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Prioritizing the performance outcomes due to adoption of critical success factors of supply chain remanufacturing
Accepted Manuscript Prioritizing the Performance Outcomes due to adoption of Critical Success Factors of Supply Chain Remanufacturing
Zulfiquar N. Ansari, Ravi Kant, Ravi Shankar PII:
S0959-6526(18)33740-5
DOI:
10.1016/j.jclepro.2018.12.038
Reference:
JCLP 15096
To appear in:
Journal of Cleaner Production
Received Date:
04 February 2018
Accepted Date:
05 December 2018
Please cite this article as: Zulfiquar N. Ansari, Ravi Kant, Ravi Shankar, Prioritizing the Performance Outcomes due to adoption of Critical Success Factors of Supply Chain Remanufacturing, Journal of Cleaner Production (2018), doi: 10.1016/j.jclepro.2018.12.038
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Title: Prioritizing the Performance Outcomes due to adoption of Critical Success Factors of Supply Chain Remanufacturing
Author 1: Zulfiquar N. Ansari Research Scholar Department of Mechanical Engineering S.V. National Institute of Technology, Surat- 395007, India E-mail:[email protected] Author 2: Ravi Kant Assistant Professor Department of Mechanical Engineering S.V. National Institute of Technology, Surat-395007, India E-mail: [email protected]; [email protected] Author 3: Ravi Shankar Department of Management Studies Indian Institute of Technology, Delhi Hauz Khas, New Delhi 110 016, India E-mail: [email protected] *Corresponding Author: Zulfiquar N. Ansari Research Scholar Department of Mechanical Engineering S.V. National Institute of Technology, Surat- 395007, India E-mail: [email protected]
Graphical Abstract
Remanufacturing in SC
Remanufacturing practices and expected performance outcomes Indian manufacturing context
Identification of CSFs and the expected POs through relevant literature analysis and discussion with decision making group
32 CSFs categorized under 6 main factors
16 POs expected due to adoption of remanufacturing practices in SC
Prioritizing CSFs using fuzzy AHP
Ranking the alternatives (POs) using fuzzy TOPSIS (weights obtained using fuzzy AHP is used to calculate the rank)
Results and Discussion
Conclusion, limitations and future scope of research
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Prioritizing the Performance Outcomes due to Adoption of Critical Success
2
Factors of Supply Chain Remanufacturing
3
Abstract
4
The objective of this study is to identify and prioritize the performance outcomes (POs) due
5
to adoption of SC remanufacturing critical success factors (CSFs). CSFs and the POs realized
6
due SC remanufacturing adoption, are identified based on past relevant literature analysis and
7
subsequent discussions with the expert decision panel. This research work propose a hybrid
8
solution methodology namely fuzzy Analytic Hierarchy Process (AHP) and fuzzy Technique
9
for Order Performance by Similarity to Ideal Solution (TOPSIS), to prioritize the POs
10
realized due to adoption of SC remanufacturing CSFs. Fuzzy AHP technique is used to obtain
11
the relative weights of the CSFs by performing pair wise comparison amongst the criteria,
12
while fuzzy TOPSIS is used to prioritize the POs due to adoption of SC remanufacturing
13
CSFs. Further, the proposed methodological framework is applied to an Indian manufacturing
14
organization to demonstrate its applicability, and prioritize the POs realized due to adoption
15
of SC remanufacturing CSFs. The study presents more accurate, structured and systematic
16
approach to the organization for improving its POs step-by-step through adoption of SC
17
remanufacturing CSFs, thus increasing its sustenance capability. The impact of vagueness in
18
the criterion weights on the prioritization is also investigated.
19
Keywords: Remanufacturing, Supply Chain, Critical Success Factors, Performance
20
Outcomes, Fuzzy AHP, Fuzzy TOPSIS
21 22 23 24 1
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1. Introduction
2
Overpopulation, increasing production of fossil fuels and worsening climate condition across
3
the globe are the major challenges the world is facing in recent years. Sustaining the existing
4
supply chain (SC) under these pressures is crucial. Till now, the traditional SC focussed on
5
the internal efficiencies, processes, responsiveness and managing the forward SC (raw
6
material-finished goods-consumer). However, organizations have realized that simply
7
optimizing the internal activities is not sufficient enough to compete in the market. According
8
to Govindan and Soleimani (2017), the traditional SC referred to as forward SC these days,
9
restrict their responsibility only up to supplying products to consumer, while do not consider
10
the end-of-life (EOL) products treatment as an integral part. The products after its end-of-use
11
(EOU) turn out to be a waste for the user which is collected by the local vendors and most of
12
the times processed at small and independent manufacturer. Improper handling and
13
processing of the returned products by unauthorized units especially in India and China has
14
significantly endangered the socio-environmental life and also lead to unfriendly work
15
environment for the workers (Awasthi and Li, 2017). However, right from the beginning of
16
this century managing the environmental issues of the SC has become an integral part of
17
organizational ethical decision where each individual contributes to help achieve their
18
company brand green image (Sarkis et al., 2010a). Sustainability concepts are being
19
implemented in SC to address the environmental, social, and economic concerns in business
20
decisions (Lorek and Spangenberg, 2014; Luthra et al., 2016; Ansari and Kant, 2017).
21
Remanufacturing is emerging as an effective strategy which meets the sustainable goals of an
22
organizational SC (Zhang et al., 2011; Subramoniam et al., 2013). For instance, 50% savings
23
in cost and 60% savings in energy and materials can be realized by producing remanufactured
24
product in comparison to a new product manufactured (Sutherland et al. 2008; Rathore et al., 2
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2011; Zhang et al., 2011). Also, remanufacturing of a product eliminates significant amount
2
of processes such as acquisition of raw material, processing and machining of material etc.
3
which ultimately leads to reduction of huge amount of resource and energy consumption and
4
reduction of emissions too (Liu et al., 2016). Hence, integrating remanufacturing in SC has
5
become a key strategy that has positive influence on the sustainable performance of the
6
organization. Remanufacturing is a multi-process that involves product recovery after its end-
7
use, inspection, disassembly, cleaning, refurbishing, reconditioning, upgrading, and
8
reassembly to transform the used product to a “like-new” product (Govindan et al., 2016;
9
Kafuku et al. 2016). Although, remanufacturing a sustainable strategy exists over the past
10
decade and already an integral part of good number of developed nation organizations, but
11
Indian economy has not yet extensively contributed to it (Sharma et al., 2016; Govindan et
12
al., 2016). Terker et al., (2013) suggest that more extensive strategies are needed to
13
implement SC remanufacturing practices in Indian manufacturing organizations. To deal with
14
this, some authors have suggested several factors for successful implementation of
15
remanufacturing practices in SC such as management initiative, technology for
16
remanufacturing, design for remanufacturing, work place impact, effective acquisition
17
process, financial budget (Mondal and Mukherjee, 2006; Mukherjee and Mondal, 2009;
18
Subramoniam et al., 2013; Sharma et al., 2016). The fact that these factors are important for
19
successful adoption of remanufacturing practices in SC, the aim of the present study is to
20
extensively explore the critical success factors (CSFs) for remanufacturing adoption in SC
21
and prioritize the performance outcomes (POs) realized due to its adoption. Prioritizing the
22
POs due to adoption of SC remanufacturing CSFs is significant, as it will aid organizational
23
decision-makers to understand the pinning issues and formulate suitable strategies to improve
24
their social, economical, and environmental performance many times referred as sustainable
25
performance. 3
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However, the POs realized due adoption of SC remanufacturing CSFs are qualitative as well
2
as quantitative in nature. In other words the problem is multi-dimensional and to handle the
3
complex relationship between the considered factors a suitable multi-criteria decision making
4
(MCDM) approach is needed that can provide useful insights. A hybrid fuzzy Analytic
5
Hierarchy Process (AHP) and fuzzy Technique for Order of Preference by Similarity to Ideal
6
Solution (TOPSIS) approach is proposed in the study. Fuzzy AHP method is used to rank the
7
SC remanufacturing CSFs. While, the fuzzy TOPSIS method is used to prioritize the POs due
8
to remanufacturing CSFs adoption in SC. Thirty-two SC remanufacturing CSFs categorized
9
under six main factors and sixteen Pos, that could be realized due to SC remanufacturing
10
CSFs adoption are identified in the study through literature analysis and decision-making
11
panel opinion.. The study presents a structured implementation plan to industrial practitioners
12
and SC managers to improve the SC performance by effectively managing the
13
remanufacturing CSFs adoption.
14
The remainder of the article is arranged as follows: Literature related to the research topic is
15
discussed in Section 2 to explore the gaps and define the objectives; Section 3 explains the
16
problem considered along with case organization description, identification of CSFs for SC
17
remanufacturing and the POs and the research framework; Solution methodology adopted is
18
explained in Section 4; Numerical application of the proposed framework is presented in
19
Section 5; Section 6 presents the results and discussion; followed by sensitivity analysis in
20
Section 7; finally the conclusion is presented in Section 8.
21
2. Literature Review
22
The literature section comprises of scope of research, SC remanufacturing issues addressed in
23
Indian context, and research gaps and objectives.
4
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2.1 Scope of research
2
The population of India is increasing at a very rapid rate (0.3 billion in the year 1950 to 1.04
3
billion in the year 2002) and predicted that by the year 2050 it might be more than 1.6 billion
4
(State of Environment Report, 2009). The report further states that Indian economy is driven
5
due to strong growth in manufacturing sectors (7.4 per cent average over the past 10 years)
6
mainly in the electronics, information technology, textiles, pharmaceuticals, chemicals etc.
7
Industrialization in a developing country like India will definitely boost its economy but at
8
the same time poses a challenge to environmental protection (release of hazardous and non-
9
hazardous waste). In recent study, the Associated Chambers of Commerce & Industry of
10
India (ASSOCHAM) suggest that waste generation in India is likely to grow at a Compound
11
Annual Growth Rate (CAGR) of 30% i.e. increase to 52 lakh Metric Tonnes (MT) per annum
12
by the year 2020 from the existing level of 18 lakh MT per annum. Also, they found that a
13
greater percent of waste generated (approx. 95%) is managed by informal sector (scrap
14
dealers) while only 1.5% wastes are processed properly (reuse, repair or recycle). Given the
15
fact that in coming years, waste generation is going to increase at a burgeoning rate, there is
16
enough scope for Indian industries to investigate as to how these wastes should be managed
17
and develop some strategic plan. SC remanufacturing is one such EOL product management
18
strategy that is finding increased attention from the researchers and practitioners. SC
19
remanufacturing offers more potential to manage the used products or waste, especially in
20
India due to the following reasons (Mondal and Mukherjee, 2006; Rathore et al., 2011):
21 22
1. SC remanufacturing being labour intensive process and availability of cheap and abundant labour force in India would make its implementation easy.
5
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2. India due to its tag of having a market that is price sensitive, remanufactured product
2
would obvious generate business, as its pricing is in line with a second hand product
3
while quality almost equivalent to a new product.
4
3. In developing countries like India reuse, repair and recycling practices across SC are
5
the most suitable strategies used to handle the EOL products, but the most alarming
6
thing is that involvement of informal sectors in these activities are causing harm to the
7
environment as well as societal health.
8
From the above discussion, it can be concluded that there is not only ample scope to
9
implement remanufacturing in SC, rather it turns out to be an attractive business strategy for
10
Indian industries leading to economical benefits, environmentally sustainable and high
11
returns to the society.
12
2.2 Remanufacturing implementation in Indian context
13
Remanufacturing in India is unorganized and still in its infancy stage with Indian industries
14
not endorsing it very aggressively (Choudhary and Singh, 2011; Rathore et al., 2011; Sharma
15
et al., 2016). However, it is not that India is completely unfamiliar with the concept, since a
16
few multinational companies like Caterpillar, Volvo, General Electric healthcare (GE),
17
Hewlett-Packard (HP), Bavarian Motor Works (BMW) and SKF are already in
18
remanufacturing business (Sharma et al., 2016). Also, a few responsible Indian organizations
19
such as Timken India Ltd., EMD Locomotive Technologies Pvt. Ltd., Indian Railway, Larsen
20
and Tubro (L&T), Xerox ModiCorp Ltd., United Van der Horst Ltd. and some cartridge
21
refilling companies are involved in remanufacturing activities and considering it as an
22
integral part of their SC (Choudhary and Singh, 2011; Terker et al., 2013; Sharma et al.,
23
2016).
6
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It is absolutely fine to believe that when a definite research field is at its initial stage many
2
studies are carried out to find out as to how it can be implemented and what are those
3
challenges that needs to be faced during implementation. With this regard a few studies has
4
been carried out in Indian context with a focus on remanufacturing. Mondal and Mukherjee
5
(2006) based on empirical study identifies six critical
6
technology for remanufacturing, the market, the customer’s attitude, profitability and
7
legislative compulsion) that Indian manufacturers should consider before they make their
8
decision, whether to engage into remanufacturing activities or not. Mukherjee and Mondal
9
(2009) analyze the remanufacturing process of an Indian photocopier remanufacturer to
10
identify the critical managerial issues. They use interpretive structural modeling (ISM) to
11
analyze complex inter-relationship among issues such as: product design, workplace
12
environment impact, level of technology, return acquisition process, reverse distribution
13
process, disassembly and reassembly planning, workforce expertise and skill role, inventory
14
management and remanufactured product marketing. Choudhary and Singh (2011) explore
15
the potentiality of remanufacturing growth in India and the challenges that may be faced by
16
the industry, environment and society during its implementation. Rathore et al., (2011)
17
evaluates the present remanufacturing status through a case analysis of Indian mobile phone.
18
They try to find ways as to how in the present socio-economic environment it can be
19
successfully implemented. Their study reveals that availability of cheap and ample labor,
20
price sensitivity of the market, and increasing environmental impacts due to the booming
21
economy are the primary drivers to promote and implement remanufacturing practices in
22
Indian industries.
23
Subramoniam et al. (2013) conducts a case study with the original equipment supplier
24
companies’ representatives and based on factors such as: strategic product planning, design
factors (acquisition of returns,
7
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for remanufacturing, plant location, production systems, physical distribution, and
2
cooperation among remanufacturing stakeholders, develops a remanufacturing decision
3
making framework (RDMF). They further rank these factors based on their importance using
4
AHP and found that financial impact of remanufacturing is the most influential criterion in
5
RDMF. Terker et al., (2013) highlights the benefits of remanufacturing operation through a
6
case study analysis of an Indian Original Equipment Manufacturer (OEM). Since the OEM is
7
also in the service business to certain clients, a study was also carried out to understand the
8
effect of remanufacturing services in the market. The findings suggest that to boost Indian
9
economy free globe trading of remanufactured product should be permitted and on an
10
average a cost savings of 57% was realized due to remanufacturing of EOL bearings. Sharma
11
et al., (2016) conducts a questionnaire based survey to explore the significant economic,
12
environmental and social drivers for remanufacturing in India. They also identified the
13
barriers to implement remanufacturing as a regular practice. Economic driver is the main
14
driving force to adopt remanufacturing in Indian industries while quality concerns and no
15
proper guidelines turns out to be major roadblock. Govindan et al., (2016) study is focused on
16
Indian auto parts remanufacturing industries to evaluate the critical barriers. They propose a
17
hybrid ISM-fuzzy Analytic Network Process (ANP) approach for analysis, wherein ISM is
18
used to address the interrelationships and interdependencies among the barriers while fuzzy
19
ANP is applied to rank the most influential barrier. Vasanthakumar et al. (2016) identifies
20
twenty factors that should be considered for successful implementation of lean
21
remanufacturing practices in Indian automotive manufacturing. They model the factors using
22
ISM methodology and propose that strong top management commitment with proper strategy
23
selection, long-term vision and participation; and a strong understanding of the current
24
product and process designs as the most important factor. Bhatia and Srivastava (2018)
25
identify and evaluate ten external barriers to remanufacturing in Indian electronic sector. The 8
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authors used Grey-DEMATEL (Decision Making Trial and Evaluation Laboratory) approach
2
analyse external barriers and suggest that lack of channels to collect used products and
3
customer willingness to return used products are the most prominent barriers for
4
remanufacturing implementation. A summary of remanufacturing works carried out in Indian
5
context is depicted in table 1.
6
Table 1 Summary of literature related to SC remanufacturing implementation in Indian context Reference
Focus of the study
Methodology; Industry
MCDM
Mondal and Mukherjee (2006) Mukherjee and Mondal (2009) Choudhary and Singh (2011) Rathore et al., (2011)
Identifies the factors to be considered before remanufacturing decision Key issues in remanufacturing business
Empirical; Manufacturing sector Case study; Photocopier
n.a
Challenges that a industry may face in remanufacturing implementation Identifies how remanufacturing can be implemented in present socio-economic environment
n.a.; Manufacturing sector Case study; Mobile business
n.a.
Subramoniam et al., (2013) Terker et al., (2013) Sharma et al., (2016) Govindan et al., (2016) Vasanthakumar et al., (2016) Bhatia and Srivastava (2018)
Develops a RDMF based
Case study; Automotive
AHP
Benefits of adopting remanufacturing operation Identifies the drivers and barriers for remanufacturing Barrier analysis of remanufacturing
Case study; Bearing
n.a.
Survey; Multiple industry
n.a.
Case organization; Automotive Survey; Automotive industries Case organization; Electronic
ISM-ANP
Evaluate the factors that influence lean remanufacturing practices Identify and evaluate external barriers to remanufacturing
ISM
ISM GreyDEMATEL
7
Note: n.a.: not applicable
8
The above discussion suggest that remanufacturing is not a new concept for Indian
9
manufacturing SC, but according to Sharma et al., (2016) majority of the organizations are
10
adopting it solely for financial benefit. Environmental performance of an organization is
11
usually compromised in Indian scenario due to weak legislation and no standardized process
12
for EOL product management (Govindan et al., 2016). Recently, introduction of a new
13
regulatory law by Indian government namely E-Waste (Management) Rules 2016 shows
14
government intentions regarding environmental protection. This suggest that evaluating the 9
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environmental impacts of the remanufacturing activities would soon become an integral part
2
of the system. In this regard, there is much need to identify and analyse SC remanufacturing
3
CSFs extensively. Literature reveals that adoption of remanufacturing practices has
4
significant influence on the organizational sustainable performance usually referred to as
5
social, economic and environmental performance (Ijomah et al., 2004; Matsumoto and
6
Ijomah, 2013; Subramonium et al., 2013). Implementation of remanufacturing practices in
7
automotive industries could annually save energy equivalent to five nuclear power plants
8
(Steinhilper, 1998). Also, remanufacturing being labour intensive process it creates job
9
opportunities for the society resulting into social benefits (King et al., 2006). Liu et al. (2016)
10
conducts a case study to evaluate the environmental impacts of cylinder head
11
remanufacturing. Their study conducts life cycle assessment and the results suggest that
12
remanufacturing of cylinder head results into improved environmental performance in
13
comparison to the new manufacturing. Kurilova-Palisaitiene et al., (2018) conducted case
14
studies in four remanufacturing organization to study the impact of lean practices adoption on
15
performance. The authors propose seven lean based practices adoption in remanufacturing to
16
reduce the unnecessary operations and reduce the lead time, thus improving the operational
17
performance of the organization. Krystofik et al. (2018) conducts a case study for an office
18
furniture remanufacturing process to identify the factors that affect the economic and
19
environmental performance. The result reveals that adoption of adaptive design based
20
remanufacturing strategies not only improve the environmental performance but, also
21
enhances the long-term economic viability of remanufacturing. The significance of
22
remanufacturing practices adoption in developing countries has increased more recently. This
23
is mainly due to investment by Multinational Companies (MNCs), ease in doing business
24
with foreign countries and inflating consumption of natural resources (Chaowanapong et al.,
25
2018). For instance, in one of the developing nation namely, China, government is promoting 10
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implementation of remanufacturing practices to reduce the resources and energy consumption
2
(Qu et al., 2017). Hence, implementation of remanufacturing practices is beneficial for
3
business organizations, for consumers, for the environment, and for the society and several
4
POs could be realized due to its adoption. The present study thus, prioritizes the POs of the
5
organization as a whole (economic, social and environmental performance), due to adoption
6
of SC remanufacturing CSFs. This study will aid the decision-makers and practitioners in
7
India to adopt the CSFs of SC remanufacturing in their organization more structurally and in
8
an organized manner to realize the performance benefits that could achieved due to its
9
adoption.
10
2.3 Research gaps and objectives
11
SC remanufacturing practices adoption has been on the agenda of developed Western
12
countries over the past decade and globally a large numbers of industries are already
13
practicing it (Abdulrahman et al., 2015). Also, its implementation in Europe and North
14
America has resulted into fruitful results such as enhanced profits and increased market share
15
for the manufacturers (Jayaraman and Luo, 2007; Giannetti et al., 2013). Many advanced
16
remanufacturing technologies have been achieved in developed countries to improve the
17
remanufactured product quality (Nasr, 2010). However, in developing economies like India it
18
is still not well established and organizations are yet to realize the complete benefits that
19
could be accrued due to its adoption. Integration of remanufacturing concept in SC has not
20
been able to capture the much needed attention from its stakeholders in India and a lot still
21
needs to be done (Rathore et al., 2011; Subramonuim et al., 2013).
22
Extensive literature analysis has been carried out to evaluate the remanufacturing studies
23
carried out in Indian scenario to justify the need for this study (Table 1). The potential gaps in
24
the present study are as follows: 11
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Most of the studies focus on analyzing the barriers or issues to remanufacturing practices implementation in SC and the benefits that could be realized.
3
There are very few studies that analyse the CSFs for remanufacturing adoption in SC.
4
In addition, these studies are based on specific industrial sector such as photocopier,
5
automotive and manufacturing sector (Mondal and Mukherjee, 2006; Mukherjee and
6
Mondal, 2009; Sharma et al., 2016; Vasanthakumar et al., 2016). The factors
7
considered by these studies are limited and further the differing research outcomes
8
gives a clear indication that the solutions are industry specific and may not hold true
9
for other sectors.
10
For economic development and growth, developing countries are eager to attract foreign
11
direct investment (FDI). India due to its status of one of the developing nations in Asia has
12
generated immense interest amongst developed nations to do business with. According to the
13
report, India has surpassed China in year 2015 and is a top destination for FDI with $31
14
billion investment in the first half of year 2015 as compared to $28 billion of China
15
(Financial Times, 2015). Adoption of remanufacturing strategy in the existing SC is one of
16
the attractive business alternative that Indian market can offer to foreign partners to generate
17
revenues. However, a large number of small organizations involved in remanufacturing
18
business and a few new organizations willing to practice remanufacturing in their SC are still
19
striving hard to adopt it effectively (Govindan et al., 2016). These industries need to be
20
provided with a set of parameters or guidelines that not only helps them in adoption of
21
remanufacturing practices in SC but also improve their POs. This study thus, extensively
22
identifies the CSFs for remanufacturing adoption in Indian manufacturing SC and lists out the
23
POs that could be realized due to adoption of remanufacturing initiatives. Further, the study
24
prioritizes the POs (alternatives) due to adoption of SC remanufacturing CSFs. 12
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3
Problem Description
2
This section is divided into three sub-sections. The first sub-section provides information of
3
the case organization considered. The second sub-section identifies the CSFs for
4
remanufacturing adoption in SC and the POs realized due to its adoption. The third sub-
5
section describes the proposed research framework.
6
3.1 Back ground of case organization
7
The rising environmental concerns combined with global competitiveness factors have forced
8
Indian organizations to think for sustainable strategies. Indian organizations have realized
9
that simply focusing on the internal operational efficiencies of the SC is not sufficient enough
10
to gain competitive position in the market. The fact that, infusing remanufacturing CSFs in
11
the existing SC improves sustainable performance has been realized by Indian organizations.
12
However, organizations that are already into the remanufacturing business and some
13
responsible organizations willing to integrate remanufacturing strategy in their SC have not
14
been able to realize the POs effectively, due to unavailability of comprehensive set of CSFs.
15
The CSFs need to be implemented, to ensure that organizations observe SC remanufacturing
16
POs effectively. Adoption of all the CSFs into the system, at a time is a complex task. Hence,
17
ranking the SC remanufacturing CSFs will enable the organization to focus on high rank
18
CSFs and implement them in stepwise manner step to improve the POs. In this regard, the
19
present study identifies an Indian remanufacturing organization for the application of the
20
proposed structural framework. Gupta and Barua (2017) and Luthra et al. (2017) adopts a
21
similar approach as adopted in this study to validate their research framework. The
22
organization selected in this study was established in the year 1962. Organization name is not
23
disclosed in this study to maintain the confidentiality and hence, named as X organization.
24
Organization X is an Indian manufacturing firm with total annual income of 18821.6 million 13
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in Indian National Rupee for the year 2016. Over 900 employees work in the organization.
2
This organization is engaged in design, manufacturing and sales of ball bearings, roller
3
bearings and bearing housings; and also aftermarket services such as replacement, warranty
4
services, remanufacturing or reconditioning or refurbishing. The organization is
5
manufacturing a special type of bearing named Tarol-6x11 which is being supplied to Indian
6
railways for installation in high speed trains and metros. Since the cost of the bearing is very
7
high, Indian railways were searching for an economical solution over the last few years. The
8
organization suggested that after a run of specific period (say one year) the bearings would be
9
remanufactured (reconditioned) and brought back to quality level of new bearing, thus
10
increasing its life. Hence, based on the contractual agreement made with Indian railways
11
during the supply of new Tarol-6x11 bearing, the organization is involved in reconditioning
12
activities. However, infusing remanufacturing practices in the existing SC creates an
13
imbalance in the manufacturing activities. Also the fact that organization is certified with ISO
14
9001 and ISO 14001, suggest that organization is concerned regarding the quality
15
management and environmental protection. Under such circumstances, to handle the problem
16
complexity and to improve organizational POs, a set of SC remanufacturing CSFs adoption
17
would be the best solution. The objective of the proposed research, to improve the POs due to
18
adoption of SC remanufacturing CSFs turns out to be main reason for top management and
19
executive’s agreeing to support the research. The senior executives of the case organization
20
were also interested in evaluating the SC remanufacturing POs. Further, prioritizing the POs
21
helps the decision-makers/stakeholders to plan a systematic and step-by-step strategy to
22
improve their sustainable performance. Hence, a hybrid fuzzy AHP-TOPSIS approach is
23
applied to help the decision makers develop an action plan for successful implementation of
24
remanufacturing practices.
25
3.2 Identification of CSFs for remanufacturing adoption in SC and the POs 14
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Initially, a total of forty-eight SC remanufacturing CSFs and twenty-five POs due to its
2
adoption were listed down from the literature analysis. During the listing process, it was
3
observed that some CSFs and POs resulted into same meaning and hence were eliminated at
4
the same stage itself. It should be noted that majority of the SC remanufacturing CSFs (main
5
factors and sub-factors) listed are from the studies carried out in Indian context; but there are
6
a few factors that are not based on Indian studies.
7
remanufacturing CSFs and the POs that can be derived due to its adoption, a similar type of
8
situation has been considered. A decision group comprising of six experts from industry and
9
academics having enough expertise in the research field were scrutinized to put forward the
10
research problem in front of them. Then, the experts were invited and a meeting was
11
conducted with them in order to explain the purpose of the study and the possible outcomes.
12
The list of identified SC remanufacturing CSFs with explanation and POs was placed in front
13
of them and based on the inputs received from the experts and frequency of the occurrences
14
of the factor in the literature, thirty-two CSFs were decided and grouped under six main
15
categories (See table 2). Also, sixteen POs were finalized due to adoption of SC
16
remanufacturing CSFs (See table 3).
17
However, to identify these SC
Table 2 List of common CSFs for remanufacturing adoption in SC Sr. Critical Success No. Factors Managerial CSFs 1 Top management support and involvement (MAN1)
2
Benchmarking (MAN2)
Description
References
Clarity in top management regarding benefits of remanufacturing practices adoption would intensify its implementation and find increased support and involvement
Subramoniam et al., (2013); Abdulrahman et al., (2015); Govindan et al., (2016); Vasanthakumar et al., (2016) Gandhi et al., (2016); Malviya and Kant (2016); Mangla et al., (2016)
Significant factors already being practiced by world class companies should be considered for implementation
15
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Redefining the firm business model (MAN3)
Several managerial implications such as staff management, stock management issues, flexibility and outsourcing come into action during remanufacturing decision. Existing business model needs to be modified to meet the challenges
Diaz and Marsillac (2017)
4
Lean tools as a continuous improvement philosophy (MAN4)
Lean tools namely total productive maintenance and 5S as a continuous improvement process should be implemented in the industry to improve product performance, process efficiency and optimized inventory level
Kurilova-Palisaitiene and Sundin (2014); Vasanthakumar et al., (2016)
5
Enhancing corporate green image (MAN5)
Remanufacturing business in the existing set-up creates a brand green image in the market and hence a definite consumer segment concerned about the waste disposal in land demands sustainable product
Atasu et al., (2008); Mukherjee and Mondal (2009)
6
Sustainability concept implementation (MAN6)
Infusing sustainable dimension in decision making at each stage of remanufacturing chain significantly improves the overall performance
Rathore et al., (2011); Govindan et al., (2016)
7
Monitoring and controlling (MAN7)
Organizations effectively monitoring their own operations can result into performance improvement
Chen et al., (2012)
Supplier certification would ensure all activities environmentally responsible and within the environmental standards
Rahman and Subramanian (2012)
High return rates of EOU products can be achieved when organization itself is involved in remanufacturing of their own product and hence effective integration and coordination with both supplier and end user is required to manage the return flow Highly damaged or worn out products not meeting the desired quality levels to be remanufactured should be eliminated at the initial stage through effective inspection, thus reducing the unnecessary handling and storage cost Third party independent operators in India are aggressively involved in remanufacturing. OEMs should technologically and financially collaborate with third party remanufacturer (TPR) to uphold the product quality, creating competitiveness for both
Abdulrahman et al., (2015)
Large population of India with low income creates distinguished market for remanufactured product
Rathore et al., (2011); Sharma et al., (2016)
Products whose components worn out rate is high and made of costly material, using remanufactured product as a spare part for replacement can significantly reduce the overall cost and green house gas
Dahane et al., (2017)
Manufacturers should be made responsible to collect the product having hazardous contaminants to ensure proper EOL treatment
Mondal and Mukherjee (2006)
Strategic CSFs 8 Pressurizing strategic suppliers to endorse environmental accreditation (STR1) 9
Supplier, consumer and organization strategic alliance (STR2)
10
Effective gate-keeping (STR3)
11
Strategic alliance with third party remanufacturer (STR4)
12
Targeting pricesensitive consumer (STR5) Using remanufactured part as spares (STR6)
13
Regulatory CSFs 14 Mandatory take-back policies for hazardous products (REG1)
Wee Kwan Tan and Kumar (2006); Rahman and Subramanian (2012) Rathore et al., (2011); Abdulrahman et al., (2015)
16
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Enforced laws and regulations banning informal waste handling sector (REG2) Subsidized loan from government financial institutions (REG3)
Stringent laws are needed to prevent dismantling and processing of used products by informal sector. Government should educate and motivate the informal sectors to formalize
Sharma et al., (2016); Awasthi and Li (2017)
Government initiative to provide loans at a subsidized rate would encourage manufacturers to go for remanufacturing
Practitioner opinion
17
Government initiative to subsidize latest technology (REG4)
Governing political force should provide tax reduction and subsidy for upcoming emergent technologies that are usually costly on its initial launch to help promote remanufacturing especially in developing economies
Willis (2010); Abdulrahman et al., (2015)
18
Standardized remanufacturing guidelines and framework (REG5)
Government initiative to develop a framework and standardized guidelines would provide an organized approach to small enterprise and new organizations
Sharrna et al., (2011)
16
Technological and Infrastructural CSFs 19 Part based designing Product design should be flexible and permit full of the product (T&I1) disassembly. Recovery of even the smallest component of the product would make it highly remanufacturable and repairable
Akturk et al., (2017)
20
Adoption of advanced technology (T&I2)
Procurement and adoption of latest technologies by the manufacturer will improve performance specifications and productivity
Mondal and Mukherjee (2006)
21
Innovation (T&I3)
Optimum design strategies should be considered right at the initial product design phase to provide ease in remanufacturing
Sharma et al., (2016); Govindan et al., (2016)
22
Infrastructural development (T&I4)
Proper infrastructure will result into efficient processing of EOU products i.e. recycling, disposal with proper treatment
Subramoniam et al., (2013)
23
Transparent information system (T&I5) Effective reverse logistics network (T&I6)
Proper information flow at each levels of remanufacturing stage within and outside the organizations results into a transparent system Acquisition of the used product is in fact the starting point of remanufacturing. Efficient logistic network, such as locating the returns, collecting and transporting back to the plant will result into cost savings
Vasanthakumar et al., (2016)
Management should take remanufacturing as a potential business alternative and to manage the cost involved in the system, necessary fund should be allocated for system development
Practitioner opinion
24
Financial CSFs 25 Separate fund allocation for remanufacturing (FIN1)
Mondal and Mukherjee (2006); Sharma et al., (2016)
26
Acquisition of additional machinery equipment (FIN2)
Procurement of surplus machining will ensure smooth processing of new product and remanufactured product simultaneously
Rahman and Subramanian (2012)
27
Investment in remanufacturing related R&D (FIN3)
It helps to enhance the product performance in terms of better quality, reduced hazardous material, ease in disassembly etc.
Abdulrahman et al. (2015)
Consumer trust can be a driving force for remanufacturing business which can be attained by providing them quality remanufactured product with extended warranty
Mitra (2016); Cui et al., (2017)
Social CSFs 28 Supplying quality product with extended warranty (SOC1)
17
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Enough expertise by providing organized training to personnel (SOC2)
Skill level of the staff should be enhanced via. training from experienced practitioners to handle complex remanufacturing production environment and reverse product flows; and also in specialized systems to avoid land fillings
Dowlatshahi (2005); Rathore et al., (2011); Govindan et al., (2016)
30
Better human resource management practices (SOC3)
Healthy practices such as employee involvement in decision making process, employee empowerment and reward system to employees will help meet desired goals and improve system effectiveness
Practitioner opinion
31
Healthy and safe working conditions (SOC4)
Subramonium et al. (2009); Rathore et al. (2011)
32
Organization initiative to promote campaigns and workshops (SOC5)
Organization providing free and safe working culture and fulfilling social-well being responsibility would find increased support for each worker to achieve the objective Creating awareness among customers via. public meetings and also informing other key stakeholders is effective for remanufacturing successful implementation
Sharma et al., (2016); Govindan et al., (2016)
1 2
Table 3: POs derived due to remanufacturing CSFs adoption in SC Sr. No.
Performance outcomes derived due to CSFs adoption
Code
Reference
1
Rise in sales
PO1
2
Creates new market opportunities and increases market share
PO2
Jayaraman and Luo (2007); Abdulrahman et al., (2015) Pagell et al., (2007); Giannetti et al., (2013); Subramoniam et al., (2013)
3
Material and energy savings
PO3
4 5
Avoids land fillings and high disposal cost Extends product life time
PO4 PO5
6
Increases employment rate
PO6
7
Improves brand image
PO7
8 9
Reduces environmental impact Quality ensured product at low cost
PO8 PO9
10
Avoids waste limitation penalties
PO10
11
Competitiveness and better market position
PO11
12
Reduces carbon and green house gas emission Savings in capital investment Increases productivity and overall profitability
PO12
15
Attracts environmentally conscious customers
PO15
Mukherjee and Mondal (2009)
16
Reduces waste
PO16
Mukherjee and Mondal (2009); Choudhary and Singh (2011)
13 14
PO13 PO14
Mondal and Mukherjee (2006); Sarkis et al., (2010a); Choudhary and Singh (2011); Terker et al., (2013) Sarkis et al. (2010b); Rathore et al., (2011) Rathore et al., (2011); Zhang et al., (2011); Abdulrahman et al., (2015) Sarkis et al., (2010b); Subramoniam et al., (2013); Abdulrahman et al., (2015) Sarkis et al., (2010b); Kapetanopoulou and Tagaras (2011) Pagell et al., (2007); Rathore et al., (2011) King et al., (2006); Rathore et al., (2011); Terker et al., (2013) Ijomah et al., (2004); Rathore et al., (2011); Subramoniam et al., (2013) Martin et al., (2010); Subramoniam et al., (2013) Choudhary and Singh (2011); Sharma et al., (2016); Govindan et al., (2016) Terker et al., (2013) Mukherjee and Mondal (2009); Terker et al., (2013)
18
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3.3 Research framework
2
The study presents a three step solution methodology to prioritize the POs that can be derived
3
due to the adoption of remanufacturing CSFs in SC. Phase 1: Identifying the common CSFs
4
for adoption of remanufacturing practices in SC and the POs that could be realized due to its
5
adoption, through relevant literature analysis combined with inputs from decision making
6
panel. Phase 2: Prioritizing the CSFs to be implemented in SC for remanufacturing practices
7
adoption using fuzzy AHP, with the objective to help practitioners’ decision-making process
8
regarding adoption of remanufacturing CSFs. Phase 3: Prioritizing the POs due to SC
9
remanufacturing CSFs adoption in SC using fuzzy TOPSIS. Fig. 1 depicts the research
10
methodology followed to evaluate the POs of remanufacturing CSFs adoption.
11 12 13 14 15 16 17 18 19 20 21 22 23 19
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Constituting decision making group
Literature analysis
Academicians, Experts and Industrial practitioners
2 3
Phase 1
4 Identifying the remanufacturing CSFs for implementation
5
Identifying the performance outcomes due to remanufacturing CSFs implementation
6 7
Hierarchical structure
8 Weights calculation using fuzzy AHP
9 Phase 2
10 No
11
Approving Weights
12
Yes
13 Weights calculation of alternatives (performance outcomes) using fuzzy TOPSIS
14 15 16 17
Weights calculated using fuzzy AHP used
Phase 3
Prioritize the alternatives (Rank the performance outcomes of remanufacturing)
18 19 20 21 22
Fig. 1 Three phase research methodology adopted in the study (Adopted from Ertugrual and Karakasoglu, 2009; Sirisawat and Kiatcharoenpol, 2018)
4. Solution Methodology 4.1 Fuzzy AHP and fuzzy TOPSIS technique
20
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Several researchers have applied fuzzy AHP and fuzzy TOPSIS technique to handle different
2
complex situations and problems. Table 4 provides a summary of fuzzy AHP and fuzzy
3
TOPSIS studies by various researchers in different areas for problem solution.
4
Table 4 Summary of fuzzy AHP and fuzzy TOPSIS studies carried out by different researchers Reference
Objective of the study
Fuzzy AHP used for
Fuzzy TOPSIS used for
Mohammed et al. (2018)
Sustainable two-stage supplier selection and order allocation problem for a meat supply chain
Calculate relative weights for sustainable criteria
Rate the suppliers based on their sustainable performance
Sirisawat and Kiatcharoenpol (2018)
To classify reverse logistics barriers and rank both barriers and solutions of reverse logistics implementation
Calculate weights of each barrier
Rank the solutions of reverse logistics implementation
Singh et al. (2018)
To select an appropriate third party logistics (3PL) in order to outsource logistics activities of perishable products. Analyse the criteria influencing the automotive components remanufacturing (ACR) industry operation and selects the main operation patterns for ACR in China To select suitable partner for effective product development processes
To rank different 3PL selection criteria
Select the best 3PL based on performance
Determine weights of influencing criteria
Select the optimal ACR production operation patterns
Determine the weights of each criteria
Rank the partner alternatives
Develops a framework of strategies to reduce water losses in water distribution systems of developing countries Evaluation and selection of best supplier
Determine the weights of criteria responsible for water loss Determine the weights of multiple criteria Determine the weights of criteria Determine the initial weights of RL criteria
Rank the potential solutions for effective water management Select the fittest supplier
Tian et al. (2017)
Büyüközkan and Güleryüz (2016) Zyoud et al. (2016) Lee et al. (2015) Sánchez-Lozano et al. (2015) Senthil et al. (2014)
To determine the best location for a solar thermoelectric power plant To select the most efficient reverse logistics contractor
Vinodh et al. (2014)
To determine the best method for recycling plastics among the various plastic recycling processes To identify an appropriate organization strategy for distribution channel management
Yang et al. (2011)
Selection of appropriate vessels for carrying out shipping activities
Determine the weights of plastic recycling process criteria Weights calculation of determinants of distribution channel management Calculate the weights of influencing criteria
Ertuğrul and Karakaşoğlu (2009)
To evaluate the performance of the Turkish cement firms by using financial ratios
Determine the weights of the criteria
Paksoy et al. (2012)
Rank the best location Selection of the best third party reverse logistics provider Selection of the best recycling process Selection of the best distribution channel Ranking the alternatives (vessels) based on closeness coefficient value Ranking the firms based on performance
5 6
The above analysis suggest that several researchers have applied hybrid approach of MCDM
7
techniques to help the decision makers, prioritize the criteria according to their relative
8
importance and also ranking of the various alternatives while addressing different problems. 21
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Application of hybrid fuzzy AHP-TOPSIS approach to a problem is such that, fuzzy AHP is
2
used to calculate the weights of the influencing criteria. Next, fuzzy TOPSIS technique is
3
applied where in each criteria is compared with each alternative and based on the intensity of
4
influence a linguistic scale is assigned. The weights derived for each criteria is used in fuzzy
5
TOPSIS analysis to prioritize the alternatives. The present study determines the weight of SC
6
remanufacturing CSFs (criteria) and ranks them using fuzzy AHP. Next, the POs
7
(alternatives) realized due to adoption of remanufacturing CSFs in SC are prioritized using
8
fuzzy TOPSIS. The detail methodology for fuzzy AHP and fuzzy TOPSIS is explained in the
9
following sub-sections.
10
4.2 Fuzzy AHP
11
Thomas L. Satty, in 1970s introduced a quantitative technique namely AHP to solve complex
12
MCDM problems. AHP systematically decomposes the complex problem into a hierarchical
13
structure, with goal at the top; alternatives at the bottom while the criteria and sub-criteria at
14
the levels and sub-levels of the hierarchy. Easily understandable, simplicity in application and
15
flexibility makes the technique more prevalent for practitioners and decision-makers to apply
16
(Wang et al., 2009; Govindan et al., 2015). However, inability to handle imprecise or
17
vagueness usually intrinsic in human decision; use of discrete scale (1-9) which is unable to
18
handle the uncertainty; subjective judgement, selection and preference of decision makers are
19
some of the limitations of Satty’s AHP application (Choudhary and Shankar, 2012; Paksoy et
20
al., 2012; Patil and Kant 2014; Prakash and Barua, 2015). Hence, there is need to apply fuzzy
21
AHP to overcome these issues. Fuzzy AHP permits to take into account the linguistic
22
vagueness and uncertainties involved in the decision-makers judgement. The use of fuzzy
23
AHP is widespread and many researchers have utilized this problem solving technique in
22
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varying areas such as manufacturing maintenance field (Carnero et al., 2014); telecom sector
2
(Kumar et al., 2015); green supply chain management (Mangla et al., 2015)
3
Definition 1. 𝐴1 = (𝑙1,𝑚1,𝑝1); 𝐴2 = (𝑙2,𝑚2,𝑝2) represent two triangular fuzzy numbers
4
(TFNs). The algebraic operations such as addition, subtraction, multiplication, division and
5
reciprocal for these two TFNs are carried out as follows:
6
𝐴1 𝐴2 = (𝑙1,𝑚1,𝑝1) (𝑙2,𝑚2,𝑝2) = (𝑙1 + 𝑙2, 𝑚1 + 𝑚2, 𝑝1 + 𝑝2)
7
𝐴1 - 𝐴2 = (𝑙1,𝑚1,𝑝1) - (𝑙2,𝑚2,𝑝2) = (𝑙1 - 𝑝2, 𝑚1 - 𝑚2, 𝑝1 - 𝑙2)
8
𝐴1 𝐴2 = (𝑙1,𝑚1,𝑝1) (𝑙2,𝑚2,𝑝2) = (𝑙1𝑙2, 𝑚1𝑚2, 𝑝1𝑝2)
9
10
11
𝐴1 𝐴2 =
(𝑙1,𝑚1,𝑝1) (𝑙2,𝑚2,𝑝2) =
(
)
𝑙1 𝑚1 𝑝1 , , 𝑝 2 𝑚2 𝑙2
𝐴1 = (𝑙1,𝑚1,𝑝1)
𝐴1
-1
= (𝑙1,𝑚1,𝑝1)
(1) (2) (3)
(4)
(5) -1
=
(6)
12
In fuzzy AHP approach, preferences of one criterion over another is given based on TFNs.
13
Next, Chang’s extent analysis method is applied to calculate the synthetic extent value of the
14
obtained pair-wise comparison matrix (Chang, 1996). The weight vectors of the criterions
15
and alternatives are calculated using this analysis.
16
If 𝑋 = {𝑥1,𝑥2,…,𝑥𝑛} is a object set and U = {𝑢1,𝑢2,…,𝑢𝑚} is a goal set, then according to
17
Chang’s extent analysis model, 𝑀𝑔 , 𝑀𝑔 , …𝑀𝑔 are the m extent values that can be calculated
18
for each object. 𝑔𝑖 is the goal set (𝑖 = 1, 2, …, 𝑛) and all the TFNs given in table 5 are
19
represented by 𝑀𝑔 (𝑗 = 1, 2, 3, 4…, 𝑚). There are different TFNs scales used by different
1
2
𝑚
𝑖
𝑖
𝑖
𝑗
𝑖
23
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authors (Duran 2011; Carnero 2014; Wang et al., 2015). However, this study adopts the
2
characteristic function of the fuzzy numbers (Table 5) used in Lee et al. (2009) which was
3
also used by authors Gumus (2009); Sun (2010); and Paksoy et al., (2012). The characteristic
4
function of the fuzzy numbers (Table 5) is Procedure for Chang’s extent analysis model as
5
applied in this paper is as follows:
6
Step 1: Fuzzy extent synthetic value (𝑆𝑖) calculation
7
With respect to 𝑖𝑡ℎ object, the value of fuzzy synthetic extent is defined as: ‒1 𝑛 𝑚 𝑗 𝑗 𝑀𝑔 𝑀𝑔 𝑖 𝑖 𝑗=1 𝑖 = 1𝑗 = 1
[∑ ∑ ]
𝑚
8
𝑆𝑖 =
∑
𝑚
∑
9
𝑛
12
𝑗=1
𝑚
[
𝑚
∑∑
𝑚
𝑚 𝑗,
𝑗=1
]
𝑗 𝑀𝑔 𝑖 𝑖 = 1𝑗 = 1
‒1
=
∑𝑝
𝑗
𝑗=1
(∑ ∑ 𝑛
∑∑
𝑛
𝑚
𝑙𝑗,
𝑗 𝑀𝑔 = 𝑖 𝑖 = 1𝑗 = 1
10
11
(∑ ∑ 𝑚
𝑗 𝑀𝑔 = 𝑖 𝑗=1
𝑛
𝑙𝑖,
𝑖=1
(∑
(7)
𝑚𝑖,
𝑝𝑖
𝑖=1
)
∑𝑝
(9)
𝑖
𝑖=1
,
𝑛
(8)
𝑛
𝑖=1
1
)
1
∑
,
𝑛
𝑚𝑖
𝑖=1
1
∑
𝑛
)
(10)
𝑙𝑖
𝑖=1
where 𝑙 is the lower limit value, m is the most promising value and p is the upper limit value.
13 14 15
24
ACCEPTED MANUSCRIPT Table 5 TFNs scale used for comparing the criterion pair-wise Linguistic variables
TFN assigned
TFN reciprocal scale
1 2 3
Equal (E) (1, 1, 1) (1, 1, 1) Very low (VL) (1, 2, 3) (1/3, 1/2, 1) Low (L) (2, 3, 4) (1/4, 1/3, 1/2) Medium (M) (3, 4, 5) (1/5, 1/4, 1/3) High (H) (4, 5, 6) (1/6, 1/5, 1/4) Very high (VH) (5, 6, 7) (1/7, 1/6, 1/5) Excellent (E) (6, 7, 8) (1/8, 1/7, 1/6) Note: For linguistic variable Equal (E) fuzzy number is (1, 1, 1), while for other linguistic variables fuzzy membership function is based on ( x 1, x, x 1) for x 2,3,4,5,6,7 (Lee et al., 2009)
4
Step 2: Fuzzy values comparison
5
The degree of possibility of 𝑆2 = (𝑙2,𝑚2,𝑝2) ≥ 𝑆1 = (𝑙1,𝑚1,𝑝1) is defined as follows:
6
𝑆𝑈𝑃
𝑉(𝑆2) ≥ 𝑉(𝑆1) = 𝑦 ≥ 𝑥[min (𝜇𝑆 (𝑥), 𝜇𝑆 (𝑦))] 1
(11)
2
7
where (𝑥,𝑦) are the values on the axis of membership function of each criterion. 𝑆1 and 𝑆2
8
being fuzzy numbers can also be expressed as follows:
9
𝑉(𝑆2) ≥ 𝑉(𝑆1) = ℎ𝑔𝑡(𝑆1 ∩ 𝑆2) = 𝜇𝑆 (𝑑)
(12)
2
10
where the highest point of intersection D between 𝜇𝑆 and 𝜇𝑆 as shown in fig. 3 represents the
11
ordinate d. 𝜇𝑆 (𝑑) can be calculated using the following expression:
1
2
2
𝜇𝑆 (𝑑) = 2
1,
𝑚2 ≥ 𝑚1
0,
𝑙1 ≥ 𝑝2 𝑙1 ‒ 𝑝2
(13)
𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
(𝑚2 ‒ 𝑝2 )–(𝑚1 ‒ 𝑙1 )
12
Both 𝑉(𝑆1) ≥ 𝑉(𝑆2) and 𝑉(𝑆2) ≥ 𝑉(𝑆1) values are needed to compare 𝑆1 and 𝑆2.
13 14 15 25
ACCEPTED MANUSCRIPT 1
S
2
𝑆2
1
3
𝑆1
4 5 6 7
𝑉(𝑆2) ≥ 𝑉(𝑆1)
D
8 9
S
0
𝑙2
𝑚2
𝑙1
𝑑
𝑝2
𝑚1
𝑝1
Fig. 3 The intersection of fuzzy numbers
10 11
Step 3: Priority weight calculation
12
The degree of possibility of convex fuzzy number to be greater than 𝑘 convex fuzzy numbers
13
𝑆𝑖 (𝑖 = 1, 2,…,𝑘) can be defined as:
14
𝑉(𝑆 ≥ 𝑆1, 𝑆2,..., 𝑆𝑘) = 𝑉[(𝑆 ≥ 𝑆1), (𝑆 ≥ 𝑆2), …, (𝑆 ≥ 𝑆𝑘)] = min 𝑉 (𝑆 ≥ 𝑆𝑖),
15
𝑖 = 1, 2,…,𝑘
'
16
Assume that 𝑑 (𝐴𝑖) = min 𝑉 (𝑆𝑖 ≥ 𝑆𝑘) for 𝑘 = 1, 2,…,𝑛; 𝑘 ≠ 𝑖
17
Then the equation of weight vectors are given as,
18
'
'
'
'
𝑊 = (𝑑 (𝐴1), 𝑑 (𝐴2), …, 𝑑 (𝐴𝑚))
𝑇
19
Step 4: Normalized weight vector calculation
20
After normalization of 𝑊 , the normalized weight vectors are calculated.
21
(14)
(15)
'
𝑊 = (𝑑(𝐴1), 𝑑(𝐴2), …, 𝑑(𝐴𝑚))
𝑇
(16) 26
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4.3 Phase 3: Fuzzy TOPSIS
2
TOPSIS is one of the MCDM techniques, Hwang and Yoon (1981) proposed to handle
3
complex multi-dimensional problems. The concept of TOPSIS approach is that, the
4
alternative that is ideal has the best level for all criteria, while the negative ideal is the one
5
that has all worst criteria values i.e. the selected best alternative will be at the shortest
6
distance from the positive ideal solution in geometrical sense, while it is at the longest
7
distance from the negative solution (Wang et al., 2008; Wang et al., 2009). The advantages of
8
TOPSIS technique as explained by Bottani and Rizzi (2006); Govindan et al. (2015); Mittal
9
and Sangwan (2015); Agrawal et al. (2016) are: (i) useful when there are large number of
10
alternatives (ii) consistent and has the ability to handle rank reversal issue (iii) integrates both
11
benefit criteria and cost criteria (iv) logical, simple to understand and can easily be
12
programmed in a spread sheet.
13
The weight ratings assigned to the alternatives in traditional TOPSIS approach are in the form
14
of crisp values. However, in real case situations crisp numbers are not sufficient to rate the
15
alternatives due to presence of vagueness in human judgement. Fuzzy theory helps to express
16
the decision-maker judgement in the form of linguistic variable represented in the form of
17
fuzzy numbers (Zadeh, 1965). Fuzzy TOPSIS has been widely used in the literature for
18
solving multi-dimensional problems such as facility location selection (Chu, 2002; Ertuğrul
19
and Karakaşoğlu, 2008); maintenance problem (Ding and Kamaruddin, 2015); reverse
20
logistics (Agrawal et al., 2016) etc. Hence, fuzzy TOPSIS methodology is used to rank the
21
alternatives. The study adopts fuzzy-TOPSIS methodology to rank the POs due to SC
22
remanufacturing CSFs adoption as introduced by Chen (2000).
23
The steps of fuzzy TOPSIS as applied in this study are as follows:
27
ACCEPTED MANUSCRIPT 1
Step 1: Linguistic variables ratings for the alternatives with respect to the criteria
2
Suppose there are m possible alternatives, 𝐴 = {𝐴1, 𝐴2…, 𝐴𝑚} that are to be evaluated against
3
the criteria, 𝐶 = {𝐶1, 𝐶2…, 𝐶𝑛}. If the triangular fuzzy rating for 𝐾𝑡ℎ decision maker is
4
𝑅𝑎𝑏𝐾 = (𝑙𝑎𝑏𝐾, 𝑝𝑎𝑏𝐾, 𝑢𝑎𝑏𝐾) where, 𝑎 = 1, 2, 3,…, 𝑚;𝑏 = 1, 2, 3,…, 𝑛; each decision maker
5
𝐷𝑘 (𝑘 = 1, 2, …, 𝐾) will provide their fuzzy rating for alternatives with respect to the criteria,
6
thus forming a matrix for alternatives in a fuzzy form. Scales provided in Table 6 is used for
7
rating the alternatives (POs). Table 6 Linguistic variables for rating the alternatives Linguistic variables
Corresponding TFNs
Very poor (VP) Poor (P) Medium poor (MP) Fair (F) Medium good (MG) Good (G) Very good (VG)
(0, 0, 1) (0, 1, 3) (1, 3, 5) (3, 5, 7) (5, 7, 9) (7, 9, 10) (9, 10, 10)
8 9
Step 2: Aggregate fuzzy ratings calculation
10
If the fuzzy rating of 𝐾𝑡ℎ decision maker is 𝑅𝑎𝑏𝐾 then the aggregated fuzzy ratings 𝑅𝑎𝑏 of
11
alternatives with respect to different criterions is given by 𝑅𝑎𝑏(𝑙𝑎𝑏, 𝑝𝑎𝑏, 𝑢𝑎𝑏), where
12
13
𝑎𝑖𝑗 = min {𝑙𝑎𝑏𝐾}, 𝐾
1 𝑏𝑖𝑗 = 𝐾
𝑁
∑𝑝
𝑎𝑛𝐾,
𝑐𝑖𝑗 = max {𝑢𝑎𝑏𝐾}
𝐾=1
(17)
𝐾
Step 3: Developing normalized fuzzy rated decision matrix
28
ACCEPTED MANUSCRIPT 1
Using linear scale transformation the aggregate fuzzy weights of different criteria scales are
2
converted into a comparable scale. The normalized fuzzy decision matrix is given by 𝑄
3
where:
4
𝑄 = [𝑟𝑖𝑗]
5
where
6
𝑟𝑖𝑗 =
7
𝑎𝑗 𝑎𝑗 𝑎𝑗 ‒ 𝑟𝑖𝑗 = , , , 𝑎 𝑗 = min 𝑎𝑖𝑗 (cost criteria) * * * 𝑐𝑗 𝑐𝑗 𝑐𝑗
8
Step 4: Weighted normalized matrix calculation
9
If 𝑉 represents the weighted fuzzy normalized matrix, it is calculated using the equation
(
(18)
)
𝑎𝑖𝑗 𝑏𝑖𝑗 𝑐𝑖𝑗 , , , 𝑐 *𝑗 = max 𝑐𝑖𝑗 (benefit criteria) * * * 𝑐𝑗 𝑐𝑗 𝑐𝑗
(
‒
‒
‒
𝑉 = [𝑣𝑖𝑗]
10
𝑖 = 1, 2, 3, …, 𝑚 𝑎𝑛𝑑 𝑗 = 1, 2, 3, …𝑛
𝑚×𝑛
)
𝑖 = 1,2,…..𝑚; 𝑗 = 1,2,…..𝑛
𝑚×𝑛
(19)
11
where 𝑣𝑖𝑗 = 𝑟𝑖𝑗 ⊗ 𝑤𝑗
12
Step 5: Determination of fuzzy positive ideal solution (FPIS,𝐴 ) and fuzzy negative ideal
13
solution (FNIS, 𝐴 )
14
FPIS and FNIS are computed as follows:
15
16
17
+
-
𝐴
+
(
+
+
+
)
+ 𝑗
)
-
= 𝑣 1 ,……𝑣 𝑗 ,……𝑣 𝑛 𝑤h𝑒𝑟𝑒 𝑣
-
(
-
-
-
(
+
(
-
+
= 𝑐 𝑗 ,𝑐 𝑗 ,𝑐
-
-
)
+ 𝑗
{𝑐𝑖𝑗} ) 𝑎𝑛𝑑 𝑐 +𝑗 = max 𝑖 -
𝐴 = 𝑣 1 ,……𝑣 𝑗 ,……𝑣 𝑛 𝑤h𝑒𝑟𝑒 𝑣 𝑗 = 𝑐 𝑗 ,𝑐 𝑗 ,𝑐 𝑗 𝑎𝑛𝑑 𝑐 𝑗 = min {𝑎𝑖𝑗}
(20)
(21)
𝑖
∀ 𝑖 = 1,2,…..𝑚; 𝑗 = 1,2,…..𝑛 29
ACCEPTED MANUSCRIPT 1
Step 6: Calculate the distance of each alternative from FPIS and FNIS 𝑛
+ 𝑑𝑖
2
=
∑ 𝑑(𝑣𝑖𝑗,𝑣 +𝑖𝑗 ), 𝑖 = 1,2,…..,𝑚; 𝑗 = 1,2,…..,𝑛
(22)
𝑗=1
𝑛
𝑑 𝑖-
3
4
=
∑ 𝑑(𝑣𝑖𝑗,𝑣𝑖𝑗- ), 𝑖 = 1,2,…..,𝑚; 𝑗 = 1,2,…..,𝑛
(23)
𝑗=1
Step 7: Calculating the closeness coefficient (𝐶𝐶𝑖) of each alternative -
5
𝐶𝐶𝑖 =
𝑑𝑖 𝑑
+ 𝑖
-
(24)
+ 𝑑𝑖
6
Step 8: Ranking the alternatives
7
According to the closeness coefficient(𝐶𝐶𝑖), the alternatives are ranked in the descending
8
order.
9 10 11 12 13 14 15 16 17 18 19 20 30
ACCEPTED MANUSCRIPT 1 2 3 4
Goal
Main factors
5
Sub-factors
Alternatives
Top management support and involvement
6
Benchmarking Rise in sales
7
Redefining firm business model
Managerial CSFs
8
Lean tools as a continuous improvement philosophy Enhancing corporate image
9
Sustainability concept implementation Material and energy savings Monitoring and controlling
10
Pressurizing strategic suppliers to endorse environmental accreditation
11 12
Creates new market opportunities and increases market share
Avoids land fillings and high disposal cost
Supplier, consumer and organization strategic alliance
Strategic CSFs
Effective gate-keeping Strategic alliance with third party remanufacturer
13
Extends product life time
Increases employment rate
Targeting price-sensitive consumer
14
Using remanufactured part as spares
15 Prioritizing the POs due to adoption of SC remanufacturing CSFs
16
Mandatory take-back policies for hazardous products
Regulatory CSFs
Enforced laws and regulations banning informal waste handling sector
Government initiative to subsidize latest technology Standardized remanufacturing guidelines and framework
18
Quality ensured product at low cost Avoids waste limitation penalties
Part based designing of the product
19
Adoption of advanced technology
21
Reduces environmental impact
Subsidized loan from governmental financial institutions
17
20
Improves brand image
Technological and Infrastructural CSFs
Innovation Infrastructural development Transparent information system
22
Competitiveness and better market position
Reduces carbon and green house gas emission
Savings in capital investment
Effective reverse logistics network
23
Financial CSFs
24
Separate fund allocation for remanufacturing
Increases productivity and overall profitability
Acquisition of additional machinery and equipment Investment in remanufacturing related R&D
25
Attracts environmentally conscious customers
Supplying quality product with extended warranty Reduces waste
Social CSFs
Enough expertise by providing organized training to personnel Better HRM practices Healthy and safe working conditions Organization initiative to promote campaigns and workshops
31
ACCEPTED MANUSCRIPT 1 2 3 4
5. Numerical Application of Proposed Framework
5
5.1
Phase 1: Identification of CSFs for remanufacturing adoption in SC and the POs
6 7
A decision making panel consisting of total six experts was formed to collect the input data
8
required for the solution. The panel consist of five industrial experts selected from the case
9
organization and an academician expert. Academician expert was a university professor. The
10
five industry experts include Head (Projects); Head (Manufacturing); Head (Technologistic);
11
Head (Industrial Engineering), and SC Manager. All the experts selected for study are
12
engineering professionals, knowledgeable and having experience more than 10 years. The
13
study proposes a total of six main CSFs and thirty-two sub-factors, both qualitative as well as
14
quantitative in nature for successful adoption of remanufacturing practices in the SC through
15
evaluation of past literature analysis and inputs received from the decision making group
16
(Table 2). Also, the above decision making group was used to evaluate the POs (alternatives)
17
due to adoption of remanufacturing CSFs in SC (Table 3). The decision hierarchy is
18
structured in four levels. The first level of the hierarchy forms the main goal: “prioritize the
19
POs due to adoption of SC remanufacturing CSFs”. Second level consist of main CSFs
20
followed by sub-factors on the third level and finally the POs (alternatives) at the fourth level
21
of the hierarchy (See fig. 2).
22 23
5.2
Phase 2: Fuzzy AHP application: weight calculation of the main factors and sub-factors for remanufacturing practices adoption
32
ACCEPTED MANUSCRIPT 1
The relative importance of the listed common CSFs and sub-factors for remanufacturing
2
adoption in SC is calculated using fuzzy AHP, with the aim to rank these factors according to
3
their degree of importance. Decision making panel was asked to make a pair wise comparison
4
matrix between the 6 main factors and 32 sub-factors based on the TFNs defined in table 5.
5
The decision making panel conducted several round of discussion regarding weight allocation
6
for pair wise comparison between the factors and sub-factors. The panel discussion lasted till
7
each group member was of the same opinion regarding the importance weights to be
8
allocated during main criteria and sub-criteria pair wise comparison. Prakash and Barua
9
(2015) adopted a similar approach, where in a combined decision from the decision group
10
was made to make pair-wise comparison between main criteria and sub-criteria. Based on the
11
hierarchical structure presented in fig 2. a questionnaire form for fuzzy AHP is prepared
12
given in Appendix I. The fuzzy comparison matrices of the main criteria and the sub-criteria
13
with their calculated weights are given in table 7-13. Chang’s extent analysis is used to
14
calculate the weights of the criteria from the pair wise comparison matrices. MS excel sheet
15
is used for calculation purpose. From table 7 the fuzzy extent synthesis values of main criteria
16
are calculated using Eq. 7. The final results obtained by calculating the weights of main
17
CSFs and sub-criteria using the Chang’s extent analysis are presented in table 14.
18
S(MAN) = (7.33, 11.5, 16) (31.5, 48.5, 69)-1 = (0.106, 0.237, 0.508)
19
S(STR) = (4.92, 7.33, 10.5) (31.5, 48.5, 69)-1 = (0.071, 0.151, 0.333)
20
S(REG) = (6.33, 10.5, 15) (31.5, 48.5, 69)-1 = (0.092, 0.216, 0.476)
21
S(T&I) = (5.58, 8.83, 12.5) (31.5, 48.5, 69)-1 = (0.081, 0.182, 0.397)
22
S(FIN) = (4.25, 5.83, 8.5) (31.5, 48.5, 69)-1 = (0.062, 0.120, 0.270)
23
S(SOC) = (3.08, 4.5, 6.5) (31.5, 48.5, 69)-1 = (0.045, 0.093, 0.206)
24
These fuzzy values are then used to calculate the V values using Eq. (13)
25
V ( MAN STR ) 1 ,
V ( MAN REG ) 1 ,
V ( MAN T & I ) 1 33
ACCEPTED MANUSCRIPT 1
V ( MAN FIN ) 1 ,
V ( MAN SOC ) 1
2
V ( STR MAN ) 0.725
V ( STR REG ) 0.787
3
V ( STR FIN ) 1
V ( STR SOC ) 1
4
V ( REG MAN ) 0.946
V ( REG STR ) 1
5
V ( REG FIN ) 1
V ( REG SOC ) 1
6
V (T & I MAN ) 0.841
V (T & I STR ) 1
7
V (T & I FIN ) 1
V (T & I SOC ) 1
8
V ( FIN MAN ) 0.58
V ( FIN STR ) 0.865
9
V ( FIN T & I ) 0.752
V ( FIN SOC ) 1
10
V ( SOC MAN ) 0.409
V ( SOC STR ) 0.699
11
V ( SOC T & I ) 0.584
V ( SOC FIN ) 0.842
12
Then using Eq. (14) the minimum degrees of possibility are determined such as:
13
d ' ( MAN ) = min (1, 1, 1, 1, 1) = 1
14
d ' ( STR ) = min (0.725, 0.787, 0.890, 1, 1) = 0.725
15
d ' ( REG ) = min (0.946, 1, 1, 1, 1) = 0.946
16
d ' (T & I ) = min (0.841, 1, 0.899, 1, 1) = 0.841
17
d ' ( FIN ) = min (0.58, 0.865, 0.649, 0.752, 1) = 0.58
18
d ' ( SOC ) = min (0.409, 0.699, 0.481, 0.584, 0.842) = 0.409
19
Priority weight vector is presented as below:
20
W ' = (1, 0.725, 0.946, 0.841, 0.58, 0.409)
21
The final weights after normalization of the priority weight vectors in reference to the main
22
criteria are computed as:
23
W = (0.222, 0.161, 0.210, 0.187, 0.129, 0.091)
V ( STR T & I ) 0.890
V ( REG T & I ) 1
V (T & I REG ) 0.899
V ( FIN REG ) 0.649
V ( SOC REG ) 0.481
34
ACCEPTED MANUSCRIPT 1
A similar process is followed to calculate the normalized weight of sub-criteria.
2 3 4
Table 7 Fuzzy pair wise comparison matrix of the main criteria MAN
STR
REG
T&I
FIN
SOC
Weight
MAN
(1.00, 1.00, 1.00)
(2.00, 3.00, 4.00)
(1.00, 2.00, 3.00)
(0.33, 0.50, 1.00)
(1.00, 2.00, 3.00)
(2.00, 3.00, 4.00)
0.222
STR
(0.25, 0.33, 0.50)
(1.00, 1.00, 1.00)
(0.33, 0.50, 1.00)
(0.33, 0.50, 1.00)
(1.00, 2.00, 3.00)
(2.00, 3.00, 4.00)
0.161
REG
(0.33, 0.50, 1.00)
(1.00, 2.00, 3.00)
(1.00, 1.00, 1.00)
(2.00, 3.00, 4.00)
(1.00, 2.00, 3.00)
(1.00, 2.00, 3.00)
0.210
T&I
(1.00, 2.00, 3.00)
(1.00, 2.00, 3.00)
(0.25, 0.33, 0.50)
(1.00, 1.00, 1.00)
(2.00, 3.00, 4.00)
(0.33, 0.50, 1.00)
0.187
FIN
(0.33, 0.50, 1.00)
(0.33, 0.50, 1.00)
(0.33, 0.50, 1.00)
(0.25, 0.33, 0.50)
(1.00, 1.00, 1.00)
(2.00, 3.00, 4.00)
0.129
SOC
(0.25, 0.33, 0.50)
(0.25, 0.33, 0.50)
(0.33, 0.50, 1.00)
(1.00, 2.00, 3.00)
(0.25, 0.33, 0.50)
(1.00, 1.00, 1.00)
0.091
5 6 7
Table 8 Fuzzy pair-wise comparison matrix of the managerial (MAN) criteria MAN1
MAN2
MAN3
MAN4
MAN5
MAN6
MAN7
Weight
MAN1
(1.00, 1.00, 1.00)
(1.00, 1.00, 1.00)
(5.00, 6.00, 7.00)
(2.00, 3.00, 4.00)
(3.00, 4.00, 5.00)
(0.20, 0.25, 0.33)
(2.00, 3.00, 4.00)
0.272
MAN2
(1.00, 1.00, 1.00)
(1.00, 1.00, 1.00)
(0.33, 0.50, 1.00)
(0.33, 0.50, 1.00)
(1.00, 2.00, 3.00)
(0.20, 0.25, 0.33)
(0.33, 0.50, 1.00)
0.018
MAN3
(0.14, 0.17, 0.20)
(1.00, 2.00, 3.00)
(1.00, 1.00, 1.00)
(2.00, 3.00, 4.00)
(0.25, 0.33, 0.50)
(1.00, 2.00, 3.00)
(0.25, 0.33, 0.50)
0.109
MAN4
(0.25, 0.33, 0.50)
(1.00, 2.00, 3.00)
(0.25, 0.33, 0.50)
(1.00, 1.00, 1.00)
(0.20, 0.25, 0.33)
(1.00, 2.00, 3.00)
(3.00, 4.00, 5.00)
0.132
MAN5
(0.20, 0.25, 0.33)
(0.33, 0.50, 1.00)
(2.00, 3.00, 4.00)
(3.00, 4.00, 5.00)
(1.00, 1.00, 1.00)
(0.33, 0.50, 1.00)
(2.00, 3.00, 4.00)
0.182
MAN6
(3.00, 4.00, 5.00)
(3.00, 4.00, 5.00)
(0.33, 0.50, 1.00)
(0.33, 0.50, 1.00)
(1.00, 2.00, 3.00)
(1.00, 1.00, 1.00)
(2.00, 3.00, 4.00)
0.229
MAN7
(0.25, 0.33, 0.50)
(1.00, 2.00, 3.00)
(2.00, 3.00, 4.00)
(0.20, 0.25, 0.33)
(0.25, 0.33, 0.50)
(0.25, 0.33, 0.50)
(1.00, 1.00, 1.00)
0.058
8 9
Table 9 Fuzzy pair-wise comparison matrix of the strategic (STR) criteria STR1
STR2
STR3
STR4
STR5
STR6
Weight
STR1
(1.00, 1.00, 1.00)
(0.25, 0.33, 0.50)
(0.33, 0.50, 1.00)
(0.20, 0.25, 0.33)
(0.33, 0.50, 1.00)
(2.00, 3.00, 4.00)
0.036
STR2
(2.00, 3.00, 4.00)
(1.00, 1.00, 1.00)
(3.00, 4.00, 5.00)
(2.00, 3.00, 4.00)
(0.20, 0.25, 0.33)
(1.00, 2.00, 3.00)
0.271
STR3
(1.00, 2.00, 3.00)
(0.20, 0.25, 0.33)
(1.00, 1.00, 1.00)
(0.25, 0.33, 0.50)
(2.00, 3.00, 4.00)
(3.00, 4.00, 5.00)
0.206
STR4
(3.00, 4.00, 5.00)
(0.25, 0.33, 0.50)
(2.00, 3.00, 4.00)
(1.00, 1.00, 1.00)
(0.17, 0.20, 0.25)
(0.25, 0.33, 0.50)
0.147
STR5
(1.00, 2.00, 3.00)
(3.00, 4.00, 5.00)
(0.25, 0.33, 0.50)
(4.00, 5.00, 6.00)
(1.00, 1.00, 1.00)
(3.00, 4.00, 5.00)
0.329
STR6
(0.25, 0.33, 0.50)
(0.33, 0.50, 1.00)
(0.20, 0.25, 0.33)
(2.00, 3.00, 4.00)
(0.20, 0.25, 0.33)
(1.00, 1.00, 1.00)
0.012
10 11
Table 10 Fuzzy pair-wise comparison matrix of the regulatory (REG) criteria REG1
REG2
REG3
REG4
REG5
Weight
REG1
(1.00, 1.00, 1.00)
(1.00, 2.00, 3.00)
(2.00, 3.00, 4.00)
(2.00, 3.00, 4.00)
(1.00, 2.00, 3.00)
0.325
REG2
(0.33, 0.50, 1.00)
(1.00, 1.00, 1.00)
(2.00, 3.00, 4.00)
(1.00, 2.00, 3.00)
(2.00, 3.00, 4.00)
0.293
REG3
(0.25, 0.33, 0.50)
(0.25, 0.33, 0.50)
(1.00, 1.00, 1.00)
(1.00, 2.00, 3.00)
(0.33, 0.50, 1.00)
0.114
REG4
(0.25, 0.33, 0.50)
(0.33, 0.50, 1.00)
(0.33, 0.50, 1.00)
(1.00, 1.00, 1.00)
(0.20, 0.25, 0.33)
0.020
REG5
(0.33, 0.50, 1.00)
(0.25, 0.33, 0.50)
(1.00, 2.00, 3.00)
(3.00, 4.00, 5.00)
(1.00, 1.00, 1.00)
0.248
35
ACCEPTED MANUSCRIPT 1 2 3 4 5 6
Table 11 Fuzzy pair-wise comparison matrix of the technological and infrastructural (T&I) criteria T&I1
T&I2
T&I3
T&I4
T&I5
T&I6
Weight
T&I1
(1.00, 1.00, 1.00)
(2.00, 3.00, 4.00)
(0.17, 0.20, 0.25)
(0.20, 0.25, 0.33)
(0.25, 0.33, 0.50)
(2.00, 3.00, 4.00)
0.096
T&I2
(0.25, 0.33, 0.50)
(1.00, 1.00, 1.00)
(0.33, 0.50, 1.00)
(3.00, 4.00, 5.00)
(2.00, 3.00, 4.00)
(0.25, 0.33, 0.50)
0.153
T&I3
(4.00, 5.00, 6.00)
(1.00, 2.00, 3.00)
(1.00, 1.00, 1.00)
(4.00, 5.00, 6.00)
(3.00, 4.00, 5.00)
(0.25, 0.33, 0.50)
0.356
T&I4
(3.00, 4.00, 5.00)
(0.20, 0.25, 0.33)
(0.17, 0.20, 0.25)
(1.00, 1.00, 1.00)
(0.33, 0.50, 1.00)
(0.20, 0.25, 0.33)
0.018
T&I5
(2.00, 3.00, 4.00)
(0.25, 0.33, 0.50)
(0.20, 0.25, 0.33)
(1.00, 2.00, 3.00)
(1.00, 1.00, 1.00)
(1.00, 2.00, 3.00)
0.143
T&I6
(0.25, 0.33, 0.50)
(2.00, 3.00, 4.00)
(2.00, 3.00, 4.00)
(3.00, 4.00, 5.00)
(0.33, 0.50, 1.00)
(1.00, 1.00, 1.00)
0.235
7 8
Table 12 Fuzzy pair-wise comparison matrix of the financial (FIN) criteria FIN1
FIN2
FIN3
Weight
FIN1
(1.00, 1.00, 1.00)
(1.00, 2.00, 3.00)
(2.00, 3.00, 4.00)
0.567
FIN2
(0.33, 0.50, 1.00)
(1.00, 1.00, 1.00)
(1.00, 2.00, 3.00)
0.356
FIN3
(0.25, 0.33, 0.50)
(0.33, 0.50, 1.00)
(1.00, 1.00, 1.00)
0.077
9 10
Table 13 Fuzzy pair-wise comparison matrix of the social (SOC) criteria SOC1
SOC2
SOC3
SOC4
SOC5
Weight
SOC1
(1.00, 1.00, 1.00)
(0.25, 0.33, 0.50)
(3.00, 4.00, 5.00)
(2.00, 3.00, 4.00)
(0.20, 0.25, 0.33)
0.239
SOC2
(2.00, 3.00, 4.00)
(1.00, 1.00, 1.00)
(3.00, 4.00, 5.00)
(4.00, 5.00, 6.00)
(0.33, 0.50, 1.00)
0.414
SOC3
(0.20, 0.25, 0.33)
(0.20, 0.25, 0.33)
(1.00, 1.00, 1.00)
(2.00, 3.00, 4.00)
(0.33, 0.50, 1.00)
0.045
SOC4
(0.25, 0.33, 0.50)
(0.17, 0.20, 0.25)
(0.25, 0.33, 0.50)
(1.00, 1.00, 1.00)
(2.00, 3.00, 4.00)
0.021
SOC5
(3.00, 4.00, 5.00)
(1.00, 2.00, 3.00)
(1.00, 2.00, 3.00)
(0.25, 0.33, 0.50)
(1.00, 1.00, 1.00)
0.282
11 12
Table 14 Final priorities of CSFs of remanufacturing adoption in SC Main factors Managerial CSFs
Weight
Sub-factors
Weight
Globalized weight
Global Rank
0.222
MAN1
0.272
0.060342
5
MAN2
0.018
0.004103
28
MAN3
0.109
0.024197
19
MAN4
0.132
0.029266
15
MAN5
0.182
0.040369
12
MAN6
0.229
0.050748
8
MAN7
0.058
0.012974
24
36
ACCEPTED MANUSCRIPT Strategic CSFs
Regulatory CSFs
Technological and Infrastructural CSFs
Financial CSFs
Social CSFs
0.161
0.210
0.187
0.129
0.091
STR1
0.036
0.005765
26
STR2
0.271
0.043582
11
STR3
0.206
0.033163
14
STR4
0.147
0.023695
21
STR5
0.329
0.052891
6
STR6
0.012
0.001904
31
REG1
0.325
0.068182
2
REG2
0.293
0.061500
4
REG3
0.114
0.024000
20
REG4
0.020
0.004227
27
REG5
0.248
0.052091
7
T&I1
0.096
0.017889
23
T&I2
0.153
0.028529
16
T&I3
0.356
0.066501
3
T&I4
0.018
0.003325
30
T&I5
0.143
0.026733
17
T&I6
0.235
0.044023
10
FIN1
0.567
0.073088
1
FIN2
0.356
0.045972
9
FIN3
0.077
0.009940
25
SOC1
0.239
0.021713
22
SOC2
0.414
0.037697
13
SOC3
0.045
0.004071
29
SOC4
0.021
0.001885
32
SOC5
0.282
0.025634
18
1 2 3
5.3
Phase 3: Fuzzy TOPSIS application: prioritizing the POs due to adoption of remanufacturing CSFs in SC
4
A fuzzy comparison matrix is constructed by the decision making panel using the linguistic
5
variables defined in table 6. Each alternative (POs) is compared with each sub-criteria (CSFs)
6
individually, thus forming a matrix as presented in table 15. The linguistic variables are then
7
substituted with the corresponding TFNs (Table 16). In a similar way, linguistic variable
8
matrix and TFN matrix was constructed for each expert, but the matrix for only one decision
9
maker is presented to limit the space and word counts. Questionnaire form is prepared to
10
compare each PO with the sub-factors (CSFs) and is given in Appendix II. Having received
11
the expert opinion from each expert, aggregate fuzzy decision matrix is formulated using Eq. 37
ACCEPTED MANUSCRIPT 1
(17) and presented in table 17. The normalized fuzzy decision matrix is then formed in table
2
18. Then after the fuzzy weighted normalized matrix is established with the use of weights
3
obtained by application of fuzzy AHP (Table 19).
4
Then using Eq. (22)-(23), calculating the distance of each of the alternative from FPIS and
5
FNIS.
...........
1 0.025634 0.0179442 0.025634 0.00243522 0.025634 0.0256342 3
10
= 0.426404
d ( A1 , A )
1 0.018103 0.0301712 0.018103 0.048722 0.018103 0.0603422 3
...........
1 0.002563 0.0179442 0.002563 0.0243522 0.002563 0.00256342 3
8
9
1 0.060342 0.0301712 0.060342 0.048722 0.060342 0.0603422 3
6
7
d ( A1 , A )
= 0.539950 Using Eq. (24), the closeness coefficient for PO1 is computed as under:
11
𝐶𝐶1 =
0.539950 = 0.558750 0.426404 + 0.539950
12
In the same way the calculations for each of the alternatives (POs) were performed to
13
compute the values of distances, d ( Ai , A ) , d ( Ai , A ) and the closeness coefficients. Finally
14
based on the computed values of 𝐶𝐶𝑖 (closeness coefficient) the POs (alternatives) are ranked
15
as shown in the table 20.
16
Table 15 Linguistic rating matrix for the POs (Decision maker 1) Alternatives (POs) PO1 PO2 PO3 ….. …..
MAN1 G G VG ….. …..
MAN2 F F VG ….. …..
MAN3 MG MG G ….. …..
Criteria ….. ….. ….. ….. ….. …..
….. ….. ….. ….. ….. …..
SOC3 G MG G ….. …..
SOC4 MG F MG ….. …..
SOC5 VG VG MG ….. …..
38
ACCEPTED MANUSCRIPT PO14 PO15 PO16
1
G MG G
F MG MG
VG F G
….. ….. …..
….. ….. …..
VG MG G
VG MG MG
VG VG G
2 3
Table 16 Fuzzy decision matrix and weights of POs (Decision maker 1) Alternatives (POs)
MAN1 (7, 9, 10) (7, 9, 10) (9, 10, 10) ….. ….. (7, 9, 10) (5, 7, 9) (7, 9, 10)
PO1 PO2 PO3 ….. ….. PO14 PO15 PO16
MAN2 (3, 5, 7) (3, 5, 7) (9, 10, 10) ….. ….. (3, 5, 7) (5, 7, 9) (5, 7, 9)
MAN3 (5, 7, 9) (5, 7, 9) (7, 9, 10) ….. ….. (9, 10, 10) (3, 5, 7) (7, 9, 10)
Criteria ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. …..
SOC3 (7, 9, 10) (5, 7, 9) (7, 9, 10) ….. ….. (9, 10, 10) (5, 7, 9) (7, 9, 10)
SOC4 (5, 7, 9) (3, 5, 7) (5, 7, 9) ….. ….. (9, 10, 10) (5, 7, 9) (5, 7, 9)
SOC5 (9, 10, 10) (9, 10, 10) (5, 7, 9) ….. ….. (9, 10, 10) (9, 10, 10) (7, 9, 10)
SOC4 (3, 6, 9) (1, 4, 7) (3, 6, 9) ….. ….. (7, 9.5, 10) (3, 6, 9) (3, 6, 9)
SOC5 (7, 9.5, 10) (7, 9.5, 10) (3, 6, 9) ….. ….. (7, 9.5, 10) (7, 9.5, 10) (5, 8, 10)
4 5
Table 17 Aggregate fuzzy decision matrix for POs Alternatives (POs) PO1 PO2 PO3 ….. ….. PO14 PO15 PO16
MAN1 (5, 8, 10) (5, 8, 10) (7, 9.5, 10) ….. ….. (5, 8, 10) (3, 6, 9) (5, 8, 10)
MAN2 (1, 4, 7) (1, 4, 7) (7, 9.5, 10) ….. ….. (1, 4, 7) (3, 6, 9) (3, 6, 9)
MAN3 (3, 6, 9) (3, 6, 9) (5, 8, 10) ….. ….. (7, 9.5, 10) (1, 4, 7) (5, 8, 10)
Criteria ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. …..
SOC3 (5, 8, 10) (3, 6, 9) (5, 8, 10) ….. ….. (7, 9.5, 10) (3, 6, 9) (5, 8, 10)
6 7
Table 18 Normalized fuzzy decision matrix for POs Criteria
Alternatives (POs)
MAN1
MAN2
MAN3
…..
…..
SOC3
SOC4
SOC5
PO1
(0.5, 0.8, 0.10)
(0.1, 0.4, 0.7)
(0.3, 0.6, 0.9)
…..
…..
(0.5, 0.8, 0.10)
(0.3, 0.6, 0.9)
(0.7, 0.95, 0.10)
8
PO2
(0.5, 0.8, 0.10)
(0.1, 0.4, 0.7)
(0.3, 0.6, 0.9)
…..
…..
(0.3, 0.6, 0.9)
(0.1, 0.4, 0.7)
(0.7, 0.95, 0.10)
PO3
(0.7, 0.95, 0.10)
(0.7, 0.95, 0.10)
(0.5, 0.8, 0.10)
…..
…..
(0.5, 0.8, 0.10)
(0.3, 0.6, 0.9)
(0.3, 0.6, 0.9)
…..
…..
…..
…..
…..
…..
…..
…..
…..
…..
…..
…..
…..
…..
…..
…..
…..
…..
PO14
(0.5, 0.8, 0.10)
(0.1, 0.4, 0.7)
(0.7, 0.95, 0.10)
…..
…..
(0.7, 0.95, 0.10)
(0.7, 0.95, 0.10)
(0.7, 0.95, 0.10)
PO15
(0.3, 0.6, 0.9)
(0.3, 0.6, 0.9)
(0.1, 0.4, 0.7)
…..
…..
(0.3, 0.6, 0.9)
(0.3, 0.6, 0.9)
(0.7, 0.95, 0.10)
PO16
(0.5, 0.8, 0.10)
(0.3, 0.6, 0.9)
(0.5, 0.8, 0.10)
…..
…..
(0.5, 0.8, 0.10)
(0.3, 0.6, 0.9)
(0.5, 0.8, 0.10)
9
Table 19 Weighted normalized fuzzy decision matrix for POs Alternatives (POs) PO1
Criteria MAN1
MAN2
…..
…..
SOC4
SOC5
(0.0301, 0.0482, 0.0603)
(0.0004, 0.0016, 0.0028)
…..
…..
(0.0005, 0.0011, 0.0016)
(0.0179, 0.0243, 0.0256)
39
ACCEPTED MANUSCRIPT PO2
(0.0004, 0.0016, 0.0028) (0.0028, 0.0038, 0.0041) …..
…..
…..
…..
…..
…..
(0.0301, 0.0482, 0.0603) (0.0422, 0.0573, 0.0303) …..
…..
…..
…..
…..
…..
PO14
(0.0301, 0.0482, 0.0603) (0.0181, 0.0362, 0.0543) (0.0301, 0.0482, 0.0603)
(0.0004, 0.0016, 0.0028) (0.0012, 0.0024, 0.0036) (0.0012, 0.0024, 0.0036)
….. …..
…..
…..
…..
PO3
PO15 PO16
1 2
…..
(0.0001, 0.0007, 0.0013) (0.0005, 0.0011, 0.0016) …..
(0.0179, 0.0243, 0.0256) (0.0076, 0.0153, 0.0230) …..
…..
…..
…..
…..
(0.0013, 0.0179, 0.0018) (0.0005, 0.0011, 0.0016) (0.0005, 0.0011, 0.0016)
(0.0179, 0.0243, 0.0256) (0.0179, 0.0243, 0.0256) (0.01281, 0.0205, 0.0256)
Table 20 Closeness coefficients(𝐶𝐶𝑖) Criteria code
Performance outcomes
𝑑
+ 𝑖
‒
𝑑𝑖
𝐶𝐶𝑖
Rank
PO1 PO2
Rise in sales Creates new market opportunities and increases market share
0.426404 0.377070
0.539950 0.579317
0.558750 0.605735
11 10
PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PO13 PO14 PO15 PO16
Material and energy savings Avoids land fillings and high disposal cost Extends product life time Increases employment rate Improves brand image Reduces environmental impact Quality ensured product at low cost Avoids waste limitation penalties Competitiveness and better market position Reduces carbon and green house gas emission Savings in capital investment Increases productivity and overall profitability Attracts environmentally conscious customers Reduces waste
0.304031 0.373363 0.452824 0.464335 0.372581 0.296230 0.438438 0.378196 0.267031 0.302095 0.448655 0.273369 0.501461 0.311327
0.639130 0.587058 0.518978 0.509438 0.586609 0.648668 0.528341 0.584374 0.668063 0.643892 0.521206 0.663967 0.475484 0.637932
0.677647 0.611251 0.534037 0.523159 0.611567 0.686495 0.546496 0.607098 0.714434 0.680656 0.537403 0.708355 0.486705 0.672032
5 8 14 15 7 3 12 9 1 4 13 2 16 6
3 4
6. Results and Discussion
5
The study attempts to improve the POs of the organization through adoption of SC
6
remanufacturing CSFs. To deal with, the present work evaluates thirty-two CSFs for
7
remanufacturing adoption in SC and sixteen POs that could be realized due to its adoption; in
8
context of an Indian manufacturing organization. The CSFs and POs were finalized through
9
literature review and consequent discussion with the decision group panel. (academician and
10
industrial personals). The relative importance of the SC remanufacturing CSFs was evaluated
11
using fuzzy AHP. Further, the calculated weights were carried forward to prioritize the POs
12
that could be realized due to remanufacturing CSFs adoption in SC, using fuzzy TOPSIS 40
ACCEPTED MANUSCRIPT 1
approach. The results of the analysis as obtained (Table 14 to 20) are elaborated in this
2
section and also discussed with industrial practitioners to enhance organizational POs
3
effectively through systematic adoption of SC remanufacturing CSFs.
4
Based on the findings of the study from table 14, main CSFs for remanufacturing adoption in
5
SC can be ranked according to their relative degree of importance value as: managerial CSFs,
6
regulatory CSFs, technological and infrastructural CSFs, strategic CSFs, financial CSFs and
7
social CSFs. This clearly suggests that of the main CSFs, managerial CSFs have significant
8
impact on adoption of remanufacturing initiatives in SC. Under this main factor there are
9
seven sub-factors, and the ranking of these factors in accordance with their priority is given
10
as: MAN1 > MAN6 > MAN5 > MAN4 > MAN3 > MAN7 > MAN2, which indicates that top
11
management support and involvement is the important factor amongst all the managerial
12
CSFs for remanufacturing adoption in SC. In the present case organization, the top
13
management is actively involved in SC remanufacturing practices, be it management
14
personnel, production executive, research and development executive, and marketing
15
executive. Motivating the workers, providing regular trainings to improve skill base,
16
incentive mechanism, transparent information flow system are some of the top management
17
initiatives for effective adoption of SC remanufacturing. Regulatory CSFs is the second most
18
important factor and the sub-factors arranged according to the degree of importance are
19
REG1 > REG2 > REG5 > REG3 > REG4, which indicates that weightage factor of
20
mandatory take-back policies is the highest while government initiative to subsidize latest
21
technology has the lowest weightage factor. In line with this, the present organization is ISO
22
14001 certified which suggest that the organization is sensitive towards the environmental
23
damage created due to their manufacturing activities. The organization has its own
24
remanufacturing facility and itself collects the used bearings from the user, reconditions it 41
ACCEPTED MANUSCRIPT 1
and supplies back the remanufactured bearings that meets the desired quality level. This
2
suggests that the organization is adhering to the government take-back policies.
3
Technological and infrastructural CSFs stands at third position based on the importance
4
weights calculated. Sub-criteria ratings of T&I CSFs are T&I3 > T&I6 > T&I2 > T&I5 >
5
T&I1 > T&I4, which suggest that degree of importance of innovation success factor for
6
remanufacturing practices implementation in SC is high and should be given priority in this
7
category. The ratings of strategic CSFs which ranks at fourth place among the main factors
8
are STR5 > STR2 > STR3 > STR4 > STR1 > STR6 which demonstrates that highest priority
9
should be given to target price-sensitive consumer and using remanufactured product as
10
spares should be given least importance under this factor category. Financial CSFs is at the
11
fifth importance level and based on the importance value, its sub-factors as arranged in
12
descending order are FIN1 > FIN2 > FIN3. Finally, the ratings of sub-criteria under social
13
CSFs category which stands at the sixth position are SOC3 > SOC2 > SOC5 > SOC1 >
14
SOC4. Now, due to adoption of SC remanufacturing CSFs, some POs (alternatives) could be
15
realized. However, it very difficult to define as to which performance outcome could be
16
highly observed due to adoption of SC remanufacturing CSFs. But, prioritizing the POs using
17
fuzzy TOPSIS by combining the CSFs weights obtained using fuzzy AHP, helps decision
18
makers to identify the best PO. Based on the value of the closeness coefficients, POs realized
19
due to adoption of SC remanufacturing CSFs are prioritized. The closeness coefficient of
20
PO11 is highest, while that of PO15 is lowest i.e. competiveness and better market position is
21
the highest priority PO and attracts environmentally conscious customers is the lowest
22
priority PO due to adoption of remanufacturing practices in SC. Adoption of SC
23
remanufacturing CSFs aids the organization to produce remanufactured product of the same
24
quality level as that of “new like” at reduced cost. Supplying the products at a lower-prices
25
not only provides a competitive edge over the peers but also, increases the market share. The 42
ACCEPTED MANUSCRIPT 1
priorities of the other POs are PO14-PO8-PO12-PO3-PO16-PO7-PO4-PO10-PO2-PO1-PO9-
2
PO13-PO5-PO6-PO15 in descending order.
3
6.1 Practical implications of the study
4
This study based on the results and subsequent discussion with the decision panel group list
5
out several managerial implications for the industrial practitioners to effectively manage the
6
remanufacturing practices implementation. Following are the recommendations suggested to
7
the industrial manager:
8
The present study explores thirty-two CSFs categorized under six main criteria. Clear
9
understanding of each criteria would help the industrial managers to overcome the
10
remanufacturing challenges effectively. Further, it is not possible to implement all the
11
CSFs at a time within the organizational decision process. Hence, ranking of the
12
CSFs based on their relative importance as carried out in this research would help the
13
practitioners to focus initially on high intensity CSFs for successful adoption of
14
remanufacturing practices in SC.
15
This study provides a structural framework to the managers/practitioners to enhance
16
their sustainable performance by providing an exhaustive list of remanufacturing
17
CSFs and POs realized due its adoption as presented in Fig. 2. Further, prioritization
18
of the POs realized due to adoption of SC remanufacturing CSFs helps the policy
19
makers to develop strategic action plan at the initial planning phase itself, thereby
20
minimizing the failure risk and enhancing the possibility of success.
21
Remanufacturing initiative is emerging in developing nations as well as densely
22
populated countries, as it not only yields environmental benefits but also results into
23
societal benefits (job creation). The findings of the study are based on the numerical 43
ACCEPTED MANUSCRIPT 1
application of the proposed framework in an Indian manufacturing organization.
2
However, the proposed study would help the researchers/practitioners of other
3
developing nations such as China, Brazil, and Bangladesh etc. to improve their
4
organizational performance by modifying their present approach.
5
It is mandatory for any organization to abide by certain regulatory laws related to
6
environment and undergo several societal responsibility, while maintaining their
7
economic growth. The present fuzzy AHP-TOPSIS framework helps the internal as
8
well as the external stakeholders by making the SC remanufacturing more transparent
9
and, thereby helps the organizations to be more accountable towards sustainable
10
performance and decrease their weakness. The proposed framework helps to mitigate
11
the social, economic and environmental risks to the society that would have arisen
12
due unplanned implementation of SC remanufacturing CSFs.
13
An action of managers/practitioners is simply not sufficient enough to realize the POs
14
accrued due to SC remanufacturing CSFs adoption. Multiple stakeholders are involved in SC
15
remanufacturing CSFs implementation.
16
recommended to the multiple stakeholders:
17
In this regard, following are some suggestions
Stringent laws regarding collection of used product at its EOL (i.e. take-back policies)
18
by the OEM should be implemented by government institutions to effectively manage
19
the end waste.
20
products and ensure that the used products undergo proper EOL treatment.
21 22 23
Government institutions should enforce laws to curb informal handling of the waste
Consumers should be aware that remanufactured product is not second hand product. They should be aware regarding the benefits of using remanufactured product. 44
ACCEPTED MANUSCRIPT 1
7. Sensitivity Analysis
2
Sensitivity analysis needs to be conducted to investigate the effect of variation of SC
3
remanufacturing CSFs priority weights on the final rankings of the alternatives (POs).
4
Sirisawat and Kiatcharoenpal (2018) and Tian et al. (2017) performed a similar analysis to
5
check the robustness of alternatives ranking due to changes in criteria weights. The study
6
conducts ten experiments to perform the sensitivity analysis (Table 21). Sensitivity analysis
7
is performed by assigning higher weight to each CSF one by one, while keeping the weight of
8
other CSFs as low. During experiment 1, CSF MAN1 was allocated a weight equal to 0.6,
9
while the remaining CSFs weight was set as 0.015, then the rankings of the POs (alternatives)
10
were determined. It indicates that PO11, PO8, PO12, PO3, PO7 are the top five outcomes that
11
can be derived due to adoption of remanufacturing CSFs in SC. Conducting experiment 2 by
12
putting weight of CSF MAN2 = 0.6 and weight of the remaining CSFs as 0.015 indicates that
13
PO11, PO14, PO8, PO12, PO3 are the five important POs. During experiment 3, where
14
weight of CSF MAN3 = 0.6 and weight of other CSFs equal to 0.015, depicts the results that
15
PO11, PO8, PO12, PO14, PO3 are the five highly rated POs. Similarly, the remaining seven
16
experiments are performed and the results obtained are presented in table 21.
17
The sensitivity analysis performed by changing the weight values of each CSF one by one
18
may result into deviation in values of closeness coefficient as well as the final ranking of the
19
POs. Based on the ten experiments performed, competitiveness and better market position
20
(PO11) is the best outcome reported in nine experiments. In addition, the factors PO11,
21
PO14, PO8, PO12, PO3 turn out to be the top five POs that could be realized due adoption of
22
SC remanufacturing CSFs in each experiment, with negligible order discrepancies. Hence,
23
sensitivity analysis experiment results represents that the ranking of the POs (alternatives)
24
due to adoption of remanufacturing CSFs in SC is relatively sensitive to the CSFs weights. 45
Table 21 Sensitivity analysis
POs
Exp. 1
Exp. 2
Exp. 3
Exp. 4
Exp. 5
Exp. 6
Exp. 7
Exp. 8
Exp. 9
Exp. 10
CC
Rank
CC
Rank
CC
Rank
CC
Rank
CC
Rank
CC
Rank
CC
Rank
CC
Rank
CC
Rank
CC
Rank
PO1
0.57627
11
0.37674
12
0.37509
12
0.37674
12
0.37368
13
0.41078
12
0.37674
12
0.37674
12
0.37368
13
0.37674
12
PO2
0.60262
9
0.38874
9
0.38712
9
0.38874
9
0.38573
9
0.42219
9
0.38712
9
0.38874
9
0.38573
9
0.38712
9
PO3
0.72452
4
0.41008
5
0.41104
5
0.41008
5
0.41376
5
0.44239
5
0.41104
5
0.41104
5
0.41104
5
0.41008
5
PO4
0.62312
8
0.39663
8
0.39663
8
0.39822
8
0.39822
8
0.43004
8
0.39526
8
0.39526
8
0.39663
8
0.39663
8
PO5
0.57455
12
0.37448
13
0.37306
13
0.37448
13
0.37448
12
0.41017
13
0.37448
13
0.37612
13
0.37612
12
0.37448
13
PO6
0.49831
15
0.36849
14
0.37032
14
0.35868
16
0.36693
15
0.40776
14
0.37032
14
0.36693
15
0.36693
14
0.37032
14
PO7
0.70582
5
0.40374
7
0.40374
7
0.40374
7
0.40277
7
0.43623
7
0.40501
7
0.40374
7
0.40501
7
0.40501
7
PO8
0.73259
2
0.41451
3
0.41451
2
0.41356
2
0.41576
1
0.44567
3
0.41451
2
0.41356
3
0.41576
2
0.41451
2
PO9
0.59451
10
0.38350
11
0.38210
11
0.38210
10
0.38350
11
0.41874
11
0.38210
11
0.38350
10
0.38350
10
0.38512
11
PO10
0.52802
14
0.38395
10
0.38242
10
0.38127
11
0.38395
10
0.42106
10
0.38395
10
0.38242
11
0.38242
11
0.38573
10
PO11
0.73852
1
0.41792
1
0.41572
1
0.41792
1
0.41572
2
0.44857
1
0.41572
1
0.41792
1
0.41667
1
0.41792
1
PO12
0.73259
3
0.41451
4
0.41451
3
0.41356
3
0.41451
4
0.44567
4
0.41451
3
0.41356
4
0.41576
3
0.41356
3
PO13
0.55636
13
0.36605
15
0.36605
15
0.36462
15
0.36772
14
0.40220
15
0.36605
15
0.36772
14
0.36605
15
0.36462
14
PO14
0.66404
6
0.41627
2
0.41239
4
0.41340
4
0.41473
3
0.44728
2
0.41239
4
0.41473
2
0.41239
4
0.41239
4
PO15
0.47578
16
0.35868
16
0.35523
16
0.36849
14
0.35682
16
0.39675
16
0.35868
16
0.35868
16
0.35682
16
0.35523
16
PO16
0.64702
7
0.40744
6
0.40609
6
0.40507
6
0.40744
6
0.43944
6
0.40609
6
0.40744
6
0.40609
6
0.40609
6
Conditions for experiment: Exp. 1: MAN1 = 0.6, MAN2-SOC5 = 0.015; Exp. 2: MAN2 = 0.6, MAN1, MAN3-SOC5= 0.015; Exp. 3: MAN3 = 0.6, MAN1-MAN2, MAN4SOC5 = 0.015; Exp. 4: MAN4 = 0.6, MAN1-MAN3, MAN5-SOC5 = 0.015; Exp. 5: MAN5 = 0.6, MAN1-MAN4, MAN6-SOC5 = 0.015; Exp. 6: MAN6 = 0.6, MAN1MAN5, MAN7-SOC5 = 0.015; Exp. 7: MAN7 = 0.6, MAN1-MAN6, STR1-SOC5 = 0.015; Exp. 8: STR1 = 0.6, MAN1-MAN7, STR2-SOC5 = 0.015; Exp. 9: STR2 = 0.6, MAN1-STR1, STR3-SOC5 = 0.015; Exp. 10: STR3 = 0.6, MAN1-STR2, STR4-SOC5 = 0.015
46
ACCEPTED MANUSCRIPT 1
8. Conclusion
2
Uneven climate, increased industrialization and growing population rate have forced the
3
organizations to search for a sustainable solution. Adoption of remanufacturing practices in
4
the existing SC is a prominent strategy, being adopted by organizations to improve their
5
sustainable performance. Remanufacturing strategy adoption in SC has been a topic of debate
6
for developed nations since long, and these nations have already started realizing the benefits
7
that could be incurred due to its adoption. However, it is still at a premature stage in
8
developing nations and especially, India due to its complex socio-environmental and
9
economic relationship. In addition, interaction of complex factors such as uncertain
10
regulations, fluctuating global economic scenario and varied consumer behaviour poses a
11
critical challenge for organizations to adopt SC remanufacturing CSFs. The present study
12
attempts to identify the CSFs for remanufacturing adoption in SC and the POs that could be
13
realized due to its adoption. A structured framework is developed in this study which
14
proposes a hybrid fuzzy AHP-TOPSIS approach for prioritizing the POs realized due to
15
adoption of SC remanufacturing CSFs.
16
The proposed hybrid fuzzy AHP-TOPSIS research framework is applied to a case
17
organization in Indian context. After assessing the relevant literature and discussion with
18
experts of the decision-making team, thirty-two SC remanufacturing CSFs and sixteen POs
19
due to its adoption were decided. The POs (alternatives) were prioritized based on the inputs
20
received from the decision making group and finally ranks were assigned using hybrid fuzzy
21
AHP-TOPSIS approach. The analysis reveals that competitiveness and better market position
22
is the best PO that could be realized due to adoption of SC remanufacturing CSFs. This is
23
because the organization would present itself that, apart from manufacturing a new product,
24
they are also in the business of remanufacturing of used product. This would create a trust 47
ACCEPTED MANUSCRIPT 1
among the customers regarding the quality of the remanufactured product which is ultimately
2
the consumer’s prime requirement. Also, the PO namely attracts environmentally conscious
3
customers has the lowest rank because major societal class in India has low income and
4
hence the environmental damage created by the organization product is a least concern for
5
them as their priority is availability of quality goods at cheaper rate. The ranking of the POs
6
will aid organizations in structured implementation of SC remanufacturing CSFs in
7
accordance with their priority to realize the POs, thus improving the sustainable performance
8
of the organization. The outcomes of the study will help the managers of the case
9
organization under study effectively, to enhance their POs through structured and systematic
10
implementation of remanufacturing CSFs in SC. Realization about the most effective actions
11
to be taken for effective adoption of remanufacturing CSFs in SC by the industry top
12
executives; forward SC and reverse SC practitioners; and government bodies would help to
13
improve socio-environmental-economic performance.
14
The structured model proposed in this study is not without limitations, which otherwise
15
provides an opportunities for other researchers to investigate the issues. The proposed model
16
combines fuzzy AHP-TOPSIS methodology, to prioritize the alternatives (16 POs) realized
17
due to adoption of thirty-two SC remanufacturing CSFs. The results computed in this study is
18
based on the inputs received from decision making panel. Hence, it is proposed that the
19
results should be computed carefully. The findings of the study is based on the application of
20
proposed framework on a single numerical case organization in India. Hence, the framework
21
should be applied in different geographical context with minor modifications to generalise the
22
results. The proposed methodology ranks the CSFs however, combining DEMATEL and
23
ISM with fuzzy AHP will help the decision makers to analyze the inter-relationships between
24
CSFs. Analyzing the problem using fuzzy AHP-DEMATEL or fuzzy AHP-ISM would 48
ACCEPTED MANUSCRIPT 1
support the practitioners and policy definers to plan flexible decision-making strategies for
2
short-term or long-term that will improve performance due to remanufacturing CSFs
3
adoption in SC from an industrial perspective (Mangla et al., 2016). Importance weight
4
allocation during pair-wise comparison of main factors and sub-factors in fuzzy AHP analysis
5
is done based on group-thinking of the experts. There may be possibility that some individual
6
tend to dominate the group or some contribute averagely to avoid any conflicts. Further,
7
using other multi-criteria decision matrix such as fuzzy ELECTRE, fuzzy PROMETHEE, or
8
fuzzy VIKOR, the results of the study should be compared. The proposed structural
9
framework should be applied to various other industry sectors that are willing to adopt
10
remanufacturing CSFs in their SC however, the opinion may vary based on industry type and
11
priorities.
12
Appendix I
13 14 15
Questionnaire form for decision making group to facilitate pair-wise comparison matrix between the main criteria. Similar form is prepared for sub-criteria but only one sub-criteria form is presented due to space limitations. Criteria
Managerial CSFs (MAN) Strategic CSFs (STR) Regulatory CSFs (REG) Technological and Infrastructural CSFs (T&I) Financial CSFs (FIN)
MAN Equal (1,1,1) Very low (1,2,3) Low (2,3,4)
STR Equal (1,1,1) Very low (1,2,3) Low (2,3,4)
REG Equal (1,1,1) Very low (1,2,3) Low (2,3,4)
T&I Equal (1,1,1) Very low (1,2,3) Low (2,3,4)
FIN Equal (1,1,1) Very low (1,2,3) Low (2,3,4)
SCO 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 (6,7,8)
Medium (3,4,5) High (4,5,6) Very high (5,6,7) Excellent (6,7,8)
Medium (3,4,5) High (4,5,6) Very high (5,6,7) Excellent (6,7,8)
Medium (3,4,5) High (4,5,6) Very high (5,6,7) Excellent (6,7,8)
Medium (3,4,5) High (4,5,6) Very high (5,6,7) Excellent (6,7,8)
Medium (3,4,5) High (4,5,6) Very high (5,6,7) Excellent (6,7,8)
(1,1,1) (1,1,1) (1,1,1)
(1,1,1)
(1,1,1)
49
ACCEPTED MANUSCRIPT Social CSFs (SOC)
(1,1,1)
1 Sub-criteria
Top management support and involvement (MAN1) Benchmarking (MAN2) Redefining the firm business model (MAN3) Lean tools as a continuous improvement philosophy (MAN4) Enhancing corporate green image (MAN5) Sustainability concept implementation (MAN6) Monitoring and controlling (MAN7)
MAN1 Equal (1,1,1)
MAN2 Equal (1,1,1)
MAN3 Equal (1,1,1)
MAN4 Equal (1,1,1)
MAN5 Equal (1,1,1)
MAN6 Equal (1,1,1)
MAN7 Equal (1,1,1)
Very low (1,2,3) Low (2,3,4)
Very low (1,2,3) Low (2,3,4)
Very low (1,2,3) Low (2,3,4)
Very low (1,2,3) Low (2,3,4)
Very low (1,2,3) Low (2,3,4)
Very low (1,2,3) Low (2,3,4)
Very low (1,2,3) Low (2,3,4)
Medium (3,4,5) High (4,5,6)
Medium (3,4,5) High (4,5,6)
Medium (3,4,5) High (4,5,6)
Medium (3,4,5) High (4,5,6)
Medium (3,4,5) High (4,5,6)
Medium (3,4,5) High (4,5,6)
Medium (3,4,5) High (4,5,6)
Very high (5,6,7)
Very high (5,6,7)
Very high (5,6,7)
Very high (5,6,7)
Very high (5,6,7)
Very high (5,6,7)
Very high (5,6,7)
Excellent (6,7,8)
Excellent (6,7,8)
Excellent (6,7,8)
Excellent (6,7,8)
Excellent (6,7,8)
Excellent (6,7,8)
Excellent (6,7,8)
(1, 1, 1)
(1, 1, 1)
(1, 1, 1)
(1, 1, 1)
(1, 1, 1)
(1, 1, 1)
(1, 1, 1)
2 3 4 5 6 7 8 50
ACCEPTED MANUSCRIPT 1
Appendix II
2
Questionnaire form to compare each PO (alternatives) with sub-criteria.
Alternatives
Criteria MAN1 Very poor (0,0,1) Poor (0,1,3)
MAN2 Very poor (0,0,1) Poor (0,1,3)
MAN3 Very poor (0,0,1) Poor (0,1,3)
…
… … …
SOC3 Very poor (0,0,1) Poor (0,1,3)
SOC4 Very poor (0,0,1) Poor (0,1,3)
SOC5 Very poor (0,0,1) Poor (0,1,3)
… …
Medium poor (1,3,5) Fair (3,5,7)
Medium poor (1,3,5) Fair (3,5,7)
Medium poor (1,3,5) Fair (3,5,7)
… …
… …
Medium poor (1,3,5) Fair (3,5,7)
Medium poor (1,3,5) Fair (3,5,7)
Medium poor (1,3,5) Fair (3,5,7)
Medium good (5,7,9) Good (7,9,10)
Medium good (5,7,9) Good (7,9,10)
Medium good (5,7,9) Good (7,9,10)
… …
… …
Medium good (5,7,9) Good (7,9,10)
Medium good (5,7,9) Good (7,9,10)
Medium good (5,7,9) Good (7,9,10)
Very good (9,10,10)
Very good (9,10,10)
Very good (9,10,10)
…
…
Very good (9,10,10)
Very good (9,10,10)
Very good (9,10,10)
PO1 PO2 PO3 . . . PO14 PO15 PO16
3 4
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ACCEPTED MANUSCRIPT Highlights
Identifies the CSFs and the POs realized due to adoption of SC remanufacturing CSFs adoption.
A hybrid MCDM approach (fuzzy AHP-TOPSIS) approach is used to evaluate the CSFs and POs.
Fuzzy AHP is used to rank the SC remanufacturing CSFs while to fuzzy TOPSIS is used to prioritize the POs due to its adoption.
Sensitivity analysis is carried out to check the robustness of POs prioritization due to variation of CSFs weights.
Zulfiquar N. Ansari, Ravi Kant, Ravi Shankar PII:
S0959-6526(18)33740-5
DOI:
10.1016/j.jclepro.2018.12.038
Reference:
JCLP 15096
To appear in:
Journal of Cleaner Production
Received Date:
04 February 2018
Accepted Date:
05 December 2018
Please cite this article as: Zulfiquar N. Ansari, Ravi Kant, Ravi Shankar, Prioritizing the Performance Outcomes due to adoption of Critical Success Factors of Supply Chain Remanufacturing, Journal of Cleaner Production (2018), doi: 10.1016/j.jclepro.2018.12.038
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT
Title: Prioritizing the Performance Outcomes due to adoption of Critical Success Factors of Supply Chain Remanufacturing
Author 1: Zulfiquar N. Ansari Research Scholar Department of Mechanical Engineering S.V. National Institute of Technology, Surat- 395007, India E-mail:[email protected] Author 2: Ravi Kant Assistant Professor Department of Mechanical Engineering S.V. National Institute of Technology, Surat-395007, India E-mail: [email protected]; [email protected] Author 3: Ravi Shankar Department of Management Studies Indian Institute of Technology, Delhi Hauz Khas, New Delhi 110 016, India E-mail: [email protected] *Corresponding Author: Zulfiquar N. Ansari Research Scholar Department of Mechanical Engineering S.V. National Institute of Technology, Surat- 395007, India E-mail: [email protected]
Graphical Abstract
Remanufacturing in SC
Remanufacturing practices and expected performance outcomes Indian manufacturing context
Identification of CSFs and the expected POs through relevant literature analysis and discussion with decision making group
32 CSFs categorized under 6 main factors
16 POs expected due to adoption of remanufacturing practices in SC
Prioritizing CSFs using fuzzy AHP
Ranking the alternatives (POs) using fuzzy TOPSIS (weights obtained using fuzzy AHP is used to calculate the rank)
Results and Discussion
Conclusion, limitations and future scope of research
ACCEPTED MANUSCRIPT 1
Prioritizing the Performance Outcomes due to Adoption of Critical Success
2
Factors of Supply Chain Remanufacturing
3
Abstract
4
The objective of this study is to identify and prioritize the performance outcomes (POs) due
5
to adoption of SC remanufacturing critical success factors (CSFs). CSFs and the POs realized
6
due SC remanufacturing adoption, are identified based on past relevant literature analysis and
7
subsequent discussions with the expert decision panel. This research work propose a hybrid
8
solution methodology namely fuzzy Analytic Hierarchy Process (AHP) and fuzzy Technique
9
for Order Performance by Similarity to Ideal Solution (TOPSIS), to prioritize the POs
10
realized due to adoption of SC remanufacturing CSFs. Fuzzy AHP technique is used to obtain
11
the relative weights of the CSFs by performing pair wise comparison amongst the criteria,
12
while fuzzy TOPSIS is used to prioritize the POs due to adoption of SC remanufacturing
13
CSFs. Further, the proposed methodological framework is applied to an Indian manufacturing
14
organization to demonstrate its applicability, and prioritize the POs realized due to adoption
15
of SC remanufacturing CSFs. The study presents more accurate, structured and systematic
16
approach to the organization for improving its POs step-by-step through adoption of SC
17
remanufacturing CSFs, thus increasing its sustenance capability. The impact of vagueness in
18
the criterion weights on the prioritization is also investigated.
19
Keywords: Remanufacturing, Supply Chain, Critical Success Factors, Performance
20
Outcomes, Fuzzy AHP, Fuzzy TOPSIS
21 22 23 24 1
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1. Introduction
2
Overpopulation, increasing production of fossil fuels and worsening climate condition across
3
the globe are the major challenges the world is facing in recent years. Sustaining the existing
4
supply chain (SC) under these pressures is crucial. Till now, the traditional SC focussed on
5
the internal efficiencies, processes, responsiveness and managing the forward SC (raw
6
material-finished goods-consumer). However, organizations have realized that simply
7
optimizing the internal activities is not sufficient enough to compete in the market. According
8
to Govindan and Soleimani (2017), the traditional SC referred to as forward SC these days,
9
restrict their responsibility only up to supplying products to consumer, while do not consider
10
the end-of-life (EOL) products treatment as an integral part. The products after its end-of-use
11
(EOU) turn out to be a waste for the user which is collected by the local vendors and most of
12
the times processed at small and independent manufacturer. Improper handling and
13
processing of the returned products by unauthorized units especially in India and China has
14
significantly endangered the socio-environmental life and also lead to unfriendly work
15
environment for the workers (Awasthi and Li, 2017). However, right from the beginning of
16
this century managing the environmental issues of the SC has become an integral part of
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organizational ethical decision where each individual contributes to help achieve their
18
company brand green image (Sarkis et al., 2010a). Sustainability concepts are being
19
implemented in SC to address the environmental, social, and economic concerns in business
20
decisions (Lorek and Spangenberg, 2014; Luthra et al., 2016; Ansari and Kant, 2017).
21
Remanufacturing is emerging as an effective strategy which meets the sustainable goals of an
22
organizational SC (Zhang et al., 2011; Subramoniam et al., 2013). For instance, 50% savings
23
in cost and 60% savings in energy and materials can be realized by producing remanufactured
24
product in comparison to a new product manufactured (Sutherland et al. 2008; Rathore et al., 2
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2011; Zhang et al., 2011). Also, remanufacturing of a product eliminates significant amount
2
of processes such as acquisition of raw material, processing and machining of material etc.
3
which ultimately leads to reduction of huge amount of resource and energy consumption and
4
reduction of emissions too (Liu et al., 2016). Hence, integrating remanufacturing in SC has
5
become a key strategy that has positive influence on the sustainable performance of the
6
organization. Remanufacturing is a multi-process that involves product recovery after its end-
7
use, inspection, disassembly, cleaning, refurbishing, reconditioning, upgrading, and
8
reassembly to transform the used product to a “like-new” product (Govindan et al., 2016;
9
Kafuku et al. 2016). Although, remanufacturing a sustainable strategy exists over the past
10
decade and already an integral part of good number of developed nation organizations, but
11
Indian economy has not yet extensively contributed to it (Sharma et al., 2016; Govindan et
12
al., 2016). Terker et al., (2013) suggest that more extensive strategies are needed to
13
implement SC remanufacturing practices in Indian manufacturing organizations. To deal with
14
this, some authors have suggested several factors for successful implementation of
15
remanufacturing practices in SC such as management initiative, technology for
16
remanufacturing, design for remanufacturing, work place impact, effective acquisition
17
process, financial budget (Mondal and Mukherjee, 2006; Mukherjee and Mondal, 2009;
18
Subramoniam et al., 2013; Sharma et al., 2016). The fact that these factors are important for
19
successful adoption of remanufacturing practices in SC, the aim of the present study is to
20
extensively explore the critical success factors (CSFs) for remanufacturing adoption in SC
21
and prioritize the performance outcomes (POs) realized due to its adoption. Prioritizing the
22
POs due to adoption of SC remanufacturing CSFs is significant, as it will aid organizational
23
decision-makers to understand the pinning issues and formulate suitable strategies to improve
24
their social, economical, and environmental performance many times referred as sustainable
25
performance. 3
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However, the POs realized due adoption of SC remanufacturing CSFs are qualitative as well
2
as quantitative in nature. In other words the problem is multi-dimensional and to handle the
3
complex relationship between the considered factors a suitable multi-criteria decision making
4
(MCDM) approach is needed that can provide useful insights. A hybrid fuzzy Analytic
5
Hierarchy Process (AHP) and fuzzy Technique for Order of Preference by Similarity to Ideal
6
Solution (TOPSIS) approach is proposed in the study. Fuzzy AHP method is used to rank the
7
SC remanufacturing CSFs. While, the fuzzy TOPSIS method is used to prioritize the POs due
8
to remanufacturing CSFs adoption in SC. Thirty-two SC remanufacturing CSFs categorized
9
under six main factors and sixteen Pos, that could be realized due to SC remanufacturing
10
CSFs adoption are identified in the study through literature analysis and decision-making
11
panel opinion.. The study presents a structured implementation plan to industrial practitioners
12
and SC managers to improve the SC performance by effectively managing the
13
remanufacturing CSFs adoption.
14
The remainder of the article is arranged as follows: Literature related to the research topic is
15
discussed in Section 2 to explore the gaps and define the objectives; Section 3 explains the
16
problem considered along with case organization description, identification of CSFs for SC
17
remanufacturing and the POs and the research framework; Solution methodology adopted is
18
explained in Section 4; Numerical application of the proposed framework is presented in
19
Section 5; Section 6 presents the results and discussion; followed by sensitivity analysis in
20
Section 7; finally the conclusion is presented in Section 8.
21
2. Literature Review
22
The literature section comprises of scope of research, SC remanufacturing issues addressed in
23
Indian context, and research gaps and objectives.
4
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2.1 Scope of research
2
The population of India is increasing at a very rapid rate (0.3 billion in the year 1950 to 1.04
3
billion in the year 2002) and predicted that by the year 2050 it might be more than 1.6 billion
4
(State of Environment Report, 2009). The report further states that Indian economy is driven
5
due to strong growth in manufacturing sectors (7.4 per cent average over the past 10 years)
6
mainly in the electronics, information technology, textiles, pharmaceuticals, chemicals etc.
7
Industrialization in a developing country like India will definitely boost its economy but at
8
the same time poses a challenge to environmental protection (release of hazardous and non-
9
hazardous waste). In recent study, the Associated Chambers of Commerce & Industry of
10
India (ASSOCHAM) suggest that waste generation in India is likely to grow at a Compound
11
Annual Growth Rate (CAGR) of 30% i.e. increase to 52 lakh Metric Tonnes (MT) per annum
12
by the year 2020 from the existing level of 18 lakh MT per annum. Also, they found that a
13
greater percent of waste generated (approx. 95%) is managed by informal sector (scrap
14
dealers) while only 1.5% wastes are processed properly (reuse, repair or recycle). Given the
15
fact that in coming years, waste generation is going to increase at a burgeoning rate, there is
16
enough scope for Indian industries to investigate as to how these wastes should be managed
17
and develop some strategic plan. SC remanufacturing is one such EOL product management
18
strategy that is finding increased attention from the researchers and practitioners. SC
19
remanufacturing offers more potential to manage the used products or waste, especially in
20
India due to the following reasons (Mondal and Mukherjee, 2006; Rathore et al., 2011):
21 22
1. SC remanufacturing being labour intensive process and availability of cheap and abundant labour force in India would make its implementation easy.
5
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2. India due to its tag of having a market that is price sensitive, remanufactured product
2
would obvious generate business, as its pricing is in line with a second hand product
3
while quality almost equivalent to a new product.
4
3. In developing countries like India reuse, repair and recycling practices across SC are
5
the most suitable strategies used to handle the EOL products, but the most alarming
6
thing is that involvement of informal sectors in these activities are causing harm to the
7
environment as well as societal health.
8
From the above discussion, it can be concluded that there is not only ample scope to
9
implement remanufacturing in SC, rather it turns out to be an attractive business strategy for
10
Indian industries leading to economical benefits, environmentally sustainable and high
11
returns to the society.
12
2.2 Remanufacturing implementation in Indian context
13
Remanufacturing in India is unorganized and still in its infancy stage with Indian industries
14
not endorsing it very aggressively (Choudhary and Singh, 2011; Rathore et al., 2011; Sharma
15
et al., 2016). However, it is not that India is completely unfamiliar with the concept, since a
16
few multinational companies like Caterpillar, Volvo, General Electric healthcare (GE),
17
Hewlett-Packard (HP), Bavarian Motor Works (BMW) and SKF are already in
18
remanufacturing business (Sharma et al., 2016). Also, a few responsible Indian organizations
19
such as Timken India Ltd., EMD Locomotive Technologies Pvt. Ltd., Indian Railway, Larsen
20
and Tubro (L&T), Xerox ModiCorp Ltd., United Van der Horst Ltd. and some cartridge
21
refilling companies are involved in remanufacturing activities and considering it as an
22
integral part of their SC (Choudhary and Singh, 2011; Terker et al., 2013; Sharma et al.,
23
2016).
6
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It is absolutely fine to believe that when a definite research field is at its initial stage many
2
studies are carried out to find out as to how it can be implemented and what are those
3
challenges that needs to be faced during implementation. With this regard a few studies has
4
been carried out in Indian context with a focus on remanufacturing. Mondal and Mukherjee
5
(2006) based on empirical study identifies six critical
6
technology for remanufacturing, the market, the customer’s attitude, profitability and
7
legislative compulsion) that Indian manufacturers should consider before they make their
8
decision, whether to engage into remanufacturing activities or not. Mukherjee and Mondal
9
(2009) analyze the remanufacturing process of an Indian photocopier remanufacturer to
10
identify the critical managerial issues. They use interpretive structural modeling (ISM) to
11
analyze complex inter-relationship among issues such as: product design, workplace
12
environment impact, level of technology, return acquisition process, reverse distribution
13
process, disassembly and reassembly planning, workforce expertise and skill role, inventory
14
management and remanufactured product marketing. Choudhary and Singh (2011) explore
15
the potentiality of remanufacturing growth in India and the challenges that may be faced by
16
the industry, environment and society during its implementation. Rathore et al., (2011)
17
evaluates the present remanufacturing status through a case analysis of Indian mobile phone.
18
They try to find ways as to how in the present socio-economic environment it can be
19
successfully implemented. Their study reveals that availability of cheap and ample labor,
20
price sensitivity of the market, and increasing environmental impacts due to the booming
21
economy are the primary drivers to promote and implement remanufacturing practices in
22
Indian industries.
23
Subramoniam et al. (2013) conducts a case study with the original equipment supplier
24
companies’ representatives and based on factors such as: strategic product planning, design
factors (acquisition of returns,
7
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for remanufacturing, plant location, production systems, physical distribution, and
2
cooperation among remanufacturing stakeholders, develops a remanufacturing decision
3
making framework (RDMF). They further rank these factors based on their importance using
4
AHP and found that financial impact of remanufacturing is the most influential criterion in
5
RDMF. Terker et al., (2013) highlights the benefits of remanufacturing operation through a
6
case study analysis of an Indian Original Equipment Manufacturer (OEM). Since the OEM is
7
also in the service business to certain clients, a study was also carried out to understand the
8
effect of remanufacturing services in the market. The findings suggest that to boost Indian
9
economy free globe trading of remanufactured product should be permitted and on an
10
average a cost savings of 57% was realized due to remanufacturing of EOL bearings. Sharma
11
et al., (2016) conducts a questionnaire based survey to explore the significant economic,
12
environmental and social drivers for remanufacturing in India. They also identified the
13
barriers to implement remanufacturing as a regular practice. Economic driver is the main
14
driving force to adopt remanufacturing in Indian industries while quality concerns and no
15
proper guidelines turns out to be major roadblock. Govindan et al., (2016) study is focused on
16
Indian auto parts remanufacturing industries to evaluate the critical barriers. They propose a
17
hybrid ISM-fuzzy Analytic Network Process (ANP) approach for analysis, wherein ISM is
18
used to address the interrelationships and interdependencies among the barriers while fuzzy
19
ANP is applied to rank the most influential barrier. Vasanthakumar et al. (2016) identifies
20
twenty factors that should be considered for successful implementation of lean
21
remanufacturing practices in Indian automotive manufacturing. They model the factors using
22
ISM methodology and propose that strong top management commitment with proper strategy
23
selection, long-term vision and participation; and a strong understanding of the current
24
product and process designs as the most important factor. Bhatia and Srivastava (2018)
25
identify and evaluate ten external barriers to remanufacturing in Indian electronic sector. The 8
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authors used Grey-DEMATEL (Decision Making Trial and Evaluation Laboratory) approach
2
analyse external barriers and suggest that lack of channels to collect used products and
3
customer willingness to return used products are the most prominent barriers for
4
remanufacturing implementation. A summary of remanufacturing works carried out in Indian
5
context is depicted in table 1.
6
Table 1 Summary of literature related to SC remanufacturing implementation in Indian context Reference
Focus of the study
Methodology; Industry
MCDM
Mondal and Mukherjee (2006) Mukherjee and Mondal (2009) Choudhary and Singh (2011) Rathore et al., (2011)
Identifies the factors to be considered before remanufacturing decision Key issues in remanufacturing business
Empirical; Manufacturing sector Case study; Photocopier
n.a
Challenges that a industry may face in remanufacturing implementation Identifies how remanufacturing can be implemented in present socio-economic environment
n.a.; Manufacturing sector Case study; Mobile business
n.a.
Subramoniam et al., (2013) Terker et al., (2013) Sharma et al., (2016) Govindan et al., (2016) Vasanthakumar et al., (2016) Bhatia and Srivastava (2018)
Develops a RDMF based
Case study; Automotive
AHP
Benefits of adopting remanufacturing operation Identifies the drivers and barriers for remanufacturing Barrier analysis of remanufacturing
Case study; Bearing
n.a.
Survey; Multiple industry
n.a.
Case organization; Automotive Survey; Automotive industries Case organization; Electronic
ISM-ANP
Evaluate the factors that influence lean remanufacturing practices Identify and evaluate external barriers to remanufacturing
ISM
ISM GreyDEMATEL
7
Note: n.a.: not applicable
8
The above discussion suggest that remanufacturing is not a new concept for Indian
9
manufacturing SC, but according to Sharma et al., (2016) majority of the organizations are
10
adopting it solely for financial benefit. Environmental performance of an organization is
11
usually compromised in Indian scenario due to weak legislation and no standardized process
12
for EOL product management (Govindan et al., 2016). Recently, introduction of a new
13
regulatory law by Indian government namely E-Waste (Management) Rules 2016 shows
14
government intentions regarding environmental protection. This suggest that evaluating the 9
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environmental impacts of the remanufacturing activities would soon become an integral part
2
of the system. In this regard, there is much need to identify and analyse SC remanufacturing
3
CSFs extensively. Literature reveals that adoption of remanufacturing practices has
4
significant influence on the organizational sustainable performance usually referred to as
5
social, economic and environmental performance (Ijomah et al., 2004; Matsumoto and
6
Ijomah, 2013; Subramonium et al., 2013). Implementation of remanufacturing practices in
7
automotive industries could annually save energy equivalent to five nuclear power plants
8
(Steinhilper, 1998). Also, remanufacturing being labour intensive process it creates job
9
opportunities for the society resulting into social benefits (King et al., 2006). Liu et al. (2016)
10
conducts a case study to evaluate the environmental impacts of cylinder head
11
remanufacturing. Their study conducts life cycle assessment and the results suggest that
12
remanufacturing of cylinder head results into improved environmental performance in
13
comparison to the new manufacturing. Kurilova-Palisaitiene et al., (2018) conducted case
14
studies in four remanufacturing organization to study the impact of lean practices adoption on
15
performance. The authors propose seven lean based practices adoption in remanufacturing to
16
reduce the unnecessary operations and reduce the lead time, thus improving the operational
17
performance of the organization. Krystofik et al. (2018) conducts a case study for an office
18
furniture remanufacturing process to identify the factors that affect the economic and
19
environmental performance. The result reveals that adoption of adaptive design based
20
remanufacturing strategies not only improve the environmental performance but, also
21
enhances the long-term economic viability of remanufacturing. The significance of
22
remanufacturing practices adoption in developing countries has increased more recently. This
23
is mainly due to investment by Multinational Companies (MNCs), ease in doing business
24
with foreign countries and inflating consumption of natural resources (Chaowanapong et al.,
25
2018). For instance, in one of the developing nation namely, China, government is promoting 10
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implementation of remanufacturing practices to reduce the resources and energy consumption
2
(Qu et al., 2017). Hence, implementation of remanufacturing practices is beneficial for
3
business organizations, for consumers, for the environment, and for the society and several
4
POs could be realized due to its adoption. The present study thus, prioritizes the POs of the
5
organization as a whole (economic, social and environmental performance), due to adoption
6
of SC remanufacturing CSFs. This study will aid the decision-makers and practitioners in
7
India to adopt the CSFs of SC remanufacturing in their organization more structurally and in
8
an organized manner to realize the performance benefits that could achieved due to its
9
adoption.
10
2.3 Research gaps and objectives
11
SC remanufacturing practices adoption has been on the agenda of developed Western
12
countries over the past decade and globally a large numbers of industries are already
13
practicing it (Abdulrahman et al., 2015). Also, its implementation in Europe and North
14
America has resulted into fruitful results such as enhanced profits and increased market share
15
for the manufacturers (Jayaraman and Luo, 2007; Giannetti et al., 2013). Many advanced
16
remanufacturing technologies have been achieved in developed countries to improve the
17
remanufactured product quality (Nasr, 2010). However, in developing economies like India it
18
is still not well established and organizations are yet to realize the complete benefits that
19
could be accrued due to its adoption. Integration of remanufacturing concept in SC has not
20
been able to capture the much needed attention from its stakeholders in India and a lot still
21
needs to be done (Rathore et al., 2011; Subramonuim et al., 2013).
22
Extensive literature analysis has been carried out to evaluate the remanufacturing studies
23
carried out in Indian scenario to justify the need for this study (Table 1). The potential gaps in
24
the present study are as follows: 11
ACCEPTED MANUSCRIPT 1 2
Most of the studies focus on analyzing the barriers or issues to remanufacturing practices implementation in SC and the benefits that could be realized.
3
There are very few studies that analyse the CSFs for remanufacturing adoption in SC.
4
In addition, these studies are based on specific industrial sector such as photocopier,
5
automotive and manufacturing sector (Mondal and Mukherjee, 2006; Mukherjee and
6
Mondal, 2009; Sharma et al., 2016; Vasanthakumar et al., 2016). The factors
7
considered by these studies are limited and further the differing research outcomes
8
gives a clear indication that the solutions are industry specific and may not hold true
9
for other sectors.
10
For economic development and growth, developing countries are eager to attract foreign
11
direct investment (FDI). India due to its status of one of the developing nations in Asia has
12
generated immense interest amongst developed nations to do business with. According to the
13
report, India has surpassed China in year 2015 and is a top destination for FDI with $31
14
billion investment in the first half of year 2015 as compared to $28 billion of China
15
(Financial Times, 2015). Adoption of remanufacturing strategy in the existing SC is one of
16
the attractive business alternative that Indian market can offer to foreign partners to generate
17
revenues. However, a large number of small organizations involved in remanufacturing
18
business and a few new organizations willing to practice remanufacturing in their SC are still
19
striving hard to adopt it effectively (Govindan et al., 2016). These industries need to be
20
provided with a set of parameters or guidelines that not only helps them in adoption of
21
remanufacturing practices in SC but also improve their POs. This study thus, extensively
22
identifies the CSFs for remanufacturing adoption in Indian manufacturing SC and lists out the
23
POs that could be realized due to adoption of remanufacturing initiatives. Further, the study
24
prioritizes the POs (alternatives) due to adoption of SC remanufacturing CSFs. 12
ACCEPTED MANUSCRIPT 1
3
Problem Description
2
This section is divided into three sub-sections. The first sub-section provides information of
3
the case organization considered. The second sub-section identifies the CSFs for
4
remanufacturing adoption in SC and the POs realized due to its adoption. The third sub-
5
section describes the proposed research framework.
6
3.1 Back ground of case organization
7
The rising environmental concerns combined with global competitiveness factors have forced
8
Indian organizations to think for sustainable strategies. Indian organizations have realized
9
that simply focusing on the internal operational efficiencies of the SC is not sufficient enough
10
to gain competitive position in the market. The fact that, infusing remanufacturing CSFs in
11
the existing SC improves sustainable performance has been realized by Indian organizations.
12
However, organizations that are already into the remanufacturing business and some
13
responsible organizations willing to integrate remanufacturing strategy in their SC have not
14
been able to realize the POs effectively, due to unavailability of comprehensive set of CSFs.
15
The CSFs need to be implemented, to ensure that organizations observe SC remanufacturing
16
POs effectively. Adoption of all the CSFs into the system, at a time is a complex task. Hence,
17
ranking the SC remanufacturing CSFs will enable the organization to focus on high rank
18
CSFs and implement them in stepwise manner step to improve the POs. In this regard, the
19
present study identifies an Indian remanufacturing organization for the application of the
20
proposed structural framework. Gupta and Barua (2017) and Luthra et al. (2017) adopts a
21
similar approach as adopted in this study to validate their research framework. The
22
organization selected in this study was established in the year 1962. Organization name is not
23
disclosed in this study to maintain the confidentiality and hence, named as X organization.
24
Organization X is an Indian manufacturing firm with total annual income of 18821.6 million 13
ACCEPTED MANUSCRIPT 1
in Indian National Rupee for the year 2016. Over 900 employees work in the organization.
2
This organization is engaged in design, manufacturing and sales of ball bearings, roller
3
bearings and bearing housings; and also aftermarket services such as replacement, warranty
4
services, remanufacturing or reconditioning or refurbishing. The organization is
5
manufacturing a special type of bearing named Tarol-6x11 which is being supplied to Indian
6
railways for installation in high speed trains and metros. Since the cost of the bearing is very
7
high, Indian railways were searching for an economical solution over the last few years. The
8
organization suggested that after a run of specific period (say one year) the bearings would be
9
remanufactured (reconditioned) and brought back to quality level of new bearing, thus
10
increasing its life. Hence, based on the contractual agreement made with Indian railways
11
during the supply of new Tarol-6x11 bearing, the organization is involved in reconditioning
12
activities. However, infusing remanufacturing practices in the existing SC creates an
13
imbalance in the manufacturing activities. Also the fact that organization is certified with ISO
14
9001 and ISO 14001, suggest that organization is concerned regarding the quality
15
management and environmental protection. Under such circumstances, to handle the problem
16
complexity and to improve organizational POs, a set of SC remanufacturing CSFs adoption
17
would be the best solution. The objective of the proposed research, to improve the POs due to
18
adoption of SC remanufacturing CSFs turns out to be main reason for top management and
19
executive’s agreeing to support the research. The senior executives of the case organization
20
were also interested in evaluating the SC remanufacturing POs. Further, prioritizing the POs
21
helps the decision-makers/stakeholders to plan a systematic and step-by-step strategy to
22
improve their sustainable performance. Hence, a hybrid fuzzy AHP-TOPSIS approach is
23
applied to help the decision makers develop an action plan for successful implementation of
24
remanufacturing practices.
25
3.2 Identification of CSFs for remanufacturing adoption in SC and the POs 14
ACCEPTED MANUSCRIPT 1
Initially, a total of forty-eight SC remanufacturing CSFs and twenty-five POs due to its
2
adoption were listed down from the literature analysis. During the listing process, it was
3
observed that some CSFs and POs resulted into same meaning and hence were eliminated at
4
the same stage itself. It should be noted that majority of the SC remanufacturing CSFs (main
5
factors and sub-factors) listed are from the studies carried out in Indian context; but there are
6
a few factors that are not based on Indian studies.
7
remanufacturing CSFs and the POs that can be derived due to its adoption, a similar type of
8
situation has been considered. A decision group comprising of six experts from industry and
9
academics having enough expertise in the research field were scrutinized to put forward the
10
research problem in front of them. Then, the experts were invited and a meeting was
11
conducted with them in order to explain the purpose of the study and the possible outcomes.
12
The list of identified SC remanufacturing CSFs with explanation and POs was placed in front
13
of them and based on the inputs received from the experts and frequency of the occurrences
14
of the factor in the literature, thirty-two CSFs were decided and grouped under six main
15
categories (See table 2). Also, sixteen POs were finalized due to adoption of SC
16
remanufacturing CSFs (See table 3).
17
However, to identify these SC
Table 2 List of common CSFs for remanufacturing adoption in SC Sr. Critical Success No. Factors Managerial CSFs 1 Top management support and involvement (MAN1)
2
Benchmarking (MAN2)
Description
References
Clarity in top management regarding benefits of remanufacturing practices adoption would intensify its implementation and find increased support and involvement
Subramoniam et al., (2013); Abdulrahman et al., (2015); Govindan et al., (2016); Vasanthakumar et al., (2016) Gandhi et al., (2016); Malviya and Kant (2016); Mangla et al., (2016)
Significant factors already being practiced by world class companies should be considered for implementation
15
ACCEPTED MANUSCRIPT 3
Redefining the firm business model (MAN3)
Several managerial implications such as staff management, stock management issues, flexibility and outsourcing come into action during remanufacturing decision. Existing business model needs to be modified to meet the challenges
Diaz and Marsillac (2017)
4
Lean tools as a continuous improvement philosophy (MAN4)
Lean tools namely total productive maintenance and 5S as a continuous improvement process should be implemented in the industry to improve product performance, process efficiency and optimized inventory level
Kurilova-Palisaitiene and Sundin (2014); Vasanthakumar et al., (2016)
5
Enhancing corporate green image (MAN5)
Remanufacturing business in the existing set-up creates a brand green image in the market and hence a definite consumer segment concerned about the waste disposal in land demands sustainable product
Atasu et al., (2008); Mukherjee and Mondal (2009)
6
Sustainability concept implementation (MAN6)
Infusing sustainable dimension in decision making at each stage of remanufacturing chain significantly improves the overall performance
Rathore et al., (2011); Govindan et al., (2016)
7
Monitoring and controlling (MAN7)
Organizations effectively monitoring their own operations can result into performance improvement
Chen et al., (2012)
Supplier certification would ensure all activities environmentally responsible and within the environmental standards
Rahman and Subramanian (2012)
High return rates of EOU products can be achieved when organization itself is involved in remanufacturing of their own product and hence effective integration and coordination with both supplier and end user is required to manage the return flow Highly damaged or worn out products not meeting the desired quality levels to be remanufactured should be eliminated at the initial stage through effective inspection, thus reducing the unnecessary handling and storage cost Third party independent operators in India are aggressively involved in remanufacturing. OEMs should technologically and financially collaborate with third party remanufacturer (TPR) to uphold the product quality, creating competitiveness for both
Abdulrahman et al., (2015)
Large population of India with low income creates distinguished market for remanufactured product
Rathore et al., (2011); Sharma et al., (2016)
Products whose components worn out rate is high and made of costly material, using remanufactured product as a spare part for replacement can significantly reduce the overall cost and green house gas
Dahane et al., (2017)
Manufacturers should be made responsible to collect the product having hazardous contaminants to ensure proper EOL treatment
Mondal and Mukherjee (2006)
Strategic CSFs 8 Pressurizing strategic suppliers to endorse environmental accreditation (STR1) 9
Supplier, consumer and organization strategic alliance (STR2)
10
Effective gate-keeping (STR3)
11
Strategic alliance with third party remanufacturer (STR4)
12
Targeting pricesensitive consumer (STR5) Using remanufactured part as spares (STR6)
13
Regulatory CSFs 14 Mandatory take-back policies for hazardous products (REG1)
Wee Kwan Tan and Kumar (2006); Rahman and Subramanian (2012) Rathore et al., (2011); Abdulrahman et al., (2015)
16
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Enforced laws and regulations banning informal waste handling sector (REG2) Subsidized loan from government financial institutions (REG3)
Stringent laws are needed to prevent dismantling and processing of used products by informal sector. Government should educate and motivate the informal sectors to formalize
Sharma et al., (2016); Awasthi and Li (2017)
Government initiative to provide loans at a subsidized rate would encourage manufacturers to go for remanufacturing
Practitioner opinion
17
Government initiative to subsidize latest technology (REG4)
Governing political force should provide tax reduction and subsidy for upcoming emergent technologies that are usually costly on its initial launch to help promote remanufacturing especially in developing economies
Willis (2010); Abdulrahman et al., (2015)
18
Standardized remanufacturing guidelines and framework (REG5)
Government initiative to develop a framework and standardized guidelines would provide an organized approach to small enterprise and new organizations
Sharrna et al., (2011)
16
Technological and Infrastructural CSFs 19 Part based designing Product design should be flexible and permit full of the product (T&I1) disassembly. Recovery of even the smallest component of the product would make it highly remanufacturable and repairable
Akturk et al., (2017)
20
Adoption of advanced technology (T&I2)
Procurement and adoption of latest technologies by the manufacturer will improve performance specifications and productivity
Mondal and Mukherjee (2006)
21
Innovation (T&I3)
Optimum design strategies should be considered right at the initial product design phase to provide ease in remanufacturing
Sharma et al., (2016); Govindan et al., (2016)
22
Infrastructural development (T&I4)
Proper infrastructure will result into efficient processing of EOU products i.e. recycling, disposal with proper treatment
Subramoniam et al., (2013)
23
Transparent information system (T&I5) Effective reverse logistics network (T&I6)
Proper information flow at each levels of remanufacturing stage within and outside the organizations results into a transparent system Acquisition of the used product is in fact the starting point of remanufacturing. Efficient logistic network, such as locating the returns, collecting and transporting back to the plant will result into cost savings
Vasanthakumar et al., (2016)
Management should take remanufacturing as a potential business alternative and to manage the cost involved in the system, necessary fund should be allocated for system development
Practitioner opinion
24
Financial CSFs 25 Separate fund allocation for remanufacturing (FIN1)
Mondal and Mukherjee (2006); Sharma et al., (2016)
26
Acquisition of additional machinery equipment (FIN2)
Procurement of surplus machining will ensure smooth processing of new product and remanufactured product simultaneously
Rahman and Subramanian (2012)
27
Investment in remanufacturing related R&D (FIN3)
It helps to enhance the product performance in terms of better quality, reduced hazardous material, ease in disassembly etc.
Abdulrahman et al. (2015)
Consumer trust can be a driving force for remanufacturing business which can be attained by providing them quality remanufactured product with extended warranty
Mitra (2016); Cui et al., (2017)
Social CSFs 28 Supplying quality product with extended warranty (SOC1)
17
ACCEPTED MANUSCRIPT 29
Enough expertise by providing organized training to personnel (SOC2)
Skill level of the staff should be enhanced via. training from experienced practitioners to handle complex remanufacturing production environment and reverse product flows; and also in specialized systems to avoid land fillings
Dowlatshahi (2005); Rathore et al., (2011); Govindan et al., (2016)
30
Better human resource management practices (SOC3)
Healthy practices such as employee involvement in decision making process, employee empowerment and reward system to employees will help meet desired goals and improve system effectiveness
Practitioner opinion
31
Healthy and safe working conditions (SOC4)
Subramonium et al. (2009); Rathore et al. (2011)
32
Organization initiative to promote campaigns and workshops (SOC5)
Organization providing free and safe working culture and fulfilling social-well being responsibility would find increased support for each worker to achieve the objective Creating awareness among customers via. public meetings and also informing other key stakeholders is effective for remanufacturing successful implementation
Sharma et al., (2016); Govindan et al., (2016)
1 2
Table 3: POs derived due to remanufacturing CSFs adoption in SC Sr. No.
Performance outcomes derived due to CSFs adoption
Code
Reference
1
Rise in sales
PO1
2
Creates new market opportunities and increases market share
PO2
Jayaraman and Luo (2007); Abdulrahman et al., (2015) Pagell et al., (2007); Giannetti et al., (2013); Subramoniam et al., (2013)
3
Material and energy savings
PO3
4 5
Avoids land fillings and high disposal cost Extends product life time
PO4 PO5
6
Increases employment rate
PO6
7
Improves brand image
PO7
8 9
Reduces environmental impact Quality ensured product at low cost
PO8 PO9
10
Avoids waste limitation penalties
PO10
11
Competitiveness and better market position
PO11
12
Reduces carbon and green house gas emission Savings in capital investment Increases productivity and overall profitability
PO12
15
Attracts environmentally conscious customers
PO15
Mukherjee and Mondal (2009)
16
Reduces waste
PO16
Mukherjee and Mondal (2009); Choudhary and Singh (2011)
13 14
PO13 PO14
Mondal and Mukherjee (2006); Sarkis et al., (2010a); Choudhary and Singh (2011); Terker et al., (2013) Sarkis et al. (2010b); Rathore et al., (2011) Rathore et al., (2011); Zhang et al., (2011); Abdulrahman et al., (2015) Sarkis et al., (2010b); Subramoniam et al., (2013); Abdulrahman et al., (2015) Sarkis et al., (2010b); Kapetanopoulou and Tagaras (2011) Pagell et al., (2007); Rathore et al., (2011) King et al., (2006); Rathore et al., (2011); Terker et al., (2013) Ijomah et al., (2004); Rathore et al., (2011); Subramoniam et al., (2013) Martin et al., (2010); Subramoniam et al., (2013) Choudhary and Singh (2011); Sharma et al., (2016); Govindan et al., (2016) Terker et al., (2013) Mukherjee and Mondal (2009); Terker et al., (2013)
18
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3.3 Research framework
2
The study presents a three step solution methodology to prioritize the POs that can be derived
3
due to the adoption of remanufacturing CSFs in SC. Phase 1: Identifying the common CSFs
4
for adoption of remanufacturing practices in SC and the POs that could be realized due to its
5
adoption, through relevant literature analysis combined with inputs from decision making
6
panel. Phase 2: Prioritizing the CSFs to be implemented in SC for remanufacturing practices
7
adoption using fuzzy AHP, with the objective to help practitioners’ decision-making process
8
regarding adoption of remanufacturing CSFs. Phase 3: Prioritizing the POs due to SC
9
remanufacturing CSFs adoption in SC using fuzzy TOPSIS. Fig. 1 depicts the research
10
methodology followed to evaluate the POs of remanufacturing CSFs adoption.
11 12 13 14 15 16 17 18 19 20 21 22 23 19
ACCEPTED MANUSCRIPT 1
Constituting decision making group
Literature analysis
Academicians, Experts and Industrial practitioners
2 3
Phase 1
4 Identifying the remanufacturing CSFs for implementation
5
Identifying the performance outcomes due to remanufacturing CSFs implementation
6 7
Hierarchical structure
8 Weights calculation using fuzzy AHP
9 Phase 2
10 No
11
Approving Weights
12
Yes
13 Weights calculation of alternatives (performance outcomes) using fuzzy TOPSIS
14 15 16 17
Weights calculated using fuzzy AHP used
Phase 3
Prioritize the alternatives (Rank the performance outcomes of remanufacturing)
18 19 20 21 22
Fig. 1 Three phase research methodology adopted in the study (Adopted from Ertugrual and Karakasoglu, 2009; Sirisawat and Kiatcharoenpol, 2018)
4. Solution Methodology 4.1 Fuzzy AHP and fuzzy TOPSIS technique
20
ACCEPTED MANUSCRIPT 1
Several researchers have applied fuzzy AHP and fuzzy TOPSIS technique to handle different
2
complex situations and problems. Table 4 provides a summary of fuzzy AHP and fuzzy
3
TOPSIS studies by various researchers in different areas for problem solution.
4
Table 4 Summary of fuzzy AHP and fuzzy TOPSIS studies carried out by different researchers Reference
Objective of the study
Fuzzy AHP used for
Fuzzy TOPSIS used for
Mohammed et al. (2018)
Sustainable two-stage supplier selection and order allocation problem for a meat supply chain
Calculate relative weights for sustainable criteria
Rate the suppliers based on their sustainable performance
Sirisawat and Kiatcharoenpol (2018)
To classify reverse logistics barriers and rank both barriers and solutions of reverse logistics implementation
Calculate weights of each barrier
Rank the solutions of reverse logistics implementation
Singh et al. (2018)
To select an appropriate third party logistics (3PL) in order to outsource logistics activities of perishable products. Analyse the criteria influencing the automotive components remanufacturing (ACR) industry operation and selects the main operation patterns for ACR in China To select suitable partner for effective product development processes
To rank different 3PL selection criteria
Select the best 3PL based on performance
Determine weights of influencing criteria
Select the optimal ACR production operation patterns
Determine the weights of each criteria
Rank the partner alternatives
Develops a framework of strategies to reduce water losses in water distribution systems of developing countries Evaluation and selection of best supplier
Determine the weights of criteria responsible for water loss Determine the weights of multiple criteria Determine the weights of criteria Determine the initial weights of RL criteria
Rank the potential solutions for effective water management Select the fittest supplier
Tian et al. (2017)
Büyüközkan and Güleryüz (2016) Zyoud et al. (2016) Lee et al. (2015) Sánchez-Lozano et al. (2015) Senthil et al. (2014)
To determine the best location for a solar thermoelectric power plant To select the most efficient reverse logistics contractor
Vinodh et al. (2014)
To determine the best method for recycling plastics among the various plastic recycling processes To identify an appropriate organization strategy for distribution channel management
Yang et al. (2011)
Selection of appropriate vessels for carrying out shipping activities
Determine the weights of plastic recycling process criteria Weights calculation of determinants of distribution channel management Calculate the weights of influencing criteria
Ertuğrul and Karakaşoğlu (2009)
To evaluate the performance of the Turkish cement firms by using financial ratios
Determine the weights of the criteria
Paksoy et al. (2012)
Rank the best location Selection of the best third party reverse logistics provider Selection of the best recycling process Selection of the best distribution channel Ranking the alternatives (vessels) based on closeness coefficient value Ranking the firms based on performance
5 6
The above analysis suggest that several researchers have applied hybrid approach of MCDM
7
techniques to help the decision makers, prioritize the criteria according to their relative
8
importance and also ranking of the various alternatives while addressing different problems. 21
ACCEPTED MANUSCRIPT 1
Application of hybrid fuzzy AHP-TOPSIS approach to a problem is such that, fuzzy AHP is
2
used to calculate the weights of the influencing criteria. Next, fuzzy TOPSIS technique is
3
applied where in each criteria is compared with each alternative and based on the intensity of
4
influence a linguistic scale is assigned. The weights derived for each criteria is used in fuzzy
5
TOPSIS analysis to prioritize the alternatives. The present study determines the weight of SC
6
remanufacturing CSFs (criteria) and ranks them using fuzzy AHP. Next, the POs
7
(alternatives) realized due to adoption of remanufacturing CSFs in SC are prioritized using
8
fuzzy TOPSIS. The detail methodology for fuzzy AHP and fuzzy TOPSIS is explained in the
9
following sub-sections.
10
4.2 Fuzzy AHP
11
Thomas L. Satty, in 1970s introduced a quantitative technique namely AHP to solve complex
12
MCDM problems. AHP systematically decomposes the complex problem into a hierarchical
13
structure, with goal at the top; alternatives at the bottom while the criteria and sub-criteria at
14
the levels and sub-levels of the hierarchy. Easily understandable, simplicity in application and
15
flexibility makes the technique more prevalent for practitioners and decision-makers to apply
16
(Wang et al., 2009; Govindan et al., 2015). However, inability to handle imprecise or
17
vagueness usually intrinsic in human decision; use of discrete scale (1-9) which is unable to
18
handle the uncertainty; subjective judgement, selection and preference of decision makers are
19
some of the limitations of Satty’s AHP application (Choudhary and Shankar, 2012; Paksoy et
20
al., 2012; Patil and Kant 2014; Prakash and Barua, 2015). Hence, there is need to apply fuzzy
21
AHP to overcome these issues. Fuzzy AHP permits to take into account the linguistic
22
vagueness and uncertainties involved in the decision-makers judgement. The use of fuzzy
23
AHP is widespread and many researchers have utilized this problem solving technique in
22
ACCEPTED MANUSCRIPT 1
varying areas such as manufacturing maintenance field (Carnero et al., 2014); telecom sector
2
(Kumar et al., 2015); green supply chain management (Mangla et al., 2015)
3
Definition 1. 𝐴1 = (𝑙1,𝑚1,𝑝1); 𝐴2 = (𝑙2,𝑚2,𝑝2) represent two triangular fuzzy numbers
4
(TFNs). The algebraic operations such as addition, subtraction, multiplication, division and
5
reciprocal for these two TFNs are carried out as follows:
6
𝐴1 𝐴2 = (𝑙1,𝑚1,𝑝1) (𝑙2,𝑚2,𝑝2) = (𝑙1 + 𝑙2, 𝑚1 + 𝑚2, 𝑝1 + 𝑝2)
7
𝐴1 - 𝐴2 = (𝑙1,𝑚1,𝑝1) - (𝑙2,𝑚2,𝑝2) = (𝑙1 - 𝑝2, 𝑚1 - 𝑚2, 𝑝1 - 𝑙2)
8
𝐴1 𝐴2 = (𝑙1,𝑚1,𝑝1) (𝑙2,𝑚2,𝑝2) = (𝑙1𝑙2, 𝑚1𝑚2, 𝑝1𝑝2)
9
10
11
𝐴1 𝐴2 =
(𝑙1,𝑚1,𝑝1) (𝑙2,𝑚2,𝑝2) =
(
)
𝑙1 𝑚1 𝑝1 , , 𝑝 2 𝑚2 𝑙2
𝐴1 = (𝑙1,𝑚1,𝑝1)
𝐴1
-1
= (𝑙1,𝑚1,𝑝1)
(1) (2) (3)
(4)
(5) -1
=
(6)
12
In fuzzy AHP approach, preferences of one criterion over another is given based on TFNs.
13
Next, Chang’s extent analysis method is applied to calculate the synthetic extent value of the
14
obtained pair-wise comparison matrix (Chang, 1996). The weight vectors of the criterions
15
and alternatives are calculated using this analysis.
16
If 𝑋 = {𝑥1,𝑥2,…,𝑥𝑛} is a object set and U = {𝑢1,𝑢2,…,𝑢𝑚} is a goal set, then according to
17
Chang’s extent analysis model, 𝑀𝑔 , 𝑀𝑔 , …𝑀𝑔 are the m extent values that can be calculated
18
for each object. 𝑔𝑖 is the goal set (𝑖 = 1, 2, …, 𝑛) and all the TFNs given in table 5 are
19
represented by 𝑀𝑔 (𝑗 = 1, 2, 3, 4…, 𝑚). There are different TFNs scales used by different
1
2
𝑚
𝑖
𝑖
𝑖
𝑗
𝑖
23
ACCEPTED MANUSCRIPT 1
authors (Duran 2011; Carnero 2014; Wang et al., 2015). However, this study adopts the
2
characteristic function of the fuzzy numbers (Table 5) used in Lee et al. (2009) which was
3
also used by authors Gumus (2009); Sun (2010); and Paksoy et al., (2012). The characteristic
4
function of the fuzzy numbers (Table 5) is Procedure for Chang’s extent analysis model as
5
applied in this paper is as follows:
6
Step 1: Fuzzy extent synthetic value (𝑆𝑖) calculation
7
With respect to 𝑖𝑡ℎ object, the value of fuzzy synthetic extent is defined as: ‒1 𝑛 𝑚 𝑗 𝑗 𝑀𝑔 𝑀𝑔 𝑖 𝑖 𝑗=1 𝑖 = 1𝑗 = 1
[∑ ∑ ]
𝑚
8
𝑆𝑖 =
∑
𝑚
∑
9
𝑛
12
𝑗=1
𝑚
[
𝑚
∑∑
𝑚
𝑚 𝑗,
𝑗=1
]
𝑗 𝑀𝑔 𝑖 𝑖 = 1𝑗 = 1
‒1
=
∑𝑝
𝑗
𝑗=1
(∑ ∑ 𝑛
∑∑
𝑛
𝑚
𝑙𝑗,
𝑗 𝑀𝑔 = 𝑖 𝑖 = 1𝑗 = 1
10
11
(∑ ∑ 𝑚
𝑗 𝑀𝑔 = 𝑖 𝑗=1
𝑛
𝑙𝑖,
𝑖=1
(∑
(7)
𝑚𝑖,
𝑝𝑖
𝑖=1
)
∑𝑝
(9)
𝑖
𝑖=1
,
𝑛
(8)
𝑛
𝑖=1
1
)
1
∑
,
𝑛
𝑚𝑖
𝑖=1
1
∑
𝑛
)
(10)
𝑙𝑖
𝑖=1
where 𝑙 is the lower limit value, m is the most promising value and p is the upper limit value.
13 14 15
24
ACCEPTED MANUSCRIPT Table 5 TFNs scale used for comparing the criterion pair-wise Linguistic variables
TFN assigned
TFN reciprocal scale
1 2 3
Equal (E) (1, 1, 1) (1, 1, 1) Very low (VL) (1, 2, 3) (1/3, 1/2, 1) Low (L) (2, 3, 4) (1/4, 1/3, 1/2) Medium (M) (3, 4, 5) (1/5, 1/4, 1/3) High (H) (4, 5, 6) (1/6, 1/5, 1/4) Very high (VH) (5, 6, 7) (1/7, 1/6, 1/5) Excellent (E) (6, 7, 8) (1/8, 1/7, 1/6) Note: For linguistic variable Equal (E) fuzzy number is (1, 1, 1), while for other linguistic variables fuzzy membership function is based on ( x 1, x, x 1) for x 2,3,4,5,6,7 (Lee et al., 2009)
4
Step 2: Fuzzy values comparison
5
The degree of possibility of 𝑆2 = (𝑙2,𝑚2,𝑝2) ≥ 𝑆1 = (𝑙1,𝑚1,𝑝1) is defined as follows:
6
𝑆𝑈𝑃
𝑉(𝑆2) ≥ 𝑉(𝑆1) = 𝑦 ≥ 𝑥[min (𝜇𝑆 (𝑥), 𝜇𝑆 (𝑦))] 1
(11)
2
7
where (𝑥,𝑦) are the values on the axis of membership function of each criterion. 𝑆1 and 𝑆2
8
being fuzzy numbers can also be expressed as follows:
9
𝑉(𝑆2) ≥ 𝑉(𝑆1) = ℎ𝑔𝑡(𝑆1 ∩ 𝑆2) = 𝜇𝑆 (𝑑)
(12)
2
10
where the highest point of intersection D between 𝜇𝑆 and 𝜇𝑆 as shown in fig. 3 represents the
11
ordinate d. 𝜇𝑆 (𝑑) can be calculated using the following expression:
1
2
2
𝜇𝑆 (𝑑) = 2
1,
𝑚2 ≥ 𝑚1
0,
𝑙1 ≥ 𝑝2 𝑙1 ‒ 𝑝2
(13)
𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
(𝑚2 ‒ 𝑝2 )–(𝑚1 ‒ 𝑙1 )
12
Both 𝑉(𝑆1) ≥ 𝑉(𝑆2) and 𝑉(𝑆2) ≥ 𝑉(𝑆1) values are needed to compare 𝑆1 and 𝑆2.
13 14 15 25
ACCEPTED MANUSCRIPT 1
S
2
𝑆2
1
3
𝑆1
4 5 6 7
𝑉(𝑆2) ≥ 𝑉(𝑆1)
D
8 9
S
0
𝑙2
𝑚2
𝑙1
𝑑
𝑝2
𝑚1
𝑝1
Fig. 3 The intersection of fuzzy numbers
10 11
Step 3: Priority weight calculation
12
The degree of possibility of convex fuzzy number to be greater than 𝑘 convex fuzzy numbers
13
𝑆𝑖 (𝑖 = 1, 2,…,𝑘) can be defined as:
14
𝑉(𝑆 ≥ 𝑆1, 𝑆2,..., 𝑆𝑘) = 𝑉[(𝑆 ≥ 𝑆1), (𝑆 ≥ 𝑆2), …, (𝑆 ≥ 𝑆𝑘)] = min 𝑉 (𝑆 ≥ 𝑆𝑖),
15
𝑖 = 1, 2,…,𝑘
'
16
Assume that 𝑑 (𝐴𝑖) = min 𝑉 (𝑆𝑖 ≥ 𝑆𝑘) for 𝑘 = 1, 2,…,𝑛; 𝑘 ≠ 𝑖
17
Then the equation of weight vectors are given as,
18
'
'
'
'
𝑊 = (𝑑 (𝐴1), 𝑑 (𝐴2), …, 𝑑 (𝐴𝑚))
𝑇
19
Step 4: Normalized weight vector calculation
20
After normalization of 𝑊 , the normalized weight vectors are calculated.
21
(14)
(15)
'
𝑊 = (𝑑(𝐴1), 𝑑(𝐴2), …, 𝑑(𝐴𝑚))
𝑇
(16) 26
ACCEPTED MANUSCRIPT 1
4.3 Phase 3: Fuzzy TOPSIS
2
TOPSIS is one of the MCDM techniques, Hwang and Yoon (1981) proposed to handle
3
complex multi-dimensional problems. The concept of TOPSIS approach is that, the
4
alternative that is ideal has the best level for all criteria, while the negative ideal is the one
5
that has all worst criteria values i.e. the selected best alternative will be at the shortest
6
distance from the positive ideal solution in geometrical sense, while it is at the longest
7
distance from the negative solution (Wang et al., 2008; Wang et al., 2009). The advantages of
8
TOPSIS technique as explained by Bottani and Rizzi (2006); Govindan et al. (2015); Mittal
9
and Sangwan (2015); Agrawal et al. (2016) are: (i) useful when there are large number of
10
alternatives (ii) consistent and has the ability to handle rank reversal issue (iii) integrates both
11
benefit criteria and cost criteria (iv) logical, simple to understand and can easily be
12
programmed in a spread sheet.
13
The weight ratings assigned to the alternatives in traditional TOPSIS approach are in the form
14
of crisp values. However, in real case situations crisp numbers are not sufficient to rate the
15
alternatives due to presence of vagueness in human judgement. Fuzzy theory helps to express
16
the decision-maker judgement in the form of linguistic variable represented in the form of
17
fuzzy numbers (Zadeh, 1965). Fuzzy TOPSIS has been widely used in the literature for
18
solving multi-dimensional problems such as facility location selection (Chu, 2002; Ertuğrul
19
and Karakaşoğlu, 2008); maintenance problem (Ding and Kamaruddin, 2015); reverse
20
logistics (Agrawal et al., 2016) etc. Hence, fuzzy TOPSIS methodology is used to rank the
21
alternatives. The study adopts fuzzy-TOPSIS methodology to rank the POs due to SC
22
remanufacturing CSFs adoption as introduced by Chen (2000).
23
The steps of fuzzy TOPSIS as applied in this study are as follows:
27
ACCEPTED MANUSCRIPT 1
Step 1: Linguistic variables ratings for the alternatives with respect to the criteria
2
Suppose there are m possible alternatives, 𝐴 = {𝐴1, 𝐴2…, 𝐴𝑚} that are to be evaluated against
3
the criteria, 𝐶 = {𝐶1, 𝐶2…, 𝐶𝑛}. If the triangular fuzzy rating for 𝐾𝑡ℎ decision maker is
4
𝑅𝑎𝑏𝐾 = (𝑙𝑎𝑏𝐾, 𝑝𝑎𝑏𝐾, 𝑢𝑎𝑏𝐾) where, 𝑎 = 1, 2, 3,…, 𝑚;𝑏 = 1, 2, 3,…, 𝑛; each decision maker
5
𝐷𝑘 (𝑘 = 1, 2, …, 𝐾) will provide their fuzzy rating for alternatives with respect to the criteria,
6
thus forming a matrix for alternatives in a fuzzy form. Scales provided in Table 6 is used for
7
rating the alternatives (POs). Table 6 Linguistic variables for rating the alternatives Linguistic variables
Corresponding TFNs
Very poor (VP) Poor (P) Medium poor (MP) Fair (F) Medium good (MG) Good (G) Very good (VG)
(0, 0, 1) (0, 1, 3) (1, 3, 5) (3, 5, 7) (5, 7, 9) (7, 9, 10) (9, 10, 10)
8 9
Step 2: Aggregate fuzzy ratings calculation
10
If the fuzzy rating of 𝐾𝑡ℎ decision maker is 𝑅𝑎𝑏𝐾 then the aggregated fuzzy ratings 𝑅𝑎𝑏 of
11
alternatives with respect to different criterions is given by 𝑅𝑎𝑏(𝑙𝑎𝑏, 𝑝𝑎𝑏, 𝑢𝑎𝑏), where
12
13
𝑎𝑖𝑗 = min {𝑙𝑎𝑏𝐾}, 𝐾
1 𝑏𝑖𝑗 = 𝐾
𝑁
∑𝑝
𝑎𝑛𝐾,
𝑐𝑖𝑗 = max {𝑢𝑎𝑏𝐾}
𝐾=1
(17)
𝐾
Step 3: Developing normalized fuzzy rated decision matrix
28
ACCEPTED MANUSCRIPT 1
Using linear scale transformation the aggregate fuzzy weights of different criteria scales are
2
converted into a comparable scale. The normalized fuzzy decision matrix is given by 𝑄
3
where:
4
𝑄 = [𝑟𝑖𝑗]
5
where
6
𝑟𝑖𝑗 =
7
𝑎𝑗 𝑎𝑗 𝑎𝑗 ‒ 𝑟𝑖𝑗 = , , , 𝑎 𝑗 = min 𝑎𝑖𝑗 (cost criteria) * * * 𝑐𝑗 𝑐𝑗 𝑐𝑗
8
Step 4: Weighted normalized matrix calculation
9
If 𝑉 represents the weighted fuzzy normalized matrix, it is calculated using the equation
(
(18)
)
𝑎𝑖𝑗 𝑏𝑖𝑗 𝑐𝑖𝑗 , , , 𝑐 *𝑗 = max 𝑐𝑖𝑗 (benefit criteria) * * * 𝑐𝑗 𝑐𝑗 𝑐𝑗
(
‒
‒
‒
𝑉 = [𝑣𝑖𝑗]
10
𝑖 = 1, 2, 3, …, 𝑚 𝑎𝑛𝑑 𝑗 = 1, 2, 3, …𝑛
𝑚×𝑛
)
𝑖 = 1,2,…..𝑚; 𝑗 = 1,2,…..𝑛
𝑚×𝑛
(19)
11
where 𝑣𝑖𝑗 = 𝑟𝑖𝑗 ⊗ 𝑤𝑗
12
Step 5: Determination of fuzzy positive ideal solution (FPIS,𝐴 ) and fuzzy negative ideal
13
solution (FNIS, 𝐴 )
14
FPIS and FNIS are computed as follows:
15
16
17
+
-
𝐴
+
(
+
+
+
)
+ 𝑗
)
-
= 𝑣 1 ,……𝑣 𝑗 ,……𝑣 𝑛 𝑤h𝑒𝑟𝑒 𝑣
-
(
-
-
-
(
+
(
-
+
= 𝑐 𝑗 ,𝑐 𝑗 ,𝑐
-
-
)
+ 𝑗
{𝑐𝑖𝑗} ) 𝑎𝑛𝑑 𝑐 +𝑗 = max 𝑖 -
𝐴 = 𝑣 1 ,……𝑣 𝑗 ,……𝑣 𝑛 𝑤h𝑒𝑟𝑒 𝑣 𝑗 = 𝑐 𝑗 ,𝑐 𝑗 ,𝑐 𝑗 𝑎𝑛𝑑 𝑐 𝑗 = min {𝑎𝑖𝑗}
(20)
(21)
𝑖
∀ 𝑖 = 1,2,…..𝑚; 𝑗 = 1,2,…..𝑛 29
ACCEPTED MANUSCRIPT 1
Step 6: Calculate the distance of each alternative from FPIS and FNIS 𝑛
+ 𝑑𝑖
2
=
∑ 𝑑(𝑣𝑖𝑗,𝑣 +𝑖𝑗 ), 𝑖 = 1,2,…..,𝑚; 𝑗 = 1,2,…..,𝑛
(22)
𝑗=1
𝑛
𝑑 𝑖-
3
4
=
∑ 𝑑(𝑣𝑖𝑗,𝑣𝑖𝑗- ), 𝑖 = 1,2,…..,𝑚; 𝑗 = 1,2,…..,𝑛
(23)
𝑗=1
Step 7: Calculating the closeness coefficient (𝐶𝐶𝑖) of each alternative -
5
𝐶𝐶𝑖 =
𝑑𝑖 𝑑
+ 𝑖
-
(24)
+ 𝑑𝑖
6
Step 8: Ranking the alternatives
7
According to the closeness coefficient(𝐶𝐶𝑖), the alternatives are ranked in the descending
8
order.
9 10 11 12 13 14 15 16 17 18 19 20 30
ACCEPTED MANUSCRIPT 1 2 3 4
Goal
Main factors
5
Sub-factors
Alternatives
Top management support and involvement
6
Benchmarking Rise in sales
7
Redefining firm business model
Managerial CSFs
8
Lean tools as a continuous improvement philosophy Enhancing corporate image
9
Sustainability concept implementation Material and energy savings Monitoring and controlling
10
Pressurizing strategic suppliers to endorse environmental accreditation
11 12
Creates new market opportunities and increases market share
Avoids land fillings and high disposal cost
Supplier, consumer and organization strategic alliance
Strategic CSFs
Effective gate-keeping Strategic alliance with third party remanufacturer
13
Extends product life time
Increases employment rate
Targeting price-sensitive consumer
14
Using remanufactured part as spares
15 Prioritizing the POs due to adoption of SC remanufacturing CSFs
16
Mandatory take-back policies for hazardous products
Regulatory CSFs
Enforced laws and regulations banning informal waste handling sector
Government initiative to subsidize latest technology Standardized remanufacturing guidelines and framework
18
Quality ensured product at low cost Avoids waste limitation penalties
Part based designing of the product
19
Adoption of advanced technology
21
Reduces environmental impact
Subsidized loan from governmental financial institutions
17
20
Improves brand image
Technological and Infrastructural CSFs
Innovation Infrastructural development Transparent information system
22
Competitiveness and better market position
Reduces carbon and green house gas emission
Savings in capital investment
Effective reverse logistics network
23
Financial CSFs
24
Separate fund allocation for remanufacturing
Increases productivity and overall profitability
Acquisition of additional machinery and equipment Investment in remanufacturing related R&D
25
Attracts environmentally conscious customers
Supplying quality product with extended warranty Reduces waste
Social CSFs
Enough expertise by providing organized training to personnel Better HRM practices Healthy and safe working conditions Organization initiative to promote campaigns and workshops
31
ACCEPTED MANUSCRIPT 1 2 3 4
5. Numerical Application of Proposed Framework
5
5.1
Phase 1: Identification of CSFs for remanufacturing adoption in SC and the POs
6 7
A decision making panel consisting of total six experts was formed to collect the input data
8
required for the solution. The panel consist of five industrial experts selected from the case
9
organization and an academician expert. Academician expert was a university professor. The
10
five industry experts include Head (Projects); Head (Manufacturing); Head (Technologistic);
11
Head (Industrial Engineering), and SC Manager. All the experts selected for study are
12
engineering professionals, knowledgeable and having experience more than 10 years. The
13
study proposes a total of six main CSFs and thirty-two sub-factors, both qualitative as well as
14
quantitative in nature for successful adoption of remanufacturing practices in the SC through
15
evaluation of past literature analysis and inputs received from the decision making group
16
(Table 2). Also, the above decision making group was used to evaluate the POs (alternatives)
17
due to adoption of remanufacturing CSFs in SC (Table 3). The decision hierarchy is
18
structured in four levels. The first level of the hierarchy forms the main goal: “prioritize the
19
POs due to adoption of SC remanufacturing CSFs”. Second level consist of main CSFs
20
followed by sub-factors on the third level and finally the POs (alternatives) at the fourth level
21
of the hierarchy (See fig. 2).
22 23
5.2
Phase 2: Fuzzy AHP application: weight calculation of the main factors and sub-factors for remanufacturing practices adoption
32
ACCEPTED MANUSCRIPT 1
The relative importance of the listed common CSFs and sub-factors for remanufacturing
2
adoption in SC is calculated using fuzzy AHP, with the aim to rank these factors according to
3
their degree of importance. Decision making panel was asked to make a pair wise comparison
4
matrix between the 6 main factors and 32 sub-factors based on the TFNs defined in table 5.
5
The decision making panel conducted several round of discussion regarding weight allocation
6
for pair wise comparison between the factors and sub-factors. The panel discussion lasted till
7
each group member was of the same opinion regarding the importance weights to be
8
allocated during main criteria and sub-criteria pair wise comparison. Prakash and Barua
9
(2015) adopted a similar approach, where in a combined decision from the decision group
10
was made to make pair-wise comparison between main criteria and sub-criteria. Based on the
11
hierarchical structure presented in fig 2. a questionnaire form for fuzzy AHP is prepared
12
given in Appendix I. The fuzzy comparison matrices of the main criteria and the sub-criteria
13
with their calculated weights are given in table 7-13. Chang’s extent analysis is used to
14
calculate the weights of the criteria from the pair wise comparison matrices. MS excel sheet
15
is used for calculation purpose. From table 7 the fuzzy extent synthesis values of main criteria
16
are calculated using Eq. 7. The final results obtained by calculating the weights of main
17
CSFs and sub-criteria using the Chang’s extent analysis are presented in table 14.
18
S(MAN) = (7.33, 11.5, 16) (31.5, 48.5, 69)-1 = (0.106, 0.237, 0.508)
19
S(STR) = (4.92, 7.33, 10.5) (31.5, 48.5, 69)-1 = (0.071, 0.151, 0.333)
20
S(REG) = (6.33, 10.5, 15) (31.5, 48.5, 69)-1 = (0.092, 0.216, 0.476)
21
S(T&I) = (5.58, 8.83, 12.5) (31.5, 48.5, 69)-1 = (0.081, 0.182, 0.397)
22
S(FIN) = (4.25, 5.83, 8.5) (31.5, 48.5, 69)-1 = (0.062, 0.120, 0.270)
23
S(SOC) = (3.08, 4.5, 6.5) (31.5, 48.5, 69)-1 = (0.045, 0.093, 0.206)
24
These fuzzy values are then used to calculate the V values using Eq. (13)
25
V ( MAN STR ) 1 ,
V ( MAN REG ) 1 ,
V ( MAN T & I ) 1 33
ACCEPTED MANUSCRIPT 1
V ( MAN FIN ) 1 ,
V ( MAN SOC ) 1
2
V ( STR MAN ) 0.725
V ( STR REG ) 0.787
3
V ( STR FIN ) 1
V ( STR SOC ) 1
4
V ( REG MAN ) 0.946
V ( REG STR ) 1
5
V ( REG FIN ) 1
V ( REG SOC ) 1
6
V (T & I MAN ) 0.841
V (T & I STR ) 1
7
V (T & I FIN ) 1
V (T & I SOC ) 1
8
V ( FIN MAN ) 0.58
V ( FIN STR ) 0.865
9
V ( FIN T & I ) 0.752
V ( FIN SOC ) 1
10
V ( SOC MAN ) 0.409
V ( SOC STR ) 0.699
11
V ( SOC T & I ) 0.584
V ( SOC FIN ) 0.842
12
Then using Eq. (14) the minimum degrees of possibility are determined such as:
13
d ' ( MAN ) = min (1, 1, 1, 1, 1) = 1
14
d ' ( STR ) = min (0.725, 0.787, 0.890, 1, 1) = 0.725
15
d ' ( REG ) = min (0.946, 1, 1, 1, 1) = 0.946
16
d ' (T & I ) = min (0.841, 1, 0.899, 1, 1) = 0.841
17
d ' ( FIN ) = min (0.58, 0.865, 0.649, 0.752, 1) = 0.58
18
d ' ( SOC ) = min (0.409, 0.699, 0.481, 0.584, 0.842) = 0.409
19
Priority weight vector is presented as below:
20
W ' = (1, 0.725, 0.946, 0.841, 0.58, 0.409)
21
The final weights after normalization of the priority weight vectors in reference to the main
22
criteria are computed as:
23
W = (0.222, 0.161, 0.210, 0.187, 0.129, 0.091)
V ( STR T & I ) 0.890
V ( REG T & I ) 1
V (T & I REG ) 0.899
V ( FIN REG ) 0.649
V ( SOC REG ) 0.481
34
ACCEPTED MANUSCRIPT 1
A similar process is followed to calculate the normalized weight of sub-criteria.
2 3 4
Table 7 Fuzzy pair wise comparison matrix of the main criteria MAN
STR
REG
T&I
FIN
SOC
Weight
MAN
(1.00, 1.00, 1.00)
(2.00, 3.00, 4.00)
(1.00, 2.00, 3.00)
(0.33, 0.50, 1.00)
(1.00, 2.00, 3.00)
(2.00, 3.00, 4.00)
0.222
STR
(0.25, 0.33, 0.50)
(1.00, 1.00, 1.00)
(0.33, 0.50, 1.00)
(0.33, 0.50, 1.00)
(1.00, 2.00, 3.00)
(2.00, 3.00, 4.00)
0.161
REG
(0.33, 0.50, 1.00)
(1.00, 2.00, 3.00)
(1.00, 1.00, 1.00)
(2.00, 3.00, 4.00)
(1.00, 2.00, 3.00)
(1.00, 2.00, 3.00)
0.210
T&I
(1.00, 2.00, 3.00)
(1.00, 2.00, 3.00)
(0.25, 0.33, 0.50)
(1.00, 1.00, 1.00)
(2.00, 3.00, 4.00)
(0.33, 0.50, 1.00)
0.187
FIN
(0.33, 0.50, 1.00)
(0.33, 0.50, 1.00)
(0.33, 0.50, 1.00)
(0.25, 0.33, 0.50)
(1.00, 1.00, 1.00)
(2.00, 3.00, 4.00)
0.129
SOC
(0.25, 0.33, 0.50)
(0.25, 0.33, 0.50)
(0.33, 0.50, 1.00)
(1.00, 2.00, 3.00)
(0.25, 0.33, 0.50)
(1.00, 1.00, 1.00)
0.091
5 6 7
Table 8 Fuzzy pair-wise comparison matrix of the managerial (MAN) criteria MAN1
MAN2
MAN3
MAN4
MAN5
MAN6
MAN7
Weight
MAN1
(1.00, 1.00, 1.00)
(1.00, 1.00, 1.00)
(5.00, 6.00, 7.00)
(2.00, 3.00, 4.00)
(3.00, 4.00, 5.00)
(0.20, 0.25, 0.33)
(2.00, 3.00, 4.00)
0.272
MAN2
(1.00, 1.00, 1.00)
(1.00, 1.00, 1.00)
(0.33, 0.50, 1.00)
(0.33, 0.50, 1.00)
(1.00, 2.00, 3.00)
(0.20, 0.25, 0.33)
(0.33, 0.50, 1.00)
0.018
MAN3
(0.14, 0.17, 0.20)
(1.00, 2.00, 3.00)
(1.00, 1.00, 1.00)
(2.00, 3.00, 4.00)
(0.25, 0.33, 0.50)
(1.00, 2.00, 3.00)
(0.25, 0.33, 0.50)
0.109
MAN4
(0.25, 0.33, 0.50)
(1.00, 2.00, 3.00)
(0.25, 0.33, 0.50)
(1.00, 1.00, 1.00)
(0.20, 0.25, 0.33)
(1.00, 2.00, 3.00)
(3.00, 4.00, 5.00)
0.132
MAN5
(0.20, 0.25, 0.33)
(0.33, 0.50, 1.00)
(2.00, 3.00, 4.00)
(3.00, 4.00, 5.00)
(1.00, 1.00, 1.00)
(0.33, 0.50, 1.00)
(2.00, 3.00, 4.00)
0.182
MAN6
(3.00, 4.00, 5.00)
(3.00, 4.00, 5.00)
(0.33, 0.50, 1.00)
(0.33, 0.50, 1.00)
(1.00, 2.00, 3.00)
(1.00, 1.00, 1.00)
(2.00, 3.00, 4.00)
0.229
MAN7
(0.25, 0.33, 0.50)
(1.00, 2.00, 3.00)
(2.00, 3.00, 4.00)
(0.20, 0.25, 0.33)
(0.25, 0.33, 0.50)
(0.25, 0.33, 0.50)
(1.00, 1.00, 1.00)
0.058
8 9
Table 9 Fuzzy pair-wise comparison matrix of the strategic (STR) criteria STR1
STR2
STR3
STR4
STR5
STR6
Weight
STR1
(1.00, 1.00, 1.00)
(0.25, 0.33, 0.50)
(0.33, 0.50, 1.00)
(0.20, 0.25, 0.33)
(0.33, 0.50, 1.00)
(2.00, 3.00, 4.00)
0.036
STR2
(2.00, 3.00, 4.00)
(1.00, 1.00, 1.00)
(3.00, 4.00, 5.00)
(2.00, 3.00, 4.00)
(0.20, 0.25, 0.33)
(1.00, 2.00, 3.00)
0.271
STR3
(1.00, 2.00, 3.00)
(0.20, 0.25, 0.33)
(1.00, 1.00, 1.00)
(0.25, 0.33, 0.50)
(2.00, 3.00, 4.00)
(3.00, 4.00, 5.00)
0.206
STR4
(3.00, 4.00, 5.00)
(0.25, 0.33, 0.50)
(2.00, 3.00, 4.00)
(1.00, 1.00, 1.00)
(0.17, 0.20, 0.25)
(0.25, 0.33, 0.50)
0.147
STR5
(1.00, 2.00, 3.00)
(3.00, 4.00, 5.00)
(0.25, 0.33, 0.50)
(4.00, 5.00, 6.00)
(1.00, 1.00, 1.00)
(3.00, 4.00, 5.00)
0.329
STR6
(0.25, 0.33, 0.50)
(0.33, 0.50, 1.00)
(0.20, 0.25, 0.33)
(2.00, 3.00, 4.00)
(0.20, 0.25, 0.33)
(1.00, 1.00, 1.00)
0.012
10 11
Table 10 Fuzzy pair-wise comparison matrix of the regulatory (REG) criteria REG1
REG2
REG3
REG4
REG5
Weight
REG1
(1.00, 1.00, 1.00)
(1.00, 2.00, 3.00)
(2.00, 3.00, 4.00)
(2.00, 3.00, 4.00)
(1.00, 2.00, 3.00)
0.325
REG2
(0.33, 0.50, 1.00)
(1.00, 1.00, 1.00)
(2.00, 3.00, 4.00)
(1.00, 2.00, 3.00)
(2.00, 3.00, 4.00)
0.293
REG3
(0.25, 0.33, 0.50)
(0.25, 0.33, 0.50)
(1.00, 1.00, 1.00)
(1.00, 2.00, 3.00)
(0.33, 0.50, 1.00)
0.114
REG4
(0.25, 0.33, 0.50)
(0.33, 0.50, 1.00)
(0.33, 0.50, 1.00)
(1.00, 1.00, 1.00)
(0.20, 0.25, 0.33)
0.020
REG5
(0.33, 0.50, 1.00)
(0.25, 0.33, 0.50)
(1.00, 2.00, 3.00)
(3.00, 4.00, 5.00)
(1.00, 1.00, 1.00)
0.248
35
ACCEPTED MANUSCRIPT 1 2 3 4 5 6
Table 11 Fuzzy pair-wise comparison matrix of the technological and infrastructural (T&I) criteria T&I1
T&I2
T&I3
T&I4
T&I5
T&I6
Weight
T&I1
(1.00, 1.00, 1.00)
(2.00, 3.00, 4.00)
(0.17, 0.20, 0.25)
(0.20, 0.25, 0.33)
(0.25, 0.33, 0.50)
(2.00, 3.00, 4.00)
0.096
T&I2
(0.25, 0.33, 0.50)
(1.00, 1.00, 1.00)
(0.33, 0.50, 1.00)
(3.00, 4.00, 5.00)
(2.00, 3.00, 4.00)
(0.25, 0.33, 0.50)
0.153
T&I3
(4.00, 5.00, 6.00)
(1.00, 2.00, 3.00)
(1.00, 1.00, 1.00)
(4.00, 5.00, 6.00)
(3.00, 4.00, 5.00)
(0.25, 0.33, 0.50)
0.356
T&I4
(3.00, 4.00, 5.00)
(0.20, 0.25, 0.33)
(0.17, 0.20, 0.25)
(1.00, 1.00, 1.00)
(0.33, 0.50, 1.00)
(0.20, 0.25, 0.33)
0.018
T&I5
(2.00, 3.00, 4.00)
(0.25, 0.33, 0.50)
(0.20, 0.25, 0.33)
(1.00, 2.00, 3.00)
(1.00, 1.00, 1.00)
(1.00, 2.00, 3.00)
0.143
T&I6
(0.25, 0.33, 0.50)
(2.00, 3.00, 4.00)
(2.00, 3.00, 4.00)
(3.00, 4.00, 5.00)
(0.33, 0.50, 1.00)
(1.00, 1.00, 1.00)
0.235
7 8
Table 12 Fuzzy pair-wise comparison matrix of the financial (FIN) criteria FIN1
FIN2
FIN3
Weight
FIN1
(1.00, 1.00, 1.00)
(1.00, 2.00, 3.00)
(2.00, 3.00, 4.00)
0.567
FIN2
(0.33, 0.50, 1.00)
(1.00, 1.00, 1.00)
(1.00, 2.00, 3.00)
0.356
FIN3
(0.25, 0.33, 0.50)
(0.33, 0.50, 1.00)
(1.00, 1.00, 1.00)
0.077
9 10
Table 13 Fuzzy pair-wise comparison matrix of the social (SOC) criteria SOC1
SOC2
SOC3
SOC4
SOC5
Weight
SOC1
(1.00, 1.00, 1.00)
(0.25, 0.33, 0.50)
(3.00, 4.00, 5.00)
(2.00, 3.00, 4.00)
(0.20, 0.25, 0.33)
0.239
SOC2
(2.00, 3.00, 4.00)
(1.00, 1.00, 1.00)
(3.00, 4.00, 5.00)
(4.00, 5.00, 6.00)
(0.33, 0.50, 1.00)
0.414
SOC3
(0.20, 0.25, 0.33)
(0.20, 0.25, 0.33)
(1.00, 1.00, 1.00)
(2.00, 3.00, 4.00)
(0.33, 0.50, 1.00)
0.045
SOC4
(0.25, 0.33, 0.50)
(0.17, 0.20, 0.25)
(0.25, 0.33, 0.50)
(1.00, 1.00, 1.00)
(2.00, 3.00, 4.00)
0.021
SOC5
(3.00, 4.00, 5.00)
(1.00, 2.00, 3.00)
(1.00, 2.00, 3.00)
(0.25, 0.33, 0.50)
(1.00, 1.00, 1.00)
0.282
11 12
Table 14 Final priorities of CSFs of remanufacturing adoption in SC Main factors Managerial CSFs
Weight
Sub-factors
Weight
Globalized weight
Global Rank
0.222
MAN1
0.272
0.060342
5
MAN2
0.018
0.004103
28
MAN3
0.109
0.024197
19
MAN4
0.132
0.029266
15
MAN5
0.182
0.040369
12
MAN6
0.229
0.050748
8
MAN7
0.058
0.012974
24
36
ACCEPTED MANUSCRIPT Strategic CSFs
Regulatory CSFs
Technological and Infrastructural CSFs
Financial CSFs
Social CSFs
0.161
0.210
0.187
0.129
0.091
STR1
0.036
0.005765
26
STR2
0.271
0.043582
11
STR3
0.206
0.033163
14
STR4
0.147
0.023695
21
STR5
0.329
0.052891
6
STR6
0.012
0.001904
31
REG1
0.325
0.068182
2
REG2
0.293
0.061500
4
REG3
0.114
0.024000
20
REG4
0.020
0.004227
27
REG5
0.248
0.052091
7
T&I1
0.096
0.017889
23
T&I2
0.153
0.028529
16
T&I3
0.356
0.066501
3
T&I4
0.018
0.003325
30
T&I5
0.143
0.026733
17
T&I6
0.235
0.044023
10
FIN1
0.567
0.073088
1
FIN2
0.356
0.045972
9
FIN3
0.077
0.009940
25
SOC1
0.239
0.021713
22
SOC2
0.414
0.037697
13
SOC3
0.045
0.004071
29
SOC4
0.021
0.001885
32
SOC5
0.282
0.025634
18
1 2 3
5.3
Phase 3: Fuzzy TOPSIS application: prioritizing the POs due to adoption of remanufacturing CSFs in SC
4
A fuzzy comparison matrix is constructed by the decision making panel using the linguistic
5
variables defined in table 6. Each alternative (POs) is compared with each sub-criteria (CSFs)
6
individually, thus forming a matrix as presented in table 15. The linguistic variables are then
7
substituted with the corresponding TFNs (Table 16). In a similar way, linguistic variable
8
matrix and TFN matrix was constructed for each expert, but the matrix for only one decision
9
maker is presented to limit the space and word counts. Questionnaire form is prepared to
10
compare each PO with the sub-factors (CSFs) and is given in Appendix II. Having received
11
the expert opinion from each expert, aggregate fuzzy decision matrix is formulated using Eq. 37
ACCEPTED MANUSCRIPT 1
(17) and presented in table 17. The normalized fuzzy decision matrix is then formed in table
2
18. Then after the fuzzy weighted normalized matrix is established with the use of weights
3
obtained by application of fuzzy AHP (Table 19).
4
Then using Eq. (22)-(23), calculating the distance of each of the alternative from FPIS and
5
FNIS.
...........
1 0.025634 0.0179442 0.025634 0.00243522 0.025634 0.0256342 3
10
= 0.426404
d ( A1 , A )
1 0.018103 0.0301712 0.018103 0.048722 0.018103 0.0603422 3
...........
1 0.002563 0.0179442 0.002563 0.0243522 0.002563 0.00256342 3
8
9
1 0.060342 0.0301712 0.060342 0.048722 0.060342 0.0603422 3
6
7
d ( A1 , A )
= 0.539950 Using Eq. (24), the closeness coefficient for PO1 is computed as under:
11
𝐶𝐶1 =
0.539950 = 0.558750 0.426404 + 0.539950
12
In the same way the calculations for each of the alternatives (POs) were performed to
13
compute the values of distances, d ( Ai , A ) , d ( Ai , A ) and the closeness coefficients. Finally
14
based on the computed values of 𝐶𝐶𝑖 (closeness coefficient) the POs (alternatives) are ranked
15
as shown in the table 20.
16
Table 15 Linguistic rating matrix for the POs (Decision maker 1) Alternatives (POs) PO1 PO2 PO3 ….. …..
MAN1 G G VG ….. …..
MAN2 F F VG ….. …..
MAN3 MG MG G ….. …..
Criteria ….. ….. ….. ….. ….. …..
….. ….. ….. ….. ….. …..
SOC3 G MG G ….. …..
SOC4 MG F MG ….. …..
SOC5 VG VG MG ….. …..
38
ACCEPTED MANUSCRIPT PO14 PO15 PO16
1
G MG G
F MG MG
VG F G
….. ….. …..
….. ….. …..
VG MG G
VG MG MG
VG VG G
2 3
Table 16 Fuzzy decision matrix and weights of POs (Decision maker 1) Alternatives (POs)
MAN1 (7, 9, 10) (7, 9, 10) (9, 10, 10) ….. ….. (7, 9, 10) (5, 7, 9) (7, 9, 10)
PO1 PO2 PO3 ….. ….. PO14 PO15 PO16
MAN2 (3, 5, 7) (3, 5, 7) (9, 10, 10) ….. ….. (3, 5, 7) (5, 7, 9) (5, 7, 9)
MAN3 (5, 7, 9) (5, 7, 9) (7, 9, 10) ….. ….. (9, 10, 10) (3, 5, 7) (7, 9, 10)
Criteria ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. …..
SOC3 (7, 9, 10) (5, 7, 9) (7, 9, 10) ….. ….. (9, 10, 10) (5, 7, 9) (7, 9, 10)
SOC4 (5, 7, 9) (3, 5, 7) (5, 7, 9) ….. ….. (9, 10, 10) (5, 7, 9) (5, 7, 9)
SOC5 (9, 10, 10) (9, 10, 10) (5, 7, 9) ….. ….. (9, 10, 10) (9, 10, 10) (7, 9, 10)
SOC4 (3, 6, 9) (1, 4, 7) (3, 6, 9) ….. ….. (7, 9.5, 10) (3, 6, 9) (3, 6, 9)
SOC5 (7, 9.5, 10) (7, 9.5, 10) (3, 6, 9) ….. ….. (7, 9.5, 10) (7, 9.5, 10) (5, 8, 10)
4 5
Table 17 Aggregate fuzzy decision matrix for POs Alternatives (POs) PO1 PO2 PO3 ….. ….. PO14 PO15 PO16
MAN1 (5, 8, 10) (5, 8, 10) (7, 9.5, 10) ….. ….. (5, 8, 10) (3, 6, 9) (5, 8, 10)
MAN2 (1, 4, 7) (1, 4, 7) (7, 9.5, 10) ….. ….. (1, 4, 7) (3, 6, 9) (3, 6, 9)
MAN3 (3, 6, 9) (3, 6, 9) (5, 8, 10) ….. ….. (7, 9.5, 10) (1, 4, 7) (5, 8, 10)
Criteria ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. ….. …..
SOC3 (5, 8, 10) (3, 6, 9) (5, 8, 10) ….. ….. (7, 9.5, 10) (3, 6, 9) (5, 8, 10)
6 7
Table 18 Normalized fuzzy decision matrix for POs Criteria
Alternatives (POs)
MAN1
MAN2
MAN3
…..
…..
SOC3
SOC4
SOC5
PO1
(0.5, 0.8, 0.10)
(0.1, 0.4, 0.7)
(0.3, 0.6, 0.9)
…..
…..
(0.5, 0.8, 0.10)
(0.3, 0.6, 0.9)
(0.7, 0.95, 0.10)
8
PO2
(0.5, 0.8, 0.10)
(0.1, 0.4, 0.7)
(0.3, 0.6, 0.9)
…..
…..
(0.3, 0.6, 0.9)
(0.1, 0.4, 0.7)
(0.7, 0.95, 0.10)
PO3
(0.7, 0.95, 0.10)
(0.7, 0.95, 0.10)
(0.5, 0.8, 0.10)
…..
…..
(0.5, 0.8, 0.10)
(0.3, 0.6, 0.9)
(0.3, 0.6, 0.9)
…..
…..
…..
…..
…..
…..
…..
…..
…..
…..
…..
…..
…..
…..
…..
…..
…..
…..
PO14
(0.5, 0.8, 0.10)
(0.1, 0.4, 0.7)
(0.7, 0.95, 0.10)
…..
…..
(0.7, 0.95, 0.10)
(0.7, 0.95, 0.10)
(0.7, 0.95, 0.10)
PO15
(0.3, 0.6, 0.9)
(0.3, 0.6, 0.9)
(0.1, 0.4, 0.7)
…..
…..
(0.3, 0.6, 0.9)
(0.3, 0.6, 0.9)
(0.7, 0.95, 0.10)
PO16
(0.5, 0.8, 0.10)
(0.3, 0.6, 0.9)
(0.5, 0.8, 0.10)
…..
…..
(0.5, 0.8, 0.10)
(0.3, 0.6, 0.9)
(0.5, 0.8, 0.10)
9
Table 19 Weighted normalized fuzzy decision matrix for POs Alternatives (POs) PO1
Criteria MAN1
MAN2
…..
…..
SOC4
SOC5
(0.0301, 0.0482, 0.0603)
(0.0004, 0.0016, 0.0028)
…..
…..
(0.0005, 0.0011, 0.0016)
(0.0179, 0.0243, 0.0256)
39
ACCEPTED MANUSCRIPT PO2
(0.0004, 0.0016, 0.0028) (0.0028, 0.0038, 0.0041) …..
…..
…..
…..
…..
…..
(0.0301, 0.0482, 0.0603) (0.0422, 0.0573, 0.0303) …..
…..
…..
…..
…..
…..
PO14
(0.0301, 0.0482, 0.0603) (0.0181, 0.0362, 0.0543) (0.0301, 0.0482, 0.0603)
(0.0004, 0.0016, 0.0028) (0.0012, 0.0024, 0.0036) (0.0012, 0.0024, 0.0036)
….. …..
…..
…..
…..
PO3
PO15 PO16
1 2
…..
(0.0001, 0.0007, 0.0013) (0.0005, 0.0011, 0.0016) …..
(0.0179, 0.0243, 0.0256) (0.0076, 0.0153, 0.0230) …..
…..
…..
…..
…..
(0.0013, 0.0179, 0.0018) (0.0005, 0.0011, 0.0016) (0.0005, 0.0011, 0.0016)
(0.0179, 0.0243, 0.0256) (0.0179, 0.0243, 0.0256) (0.01281, 0.0205, 0.0256)
Table 20 Closeness coefficients(𝐶𝐶𝑖) Criteria code
Performance outcomes
𝑑
+ 𝑖
‒
𝑑𝑖
𝐶𝐶𝑖
Rank
PO1 PO2
Rise in sales Creates new market opportunities and increases market share
0.426404 0.377070
0.539950 0.579317
0.558750 0.605735
11 10
PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PO13 PO14 PO15 PO16
Material and energy savings Avoids land fillings and high disposal cost Extends product life time Increases employment rate Improves brand image Reduces environmental impact Quality ensured product at low cost Avoids waste limitation penalties Competitiveness and better market position Reduces carbon and green house gas emission Savings in capital investment Increases productivity and overall profitability Attracts environmentally conscious customers Reduces waste
0.304031 0.373363 0.452824 0.464335 0.372581 0.296230 0.438438 0.378196 0.267031 0.302095 0.448655 0.273369 0.501461 0.311327
0.639130 0.587058 0.518978 0.509438 0.586609 0.648668 0.528341 0.584374 0.668063 0.643892 0.521206 0.663967 0.475484 0.637932
0.677647 0.611251 0.534037 0.523159 0.611567 0.686495 0.546496 0.607098 0.714434 0.680656 0.537403 0.708355 0.486705 0.672032
5 8 14 15 7 3 12 9 1 4 13 2 16 6
3 4
6. Results and Discussion
5
The study attempts to improve the POs of the organization through adoption of SC
6
remanufacturing CSFs. To deal with, the present work evaluates thirty-two CSFs for
7
remanufacturing adoption in SC and sixteen POs that could be realized due to its adoption; in
8
context of an Indian manufacturing organization. The CSFs and POs were finalized through
9
literature review and consequent discussion with the decision group panel. (academician and
10
industrial personals). The relative importance of the SC remanufacturing CSFs was evaluated
11
using fuzzy AHP. Further, the calculated weights were carried forward to prioritize the POs
12
that could be realized due to remanufacturing CSFs adoption in SC, using fuzzy TOPSIS 40
ACCEPTED MANUSCRIPT 1
approach. The results of the analysis as obtained (Table 14 to 20) are elaborated in this
2
section and also discussed with industrial practitioners to enhance organizational POs
3
effectively through systematic adoption of SC remanufacturing CSFs.
4
Based on the findings of the study from table 14, main CSFs for remanufacturing adoption in
5
SC can be ranked according to their relative degree of importance value as: managerial CSFs,
6
regulatory CSFs, technological and infrastructural CSFs, strategic CSFs, financial CSFs and
7
social CSFs. This clearly suggests that of the main CSFs, managerial CSFs have significant
8
impact on adoption of remanufacturing initiatives in SC. Under this main factor there are
9
seven sub-factors, and the ranking of these factors in accordance with their priority is given
10
as: MAN1 > MAN6 > MAN5 > MAN4 > MAN3 > MAN7 > MAN2, which indicates that top
11
management support and involvement is the important factor amongst all the managerial
12
CSFs for remanufacturing adoption in SC. In the present case organization, the top
13
management is actively involved in SC remanufacturing practices, be it management
14
personnel, production executive, research and development executive, and marketing
15
executive. Motivating the workers, providing regular trainings to improve skill base,
16
incentive mechanism, transparent information flow system are some of the top management
17
initiatives for effective adoption of SC remanufacturing. Regulatory CSFs is the second most
18
important factor and the sub-factors arranged according to the degree of importance are
19
REG1 > REG2 > REG5 > REG3 > REG4, which indicates that weightage factor of
20
mandatory take-back policies is the highest while government initiative to subsidize latest
21
technology has the lowest weightage factor. In line with this, the present organization is ISO
22
14001 certified which suggest that the organization is sensitive towards the environmental
23
damage created due to their manufacturing activities. The organization has its own
24
remanufacturing facility and itself collects the used bearings from the user, reconditions it 41
ACCEPTED MANUSCRIPT 1
and supplies back the remanufactured bearings that meets the desired quality level. This
2
suggests that the organization is adhering to the government take-back policies.
3
Technological and infrastructural CSFs stands at third position based on the importance
4
weights calculated. Sub-criteria ratings of T&I CSFs are T&I3 > T&I6 > T&I2 > T&I5 >
5
T&I1 > T&I4, which suggest that degree of importance of innovation success factor for
6
remanufacturing practices implementation in SC is high and should be given priority in this
7
category. The ratings of strategic CSFs which ranks at fourth place among the main factors
8
are STR5 > STR2 > STR3 > STR4 > STR1 > STR6 which demonstrates that highest priority
9
should be given to target price-sensitive consumer and using remanufactured product as
10
spares should be given least importance under this factor category. Financial CSFs is at the
11
fifth importance level and based on the importance value, its sub-factors as arranged in
12
descending order are FIN1 > FIN2 > FIN3. Finally, the ratings of sub-criteria under social
13
CSFs category which stands at the sixth position are SOC3 > SOC2 > SOC5 > SOC1 >
14
SOC4. Now, due to adoption of SC remanufacturing CSFs, some POs (alternatives) could be
15
realized. However, it very difficult to define as to which performance outcome could be
16
highly observed due to adoption of SC remanufacturing CSFs. But, prioritizing the POs using
17
fuzzy TOPSIS by combining the CSFs weights obtained using fuzzy AHP, helps decision
18
makers to identify the best PO. Based on the value of the closeness coefficients, POs realized
19
due to adoption of SC remanufacturing CSFs are prioritized. The closeness coefficient of
20
PO11 is highest, while that of PO15 is lowest i.e. competiveness and better market position is
21
the highest priority PO and attracts environmentally conscious customers is the lowest
22
priority PO due to adoption of remanufacturing practices in SC. Adoption of SC
23
remanufacturing CSFs aids the organization to produce remanufactured product of the same
24
quality level as that of “new like” at reduced cost. Supplying the products at a lower-prices
25
not only provides a competitive edge over the peers but also, increases the market share. The 42
ACCEPTED MANUSCRIPT 1
priorities of the other POs are PO14-PO8-PO12-PO3-PO16-PO7-PO4-PO10-PO2-PO1-PO9-
2
PO13-PO5-PO6-PO15 in descending order.
3
6.1 Practical implications of the study
4
This study based on the results and subsequent discussion with the decision panel group list
5
out several managerial implications for the industrial practitioners to effectively manage the
6
remanufacturing practices implementation. Following are the recommendations suggested to
7
the industrial manager:
8
The present study explores thirty-two CSFs categorized under six main criteria. Clear
9
understanding of each criteria would help the industrial managers to overcome the
10
remanufacturing challenges effectively. Further, it is not possible to implement all the
11
CSFs at a time within the organizational decision process. Hence, ranking of the
12
CSFs based on their relative importance as carried out in this research would help the
13
practitioners to focus initially on high intensity CSFs for successful adoption of
14
remanufacturing practices in SC.
15
This study provides a structural framework to the managers/practitioners to enhance
16
their sustainable performance by providing an exhaustive list of remanufacturing
17
CSFs and POs realized due its adoption as presented in Fig. 2. Further, prioritization
18
of the POs realized due to adoption of SC remanufacturing CSFs helps the policy
19
makers to develop strategic action plan at the initial planning phase itself, thereby
20
minimizing the failure risk and enhancing the possibility of success.
21
Remanufacturing initiative is emerging in developing nations as well as densely
22
populated countries, as it not only yields environmental benefits but also results into
23
societal benefits (job creation). The findings of the study are based on the numerical 43
ACCEPTED MANUSCRIPT 1
application of the proposed framework in an Indian manufacturing organization.
2
However, the proposed study would help the researchers/practitioners of other
3
developing nations such as China, Brazil, and Bangladesh etc. to improve their
4
organizational performance by modifying their present approach.
5
It is mandatory for any organization to abide by certain regulatory laws related to
6
environment and undergo several societal responsibility, while maintaining their
7
economic growth. The present fuzzy AHP-TOPSIS framework helps the internal as
8
well as the external stakeholders by making the SC remanufacturing more transparent
9
and, thereby helps the organizations to be more accountable towards sustainable
10
performance and decrease their weakness. The proposed framework helps to mitigate
11
the social, economic and environmental risks to the society that would have arisen
12
due unplanned implementation of SC remanufacturing CSFs.
13
An action of managers/practitioners is simply not sufficient enough to realize the POs
14
accrued due to SC remanufacturing CSFs adoption. Multiple stakeholders are involved in SC
15
remanufacturing CSFs implementation.
16
recommended to the multiple stakeholders:
17
In this regard, following are some suggestions
Stringent laws regarding collection of used product at its EOL (i.e. take-back policies)
18
by the OEM should be implemented by government institutions to effectively manage
19
the end waste.
20
products and ensure that the used products undergo proper EOL treatment.
21 22 23
Government institutions should enforce laws to curb informal handling of the waste
Consumers should be aware that remanufactured product is not second hand product. They should be aware regarding the benefits of using remanufactured product. 44
ACCEPTED MANUSCRIPT 1
7. Sensitivity Analysis
2
Sensitivity analysis needs to be conducted to investigate the effect of variation of SC
3
remanufacturing CSFs priority weights on the final rankings of the alternatives (POs).
4
Sirisawat and Kiatcharoenpal (2018) and Tian et al. (2017) performed a similar analysis to
5
check the robustness of alternatives ranking due to changes in criteria weights. The study
6
conducts ten experiments to perform the sensitivity analysis (Table 21). Sensitivity analysis
7
is performed by assigning higher weight to each CSF one by one, while keeping the weight of
8
other CSFs as low. During experiment 1, CSF MAN1 was allocated a weight equal to 0.6,
9
while the remaining CSFs weight was set as 0.015, then the rankings of the POs (alternatives)
10
were determined. It indicates that PO11, PO8, PO12, PO3, PO7 are the top five outcomes that
11
can be derived due to adoption of remanufacturing CSFs in SC. Conducting experiment 2 by
12
putting weight of CSF MAN2 = 0.6 and weight of the remaining CSFs as 0.015 indicates that
13
PO11, PO14, PO8, PO12, PO3 are the five important POs. During experiment 3, where
14
weight of CSF MAN3 = 0.6 and weight of other CSFs equal to 0.015, depicts the results that
15
PO11, PO8, PO12, PO14, PO3 are the five highly rated POs. Similarly, the remaining seven
16
experiments are performed and the results obtained are presented in table 21.
17
The sensitivity analysis performed by changing the weight values of each CSF one by one
18
may result into deviation in values of closeness coefficient as well as the final ranking of the
19
POs. Based on the ten experiments performed, competitiveness and better market position
20
(PO11) is the best outcome reported in nine experiments. In addition, the factors PO11,
21
PO14, PO8, PO12, PO3 turn out to be the top five POs that could be realized due adoption of
22
SC remanufacturing CSFs in each experiment, with negligible order discrepancies. Hence,
23
sensitivity analysis experiment results represents that the ranking of the POs (alternatives)
24
due to adoption of remanufacturing CSFs in SC is relatively sensitive to the CSFs weights. 45
Table 21 Sensitivity analysis
POs
Exp. 1
Exp. 2
Exp. 3
Exp. 4
Exp. 5
Exp. 6
Exp. 7
Exp. 8
Exp. 9
Exp. 10
CC
Rank
CC
Rank
CC
Rank
CC
Rank
CC
Rank
CC
Rank
CC
Rank
CC
Rank
CC
Rank
CC
Rank
PO1
0.57627
11
0.37674
12
0.37509
12
0.37674
12
0.37368
13
0.41078
12
0.37674
12
0.37674
12
0.37368
13
0.37674
12
PO2
0.60262
9
0.38874
9
0.38712
9
0.38874
9
0.38573
9
0.42219
9
0.38712
9
0.38874
9
0.38573
9
0.38712
9
PO3
0.72452
4
0.41008
5
0.41104
5
0.41008
5
0.41376
5
0.44239
5
0.41104
5
0.41104
5
0.41104
5
0.41008
5
PO4
0.62312
8
0.39663
8
0.39663
8
0.39822
8
0.39822
8
0.43004
8
0.39526
8
0.39526
8
0.39663
8
0.39663
8
PO5
0.57455
12
0.37448
13
0.37306
13
0.37448
13
0.37448
12
0.41017
13
0.37448
13
0.37612
13
0.37612
12
0.37448
13
PO6
0.49831
15
0.36849
14
0.37032
14
0.35868
16
0.36693
15
0.40776
14
0.37032
14
0.36693
15
0.36693
14
0.37032
14
PO7
0.70582
5
0.40374
7
0.40374
7
0.40374
7
0.40277
7
0.43623
7
0.40501
7
0.40374
7
0.40501
7
0.40501
7
PO8
0.73259
2
0.41451
3
0.41451
2
0.41356
2
0.41576
1
0.44567
3
0.41451
2
0.41356
3
0.41576
2
0.41451
2
PO9
0.59451
10
0.38350
11
0.38210
11
0.38210
10
0.38350
11
0.41874
11
0.38210
11
0.38350
10
0.38350
10
0.38512
11
PO10
0.52802
14
0.38395
10
0.38242
10
0.38127
11
0.38395
10
0.42106
10
0.38395
10
0.38242
11
0.38242
11
0.38573
10
PO11
0.73852
1
0.41792
1
0.41572
1
0.41792
1
0.41572
2
0.44857
1
0.41572
1
0.41792
1
0.41667
1
0.41792
1
PO12
0.73259
3
0.41451
4
0.41451
3
0.41356
3
0.41451
4
0.44567
4
0.41451
3
0.41356
4
0.41576
3
0.41356
3
PO13
0.55636
13
0.36605
15
0.36605
15
0.36462
15
0.36772
14
0.40220
15
0.36605
15
0.36772
14
0.36605
15
0.36462
14
PO14
0.66404
6
0.41627
2
0.41239
4
0.41340
4
0.41473
3
0.44728
2
0.41239
4
0.41473
2
0.41239
4
0.41239
4
PO15
0.47578
16
0.35868
16
0.35523
16
0.36849
14
0.35682
16
0.39675
16
0.35868
16
0.35868
16
0.35682
16
0.35523
16
PO16
0.64702
7
0.40744
6
0.40609
6
0.40507
6
0.40744
6
0.43944
6
0.40609
6
0.40744
6
0.40609
6
0.40609
6
Conditions for experiment: Exp. 1: MAN1 = 0.6, MAN2-SOC5 = 0.015; Exp. 2: MAN2 = 0.6, MAN1, MAN3-SOC5= 0.015; Exp. 3: MAN3 = 0.6, MAN1-MAN2, MAN4SOC5 = 0.015; Exp. 4: MAN4 = 0.6, MAN1-MAN3, MAN5-SOC5 = 0.015; Exp. 5: MAN5 = 0.6, MAN1-MAN4, MAN6-SOC5 = 0.015; Exp. 6: MAN6 = 0.6, MAN1MAN5, MAN7-SOC5 = 0.015; Exp. 7: MAN7 = 0.6, MAN1-MAN6, STR1-SOC5 = 0.015; Exp. 8: STR1 = 0.6, MAN1-MAN7, STR2-SOC5 = 0.015; Exp. 9: STR2 = 0.6, MAN1-STR1, STR3-SOC5 = 0.015; Exp. 10: STR3 = 0.6, MAN1-STR2, STR4-SOC5 = 0.015
46
ACCEPTED MANUSCRIPT 1
8. Conclusion
2
Uneven climate, increased industrialization and growing population rate have forced the
3
organizations to search for a sustainable solution. Adoption of remanufacturing practices in
4
the existing SC is a prominent strategy, being adopted by organizations to improve their
5
sustainable performance. Remanufacturing strategy adoption in SC has been a topic of debate
6
for developed nations since long, and these nations have already started realizing the benefits
7
that could be incurred due to its adoption. However, it is still at a premature stage in
8
developing nations and especially, India due to its complex socio-environmental and
9
economic relationship. In addition, interaction of complex factors such as uncertain
10
regulations, fluctuating global economic scenario and varied consumer behaviour poses a
11
critical challenge for organizations to adopt SC remanufacturing CSFs. The present study
12
attempts to identify the CSFs for remanufacturing adoption in SC and the POs that could be
13
realized due to its adoption. A structured framework is developed in this study which
14
proposes a hybrid fuzzy AHP-TOPSIS approach for prioritizing the POs realized due to
15
adoption of SC remanufacturing CSFs.
16
The proposed hybrid fuzzy AHP-TOPSIS research framework is applied to a case
17
organization in Indian context. After assessing the relevant literature and discussion with
18
experts of the decision-making team, thirty-two SC remanufacturing CSFs and sixteen POs
19
due to its adoption were decided. The POs (alternatives) were prioritized based on the inputs
20
received from the decision making group and finally ranks were assigned using hybrid fuzzy
21
AHP-TOPSIS approach. The analysis reveals that competitiveness and better market position
22
is the best PO that could be realized due to adoption of SC remanufacturing CSFs. This is
23
because the organization would present itself that, apart from manufacturing a new product,
24
they are also in the business of remanufacturing of used product. This would create a trust 47
ACCEPTED MANUSCRIPT 1
among the customers regarding the quality of the remanufactured product which is ultimately
2
the consumer’s prime requirement. Also, the PO namely attracts environmentally conscious
3
customers has the lowest rank because major societal class in India has low income and
4
hence the environmental damage created by the organization product is a least concern for
5
them as their priority is availability of quality goods at cheaper rate. The ranking of the POs
6
will aid organizations in structured implementation of SC remanufacturing CSFs in
7
accordance with their priority to realize the POs, thus improving the sustainable performance
8
of the organization. The outcomes of the study will help the managers of the case
9
organization under study effectively, to enhance their POs through structured and systematic
10
implementation of remanufacturing CSFs in SC. Realization about the most effective actions
11
to be taken for effective adoption of remanufacturing CSFs in SC by the industry top
12
executives; forward SC and reverse SC practitioners; and government bodies would help to
13
improve socio-environmental-economic performance.
14
The structured model proposed in this study is not without limitations, which otherwise
15
provides an opportunities for other researchers to investigate the issues. The proposed model
16
combines fuzzy AHP-TOPSIS methodology, to prioritize the alternatives (16 POs) realized
17
due to adoption of thirty-two SC remanufacturing CSFs. The results computed in this study is
18
based on the inputs received from decision making panel. Hence, it is proposed that the
19
results should be computed carefully. The findings of the study is based on the application of
20
proposed framework on a single numerical case organization in India. Hence, the framework
21
should be applied in different geographical context with minor modifications to generalise the
22
results. The proposed methodology ranks the CSFs however, combining DEMATEL and
23
ISM with fuzzy AHP will help the decision makers to analyze the inter-relationships between
24
CSFs. Analyzing the problem using fuzzy AHP-DEMATEL or fuzzy AHP-ISM would 48
ACCEPTED MANUSCRIPT 1
support the practitioners and policy definers to plan flexible decision-making strategies for
2
short-term or long-term that will improve performance due to remanufacturing CSFs
3
adoption in SC from an industrial perspective (Mangla et al., 2016). Importance weight
4
allocation during pair-wise comparison of main factors and sub-factors in fuzzy AHP analysis
5
is done based on group-thinking of the experts. There may be possibility that some individual
6
tend to dominate the group or some contribute averagely to avoid any conflicts. Further,
7
using other multi-criteria decision matrix such as fuzzy ELECTRE, fuzzy PROMETHEE, or
8
fuzzy VIKOR, the results of the study should be compared. The proposed structural
9
framework should be applied to various other industry sectors that are willing to adopt
10
remanufacturing CSFs in their SC however, the opinion may vary based on industry type and
11
priorities.
12
Appendix I
13 14 15
Questionnaire form for decision making group to facilitate pair-wise comparison matrix between the main criteria. Similar form is prepared for sub-criteria but only one sub-criteria form is presented due to space limitations. Criteria
Managerial CSFs (MAN) Strategic CSFs (STR) Regulatory CSFs (REG) Technological and Infrastructural CSFs (T&I) Financial CSFs (FIN)
MAN Equal (1,1,1) Very low (1,2,3) Low (2,3,4)
STR Equal (1,1,1) Very low (1,2,3) Low (2,3,4)
REG Equal (1,1,1) Very low (1,2,3) Low (2,3,4)
T&I Equal (1,1,1) Very low (1,2,3) Low (2,3,4)
FIN Equal (1,1,1) Very low (1,2,3) Low (2,3,4)
SCO 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 (6,7,8)
Medium (3,4,5) High (4,5,6) Very high (5,6,7) Excellent (6,7,8)
Medium (3,4,5) High (4,5,6) Very high (5,6,7) Excellent (6,7,8)
Medium (3,4,5) High (4,5,6) Very high (5,6,7) Excellent (6,7,8)
Medium (3,4,5) High (4,5,6) Very high (5,6,7) Excellent (6,7,8)
Medium (3,4,5) High (4,5,6) Very high (5,6,7) Excellent (6,7,8)
(1,1,1) (1,1,1) (1,1,1)
(1,1,1)
(1,1,1)
49
ACCEPTED MANUSCRIPT Social CSFs (SOC)
(1,1,1)
1 Sub-criteria
Top management support and involvement (MAN1) Benchmarking (MAN2) Redefining the firm business model (MAN3) Lean tools as a continuous improvement philosophy (MAN4) Enhancing corporate green image (MAN5) Sustainability concept implementation (MAN6) Monitoring and controlling (MAN7)
MAN1 Equal (1,1,1)
MAN2 Equal (1,1,1)
MAN3 Equal (1,1,1)
MAN4 Equal (1,1,1)
MAN5 Equal (1,1,1)
MAN6 Equal (1,1,1)
MAN7 Equal (1,1,1)
Very low (1,2,3) Low (2,3,4)
Very low (1,2,3) Low (2,3,4)
Very low (1,2,3) Low (2,3,4)
Very low (1,2,3) Low (2,3,4)
Very low (1,2,3) Low (2,3,4)
Very low (1,2,3) Low (2,3,4)
Very low (1,2,3) Low (2,3,4)
Medium (3,4,5) High (4,5,6)
Medium (3,4,5) High (4,5,6)
Medium (3,4,5) High (4,5,6)
Medium (3,4,5) High (4,5,6)
Medium (3,4,5) High (4,5,6)
Medium (3,4,5) High (4,5,6)
Medium (3,4,5) High (4,5,6)
Very high (5,6,7)
Very high (5,6,7)
Very high (5,6,7)
Very high (5,6,7)
Very high (5,6,7)
Very high (5,6,7)
Very high (5,6,7)
Excellent (6,7,8)
Excellent (6,7,8)
Excellent (6,7,8)
Excellent (6,7,8)
Excellent (6,7,8)
Excellent (6,7,8)
Excellent (6,7,8)
(1, 1, 1)
(1, 1, 1)
(1, 1, 1)
(1, 1, 1)
(1, 1, 1)
(1, 1, 1)
(1, 1, 1)
2 3 4 5 6 7 8 50
ACCEPTED MANUSCRIPT 1
Appendix II
2
Questionnaire form to compare each PO (alternatives) with sub-criteria.
Alternatives
Criteria MAN1 Very poor (0,0,1) Poor (0,1,3)
MAN2 Very poor (0,0,1) Poor (0,1,3)
MAN3 Very poor (0,0,1) Poor (0,1,3)
…
… … …
SOC3 Very poor (0,0,1) Poor (0,1,3)
SOC4 Very poor (0,0,1) Poor (0,1,3)
SOC5 Very poor (0,0,1) Poor (0,1,3)
… …
Medium poor (1,3,5) Fair (3,5,7)
Medium poor (1,3,5) Fair (3,5,7)
Medium poor (1,3,5) Fair (3,5,7)
… …
… …
Medium poor (1,3,5) Fair (3,5,7)
Medium poor (1,3,5) Fair (3,5,7)
Medium poor (1,3,5) Fair (3,5,7)
Medium good (5,7,9) Good (7,9,10)
Medium good (5,7,9) Good (7,9,10)
Medium good (5,7,9) Good (7,9,10)
… …
… …
Medium good (5,7,9) Good (7,9,10)
Medium good (5,7,9) Good (7,9,10)
Medium good (5,7,9) Good (7,9,10)
Very good (9,10,10)
Very good (9,10,10)
Very good (9,10,10)
…
…
Very good (9,10,10)
Very good (9,10,10)
Very good (9,10,10)
PO1 PO2 PO3 . . . PO14 PO15 PO16
3 4
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ACCEPTED MANUSCRIPT Highlights
Identifies the CSFs and the POs realized due to adoption of SC remanufacturing CSFs adoption.
A hybrid MCDM approach (fuzzy AHP-TOPSIS) approach is used to evaluate the CSFs and POs.
Fuzzy AHP is used to rank the SC remanufacturing CSFs while to fuzzy TOPSIS is used to prioritize the POs due to its adoption.
Sensitivity analysis is carried out to check the robustness of POs prioritization due to variation of CSFs weights.