s enriched with human skills and technology, although; RL prac- ices are not yet extensively implemented. The market of Indian lectronics and durables products is huge and growing at about 0% CAGR. The rural and semi-urban market contributes approx. 0% to the overall Indian electronics market (Prakash and Barua, 015b). The growth of this industry in terms of production, internal onsumption and export can be estimated through these statistics. he PC ownership growth per capita from 1993 to 2000 was 604%; owever the world growth rate was 181% during the same time
rame (Prakash and Barua, 2015a; Dwivedy and Mittal, 2010a,b). he e-waste generation from old computers will increase approx. 00% from 2007 to 2020. The volume of discarded mobile phones ill rise approx. 1800% from 2007 to 2020 (Schluep et al., 2009). nd the amount of e-waste from discarded televisions and refrig- rators by 2020 will increase approx. 200% and 300% respectively UNEP, 2010).
Huge amount of e-waste generated from different products need urther treatments, i.e. reuse, recycle, remanufacturing, repair or nvironment friendly disposal. So assets or resource recovery can e done efficiently from returned products. Collection, inspection nd disposition decision of collected return products and e-waste re done by 3rd party. But, the industry people are facing some dif- culties in the selection of the third party RL partners. Therefore,
ndustry aims a flexible, robust and systematic mode to select the ppropriate 3rd party RL partner. The management is also seeking rioritization of the RL selection criteria and wants to select the est partner among alternatives. After analyzing the problem and iscussing the aim of this research, the proposed Fuzzy AHP Fuzzy
OPSIS based framework is applied to the discussed case. Now ethodology discussed above will follow in subsections (Fig. 3).
able 5 he fuzzy comparison matrix of the criteria.
CC FA IT SQ
CC (1, 1, 1) (2, 3, 4) (3, 4, 5) (2, 3, 4) FA (0.25, 0.33, 0.5) (1, 1, 1) (0.25, 0.33, 0.5) (0.2, 0.25,0.33) IT (0.2, 0.25,0.33) (2, 3, 4) (1, 1, 1) (3, 4, 5) SQ (0.25, 0.33, 0.5) (3, 4, 5) (0.2, 0.25,0.33) (1, 1, 1) RA (0.2, 0.25,0.33) (0.2, 0.25,0.33) (0.33, 0.5, 1) (0.2, 0.25, 0.33) GL (0.25, 0.33, 0.5) (0.2, 0.25, 0.33) (0.25, 0.33, 0.5) (2, 3, 4) PE (3, 4, 5) (2, 3, 4) (2, 3, 4) (0.25, 0.33, 0.5)
High (1, 2, 3) Very high (2, 3, 4) Excellent (3, 4, 5)
5.1. Application of FAHP to evaluate weights of the criteria
Decision group has to make pair-wise comparison among 7 criteria, defined by TFNs as given in Table 3. The fuzzy compari- son matrix of criteria with calculated weights using Chang’s extent analysis is highlighted in Table 5.
Calculation of the fuzzy synthetic extent of 7 criteria using Eq. (4.1) is given below. The final result shows that capacity criteria have highest weightage value (see Table 5)
CC = (13.20, 18.25, 23.33) ∗ [(63.73), (86.17), (110)]−1
= (0.12, 0.212, 0.366) Similarly
FA = (7.9, 10.17, 12.67) ∗ [(63.73), (86.17), (110)]−1
= (0.072, 0.118, 0.119)
IT = (9.45, 13.58, 17.83) ∗ [(63.73), (86.17), (110)]−1
= (0.085, 0.158, 0.280)
SQ = (9.70, 12.92, 16.33) ∗ [(63.73), (86.17), (110)]−1
= (0.088, 0.150, 0.256)
RA = (7.93, 10.25, 13) ∗ [(63.73), (86.17), (110)]−1
= (0.072, 0.119, 0.204)
GL = (6.9, 9.17, 11.67) ∗ [(63.73), (86.17), (110)]−1
= (0.063, 0.106, 0.183)
RA GL PE Weights Rank
(3, 4, 5) (2, 3, 4) (0.2, 0.25,0.33) 0.243 1 (3, 4, 5) (3, 4, 5) (0.2, 0.25,0.33) 0.111 6 (1, 2, 3) (2, 3, 4) (0.25, 0.33, 0.5) 0.182 2 (3, 4, 5) (0.25, 0.33, 0.5) (2, 3, 4) 0.167 3 (1, 1, 1) (3, 4, 5) (3, 4, 5) 0.115 5 (0.2, 0.25, 0.33) (1, 1, 1) (3, 4, 5) 0.032 7 (0.2, 0.25,0.33) (0.2, 0.25, 0.33) (1, 1, 1) 0.149 4
C. Prakash, M.K. Barua / Resources, Conserva
Table 6 V values for criteria.
CC FA IT SQ RA GL PE CC 0.456 0.746 0.687 0.474 0.374 0.613 FA 1 1 1 1 0.905 1 IT 1 0.739 0.956 0.753 0.654 0.882 SQ 1 0.776 1 0.878 0.132 0.922
P
E
m
m
s
m
W
W
5
a f i E
T F
T N
RA 1 0.992 1 1 0.277 1 GL 1 1 1 1 1 1 PE 1 0.861 1 1 0.872 0.204
E = (8.65, 11.83, 15.17) ∗ [(63.73), (86.17), (110)]−1
= (0.079, 0.137, 0.238)
V values are determined using Eq. (4.2) (see Table 6). Then minimum possibility degree values are calculated using
q. (4.3) as
(CC) = min V (S1 ≥ Sk) = min(1, 1, 1, 1, 1, 1) = 1 (FA) = min V (S2 ≥ Sk) = min(0.456, 0.739, 0.776, 0.992, 1, 0.861) = 0.456
imilarly
(IT) = 0.746, m(SQ) = 0.687, m(RA) = 0.474, m(GL) = 0.132, m(PE) = 0.613
Then the weight vector is given by:
p = (1, 0.456, 0.746, 0.687, 0.474, 0.132, 0.613)T
After normalization final weights of the criteria are obtained as
= (0.243, 0.111, 0.182, 0.167, 0.115, 0.032, 0.0149)T
.2. Application of Fuzzy TOPSIS to select the RL partner
Fuzzy evaluation matrix is constructed using linguistic variables
s shown in Table 4 and presented through TFN and corresponding uzzy assessment matrix is expressed in Table 7. Then it is normal- zed using Eqs. (4.7) and (4.8) as shown in Table 8. After that using q. (4.9), weighted normalized fuzzy decision matrix is obtained
able 7 uzzy evaluation matrix.
CC FA IT SQ
RL Partner 1 (3, 4, 5) (0.333, 0.5, 1) (3, 4, 5) (0. RL Partner 2 (2, 3, 4) (1, 2, 3) (0.2, 0.25, 0.333) (2, RL Partner 3 (0.333, 0.5, 1) (3, 4, 5) (1, 2, 3) (3, RL Partner 4 (2, 3, 4) (0.25, 0.333, 0.5) (0.2, 0.25, 0.333) (1, RL Partner 5 (0.333, 0.5, 1) (2, 3, 4) (0.333, 0.5, 1) (0. RL Partner 6 (1, 2, 3) (0.2, 0.25, 0.333) (0.333, 0.5, 1) (1, RL Partner 7 (0.25, 0.333, 0.5) (1, 2, 3) (2, 3, 4) (2,
able 8 ormalized Fuzzy decision matrix.
CC FA IT SQ
RL Partner 1 (0.2, 0.25, 0.333) (0.2, 0.4, 0.606) (0.6, 0.8, 1) (0 RL Partner 2 (0.5, 0.75, 1) (0.333, 0.666, 1) (0.6, 0.8, 1) (0 RL Partner 3 (0.25, 0.666, 0.75) (0.2, 0.25, 0.333) (0.333, 0.666, 1) (0 RL Partner 4 (0.25, 0.333, 0.5) (0.4, 0.6, 0.8) (0.6, 0.8, 1) (0 RL Partner 5 (0.25, 0.666, 0.75) (0.2, 0.25, 0.333) (0.2, 0.4, 0.606) (0 RL Partner 6 (0.333, 0.666, 1) (0.6, 0.8, 1) (0.2, 0.4, 0.606) (0 RL Partner 7 (0.5, 0.666, 1) (0.333, 0.666, 1) (0.5, 0.75, 1) (0
tion and Recycling 108 (2016) 63–81 71
as shown in Table 9. This work assumed all selective attributes as
cost criteria and assigned the FPIS as ∨ v
+ 1 = (0, 0, 0) and the FNIS
as ∨ v
− 1 = (1, 1, 1), thereafter each alternative distance is calculated
by using Eq. (4.12) and closeness coefficient is obtained using Eq. (4.13) and RL partners selection is done through CCi values, which is indicated in Table 10. This entire process follows the methodology discussed in phase III.
6. Results and discussion
Reverse logistics practices have received considerable atten- tion recently; especially in electronics and other industry. Reverse logistics partner plays crucial role in return management prac- tices, which would affect the overall supply chain management. This paper identifies various RL partner selection criteria and sub- criteria from the relevant literature and industrial experts’ views; than identified criteria are evaluated and analyzed for selection of appropriate reverse logistics partner for Indian electronics indus- try. Idea generation seminar is conducted for identification of selection criteria of third party RL partners. Then decision team has identified various quantitative and qualitative criteria and their sub-criteria for partner selection with the help of relevant literature and finalized major seven criteria under various sub-criteria. It is very hard to say which of the selection criteria for reverse logistics partner selection is more important than others, but prioritizing them by using this approach made it more logical, flexible and supportive for management. Fuzzy AHP has been used for eval- uation and ranking of the finalized criteria and Fuzzy TOPSIS is applied to select appropriate reverse logistics partner. The rank- ing of the criteria have been done by seeing the highest weightage values. It shows Capacity Criteria (CC) have maximum weigh- tage value (0.243) and got 1st rank, IT system (IT) criteria have 2nd rank with weightage value (0.182) and Service Quality (SQ) criteria has 3rd rank with weightage value (0.167) (see Table 5). Further, ranking of various criteria in descending order with respec- tive weightage values are Partner Image & Experience (0.149) > RL Activities (0.115) > Financial Ability (0.111) > Geographical Loca- tion (0.032) (see Table 5). This shows Capacity Criteria is the most important criteria among others while selecting reverse logistics
partner and Geographical Location criteria is the least important criteria while choosing appropriate RL partner. Since capacity crite- ria received first rank among other selection criteria; it means firm should be capable enough to employ inadequate infrastructure,
RA GL PE
25, 0.333, 0.5) (1, 2, 3) (0.333, 0.5, 1) (1, 2, 3) 3, 4) (0.2, 0.25, 0.333) (3, 4, 5) (0.333, 0.5, 1) 4, 5) (1, 2, 3) (1, 2, 3) (0.25, 0.333, 0.5) 2, 3) (3, 4, 5) (0.333, 0.5, 1) (2, 3, 4) 2, 0.25, 0.333) (2, 3, 4) (1, 2, 3) (0.2, 0.25, 0.333)
2, 3) (0.333, 0.5, 1) (2, 3, 4) (3, 4, 5) 3, 4) (0.333, 0.5, 1) (0.2, 0.25, 0.333) (1, 2, 3)
RA GL PE
.4, 0.6, 0.8) (0.333, 0.666, 1) (0.2, 0.4, 0.606) (0.333, 0.666, 1)
.25, 0.333, 0.5) (0.6, 0.8, 1) (0.2, 0.25, 0.333) (0.2, 0.4, 0.606)
.2, 0.25, 0.33) (0.333, 0.666, 1) (0.333, 0.666, 1) (0.4, 0.6, 0.8)
.333, 0.666, 1) (0.2, 0.25, 0.333) (0.2, 0.4, 0.606) (0.25, 0.33, 0.5)
.6, 0.8, 1) (0.25, 0.333, 0.5) (0.333, 0.666, 1) (0.6, 0.8, 1)
.333, 0.666, 1) (0.2, 0.4, 0.606) (0.25, 0.333, 0.5) (0.2, 0.25, 0.333)
.25, 0.333, 0.5) (0.2, 0.4, 0.606) (0.6, 0.8, 1) (0.333, 0.666, 1)
72 C. Prakash, M.K. Barua / Resources, Conservation and Recycling 108 (2016) 63–81
Table 9 Weighted normalized Fuzzy decision matrix.
CC FA IT SQ RA GL PE
RL Partner 1 (0.049, 0.061, 0.080) (0.022, 0.044, 0.066) (0.109, 0.146, 0.182) (0.068, 0.103, 0.137) (0.038, 0.077, 0.115) (0.006, 0.013, 0.019) (0.05, 0.099, 0.149) RL Partner 2 (0.122, 0.182, 0.243) (0.037, 0.074, 0.111) (0.109, 0.146, 0.182) (0.043, 0.057, 0.086) (0.07, 0.092, 0.115) (0.006, 0.008, 0.013) (0.03, 0.06, 0.09) RL Partner 3 (0.061, 0.162, 0.182) (0.022, 0.028, 0.037) (0.061, 0.121, 0.182) (0.034, 0.043, 0.057) (0.038, 0.077, 0.115) (0.011, 0.021, 0.032) (0.06, 0.09, 0.119) RL Partner 4 (0.061, 0.081, 0.121) (0.044, 0.067, 0.089) (0.109, 0.146, 0.182) (0.057, 0.114, 0.171) (0.023, 0.029, 0.038) (0.006, 0.013, 0.019) (0.037, 0.05, 0.075) RL Partner 5 (0.061, 0.162, 0.182) (0.022, 0.028, 0.037) (0.036, 0.073, 0.109) (0.104, 0.137, 0.171) (0.029, 0.038, 0.058) (0.011, 0.021, 0.032) (0.09, 0.119, 0.149) RL Partner 6 (0.081, 0.162, 0.243) (0.067, 0.089, 0.111) (0.036, 0.073, 0.109) (0.057, 0.114, 0.171) (0.023, 0.046, 0.07) (0.008, 0.011, 0.016) (0.03, 0.037, 0.049) RL Partner 7 (0.122, 0.162, 0.243) (0.037, 0.074, 0.111) (0.091, 0.137, 0.182) (0.043, 0.057, 0.086) (0.023, 0.046, 0.07) (0.019, 0.026, 0.032) (0.05, 0.099, 0.149)
∨+ ∨+ ∨+ ∨ v
+ 1 = (0, 0, 0)
∨ v
+ 1 = (0, 0, 0)
∨ v
+ 1 = (0, 0, 0)
∨ v
+ 1 = (0, 0, 0)
∨ v
− 1 = (1, 1, 1)
∨ v
− 1 = (1, 1, 1)
∨ v
− 1 = (1, 1, 1)
∨ v
− 1 = (1, 1, 1)
fl r T t i n s o a a s w a m i t T l o o t 2 i p d c s s w s a R i p e i T t t m & M c r 2 t r m ( t & a s
Table 10 Closeness coefficient values and final ranking.
Alternatives d+ i
d− i
CCi Rank
RLP1 0.697 6.357 0.901 3 RLP2 0.725 6.329 0.897 4 RLP3 0.758 6.313 0.893 5 RLP4 0.677 6.375 0.904 2 RLP5 0.789 6.269 0.888 6
A+ , A− v1 = (0, 0, 0) v1 = (0, 0, 0) v1 = (0, 0, 0) ∨ v
− 1 = (1, 1, 1)
∨ v
− 1 = (1, 1, 1)
∨ v
− 1 = (1, 1, 1)
exible capacity, facilities for collection, warehousing, storage & epairing centers, according to customer convenience and reach. herefore managers and practioners have to understand subcon- racting is very effective provided that organization is fully utilizing ts resources, capacity and competencies while dealing with exter- al opportunities (Espino-Rodriguez and Padron-Robaina, 2006). IT ystem criteria comes second and it is defined as control or support f information technology in the management and execution of RL doption. It helps in timely management of information assessment bout shipment status, freight tracking and tracing. It assists in haring data through EDI in intra and inter-organizations through eb-based services (Bowersox et al., 2002). Hence, management
nd planners have to seek partner’s technological skills, infor- ation approachability, accessibility and availability of computer
nternet & network, hardware & software capability and informa- ion security systems, while selecting 3PRLPs (Aguezzoul, 2014). he third rank is obtained by Service Quality criteria; it includes evel of service before and after sales, customization & breadth f services and customer problems handling abilities. Selection f third party service providers encourages innovative and cus- omized services, and improves overall service quality (Lai et al., 008). Partner Image and Experience criteria received fourth rank
n list; it shows mutual benefits & rewards, risk sharing ability, past erformance and expertize and skills of partner to perform a job iligently. Hsu et al. (2013) studied that partner relationship and ompatibility is the most important criterion for 3rd party provider election so managers have to keep this dimension in minds while electing outsourcing partner. Fifth rank is obtained by RL activities hich comprise collection, inspection/sorting and further dispo-
ition decisions such as repairing, refurbishing, remanufacturing nd proper disposal. Goggin and Brown (2000) described effective L practices focused on asset/resource recovery process and offers
nsights into the requirements for product & material retrieval, arts & component reclamation otherwise remanufacturing or nvironment friendly disposal. Therefore asset recovery capabil- ty/skills of partner plays crucial role in the selection of RL partner. he sixth rank is gained by Financial Ability criteria; it refers to he sound financial position of outsourcing partner and ability o ensure constant and consistent service and upgrade equip-
ent’s & machinery. It also includes acquisition cost, processing operation cost and other associated cost of outsourcing partner. anagement has to analyze and evaluate economic and finan-
ial stability of 3PRLP so that firm could minimize or significantly educe the materials and business processes cost (Anttonen et al., 013). Additionally subcontracting allows flexibility to organiza- ion by focusing on central business activities and better use of esources; it increases responsiveness toward customer needs and aintain financial stability by less allocation of funds’ investments
Ellram et al., 2008). Final rank is assigned to Geographical Loca-
ion criteria; it means partner’s coverage of region, destination
market; shipment, distribution & delivery ability and speed & ccuracy of delivery. Managers outsourcing decision favor to that ervice provider which provides multiple services under single roof.
RLP6 0.658 6.385 0.907 1 RLP7 0.887 6.165 0.874 7
He will be able to cover wide geographical area and region with speed and accuracy through involvement of technology (Rafiq and Jaafar, 2007). The research results of Fuzzy AHP and importance of the criteria and sub-criteria have been understood as per ranking obtained in Table 4; were conferred and validate with the industrial experts with the goal to get further insights of selection criteria of the RL partners, which will be helpful in the selection, and hence, improve the reverse supply chain of the organization.
The weights of the criteria obtained through Fuzzy AHP are used in Fuzzy TOPSIS for selection of the reverse logistics part- ner. In this study seven potential reverse logistics partners (RLPs) have been identified; and with the help of decision making group, evaluation matrix of reverse logistics partner selection alternatives are constructed. The selection of the reverse logistics partner has done by observing highest closeness coefficient values. According to Table 10 alternative RLP6 is the best reverse logistics partner with high CCi value (0.907) and RLP7 is the lowest rating partner with low CCi value (0.874). The ranking of other alternatives with CCi values based on Table 10 are RLP4 (0.904) > RLP1 (0.901) > RLP2 (0.897) > RLP3 (0.893) > RLP5 (0.888) in descending orders. Results show that RLP6, RLP4 and RLP1 can be three potential strategic partners for case industry. By using this approach decision makers can identify, evaluate and select reverse logistics partner for Indian electronics industry.
7. Sensitivity analysis
Among all criteria of 3PRLPs selection, the capacity criteria cat- egory receives the highest priority weights. This criterion ranked as the highest amongst the other categories, hence this crite- ria carries the potential to influence other categories of 3PRLPs selection. Chang et al. (2007) suggested that slight changes in rel- ative weights would give great changes in the final ranking. As, human judgment input is utilized to calculate the weights for the listed categories of 3PRLPs selection and specific selection crite- ria, thereby, it is recommended to test the final ranking by varying the weights of all the categories of selection criteria (Govindan et al., 2012; Mangla et al., 2015; Vishwakarma et al., 2015). Thus,
sensitivity analysis may provide a further insight to the causes of selection of appropriate 3PRLPs for reverse logistics implementa- tion in organizations. In this study two stage sensitivity analyses is performed. In first stage the effect of an incremental change in
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Table 11 Ranking of other criteria by sensitivity analysis when Capacity criteria vary.
Specific criteria CC category values in performing the sensitivity analysis test
Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9 Run 10 0.100 0.200 Normalized (0.243) 0.300 0.400 0.500 0.600 0.700 0.800 0.900
FA 0.135 0.118 0.111 0.102 0.085 0.066 0.046 0.026 0.006 0.001 IT 0.206 0.189 0.182 0.172 0.156 0.137 0.117 0.097 0.077 0.054 SQ 0.191 0.174 0.167 0.158 0.141 0.122 0.102 0.082 0.062 0.029 RA 0.139 0.123 0.115 0.106 0.089 0.071 0.050 0.031 0.011 0.002 GL 0.056 0.039 0.032 0.023 0.006 0.000 0.000 0.000 0.000 0.000 PE 0.173 0.156 0.149 0.140 0.123 0.104 0.084 0.064 0.043 0.014
Table 12 Ranking of RL partner in sensitivity analysis run when Capacity criteria vary.
Sensitivity analysis rank Obtained rank by change in CC category values in the sensitivity analysis run
Run 1 (0.100)
Run 2 (0.20)
Run 3 (0.243)
Run 4 (0.30)
Run 5 (0.40)
Run 6 (0.50)
Run 7 (0.60)
Run 8 (0.70)
Run 9 (0.80)
Run 10 (0.90)
1 RLP6 RLP5 RLP6 RLP2 RLP2 RLP1 RLP1 RLP1 RLP1 RLP1 2 RLP5 RLP6 RLP4 RLP7 RLP7 RLP7 RLP7 RLP7 RLP7 RLP7 3 RLP2 RLP2 RLP1 RLP3 RLP3 RLP3 RLP3 RLP3 RLP3 RLP3 4 RLP3 RLP1 RLP2 RLP1 RLP1 RLP2 RLP2 RLP2 RLP2 RLP2 5 RLP4 RLP4 RLP3 RLP5 RLP4 RLP4 RLP4 RLP4 RLP4 RLP4 6 RLP1 RLP3 RLP5 RLP4 RLP5 RLP5 RLP5 RLP5 RLP5 RLP5 7 RLP7 RLP7 RLP7 RLP6 RLP6 RLP6 RLP6 RLP6 RLP6 RLP6
v d s s e a c i r 3 ( w g t T 3 p T d p b t
Table 13 Ranking of RL partner in sensitivity analysis Run 1 when CC (0.100).
S. no. Expt. no. Ranking
1 2 3 4 5 6 7
1 WR12 RLP6 RLP4 RLP3 RLP5 RLP1 RLP2 RLP7 2 WR13 RLP3 RLP6 RLP2 RLP1 RLP5 RLP4 RLP7 3 WR14 RLP5 RLP6 RLP4 RLP1 RLP3 RLP2 RLP7 4 WR15 RLP5 RLP6 RLP1 RLP3 RLP4 RLP2 RLP7 5 WR16 RLP6 RLP5 RLP2 RLP3 RLP4 RLP1 RLP7 6 WR17 RLP5 RLP7 RLP1 RLP2 RLP3 RLP4 RLP6 7 WR23 RLP5 RLP6 RLP1 RLP2 RLP4 RLP3 RLP7 8 WR24 RLP5 RLP6 RLP2 RLP4 RLP3 RLP1 RLP7 9 WR25 RLP6 RLP5 RLP2 RLP3 RLP4 RLP1 RLP7 10 WR26 RLP6 RLP4 RLP3 RLP2 RLP5 RLP1 RLP7 11 WR27 RLP5 RLP6 RLP1 RLP4 RLP2 RLP3 RLP7 12 WR34 RLP6 RLP5 RLP1 RLP3 RLP4 RLP2 RLP7 13 WR35 RLP6 RLP7 RLP3 RLP1 RLP4 RLP2 RLP5 14 WR36 RLP5 RLP3 RLP4 RLP1 RLP6 RLP2 RLP7 15 WR37 RLP6 RLP5 RLP2 RLP3 RLP4 RLP1 RLP7 16 WR45 RLP5 RLP7 RLP4 RLP2 RLP3 RLP1 RLP6 17 WR46 RLP4 RLP5 RLP6 RLP2 RLP3 RLP1 RLP7 18 WR47 RLP6 RLP5 RLP3 RLP2 RLP4 RLP1 RLP7 19 WR56 RLP5 RLP4 RLP2 RLP3 RLP6 RLP1 RLP7
Fig. 4. Ranking of specific criteria in sensitivity runs
alue from 0.1 to 0.9, to the capacity criteria category (CC) was etermined as shown in Table 11. The results of the stage one sen- itivity analysis and the relative change in selection criteria can be een through Fig. 4. Further, due to variation in the selection cat- gory weights, the specific criteria weights and their final ranking lso varied. Second stage sensitivity analysis is performed through hanging weights of two criteria simultaneously within the capac- ty criteria criterion run1 and selection of 3PRLPs is done as per anking obtained in this run. Then final ranking for selection of PRLPs is done by change observed by capacity criteria category see Table 12). To illustrate sensitivity analysis run 1 stage 1st, hen capacity criteria category criterion value is 0.1, IT criteria
ot first rank and GL criteria received last rank and ranking of he other selection criteria are SQ > PE > RA > FA > CC (see Fig. 4). hen Fuzzy TOPSIS is applied to get final ranking for selection of PRLPs, which shows RLP6 is the best partner and ranking of other artners are RLP5 > RLP2 > RLP3 > RLP4 > RLP1 > RLP7 (please refer able 12). In the 2nd stage sensitivity analysis run 1; we have con-
ucted 21 experiments to check the effect on the selection of RL artners by change in criteria weights simultaneously. This is done y changing the weights of two decision attributes while keeping he other weights constants. For example WR12 indicates in the
20 WR57 RLP5 RLP7 RLP2 RLP3 RLP4 RLP1 RLP6 21 WR67 RLP5 RLP6 RLP2 RLP3 RLP4 RLP1 RLP7
sensitivity analysis experiment 1 of Table 13, weight of the crite- ria 1, i.e. CC has changed with criteria 2, i.e. FA and weights of all others criteria, i.e. IT, SQ, RA, GL, PE remains constants. Then CCi scores are calculated by using Fuzzy TOPSIS method. That shows RLP6 is the best rating partner with high CCi value and RLP7 is the lowest rating partner with less CCi value. Other strategic part- ners ranking are RLP4–RLP3–RLP5–RLP1–RLP2 in descending order (see Table 13). Again in the sensitivity analysis experiment 3, i.e. WR14, weight of the criteria 1, i.e. CC has changed with criteria 4, i.e. SQ and weights of all others criteria, i.e. FA, IT, RA, GL, PE remains constants and CCi values are calculated to get final ranks. The details
of the experiment are given in Table 13. The result of the sensitivity analysis run 1 is shown in Table 13. And Fig. 5 shows that out of 21 experiments, RLP5 has highest value in 11 experiments and RLP6 has highest value in 8 experiments and RLP7 has lowest score in 17
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Fig. 5. Results of sensitivity analysis run 1.
e c b o o i R s o t t I r r T l R i c a r c e h T F
Table 14 Ranking of RL partner in sensitivity analysis Run 2 when CC (0.200).
S. no. Expt. no. Ranking
1 2 3 4 5 6 7
1 WR12 RLP5 RLP6 RLP1 RLP3 RLP2 RLP4 RLP7 2 WR13 RLP5 RLP6 RLP2 RLP1 RLP4 RLP3 RLP7 3 WR14 RLP5 RLP6 RLP1 RLP2 RLP4 RLP3 RLP7 4 WR15 RLP5 RLP7 RLP3 RLP1 RLP4 RLP2 RLP6 5 WR16 RLP6 RLP3 RLP4 RLP2 RLP5 RLP1 RLP7 6 WR17 RLP5 RLP6 RLP3 RLP2 RLP4 RLP1 RLP7 7 WR23 RLP3 RLP6 RLP1 RLP2 RLP4 RLP5 RLP7 8 WR24 RLP5 RLP6 RLP2 RLP1 RLP3 RLP4 RLP7 9 WR25 RLP5 RLP6 RLP2 RLP1 RLP4 RLP3 RLP7 10 WR26 RLP4 RLP5 RLP3 RLP2 RLP6 RLP1 RLP7 11 WR27 RLP4 RLP7 RLP1 RLP2 RLP3 RLP5 RLP6 12 WR34 RLP4 RLP6 RLP2 RLP1 RLP5 RLP3 RLP7 13 WR35 RLP5 RLP7 RLP2 RLP1 RLP4 RLP3 RLP6 14 WR36 RLP2 RLP5 RLP3 RLP1 RLP6 RLP4 RLP7 15 WR37 RLP4 RLP6 RLP2 RLP1 RLP5 RLP3 RLP7 16 WR45 RLP5 RLP7 RLP4 RLP1 RLP3 RLP2 RLP6 17 WR46 RLP3 RLP6 RLP5 RLP1 RLP4 RLP2 RLP7 18 WR47 RLP5 RLP6 RLP3 RLP1 RLP4 RLP2 RLP7 19 WR56 RLP4 RLP5 RLP1 RLP2 RLP6 RLP3 RLP7 20 WR57 RLP5 RLP7 RLP2 RLP1 RLP4 RLP3 RLP6 21 WR67 RLP2 RLP6 RLP3 RLP1 RLP4 RLP5 RLP7
Table 15 Ranking of RL partner in sensitivity analysis Run 3 when CC (0.243).
S. no. Expt. no. Ranking
1 2 3 4 5 6 7
1 WR12 RLP6 RLP4 RLP3 RLP2 RLP1 RLP5 RLP7 2 WR13 RLP6 RLP4 RLP2 RLP1 RLP3 RLP5 RLP7 3 WR14 RLP6 RLP4 RLP2 RLP1 RLP3 RLP5 RLP7 4 WR15 RLP5 RLP1 RLP4 RLP6 RLP3 RLP2 RLP7 5 WR16 RLP4 RLP1 RLP6 RLP3 RLP2 RLP5 RLP7 6 WR17 RLP6 RLP4 RLP2 RLP1 RLP3 RLP5 RLP7 7 WR23 RLP4 RLP1 RLP6 RLP2 RLP3 RLP5 RLP7 8 WR24 RLP4 RLP6 RLP1 RLP2 RLP5 RLP3 RLP7 9 WR25 RLP4 RLP6 RLP1 RLP2 RLP3 RLP5 RLP7 10 WR26 RLP3 RLP5 RLP1 RLP4 RLP6 RLP2 RLP7 11 WR27 RLP4 RLP1 RLP6 RLP2 RLP3 RLP5 RLP7 12 WR34 RLP4 RLP6 RLP1 RLP2 RLP3 RLP5 RLP7 13 WR35 RLP4 RLP6 RLP1 RLP2 RLP3 RLP5 RLP7 14 WR36 RLP6 RLP4 RLP5 RLP1 RLP3 RLP2 RLP7 15 WR37 RLP4 RLP6 RLP1 RLP2 RLP3 RLP5 RLP7 16 WR45 RLP4 RLP6 RLP1 RLP2 RLP5 RLP3 RLP7 17 WR46 RLP3 RLP4 RLP1 RLP6 RLP2 RLP5 RLP7 18 WR47 RLP4 RLP6 RLP1 RLP2 RLP3 RLP5 RLP7 19 WR56 RLP4 RLP6 RLP5 RLP1 RLP3 RLP7 RLP2
Fig. 6. Results of sensitivity analysis run 2.
xperiments consistently. Again in sensitivity run 2 Stage 1st, when apacity criteria category criterion value is 0.2, first rank is gained y CC criterion and last rank is hold by GL criterion. The ranking of ther criteria are IT > SQ > PE > RA > FA > GL (see Fig. 4) and results f Fuzzy TOPSIS depicts final ranking of selection of 3PRLPs, which ndicates RLP5 is the best partner and ranking of other partners are LP6 > RLP2 > RLP1 > RLP4 > RLP3 > RLP7 (please refer Table 12). In tage 2; similar 21 experiments are performed to assess the effect n the selection of RL partners by change in criteria weights simul- aneously. This has been conducted by replacing the weights for wo decision attributes while putting the other weights constant. n WR12, weight of the criteria 1, i.e. CC has changed with crite- ia 2, i.e. FA and weights of all others criteria, i.e. IT, SQ, RA, GL, PE emains constants. Then selection of RL partner is done by Fuzzy OPSIS which shows RLP5 is the best rating partner and RLP7 is the owest rating partner. Other RL partners ranking are RLP6-RLP1- LP3-RLP2-RLP4 in descending order (please refer Table 14). Again
n the sensitivity analysis experiment 2, i.e. WR13, weight of the riteria 1, i.e. CC has changed with criteria 3, i.e. IT and weights of ll others criteria, i.e. FA, SQ, RA, GL, PE remains constants and final anks are calculated. The results of the sensitivity analysis is indi- ated in Table 14 and Fig. 6 shows that RLP5 has highest value in 11 xperiments and RLP4 has highest value in 5 experiments and RLP7
as lowest score in 16 experiments out of total 21 experiments. he details of the remaining experiments are given in Table 14 and ig. 6.
20 WR57 RLP4 RLP6 RLP1 RLP2 RLP3 RLP5 RLP7 21 WR67 RLP4 RLP6 RLP3 RLP1 RLP2 RLP5 RLP7
At normalized level in sensitivity run 3 in phase I; when capac- ity criteria category value is 0.243 then CC criterion received first rank and GL criterion acquired last rank. Other criteria ranking are IT > SQ > PE > RA > FA (see Fig. 4). And selection results suggest that RLP6 is the best reverse logistics partner and RLP7 is the lowest rating partner The ranking of other alternatives with CCi values based on Table 11 are RLP4 > RLP1 > RLP2 > RLP3 > RLP5 in descen- ding orders. In stage II of sensitivity analysis; experiment WR12 of Table 15 indicates while changing weights of the criterion 1, i.e. CC with criterion 2, i.e. FA and keeping weights of others criteria con- stant; RLP6 is the top rating partner and other RL partners ranking are RLP4–RLP3–RLP2–RLP1–RLP5–RLP7 in descending order. The results of the sensitivity analysis is shown in Table 15 and Fig. 7 shows that out of 21 experiments, RLP4 has highest value in 13 experiments and RLP6 has highest value in 6 experiments and RLP7
has lowest score in 20 experiments. Details of other experiments can be observed through Table 15 and Fig. 7. Now onwards capacity criteria category value varies from 0.3 to 0.9 in stage 1st of run
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Fig. 7. Results of sensitivity analysis run 3.
Table 16 Ranking of RL partner in sensitivity analysis Run 4 when CC (0.300).
S. no. Expt. no. Ranking
1 2 3 4 5 6 7
1 WR12 RLP4 RLP6 RLP1 RLP3 RLP2 RLP5 RLP7 2 WR13 RLP5 RLP7 RLP3 RLP4 RLP2 RLP1 RLP6 3 WR14 RLP4 RLP5 RLP1 RLP2 RLP7 RLP3 RLP6 4 WR15 RLP6 RLP7 RLP4 RLP1 RLP3 RLP2 RLP5 5 WR16 RLP5 RLP2 RLP4 RLP3 RLP6 RLP1 RLP7 6 WR17 RLP6 RLP4 RLP3 RLP2 RLP5 RLP1 RLP7 7 WR23 RLP3 RLP6 RLP2 RLP1 RLP4 RLP5 RLP7 8 WR24 RLP2 RLP7 RLP4 RLP1 RLP3 RLP5 RLP6 9 WR25 RLP2 RLP7 RLP3 RLP1 RLP5 RLP4 RLP6 10 WR26 RLP3 RLP6 RLP4 RLP1 RLP5 RLP2 RLP7 11 WR27 RLP3 RLP7 RLP2 RLP1 RLP4 RLP5 RLP6 12 WR34 RLP3 RLP6 RLP2 RLP1 RLP5 RLP4 RLP7 13 WR35 RLP2 RLP7 RLP3 RLP1 RLP4 RLP5 RLP6 14 WR36 RLP2 RLP5 RLP3 RLP1 RLP6 RLP4 RLP7 15 WR37 RLP2 RLP6 RLP3 RLP1 RLP5 RLP4 RLP7 16 WR45 RLP2 RLP7 RLP5 RLP1 RLP3 RLP4 RLP6 17 WR46 RLP2 RLP6 RLP5 RLP1 RLP4 RLP3 RLP7 18 WR47 RLP2 RLP6 RLP3 RLP1 RLP5 RLP4 RLP7 19 WR56 RLP1 RLP6 RLP3 RLP2 RLP5 RLP4 RLP7 20 WR57 RLP2 RLP7 RLP3 RLP1 RLP4 RLP5 RLP6
4 o s t t a 4 R t R h b r h 1 t 2 R i T R
Fig. 8. Results of sensitivity analysis run 4.
Table 17 Ranking of RL partner in sensitivity analysis Run 5 when CC (0.400).
S. no. Expt. no. Ranking
1 2 3 4 5 6 7
1 WR12 RLP3 RLP7 RLP1 RLP4 RLP2 RLP5 RLP6 2 WR13 RLP5 RLP7 RLP3 RLP4 RLP2 RLP1 RLP6 3 WR14 RLP6 RLP3 RLP1 RLP4 RLP7 RLP5 RLP2 4 WR15 RLP6 RLP7 RLP5 RLP1 RLP3 RLP2 RLP4 5 WR16 RLP4 RLP2 RLP5 RLP3 RLP6 RLP1 RLP7 6 WR17 RLP5 RLP4 RLP3 RLP2 RLP6 RLP1 RLP7 7 WR23 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 8 WR24 RLP2 RLP7 RLP4 RLP1 RLP3 RLP5 RLP6 9 WR25 RLP2 RLP7 RLP3 RLP1 RLP4 RLP5 RLP6 10 WR26 RLP2 RLP6 RLP4 RLP1 RLP5 RLP3 RLP7 11 WR27 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 12 WR34 RLP2 RLP7 RLP3 RLP1 RLP4 RLP5 RLP6 13 WR35 RLP2 RLP7 RLP3 RLP1 RLP4 RLP5 RLP6 14 WR36 RLP2 RLP5 RLP3 RLP1 RLP6 RLP4 RLP7 15 WR37 RLP2 RLP7 RLP3 RLP1 RLP5 RLP4 RLP6 16 WR45 RLP2 RLP7 RLP4 RLP1 RLP3 RLP5 RLP6 17 WR46 RLP2 RLP6 RLP5 RLP1 RLP4 RLP3 RLP7 18 WR47 RLP2 RLP7 RLP3 RLP1 RLP4 RLP5 RLP6 19 WR56 RLP1 RLP6 RLP3 RLP2 RLP5 RLP4 RLP7 20 WR57 RLP2 RLP7 RLP3 RLP1 RLP4 RLP5 RLP6 21 WR67 RLP1 RLP6 RLP3 RLP2 RLP4 RLP5 RLP7
21 WR67 RLP1 RLP6 RLP3 RLP2 RLP4 RLP5 RLP7
–10; the first rank is acquired by CC criteria, however last rank is btained by GL criteria and the ranks of other criteria vary in the ame manner (for details see Fig. 4). And selection results shows hat RLP1 is the best reverse logistics partner in 5 runs and RLP2 is he top rating partner in remaining runs and RLP6 got last rank in ll runs (please refer Table 12). 2nd stage sensitivity analysis run –10 are performed to connote the effect on the selection of the L partner by variation in the priority weights of the criteria. In he 2nd stage run 4; RLP2 has highest value in 9 experiments and LP3 has highest value in 4 experiments and RLP7 is consistently as lowest score in 12 experiments. Other experiments details can e seen through Table 16 and Fig. 8. Similarly in the 2nd stage un 5; RLP2 received highest rank in 11 experiments and RLP1 as highest value in 4 experiments and RLP6 got lowest rank in 2 experiments. Specification of other experiments can be viewed hrough Table 17 and Fig. 9. Also in the 2nd stage run 6; out of 1 experiments, RLP1 acquired first rank in 11 experiments and
LP2 received first rank in 3 experiments and RLP6 got lowest rank
n 13 experiments. Details of other experiments are specified in able 19 and Fig. 10. In the sensitivity analysis run 7 of 2nd stage; LP1 has attained first position in 15 experiments and RLP6 has
Fig. 9. Results of sensitivity analysis run 5.
got last position in 14 experiments. Other experiments details are
available in Table 19 and Fig. 11. In 2nd stage run 8 of sensitivity analysis; it has been observed that RLP1 has achieved first spot in 15 experiments and RLP6 has acquired last rank in 13 experiments.
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Table 18 Ranking of RL partner in sensitivity analysis Run 6 when CC (0.500).
S. no. Expt. no. Ranking
1 2 3 4 5 6 7
1 WR12 RLP3 RLP7 RLP1 RLP4 RLP2 RLP5 RLP6 2 WR13 RLP5 RLP7 RLP3 RLP4 RLP2 RLP1 RLP6 3 WR14 RLP4 RLP3 RLP1 RLP5 RLP7 RLP6 RLP2 4 WR15 RLP6 RLP7 RLP5 RLP1 RLP2 RLP3 RLP4 5 WR16 RLP4 RLP1 RLP5 RLP3 RLP6 RLP2 RLP7 6 WR17 RLP5 RLP3 RLP4 RLP2 RLP7 RLP1 RLP6 7 WR23 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 8 WR24 RLP1 RLP7 RLP4 RLP2 RLP3 RLP5 RLP6 9 WR25 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 10 WR26 RLP2 RLP6 RLP4 RLP1 RLP5 RLP3 RLP7 11 WR27 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 12 WR34 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 13 WR35 RLP2 RLP7 RLP3 RLP1 RLP4 RLP5 RLP6 14 WR36 RLP1 RLP5 RLP3 RLP2 RLP6 RLP4 RLP7 15 WR37 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 16 WR45 RLP2 RLP7 RLP4 RLP1 RLP3 RLP5 RLP6 17 WR46 RLP1 RLP6 RLP5 RLP2 RLP3 RLP4 RLP7 18 WR47 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 19 WR56 RLP1 RLP6 RLP3 RLP2 RLP4 RLP5 RLP7 20 WR57 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 21 WR67 RLP1 RLP6 RLP3 RLP2 RLP4 RLP5 RLP7
Table 19 Ranking of RL partner in sensitivity analysis Run 7 when CC (0.600).
S. no. Expt. no. Ranking
1 2 3 4 5 6 7
1 WR12 RLP3 RLP6 RLP1 RLP4 RLP2 RLP7 RLP5 2 WR13 RLP5 RLP7 RLP3 RLP4 RLP2 RLP1 RLP6 3 WR14 RLP4 RLP5 RLP7 RLP2 RLP1 RLP3 RLP6 4 WR15 RLP6 RLP7 RLP5 RLP1 RLP2 RLP3 RLP4 5 WR16 RLP4 RLP1 RLP5 RLP3 RLP6 RLP2 RLP7 6 WR17 RLP5 RLP3 RLP4 RLP2 RLP7 RLP1 RLP6 7 WR23 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 8 WR24 RLP1 RLP7 RLP4 RLP2 RLP3 RLP5 RLP6 9 WR25 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 10 WR26 RLP1 RLP6 RLP3 RLP2 RLP4 RLP5 RLP7 11 WR27 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 12 WR34 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 13 WR35 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 14 WR36 RLP1 RLP6 RLP3 RLP2 RLP4 RLP5 RLP7 15 WR37 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 16 WR45 RLP1 RLP7 RLP4 RLP2 RLP3 RLP5 RLP6 17 WR46 RLP1 RLP6 RLP5 RLP2 RLP3 RLP4 RLP7 18 WR47 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 19 WR56 RLP1 RLP6 RLP3 RLP2 RLP4 RLP5 RLP7 20 WR57 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 21 WR67 RLP1 RLP7 RLP4 RLP2 RLP3 RLP5 RLP6
Fig. 10. Results of sensitivity analysis run 6.
Fig. 11. Results of sensitivity analysis run 7.
Table 20 Ranking of RL partner in sensitivity analysis Run 8 when CC (0.700).
S. no. Expt. no. Ranking
1 2 3 4 5 6 7
1 WR12 RLP3 RLP5 RLP1 RLP4 RLP2 RLP7 RLP6 2 WR13 RLP6 RLP7 RLP3 RLP4 RLP2 RLP1 RLP5 3 WR14 RLP4 RLP2 RLP1 RLP6 RLP7 RLP5 RLP3 4 WR15 RLP6 RLP7 RLP5 RLP1 RLP2 RLP3 RLP4 5 WR16 RLP4 RLP1 RLP5 RLP3 RLP6 RLP2 RLP7 6 WR17 RLP5 RLP3 RLP4 RLP2 RLP7 RLP1 RLP6 7 WR23 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 8 WR24 RLP1 RLP7 RLP4 RLP2 RLP3 RLP5 RLP6 9 WR25 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 10 WR26 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 11 WR27 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 12 WR34 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 13 WR35 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 14 WR36 RLP1 RLP6 RLP3 RLP2 RLP4 RLP5 RLP7 15 WR37 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 16 WR45 RLP1 RLP7 RLP4 RLP2 RLP3 RLP5 RLP6 17 WR46 RLP1 RLP6 RLP4 RLP2 RLP3 RLP5 RLP7 18 WR47 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 19 WR56 RLP1 RLP6 RLP3 RLP2 RLP4 RLP5 RLP7 20 WR57 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 21 WR67 RLP1 RLP6 RLP3 RLP2 RLP4 RLP5 RLP7
Other experiments results can be seen through Table 20 and Fig. 12. The results of 2nd stage sensitivity analysis run 9 conclude that RLP1 has reached first position in 15 experiments and RLP6 has attained last rank in 15 experiments. Details of remaining tests can be collect from Table 21 and Fig. 13. At last experiment in run 10 of 2nd stage sensitivity analysis depicts that again RLP1 has grasped first position in 15 experiments and RLP6 has man- aged last rank in 15 experiments. Other tests details are available in Table 22 and Fig. 14. At this stance, it may be concluded that capac- ity criteria category is very important in adopting and managing in 3PRLP selection, and so, needs greater managerial concentration. If the managers are able to manage the capacity criteria category and its related concerns in effective manner, it will be quite useful in selection of 3PRLP. In totality in 1st stage, we performed total 10 experiments in which RLP1 has ranked first in 5 experiments (please refer Table 12) and in 2nd stage sensitivity analysis when
experiment is performed by replacing the weights for two decision attributes while keeping the other weights are constant; RLP1 has ranked first 78 times out of 210 experiments (see Tables 13–22). It
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Fig. 12. Results of sensitivity analysis run 8.
Table 21 Ranking of RL partner in sensitivity analysis Run 9 when CC (0.800).
S. no. Expt. no. Ranking
1 2 3 4 5 6 7 1 WR12 RLP3 RLP5 RLP1 RLP4 RLP2 RLP7 RLP6 2 WR13 RLP6 RLP7 RLP3 RLP5 RLP2 RLP1 RLP4 3 WR14 RLP4 RLP2 RLP1 RLP6 RLP7 RLP5 RLP3 4 WR15 RLP6 RLP7 RLP5 RLP1 RLP2 RLP3 RLP4 5 WR16 RLP4 RLP1 RLP5 RLP3 RLP6 RLP2 RLP7 6 WR17 RLP5 RLP3 RLP4 RLP2 RLP7 RLP1 RLP6 7 WR23 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 8 WR24 RLP1 RLP7 RLP4 RLP2 RLP3 RLP5 RLP6 9 WR25 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 10 WR26 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 11 WR27 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 12 WR34 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 13 WR35 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 14 WR36 RLP1 RLP6 RLP3 RLP2 RLP4 RLP5 RLP7 15 WR37 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 16 WR45 RLP1 RLP7 RLP4 RLP2 RLP3 RLP5 RLP6 17 WR46 RLP1 RLP6 RLP4 RLP2 RLP3 RLP5 RLP7 18 WR47 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 19 WR56 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 20 WR57 RLP1 RLP7 RLP4 RLP2 RLP3 RLP5 RLP6 21 WR67 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6
Fig. 13. Results of sensitivity analysis run 9.
Table 22 Ranking of RL partner in sensitivity analysis Run 10 when CC (0.900).
S. no. Expt. no. Ranking
1 2 3 4 5 6 7 1 WR12 RLP3 RLP5 RLP1 RLP4 RLP2 RLP7 RLP6 2 WR13 RLP5 RLP7 RLP3 RLP6 RLP2 RLP1 RLP4 3 WR14 RLP4 RLP2 RLP1 RLP6 RLP7 RLP5 RLP3 4 WR15 RLP6 RLP7 RLP5 RLP1 RLP2 RLP3 RLP4 5 WR16 RLP4 RLP1 RLP5 RLP3 RLP6 RLP2 RLP7 6 WR17 RLP6 RLP3 RLP4 RLP2 RLP7 RLP1 RLP5 7 WR23 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 8 WR24 RLP1 RLP7 RLP4 RLP2 RLP3 RLP5 RLP6 9 WR25 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 10 WR26 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 11 WR27 RLP1 RLP7 RLP4 RLP2 RLP3 RLP5 RLP6 12 WR34 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 13 WR35 RLP1 RLP7 RLP4 RLP2 RLP3 RLP5 RLP6 14 WR36 RLP1 RLP6 RLP3 RLP2 RLP4 RLP5 RLP7 15 WR37 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 16 WR45 RLP1 RLP7 RLP4 RLP2 RLP3 RLP5 RLP6 17 WR46 RLP1 RLP7 RLP4 RLP2 RLP3 RLP5 RLP6 18 WR47 RLP1 RLP7 RLP4 RLP2 RLP3 RLP5 RLP6 19 WR56 RLP1 RLP7 RLP3 RLP2 RLP4 RLP5 RLP6 20 WR57 RLP1 RLP7 RLP4 RLP2 RLP3 RLP5 RLP6 21 WR67 RLP1 RLP7 RLP4 RLP2 RLP3 RLP5 RLP6
Fig. 14. Results of sensitivity analysis run 10.
indicates that proposed framework is robust and less sensitive to criteria weights.
8. Managerial implications
Increasing industrial outsourcing activities have given atten- tion for the selection of 3rd Party RLPs. This can reduce the cost and improve customer service significantly. In this paper bench- marking framework is presented to select reverse logistics partners to achieve efficiency and effectiveness in RL practices. It was found that to succeed in RL practices, partners have to focus more on Capacity Criteria (CC), IT systems (IT) and Service Delivery (SD) and their relative concerns. The proposed approach allows man- agers/practitioners to make decision about 3rd Party RLP selection in their organizations. The results obtained are discussed with the industry and they found it meaningful according to the used crite-
ria. In this study two stage sensitivity analyses are performed to get further insights to the causes of selection of appropriate 3PRLPs for reverse logistics implementation in organizations. In 1st stage; RLP1 has the highest frequencies of selection as RL partner among
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c s t e i o a i b b p b t o a t i l m F m c v T c R o n o t e c t o r a t s o t s e f p T v c
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8 C. Prakash, M.K. Barua / Resources, Co
thers and also, in 2nd stage; RLP1 has the highest probability f selection as RL partner for case industry. This study is helpful o industry to select optimized partners who integrate recovery ptions and disposal activities in RL process in order to achieve the oal of sustainability. Manufacturers of allied industries may use ur proposed approach to evaluate and select their third party RL artners in most effective and efficient way.
. Conclusions and unique contributions
RL practices are gaining momentum due to enforced legislation, orporate citizenship, ecological concern, financial benefits, and ustainability issues. In today’s scenario, it is necessary for industry o offer safe, economical and environment friendly asset recov- ry options. Such goal demand, top management commitment as t involves financial, operational and strategic outlook. Therefore, rganizations lacks in resources and capabilities outsource RL oper- tions to achieve efficiency and effectiveness. This is only possible f, selected RL partner would share its resources and derive synergy y commitment. Partner should cooperate and capable enough so oth can achieve their objectives. Now the question is how com- any can select RL partner. This study presents a flexible and fuzzy ased integrated hybrid decision model for evaluation and selec- ion of the partner. This has been done through the identification f criteria of RL partnership based on literature, industry experts nd industry associates and idea generating workshop is conducted o identify and finalize 3PRLPs selection criteria. This study final- zed 7 major selection criteria along with various sub-criteria. Then inguistic ratings to the criteria have being assigned by decision
aking team. Evaluation of the selection criteria have been done by uzzy AHP which indicates capacity criteria categories have maxi- um weightage value than other criteria and geographical location
riteria categories have least weightage value. These weightage alues are used in Fuzzy TOPSIS for selection of the RL partner. he selection of the partner has been done by observing highest loseness coefficient values which shows RLP6, RLP4, RLP1, RLP2, LP3, RLP5 and RLP7 reverse logistics partners are in descending rders. At normalized level results show RLP6 is the best RL part- er and RLP7 is the lowest rating partner. Additionally we work ut on the two stage sensitivity analysis, to determine the effect on he decisions by change in the criteria weights. In stage 1st the ffect of an incremental change in value from 0.1 to 0.9, to the apacity criteria category (CC) and relative change in other selec- ion criteria is calculated (see Table 11). The results of the stage ne sensitivity analysis and the relative change in selection crite- ia can be seen through Fig. 4. The result of 1st stage sensitivity nalysis indicates that RLP1 has first rank out of 10 runs. Addi- ionally, due to variation in the selection category weights, the pecific criteria weights and their final ranking also varied. Sec- nd stage sensitivity analysis is performed by changing weights of wo criteria simultaneously within the capacity criteria run 1 and election of 3PRLPs is done as per ranking obtained in this run. In ach run 21 experiments are conducted and final rank is obtained or selection of 3PRLPs. In stage 2nd, totally 210 experiments are erformed and the results of the sensitivity analysis is shown in ables 13–22 and Figs. 5–14, which indicates that RLP1 has highest alue in majority of experiments so RLP1 is potential RL partner for ase industry.
In a nutshell, the study empirically presents and analy- es all the obligatory components of electronics manufacturers’
ases in particular, however, owing to the flexibility of this pproach it can be extended to the other allied industries as ell. This approach measures the vagueness/impreciseness of
xperts’ opinions in the evaluation and selection process that
tion and Recycling 108 (2016) 63–81
makes multi criteria making process more influential, effective and comprehensive.
9.1. Unique contributions
This study offers unique theoretical as well as practical contribution in context to 3rd Party reverse logistics partner selection and reverse supply chain management, given as fol- lows:
• This study explores possible criteria of 3PRLPs selection under seven dimensions and incorporates the industry requirement.
• This study has selected optimized partner using fuzzy based inte- grated AHP-TOPSIS framework. Business organizations may use developed framework to evaluate and select their third party RL partners in most effective and efficient way.
• The two stage sensitivity analysis is carried out in this research in order to analyze the evaluation and selection process of the part- ners and verification of the robustness of the proposed integrated framework.
10. Limitations and future scope of the study
This study has some limitations. This study has identified major selection criteria under seven dimensions for 3PRLPs selection through relevant literature and then validated by industry experts. The identified criteria are specific to one industry and other criteria and dimensions have not been identified. We have used fuzzy based integrated AHP-TOPSIS framework to evaluate 3PRLPs selection criteria and to select the most efficient RL partner among alternatives. The nec- essary computations were performed by taking the decision making team inputs into the considerations. With regard to this, it is suggested to perform these computations in a careful manner.
Several extensions of this study are possible by inculcating any number of quantitative and qualitative attributes of 3PRLPs selection and framework can developed for stochastic environ- ment. This study can also extend, explore and compare by using other approaches such as IRP, MILP, DEMATEL, MAUT, VIKOR and Rough Set Theory either utilizing single and integrated approach.
Acknowledgements
The authors are very much thankful to the Prof. Dr. Ming Xu (Editor-in-Chief, RCR) to provide us opportunity and motivated us to submit the paper. We are also grateful to two anonymous reviewers of the paper for their constructive and helpful comments that improved the quality of the paper. This work is supported by research grant (UGC-JRF/Grant No. 6499-112-044) of the University Grant Commission Delhi, India. The authors acknowledge thanks for the support to the research facilities provided by the Depart- ment of Management Studies, in Indian Institute of Technology Roorkee, India.
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C. Prakash, M.K. Barua / Resources, Co
Chandra Prakash, doctoral student in Department of Man- agement Studies, IIT Roorkee, India. His current areas of research are Reverse Logistics, Modeling, Optimiza-
tion, Risk Management, Green Supply Chain Management, Inventory Control and Aviation management. He has completed Master in Math and MBA. He has pub- lished/presented papers in journals of reputes and in conference proceedings.
tion and Recycling 108 (2016) 63–81 81
Dr. Mukesh Kumar Barua, Associate Professor in the Department of Management Studies, IIT Roorkee, India. His research area includes supply chain management,
quality management, operations research, and opera- tions management. He obtained Master of Technology in Mechanical Engineering and Doctor of Philosophy from IIT Madras. He has published more than 45 research papers in reputed journals and conferences.
- An analysis of integrated robust hybrid model for third-party reverse logistics partner selection under fuzzy environment
- 1 Introduction
- 1.1 Research motives
- 1.2 Research goals
- 2 Background of the research
- 2.1 Reverse logistics & supplier selection
- 2.2 Modeling in RL supplier selection
- 3 Problem definition
- 4 Research methodology
- 4.1 Phase I selection criteria
- 4.2 Phase II fuzzy analytical hierarchy process
- 4.3 Phase III Fuzzy TOPSIS
- 5 Applicability of the proposed framework to case analysis
- 5.1 Application of FAHP to evaluate weights of the criteria
- 5.2 Application of Fuzzy TOPSIS to select the RL partner
- 6 Results and discussion
- 7 Sensitivity analysis
- 8 Managerial implications
- 9 Conclusions and unique contributions
- 9.1 Unique contributions
- 10 Limitations and future scope of the study
- Acknowledgements
- References