Prioritizing Circular Supply Chain Management Barriers Using Fuzzy AHP: Case of the Indian Plastic Industry

Abstract

This article provides a perspective to implement circular supply chain management (CSCM) in the plastic industry in emerging economies, especially in India. CSCM integrates the philosophy of circular economy (CE) into supply chain management and offers a new view towards sustainability. However, the implementation of CSCM is an arduous task due to the presence of several barriers. Therefore, this study aims to investigate and prioritize barriers to implementing CSCM practices in Indian plastic industries. It employs a two-phase methodology to identify and prioritize the barriers to implementing CSCM. A total of 24 barriers were identified under five major categories through an extensive literature review and experts’ opinions. A fuzzy analytical hierarchy process (AHP) methodology is employed to rank the barriers. The fuzzy framework is considered to handle the uncertainty and vagueness. An empirical case of the Indian plastic industry is taken to illustrate the proposed model. A sensitivity analysis was performed to check the robustness of the model. The results indicate that the lack of tax relaxation policies and poor enforcement of rules and regulations to protect the environment are the most prominent barriers. The finding of this study can act as a stepping stone for managers and policymakers to effective implementation of CSCM.

Keywords

Barriers circular supply chain management fuzzy-AHP plastic industry

Introduction

The current patterns of production, consumption and trade of the supply chain model affect the environment and society negatively. Most of the current business practices still tend to work on the principle of ‘extract-transform-dispose of’ (Patwa et al., 2020). Organizations extract many natural resources, process them into finished products and sell them in the market for consumption, and they are finally discarded by the consumer after the end of their use, resulting in waste (Gong et al., 2020; Jakhar et al., 2019). This linear approach of business organization does not significantly consider the society and environment. Therefore, circular economy (CE) emerged as a new approach to respond to resource scarcity and minimize waste generation, while also helping to achieve a sustainable society and economy (Batista et al., 2019; Khan et al., 2020). The CE approach aims to keep products or materials usable through the extension of their life cycle in a circular design of supply chain to utilize the highest value and minimize waste generation while optimizing natural resources (Batista et al., 2019; Govindan & Hasanagic, 2018; Mangla et al., 2018). This economic model is based on restoration and regeneration (WEF & Ellen Macarthur Foundation, 2017). In CE principles, the waste of one organization would be a resource for another (Patwa et al., 2020).

The adoption of CE is the need for the hour for organizations to minimize and manage waste effectively and remain sustainable in the current scenario of resource depletion and environment protection. Globally, various industries, such as automobile (Agyemang et al., 2019), packaging (Batista et al., 2019), textile and apparel (Jia et al., 2020), manufacturing (Kumar et al., 2019), electronics (Sharma et al., 2019), construction (Ghisellini et al., 2016), food (Sharma et al., 2019) and leather (Moktadir et al., 2019), are giving priority to implementing a CE system in their supply chain. However, implementing circular practices in developing countries like India is challenging as compared to developed countries, due to the difference in specific laws, infrastructure and social and cultural conditions. A few studies (Agyemang et al., 2019; Batista et al., 2019; Bhandari et al., 2019; Moktadir et al., 2019; Sharma et al., 2019) have tried to identify barriers to implementing circular practices in the supply chain of a developing nation’s context, but none of them provides a holistic view of implementing circular supply chain management (CSCM) in the Indian plastic industry.

To contribute to the CE literature, the objective of this study is to identify the barriers that impede the adoption of CE, primarily focusing on the Indian plastic industry. Plastic has emerged as a high-priority sector to implement CE, due to its high versatility and durability in various applications like packaging, automotive, electronics, food, etc. (Khandelwal & Barua, 2020; MoEFCC, 2018; WEF & Ellen Macarthur Foundation, 2017). However, the extensive use of plastic products has caused an increase in pollution and waste generation, affecting the environment and social well-being (Aryan et al., 2019; Gong et al., 2020; Satapathy, 2017). In India, it is estimated that 15,342 tons of plastic waste is generated per day; only 60 per cent of the total waste is recycled, and the remaining is left untreated or discarded (CPCB, 2015; 2017; Khandelwal & Barua, 2020). This improperly disposed or untreated waste is illegally dumped into landfills or burnt. The rising quantity of waste and its management are significant threats for the government and policymakers to tackle (Aryan et al., 2019; Satapathy, 2017). They can be addressed through the adoption of CE practices in the plastic supply chain. Therefore, the present study aims to provide managers and policymakers of plastic sectors a holistic insight into the obstacles of CSCM implementation in India. A fuzzy analytical hierarchy process (AHP) methodology has been utilized to evaluate the weights of identified barriers selected for this study and prioritize them. The preference ranking can help all stakeholders, including plastic producers, manufacturers, recyclers, suppliers, consumers, governments and monitoring institutions, to focus on the most prominent barriers.

This article is composed of eight sections, which are structured in the following manner: the second section discusses a review of barriers to implementing CSCM in emerging economies; the third section highlights the problem definition; the fourth section describes the two-phase methodology proposed in this research; the fifth section illustrates an application of the proposed methodology; the result and discussion are presented in the sixth section; the seventh section summarizes the conclusion and managerial implications of the study; and the last section presents the limitations.

Review of Literature

Barriers of CSCM Implementation in Emerging Countries

Over the past few years, researchers and academicians have started paying attention to implementing CE initiatives in the supply chain (Batista et al., 2019; De Angelis et al., 2018; Farooque et al., 2019; Masi et al., 2018; Paletta et al., 2019; Rizos et al., 2016). However, the implementation of CSCM is still at a nascent stage in developing countries due to the presence of several barriers (Patwa et al., 2020). The literature has tried to address several barriers that hinder the implementation of CSCM in developing countries (Agyemang et al., 2019; Govindan & Hasanagic, 2018; Mangla et al., 2018; Moktadir et al., 2019; Sharma et al., 2019). In this article, we categorize the barriers into five major categories, along with their sub-criteria from the Indian producer’s perspective based on the literature review and various rounds of deliberations with experts. The finalized identified barriers are depicted in Table 1.

Legislative Barriers

Most developing nations are characterized by a lack of robust legal and regulatory mechanisms promoting the implementation of CSCM. Due to the lack of proper legislative policies, plastic waste is illegally imported to India and processed by informal sectors (Liu et al., 2018). It demotivates the organizations that plan to shift towards CSCM implementation. Also, the lack of a standardization process to measure the performance of operating processes poses an additional challenge for organizations to shift towards CSCM (Govindan & Hasanagic, 2018; Zhang et al., 2019). The adoption of circular practices requires high investments. Therefore, tax relaxation policies from the government can be a motivating factor for plastic industries to implement CSCM (Luthra et al., 2016; Mangla et al., 2018; Sivakumar et al., 2018). The major barriers under this category include weak enforcement of government rules and regulations to protect the environment (Kabra & Ramesh, 2015; Katiyar et al., 2018; Kumar & Dixit, 2018; Mathiyazhagan et al., 2016; Singh & Sarkar, 2019), lack of global standards to measure the performance of CSCM (Mangla et al., 2018; Moktadir et al., 2019; Prakash & Barua, 2016), informal waste disposal practices (Zhang et al., 2019) and lack of tax rebate policies to promote CSCM (Geng & Doberstein, 2008; Kumar et al., 2019; Sivakumar et al., 2018).

Organizational Barriers

Lack of strong commitment and support from the senior management is a significant impediment to implementing CSCM in organizations (Gupta & Barua, 2016). Leaders need to form common strategies and understanding to develop a framework of CE implementation (Mangla et al., 2018). Organizations often tend to work in their traditional way and are scared to shift towards circular practices, wanting to avoid risks. Lack of communication between departments delays the monitoring of activities in the supply chain (Yadav et al., 2020). Zhang et al. (2019) suggested that various incentive schemes to engage with CSCM projects can be a motivating factor for stakeholders to adopt CSCM. The major barriers coming under this category are poor support and commitment from the management (Gupta & Barua, 2016; Luthra et al., 2016; Zhu & Geng, 2013), lack of strategic planning (Balon et al., 2016; Prakash & Barua, 2015), strong industrial focus on the traditional business model of ‘take-make-dispose’ (Masi et al., 2018; Paletta et al., 2019; Tura et al., 2019), lack of interdepartmental flexibility and coordination (Balon et al., 2016, Katiyar et al., 2018; Prakash & Barua, 2015) and lack of incentives to promote CSCM (Mangla et al., 2017; Satapathy, 2017).

Technical Barriers

CE practices are relatively new in many organizations. Adoption of modern technology and innovation is essential to change the existing business culture and facilitate the shift towards a circular model (De Angelis et al., 2018; Gupta & Barua 2016). Organizational performance directly depends on the available technology and resources. Many organizations do not have technical expertise or knowledge of how to transform traditional operations into circular practices (Rizos et al., 2016; Sharma et al., 2019). Also, organizations are often found to be resource-constrained. The unavailability of smart technologies poses one of the major constraints to tracking information on material flow in the supply chain for organizations (Mangla et al., 2018; Zhang et al., 2019). The major barriers in this category include limited technology to design for the end of life of products (Geng & Doberstein, 2008; Prakash & Barua, 2015, 2016; Singh & Sarkar, 2019), lack of useful models and technical expertise (Agyemang et al., 2019; Bhandari et al., 2019; Zhu & Geng, 2013), insufficient collection centres and recycling plants (Prakash & Barua, 2015, 2016), poor availability of resources (Agyemang et al., 2019) and lack of an information system to track recycled materials (Govindan & Hasanagic, 2018; Sharma et al., 2019).

Market-related Barriers

A lack of market for reused or refurbished products is a severe concern in India, where the majority of people do not care about environmental protection (Sivakumar et al., 2018; Zhang et al., 2019). The market for recycled products depends on consumer demands, and thus people’s perspective is essential for the implementation of circular practices in organizations (Gupta & Barua, 2018; Moktadir et al., 2019). Most often, consumers prefer virgin plastic products over recycled products and harbour scepticism towards the quality of recycled plastic materials (Agyemang et al., 2019; Kumar et al., 2019; Mathiyazhagan et al., 2016). Also, the pricing of these products is a sensitive issue in India due to the highly competitive market. The various barriers under this category involve lack of training and development among stakeholders (Khandelwal & Barua, 2020; Mangla et al., 2018), scepticism about the quality of refurbished and recycled products (Mathiyazhagan et al., 2016; Paletta et al., 2019; Prakash & Barua, 2016; Sivakumar et al., 2018), lack of information sharing among supply chain partners (Agyemang et al., 2019; Mangla et al., 2017, 2018; Sivakumar et al., 2018; Tura et al., 2019), lack of customer awareness about the return of used products (Katiyar et al., 2018; Satapathy, 2017; Zhang et al., 2019) and lack of a cohesive reverse logistics network (Balon et al., 2016; Prakash & Barua, 2015, 2016).

Financial Barriers

High costs often act as a deterrent to financing CE practices in the supply chain (Ghisellini et al., 2016; Govindan & Hasanagic, 2018). Organizations face a financial crunch due to a lack of internal and external funding support from banks or financial institutions (Bhandari et al., 2019; Gupta & Barua, 2018; Tura et al., 2019). These financial barriers hamper plastic organizations from investing in the purchase and packaging of eco-friendly materials. Fear of failure and high cost of waste collection, transportation, segregation, and processing system restricts the organizations to switch to new operational practices (Prakah & Barua, 2015, 2016). Huge initial capital investment is required to implement CSCM, but defining and measuring short-term economic benefits is challenging (Rizos et al., 2016). Also, the competitive price of virgin plastic products over recycled products makes it challenging for organizations to switch. The financial barriers include the high cost of eco-friendly materials’ purchase and packaging (Govindan & Hasanagic, 2018; Zhu & Geng, 2013), high cost of waste collection and segregation (Masi et al., 2018; Paletta et al., 2019; Satapathy, 2017), short term perspective towards economic benefits (Bhandari et al., 2019; Luthra et al., 2016; Kumar et al., 2019; Mathiyazhagan et al., 2016; Zhu & Geng, 2013), the higher price of recycled products (Masi et al., 2018; Tura et al., 2019; Zhang et al., 2019; Zhu & Geng, 2013) and limited financial resources (Balon et al., 2016; Satapathy, 2017; Sivakumar et al., 2018).

Table 1.

Identification of CSCM Implementation Barriers

Major Barrier	Barrier Code	Sub-criteria	References
Legislative barriers	LB1	Weak enforcement of rules and regulations for environmental protection	Tura et al. (2019), Geng and Doberstein (2008), Govindan and Hasanagic (2018), Kumar and Dixit (2018), Kumar et al. (2019), Luthra et al. (2016), Mangla et al. (2017, 2018), Masi et al. (2018), Satapathy (2017), Sharma et al. (2019), Sivakumar et al. (2018), Zhang et al. (2019) and Khandelwal and Barua (2020)
	LB2	Lack of global standards to measure the performance of CSCM
	LB3	Informal disposal of waste from recovered plastics
	LB4	Lack of tax rebate policies to promote CSCM
Organizational barriers	OB1	Poor support and commitment from management	Balon et al. (2016), Govindan and Hasanagic (2018), Gupta and Barua (2016), Kumar et al. (2019), Kumar and Dixit (2018), Luthra et al. (2016), Mangla et al. (2017, 2018), Narayanan et al. (2019), Prakash and Barua (2015, 2016), Rizos et al. (2016) and Tura et al. (2019)
	OB2	Lack of strategic planning
	OB3	Strong industrial focus on traditional business model of ‘take-make-dispose’
	OB4	Lack of interdepartmental flexibility and coordination
	OB5	Lack of incentives to promote CSCM
Technical barriers	TB1	Limited technology to design for end of life of products	Agyemang et al. (2019), Bhandari et al. (2019), Geng and Doberstein (2008), Gupta and Barua (2016), Kumar and Dixit (2018), Masi et al., (2018), Moktadir et al. (2019), Sharma et al. (2019), Prakash and Barua (2016), Rizos et al. (2016), Singh and Sarkar (2019), Sivakumar et al. (2018) and Mangla et al. (2018)
	TB2	Lack of useful models and technical expertise
	TB3	Insufficient collection centres and recycling plants
	TB4	Poor availability of resources
	TB5	Lack of an information system to track recycled materials
Market-related barriers	MRB1	Lack of training and development among stakeholders	Agyemang et al. (2019), Batista et al. (2019), Geng and Doberstein (2008), Govindan and Hasanagic (2018), Gupta and Barua (2018), Katiyar et al. (2018), Mangla et al. (2017, 2018), Mathiyazhagan et al. (2016), Prakash and Barua (2015, 2016), Satapathy (2017), Zhang et al. (2019) and Khandelwal and Barua (2020)
	MRB2	Scepticism about the quality of refurbished and recycled products
	MRB3	Lack of information sharing among supply chain partners
	MRB4	Lack of customer awareness about the return of used products
	MRB5	Lack of a cohesive reverse logistics network
Financial barriers	FB1	High cost of eco-friendly materials’ purchase and packaging	Agyemang et al. (2019), Bhandari et al. (2019), Kumar et al. (2019), Mangla et al. (2018), Masi et al. (2018), Moktadir et al. (2019), Prakash and Barua (2016), Rizos et al. (2016), Satapathy (2017), Sivakumar et al. (2018), Tura et al. (2019), Zhang et al. (2019) and Zhu and Geng (2013)
	FB2	High cost of waste collection and segregation
	FB3	Short-term perspective towards economic benefits
	FB4	Higher price of recycled products
	FB5	Limited financial resources to implement CSCM

Source: The authors.

Objective of the Study

The present study addresses the issues and challenges faced by the Indian plastic industry in the implementation of CSCM. This article aims to achieve two objectives: (a) the primary objective of this research is to identify barriers that influence the adoption of CSCM in the Indian plastic industry; (b) the secondary objective of this research is to prioritize the identified barriers using fuzzy AHP.

Methodology

This study adopts a two-phase hybrid approach for identifying and ranking the barriers. The initial phase involves identifying the barriers to CSCM adoption in the Indian plastic industry through a literature review and experts’ opinions. The second phase involves evaluating and ranking the barriers and sub-barriers using the fuzzy AHP approach. The proposed research design is represented in Figure 1.

Figure 1.

Source: Modified from Mahtani and Garg (2018).

Fuzzy AHP

AHP is a popular numerical technique of Multi Criteria Decision Making (MCDM) to rank and prioritize factors, introduced by Saaty (1980). The application of AHP is often restricted due to some factors, such as its inability to handle ambiguity and imprecise human judgement in an uncertain environment (Mahtani & Garg, 2018). The choices of experts greatly influence the results derived through conventional AHP, which involves the usage of an unbalanced scale of judgement. Therefore, the fuzzy AHP approach has been utilized to handle such shortcomings in this research (Kumar & Garg, 2017; Prakash & Barua, 2015). The fuzzy AHP technique addresses uncertainty and ambiguity present in the viewpoints of experts through the use of linguistic variables (Kabra & Ramesh, 2015; Mangla et al., 2017). The recent applications of fuzzy AHP are depicted in Table 2.

Table 2.

Recent Applications of Fuzzy AHP

Sl. No.	Author(s)	Application Area
1	Kabra and Ramesh (2015)	Exploring and prioritizing barriers to humanitarian supply chain
2	Prakash and Barua (2015)	Prioritizing solutions to overcome reverse logistics barriers
3	Vishwakarma et al. (2016)	Assessment barriers of pharmaceutical supply chain risk
4	Kumar and Garg (2017)	Evaluation of sustainable supply chain indicators
5	Mangla et al. (2017)	Prioritization of challenges of sustainable consumption and production
6	Mahtani and Garg (2018)	Assessment of financial distress factors in India’s airline industry
7	Kumar and Dixit (2018)	Identification and selection of Waste Electrical and Electronic Equipment (WEEE) recycling partners
8	Narayanan et al. (2019)	Analysis of barriers to implementing sustainable practices in India’s rubber industry
9	Ocampo (2019)	Prioritization of strategies for manufacturing sustainability
10	Singh and Sarkar (2019)	Overcoming the barriers to eco-design implementation

Source: The authors.

As per the extent analysis method developed by Chang (1996), extent analysis is performed on each criterion g_i.

M_{g i}^{1}, M_{g i}^{2}, \dots, M_{g i}^{m}, i = 1, 2, 3, 4, \dots, n

The $M_{g i}^{j} (j = 1, 2, 3, 4, \dots, m)$ are triangular fuzzy numbers (TFN), as presented in Table 3.

The steps given by Chang’s analysis are as follows:

Step 1: The value of fuzzy synthetic (S_i) corresponding to the ith criterion is given as:

S_{i} = {\sum_{j = 1}^{m} M_{g i}^{j} \times [\sum_{i = 1}^{n} \sum_{j = 1}^{m} M_{g i}^{j}]}^{- 1}

(1)

Table 3.

TFN Scale of Linguistic Variables

Linguistic Variables	Fuzzy Triangular Numbers
Equal	(1, 1, 1)
Very low	(1, 2, 3)
Low	(2, 3, 4)
Moderate	(3, 4, 5)
High	(4, 5, 6)
Very strong	(5, 6, 7)
Extreme	(7, 8, 9)

Source: Prakash and Barua (2015).

\begin{array}{l} \sum_{j = 1}^{m} M_{g i}^{j} = (\sum_{j = 1}^{m} l_{i j}, \sum_{j = 1}^{m} m_{i j}, \sum_{j = 1}^{m} u_{i j}) \\ {[\sum_{i = 1}^{n} \sum_{j = 1}^{m} M_{g i}^{j}]}^{- 1} = (\frac{1}{\sum_{i = 1}^{n} \sum_{j = 1}^{m} u_{i j}}, \frac{1}{\sum_{i = 1}^{n} \sum_{j = 1}^{m} m_{i j}}, \frac{1}{\sum_{i = 1}^{n} \sum_{j = 1}^{m} l_{i j}}) \end{array}

where l is the lowest value, m is the most promising value, and u is the highest value.

Step 2: The degree of possibility of

$S_{2} = (l_{2}, m_{2}, u_{2}) \geq S_{1} = (l_{1}, m_{1}, u_{1})$ is defined as

V (S_{2} \geq S_{1}) = {}_{x \geq y}^{sup}{[min (μ_{S_{1}} (x), μ_{S_{2}} (y))]}

V (S_{-} 2 \geq S_{-} 1) = {\begin{array}{l} 1, if m_{2} \geq m_{1} \\ V (S_{2} \geq S_{1}) = 0, if l_{1} \geq u_{2} \\ \frac{l_{1} - u_{2}}{(m_{2} - u_{2}) - (m_{1} - l_{1})} = μ_{d} \end{array}

(2)

where µ_d is the highest junction between fuzzy number $μ_{s_{1}}$ and $μ_{s 2}$ . (See in Figure 2)

Step 3: The degree of possibility for a convex fuzzy number to be greater than k convex fuzzy numbers $S_{i} (i = 1, 2, \dots, k)$ can be defined as:

Figure 2.

Source: The authors.

\begin{array}{l} V (S \geq S_{1}, S_{2}, \dots, S_{k}) = V [(S \geq S_{1}), (S \geq S_{2}), \dots, (S \geq S_{k})] = minV [(S \geq S_{i}), i = 1, 2, \dots, k \\ assuming that d^{'} (A_{i}) = \min V (S_{i} \geq S_{k}) \end{array}

(3)

For $k = 1, 2, \dots, n, (k \neq i),$ the weight vectors are represented by

W^{'} = {(d^{'} (A_{1}), d^{'} (A_{2}), \dots, d^{'} (A_{m}))}^{T}

(4)

Step 4: After normalization, the normalized weight vectors are represented by

W^{'} = {(W'_{1}, W'_{2}, \dots, W'_{n})}^{T}

(5)

An Application of the Proposed Model

The application of the proposed model considers a case of plastic products–manufacturing company located in the northern part of India. The company chosen for this case study started its operations in 1942 and is a leading plastic manufacturer in India. The company was selected due to its vast experience in the manufacturing and recycling of plastic products.

Data Collection

A questionnaire was framed based on the literature review and circulated among experts/decision-makers. The experts’ opinions were sought to identify common barriers that obstruct CSCM implementation in the Indian plastic industry. The decision-makers selected for this study were from both industry and academia, to avoid any bias by industrial experts towards their organization. Eight experts were chosen to finalize the barriers and frame the pairwise decision matrix. All experts are specialized in plastic manufacturing and recycling and have more than 10 years of experience. A total of 24 barriers were finalized under five major categories based upon the literature review and experts’ opinions, as illustrated in Table 1. The graphical representation of the fuzzy AHP framework is provided in Figure 3.

Figure 3.

Source: The author.

Calculation of the Value of Fuzzy Synthetic Extent

The group of decision-makers made a pairwise assessment of barriers and criteria using a TFN scale, as shown in Table 3. The pairwise ratings derived using linguistic statements and expressions of the primary barriers are shown in Table 4.

Table 4.

Pairwise Comparison of the Five Major Barriers

	LB	OB	TB	MRB	FB
LB	(1, 1, 1)	(1, 2, 3)	(1, 2, 3)	(2, 3, 4)	(0.25, 0.33, 0.50)
OB	(0.33, 0.50, 1)	(1, 1, 1)	(0.33, 0.50, 1)	(3, 4, 5)	(0.25, 0.33, 0.50)
TB	(0.33, 0.50, 1)	(1, 2, 3)	(1, 1, 1)	(0.33, 0.50, 1)	(2, 3, 4)
MRB	(0.25, 0.33, 0.50)	(0.20, 0.25, 0.33)	(1, 2, 3)	(1, 1, 1)	(1, 2, 3)
FB	(2, 3, 4)	(2, 3, 4)	(0.25, 0.33, 0.50)	(0.33, 0.50, 1)	(1, 1, 1)

Source: The authors.

Table 5.

Priority Weight of Significant Barriers

Criteria	Preference Weights	Ranking
LB	0.2273	1
OB	0.1848	4
TB	0.2026	3
MRB	0.1675	5
FB	0.2179	2

Source: The authors.

Calculation of fuzzy synthetic weights of five primary barriers are determined using Equation (1), as given below:

S(LB) = (5.25, 8.33, 11.5) × [23.85, 35.07, 48.33]⁻¹ = (0.109, 0.238, 0.482)

S(OB) = (4.91, 6.33, 8.5) × [23.85, 35.07, 48.33] ⁻¹ = (0.102, 0.18, 0.356)

S(TB) = (4.66, 7, 10) × [23.85, 35.07, 48.33] ⁻¹ = (0.096, 0.2, 0.419)

S(MRB) = (3.45, 5.58, 7.83) × [23.85, 35.07, 48.33] ⁻¹ = (0.071, 0.159, 0.328)

S(FB) = (5.58, 7.83, 10.5) × [23.85, 35.07, 48.33] ⁻¹ = (0.115, 0.223, 0.440)

The minimum possible degree is obtained by the use of Equations (2) and (3):

m(LB) = min V(S₁ ≥ S_k) = min (1, 1, 1, 1) = 1

The other values are m(OB) = 0.813, m(TB) = 0.891, m(MRB) = 0.737, and m(FB) = 0.958.

The weight vector is determined by:

W_v = (1, 0.813, 0.891, 0.737, 0.958)^T

The final weight values are calculated after normalization as:

W = (0.2273, 0.1848, 0.2026, 0.1675, 0.2179)

The final weights of the main barriers and their ranking are shown in Table 5.

According to Table 5, the ranking of the main barriers is LB > FB > TB > OB > MRB.

Similarly, the pairwise assessment of sub-barriers was carried out. The same procedure was used to evaluate the relative weights of sub-barriers and rank them, as shown in Tables 6 –10.

Table 6.

Pairwise Comparison of Sub-barriers in ‘LB’

	LB1	LB2	LB3	LB4
LB1	(1, 1, 1)	(2, 3, 4)	(0.33, 0.5, 1)	(3, 4, 5)
LB2	(0.25, 0.33, 0.5)	(1, 1, 1)	(0.33, 0.5, 1)	(1, 2, 3)
LB3	(1, 2, 3)	(1, 2, 3)	(1, 1, 1)	(0.25, 0.33, 0.5)
LB4	(0.2, 0.25, 0.33)	(0.33, 0.5, 1)	(2, 3, 4)	(1, 1, 1)

Source: The authors.

Table 7.

Pairwise Comparison of Sub-barriers in ‘OB’

	OB1	OB2	OB3	OB4	OB5
OB1	(1, 1, 1)	(3, 4, 5)	(0.33, 0.5, 1)	(2, 3, 4)	(0.2, 0.25, 0.33)
OB2	(0.2, 0.25, 0.33)	(1, 1, 1)	(2, 3, 4)	(0.25, 0.33, 0.5)	(1, 2, 3)
OB3	(1, 2, 3)	(0.25, 0.33, 0.5)	(1, 1, 1)	(1, 2, 3)	(0.33, 0.5, 1)
OB4	(0.25, 0.33, 0.5)	(2, 3, 4)	(0.33, 0.5, 1)	(1, 1, 1)	(0.33, 0.5, 1)
OB5	(3, 4, 5)	(0.33, 0.5, 1)	(2, 3, 4)	(1, 2, 3)	(1, 1, 1)

Source: The authors.

Table 8.

Pairwise Comparison of Sub-barriers in ‘TB’

	TB1	TB2	TB3	TB4	TB5
TB1	(1, 1, 1)	(0.2, 0.25, 0.33)	(3, 4, 5)	(0.25, 0.33, 0.5)	(1, 2, 3)
TB2	(3, 4, 5)	(1, 1, 1)	(0.33, 0.5, 1)	(0.2, 0.25, 0.33)	(0.25, 0.33, 0.5)
TB3	(0.2, 0.25, 0.33)	(1, 2, 3)	(1, 1, 1)	(2, 3, 4)	(1, 2, 3)
TB4	(2, 3, 4)	(3, 4, 5)	(0.25, 0.33, 0.5)	(1, 1, 1)	(1, 2, 3)
TB5	(0.33, 0.5, 1)	(2, 3, 4)	(0.33, 0.5, 1)	(0.33, 0.5, 1)	(1, 1, 1)

Source: The authors.

Table 9.

Pairwise Comparison of Sub-barriers in ‘MRB’

	MRB1	MRB2	MRB3	MRB4	MRB5
MRB1	(1, 1, 1)	(0.25, 0.33, 0.5)	(0.2, 0.25, 0.33)	(3, 4, 5)	(0.25, 0.33, 0.5)
MRB2	(2, 3, 4)	(1, 1, 1)	(2, 3, 4)	(0.33, 0.5, 1)	(1, 2, 3)
MRB3	(3, 4, 5)	(0.25, 0.33, 0.5)	(1, 1, 1)	(1, 2, 3)	(0.2, 0.25, 0.33)
MRB4	(0.2, 0.25, 0.33)	(1, 2, 3)	(0.33, 0.5, 1)	(1, 1, 1)	(2, 3, 4)
MRB5	(2, 3, 4)	(0.33, 0.5, 1)	(3, 4, 5)	(0.25, 0.33, 0.5)	(1, 1, 1)

Source: The authors.

Table 10.

Pairwise Comparison of Sub-barriers in ‘FB’

	FB1	FB2	FB3	FB4	FB5
FB1	(1, 1, 1)	(2, 3, 4)	(1, 2, 3)	(0.2, 0.25, 0.33)	(2, 3, 4)
FB2	(0.25, 0.33, 0.5)	(1, 1, 1)	(0.33, 0.5, 1)	(3, 4, 5)	(0.33, 0.5, 1)
FB3	(0.33, 0.5, 1)	(1, 2, 3)	(1, 1, 1)	(2, 3, 4)	(0.25, 0.33, 0.5)
FB4	(3, 4, 5)	(0.2, 0.25, 0.33)	(0.25, 0.33, 0.5)	(1, 1, 1)	(0.33, 0.5, 1)
FB5	(0.25, 0.33, 0.5)	(1, 2, 3)	(2, 3, 4)	(1, 2, 3)	(1, 1, 1)

Source: The authors.

Finally, the global weights of each sub-barrier were obtained through multiplying the relative weights of the main barriers with the relative weights of the sub-barriers. Using the global weights of each barrier, the global ranking of CSCM implementation barriers was established, as presented in Table 11.

Results and Discussions

The fuzzy AHP approach is logical and removes the difficulty involved in the comparison of two barriers. It assists decision-makers in selecting the most critical barrier through prioritizing and ranking processes. In this article, this methodology has been used to analyse the barriers to implementing CSCM in the Indian plastic industry. It motivates organizations to understand the hindrances present in implementing the circular system in their supply chain and encourages them to move towards the path of sustainability. It presents the ranking of barriers based on their prioritized weightage values. It declares that the legislative, financial, technical, organizational and market-related barriers stand in decreasing order, as presented in Table 5. Legislative barriers are the most prominent barriers to CSCM adoption. One of the viable ways to execute CSCM implementation is through the formulation of government policies and frameworks, but these have not been well greeted by organizations (Luthra et al., 2016; Prakash & Barua, 2015). The results are in conformance with past studies (Govindan & Hasanagic, 2018) that also found legislative policies and regulations to be one of the critical barriers to adopting circular practices. Poor enforcement of policies and regulations may adversely affect the intention of organizations to adopt circular practices (Bhandari et al., 2019). The relative importance of the sub-criteria under the category of legislative barriers is LB4 > LB1 > LB3 > LB2, which depicts that the lack of tax rebate policies to promote CSCM and weak enforcement of rules and regulations relating to environmental protection are the major hurdles in adopting CSCM. Policies with regard to taxation on the collection and treatment of waste will encourage the organizations to shift from linear to circular model (Mangla et al., 2018). The current policies formed by the government to make plastic companies adopt circular practices have received a backlash from organizations due to their stringent nature and unclear procedure (Aryan et al., 2019). The findings corroborate with previous studies that found that lack of tax rebate policies is a major challenge to initiating circular practices (Khandelwal & Barua, 2020; Mathiyazhagan et al., 2016; Narayanan et al., 2019; Rizos et al., 2016; Singh & Sarkar, 2019; Sivakumar et al., 2018). Financial barriers ranked second among the main barriers; financial support is necessary for plastic organizations for their adoption of CE practices, but despite the need, a proper capital investment system is not developed. Plastic organizations are facing difficulty in securing financial resources for waste management (Zhang et al., 2019). In line with previous studies (Agyemang et al., 2019; Bhandari et al., 2019), the cost- and finance-related factors are found to be the most relevant barriers to implementing CE. Among the financial category’s sub-barriers, the high cost of eco-friendly materials’ purchase and packaging acts as a major impediment and carries the topmost priority, and the sub-barriers are ranked as FB1 > FB5 > FB3 > FB2 > FB4. Infrastructure and technology are necessary for waste collection and segregation, but they require financial support from public and private bodies, which are often unable to provide support (Prakash & Barua, 2015). Third among the major barriers is technical barriers. Technology and innovation capability play an important role in the adoption of CE practices, but plastic organizations lack an effective circular model and technical expertise that would help them design for the end of life of products (Sivakumar et al., 2018). In the plastic industry, technology backwardness is attributed to lack of eco-literacy, which poses a challenge for plastic organizations in developing efficient waste management mechanisms and tracking the flow of materials. The sub-criteria ranking of technical barriers is TB4 > TB3 > TB1 > TB5 > TB2, which implies that lack of availability of resources to implement CSCM is the most significant sub-barrier in this category. For any organization to sustain itself in the long run, resource capabilities are necessary (Satapathy, 2017). Poor resource capability hinders plastic companies from recycling and reusing products or materials. The deficiency of resources acts as a major barrier for CSCM implementation (Gupta & Barua, 2018). Other than these sub-barriers, poor cohesive reverse logistic network to transport recovered plastic is also a significant barrier in this category. The same is discovered by Sivakumar et al. (2018). Organizational barriers’ ranking stands as OB5 > OB1 > OB2 > OB3 > OB4, implying that the very few incentives to promote CSCM and inadequate support and commitment from the management rank highest in this category. Due to several political, economic, social and technical developments and the competitive market, the management has to form policies and strategies to shift towards circular business practices (Gupta & Barua 2016; Kumar & Dixit, 2018; Luthra et al., 2016; Prakash & Barua, 2015). Finally, market-related barriers’ ranking is MRB2 > MRB5 > MRB3 > MRB4 > MRB1, which depicts that in this category, lack of customer awareness about the return of used products is the most critical barrier in the way of implementing CSCM.

Table 11.

Weights of Barriers and Sub-barriers

Criteria	Relative Weights	Barriers	Relative Weights	Global Weights	Rank
LB	0.2273	LB1	0.3078	0.0696	2
		LB2	0.1456	0.0329	19
		LB3	0.2245	0.0508	6
		LB4	0.3221	0.0728	1
OB	0.1848	OB1	0.2383	0.0425	8
		OB2	0.1799	0.032	18
		OB3	0.1641	0.0292	22
		OB4	0.141	0.0251	23
		OB5	0.2767	0.0493	5
TB	0.2026	TB1	0.1948	0.0389	12
		TB2	0.1495	0.0299	21
		TB3	0.2145	0.0428	9
		TB4	0.2541	0.0507	4
		TB5	0.1871	0.0374	15
MRB	0.1675	MRB1	0.1464	0.0232	24
		MRB2	0.2427	0.0385	11
		MRB3	0.1998	0.0317	17
		MRB4	0.182	0.0289	20
		MRB5	0.2291	0.0363	13
FB	0.2179	FB1	0.2431	0.0577	3
		FB2	0.1754	0.0416	14
		FB3	0.1916	0.0455	10
		FB4	0.1643	0.039	16
		FB5	0.2256	0.0536	7

Source: The authors.

Sensitivity Analysis

According to Chang et al. (2007) and Gupta and Barua (2018), a small variation in the relative weights would result in a change in the final ranking of barriers. The sensitivity analysis is conducted to validate the consistency in barriers’ ranking with the variation in the relative weights of criteria for implementing CSCM (Alora & Barua, 2019). In this study, the legislative barrier receives high priority so that it can influence the other barriers. The legislative barrier’s value is increased from 0.1 to 0.9, with 0.1 increments, and the related changes in the other criteria’s ranking are obtained. The results show that technical barrier weight varies the most, as shown in Table 12. Along with the difference in the main barriers, the specific sub-barriers’ ranking also varies. The variation in the weight of legislative barrier to 0.1; the high cost of eco-friendly materials’ purchase and packaging (FB1) holds the first rank, and lack of global standards to measure the performance of CSCM (LB2) holds the lowest position. Lack of tax rebate policies to promote CSCM (LB4) holds the first rank, after the normalized value of 0.227. Figure 4 indicates the differences in the ranking of criteria. The results of the sensitivity analysis are summarized in Table 13.

Table 12.

Variation in Weights of Major Barriers After Increasing the LB Weight Value

Criteria	Normalized Weight	Run 1	Run 2	Run 3	Run 4	Run 5	Run 6	Run 7	Run 8	Run 9
LB	0.227	0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	0.9
OB	0.185	0.215	0.191	0.167	0.144	0.12	0.096	0.072	0.048	0.024
TB	0.203	0.236	0.21	0.184	0.157	0.131	0.105	0.079	0.053	0.026
MRB	0.168	0.195	0.173	0.152	0.13	0.108	0.087	0.065	0.043	0.022
FB	0.218	0.254	0.226	0.197	0.169	0.141	0.113	0.085	0.056	0.028

Source: The authors.

Table 13.

Ranking of Barriers After Sensitivity Analysis

Identified Barriers	0.1	0.2	Normalized (0.227)	0.3	0.4	0.5	0.6	0.7	0.8	0.9
LB1	20	2	2	2	2	2	2	2	2	2
LB2	24	22	19	8	4	4	4	4	4	4
LB3	23	9	6	3	3	3	3	3	3	3
LB4	19	1	1	1	1	1	1	1	1	1
OB1	5	7	8	9	9	9	9	9	9	9
OB2	15	18	18	19	19	19	19	19	19	19
OB3	18	20	22	21	21	21	21	21	21	21
OB4	21	23	23	23	23	23	23	23	23	23
OB5	3	5	5	6	7	7	7	7	7	7
TB1	9	12	12	13	13	13	13	13	13	13
TB2	17	21	21	22	22	22	22	22	22	22
TB3	6	8	9	10	10	10	10	10	10	10
TB4	2	4	4	5	6	6	6	6	6	6
TB5	12	15	15	16	16	16	16	16	16	16
MRB1	22	24	24	24	24	24	24	24	24	24
MRB2	8	11	11	12	12	12	12	12	12	12
MRB3	14	17	17	18	18	18	18	18	18	18
MRB4	16	19	20	20	20	20	20	20	20	20
MRB5	10	13	13	14	14	14	14	14	14	14
FB1	1	3	3	4	5	5	5	5	5	5
FB2	11	14	14	15	15	15	15	15	15	15
FB3	7	10	10	11	11	11	11	11	11	11
FB4	13	16	16	17	17	17	17	17	17	17
FB5	4	6	7	7	8	8	8	8	8	8

Source: The authors.

Figure 4.

Source: The authors.

Conclusion

Organizations have initiated the implementation of circular practices in their supply chains due to the growing risk of resource scarcity and pressure to shift towards sustainable business. However, in developing nations like India, the presence of several challenges makes it tough for efficient implementation of CSCM in the plastic industry. Considering real-world application, decision-makers are unable to make sound decisions due to the presence of a multitude of barriers that makes it hard to overcome all of them simultaneously. This study identifies and prioritizes the barriers based on a robust MCDM method for CSCM implementation in the Indian plastic industry. It includes 24 barriers under five major categories found through a literature review and consultation with experts. The fuzzy AHP methodology was applied to obtain the ranking of influence of each barrier. A plastics company’s empirical case supports the projected approach. The results of this study exhibit that unsupportive government taxation policies, weak enforcement of rules and regulations, high cost of eco-friendly materials’ purchase and packaging and informal waste collection mechanisms are the most prominent barriers to CSCM implementation. The findings of this study would help decision-makers and government bodies form a set of comprehensive and concrete regulations that motivate organizations to implement CSCM. Finally, a sensitivity analysis was performed to measure the variability in the ranking of barriers while varying the criteria’s weights.

Managerial Implications

This study has several implications for business managers, decision-makers and policymakers regarding implementing CSCM in India. It offers support to extend the understanding of the significant barriers to implementing circular practices in the supply chain. The empirical study of the Indian plastic industry provides an exhaustive list of barriers to adopting CSCM that may help managers understand and formulate strategies accordingly to eradicate them most effectively. The findings of this study put forward the priority ranking of barriers, which depicts that legislative barriers in the way of effective adoption of CSCM in the Indian plastic industry hold the highest priority. Presently, plastics are not actively managed after end of life due to a stringent policy framework and high involvement of informal sectors. Therefore, the government may develop policies favouring organizations’ adoption of circular practices. The findings of this article also highlight that the government must come up with economic incentives and tax benefits for organizations to build technology and innovation. These initiatives may help boost organizations’ interest to shift towards CSCM. Also, managers are advised to conduct training and awareness programmes to enhance the knowledge and skills of stakeholders for recyclability. The findings of this study not only assist the case company to eradicate significant barriers but also enable it in moving towards sustainable development, thus enhancing the nation’s economy.

Limitations

This study is limited to identifying and prioritizing barriers in the Indian plastic industry to implementing CSCM. Future studies can investigate some other barriers to implementing CSCM in different sectors. Here, the ranking of barriers is evaluated, which can be further analysed to measure the causal relationship among barriers through the fuzzy DEMATEL approach. Further, the proposed theoretical model can also be validated using structural equation modelling. The results of this study can also be compared using different MCDM techniques like TOPSIS and VIKOR under a fuzzy environment.

Footnotes

Acknowledgement

The authors are grateful to the anonymous referees of the journal for their extremely useful suggestions to improve the quality of the article. Usual disclaimers apply.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Chitranshu Khandelwal

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