Modeling a Reverse Logistics Supply Chain for End-of-Life Vehicle Recycling Risk Management: A Fuzzy Risk Analysis Approach

Abstract

The automotive industry is one of the largest consumers of natural resources, and End-of-Life Vehicles (ELVs) form bulky wastes when they reach the end of their useful life, hence environmental concerns. Efficiency in recycling ELVs is therefore becoming a major concern to address the number of ELVs collected and recycled to minimize environmental impacts. This paper seeks to describe several activities of a closed-loop reverse logistics supply chain for the collection and recycling of ELVs and to identify the related potential risks involved. This study further investigated the potential risks for managing the efficient recycling of ELVs by modeling and viewing the end-of-life vehicle (ELV) recycling system as a reverse logistics supply chain. ELV recycling steps and processes, including collection and transportation, as well as the laws and technologies, were analyzed for risk factor identification and analysis. The major aim of this research is to perform a unified hierarchical risk analysis to estimate the degree of risk preference to efficiently manage the ELV supply chain. This study also proposes a risk assessment procedure using fuzzy knowledge representation theory to support ELV risk analysis. As a result, the identified key risks were ranked in terms of their preference for occurrence in a reverse supply chain of ELV products and mapped into five risk zones, Very Low, Low, Medium-Low, Moderate, Serious, and Critical, for ease of visualization. Hence, with a step-by-step implementation of the presented solution, ELV recycling organizations will see benefits in terms of an improvement in their activities and thus reduced costs that may occur due to uncertainties in their overall ELV business.

1. Introduction

The recycling of ELVs has gained much attention in recent years around the globe, as a vital source of secondary raw materials in major global economies, when reused and recycled [1,2]. ELV recycling helps close the sustainable resources loop and decreases the demand for primary raw materials [2,3]. The recycling of ELVs is a source of secondary raw materials for use in industry, including ferrous and non-ferrous metals, rubber, glass, and other reusable components [1,2,4]. The circular economy concept that emphasizes reusing and recycling ELV products has recently been widely adopted in many developed and emerging economies. This calls for the efficient management of the ELV supply chain for the maximum reclamation of ELV value [5,6]. Increasing environmental awareness campaigns and recognizing valuable resources in waste products in creating wealth from waste have raised concerns about managing ELV supply chains for optimum efficiency [7,8]. Vehicle ownership has drastically increased globally, increasing the number of ELVs containing valuable secondary resources [9,10]. The literature indicates that approximately 26 million vehicles were produced in 2021 after the COVID-19 pandemic [11]. The poor management of ELVs has been reported as the primary cause of various ELV environmental issues. Groundwater and heavy metal soil pollution are common problems resulting from the improper management of ELVs [12,13]. Except for some developed economies, most countries around the globe are deprived of efficient strategies and recycling systems to deal with the increasing number of ELVs [10]. Therefore, it has become crucial to carry out studies to identify the various risks involved in ELV supply chains that contribute to inefficient and sustainable ELV management to achieve sustainability in the context of both environmental and resource efficiency.

Hence, this research systematically analyzes the risks involved in the management and recycling of ELVs. The study’s primary aim is to present a detailed analysis of the risks involved in the ELV supply chain. Specifically, a qualitative analysis was performed to analyze the existing risk trends and answer the following research questions:

• RQ1: What risk factors are occurring in the ELV recycling system?
• RQ2: What is the likelihood of the occurrence and effects of various identified risks?
• RQ3: How to rank various risks in the ELV recycling system?

In ELV recycling systems, both natural and premature ELVs can go back to the ELV supply network from various sources, including owners, accident vehicles, insurance companies, police yards, and fleet companies that own a majority of the ELVs [2,14].

The ELV material reprocessing activities include collection, manual dismantling, sorting, inspection, testing, mechanical and thermochemical processing, and landfilling [4,15]. Figure 1 shows the flowchart for the environmentally friendly ELV recycling process.

Each reprocessing activity is influenced by both internal and external risks worth noting [16]. ELV collection, manual sorting, dismantling, inspection, testing, and thermomechanical processes are characterized by internal risks associated with planning and forecasting, procurement, manufacturing, transportation, inventory management, distribution and warehousing, finance, management, and customer service. When ELVs are collected and received at the reprocessing center, all the liquids (motor oil, brake oil, refrigerant) as well as hazardous material pollutants, including mercury, cadmium, chromium, zinc, etc., which could be discharged to the environment and contaminate the natural resources to a greater extent [16], constitute external risks. The proper management of ELVs is an important part of sustainable transport concepts [2,4,17].

Thorough risk identification and analysis of each ELV recycling activity is vital in ensuring an efficient risk management system, preventing losses that could occur, and guaranteeing an efficient ELV recycling system for maximum returns on investments [18]. However, most ELV recycling systems need to consider the probability of uncertain risks, which may affect the efficiency of reprocessing all ELV materials. This ignorance can affect the system’s performance in various areas, including finance and unsatisfied recycling demands. Most developing countries are unaware of the losses they face due to the lack of risk management strategies. Risk management plays a significant role in the overall revenue of ELV recycling businesses, and, thus, the net income of their GDP. It has become critical, necessitating a logical analysis of the impact and occurrence of risks involved in ELV recycling activities. In today’s business sectors, the tremendous change in a business strategy results in more competition toward achieving a competitive advantage over lower costs and the ability to meet customer satisfaction through reverse logistics. ELV recycling systems are experiencing growing pressures from environmental advocacy groups, non-governmental organizations, and some customers related to their supply chains [19]. Stakeholders demand corporate sustainability, non-financial accounting, reporting, procurement, and supplier relations [20,21]. In addition, modern enterprises operate in highly competitive markets, which also determines the need for managers to make effective decisions on vehicle recycling [22]. ELV recycling systems are expected to deliver a simultaneous balance of economic, environmental, and social society, resulting in long-term economic benefits. Developing and emerging countries must improve and sustain their ELV recycling systems to remain competitive, and cost-effectively accommodate this unpredictable business situation. Therefore, the decision to carry out risk analysis in the reverse logistics functions has been proven beneficial toward gaining an increasing advantage in the current global market [23].

A holistic view is presented concerning various risks relating to ELV supply chain management and recycling. Literature gaps are ultimately identified to suggest valuable future research opportunities.

The rest of this paper is organized in the following manner: The second section describes the research materials and methods adopted in this study, including the risk management framework and research methodology. Next, Section 3 presents the results of the research methodology including the identification of risk factors, the assessment of risk factors, and ratings. A discussion of the obtained results is described in Section 4. Finally, Section 5 presents concluding remarks, and insightful recommendations for future research, as well as other presented research limitations.

2. Materials and Methods

A risk is a potential future loss or undesirable outcome that may arise from some present action [24]. Risk factors are defined as sources that can pose a severe threat to the outcome. On the contrary, risk assessment determines the quantitative/qualitative value of risk related to a specific situation and a well-recognized threat. Although some of the individual risk factors are more significant than others, the successful recycling of ELVs is often dependent on the effective management of risks, response strategies used to assess risks, and the capacity of the system to overcome them. Therefore, it is necessary to develop a unified risk understanding model containing perceived risks related to ELV systems and factors that affect the manageability of these risks. An exhaustive literature review reveals that limited studies have highlighted essential sources of risks and associated risk-influencing factors in ELV recycling systems [25]. The previous literature has mainly demonstrated various factors that could strengthen ELV management and recycling systems [26]. The concept of ELV management and recycling is based on the principle of extended producer responsibility (EPR), which holds manufacturers responsible for their goods through third-party (3PLs) and fourth-party (4PLs) logistics partners. It requires manufacturers to take over their end-of-life products and encourage consumers to deliver their used goods to authorized recyclers [27]. However, EPR also requires the systematic management of ELVs involving such activities as collection, reporting, reverse flow, recycling, etc. This complicates the ELV supply chain, necessitating the identification and analysis of the risks involved and shared by each stakeholder. In a study by Zhou et al., they stated that ELV management and recycling are affected by several factors across different levels, including consumers, industrial organizations, and authorities [28].

Moreover, the concept of risk needs to be more consistent and utilized to analyze various issues such as risk assessment, mitigation, and the development of best practices [19], including ELV recycling. In a study by Kou and Lu, they pointed out that personal knowledge, experience, and intuitive judgment provide a better risk assessment than the probabilistic approach [25]. They further highlighted the applicability of fuzzy set theory for risk assessment in capturing the individual intuitive assessment. Risk management is an organized/structured approach, which is useful for identifying, mitigating, and assessing/evaluating the risks of reduced losses incurred due to the absence of risk management [29]. It includes four steps, namely risk classification, risk identification, risk assessment, and risk response [30], as summarized in Figure 2.

The investigation begins with the major ELV recycling activities as well as the identification of risks associated with the ELV recycling system, including the internal and external risk factors. To aid in ELV risk management, “a fuzzy risk analysis approach” is used in the analysis and ranking of the risks according to their preference of occurrence. The expected results representing the outcomes of such a system are also discussed.

Based on the initial literature review made within the SCOPUS database, the authors have identified a research gap in the case of modeling the reverse logistics supply chain, with regard to the management of risks in the recycling of ELVs, as shown in

Table 1. Initial literature review results (SCOPUS database).

Research Topic		Research Object		Research Subject		Research Scope
Modeling OR model	14,433,770	Reverse logistics OR reverse supply chain OR reverse flow	50,053	Risk management	243	End-of-Life Vehicle (ELV)	2

.

With reference to the results presented in Table 1, it was seen that modeling is a common research topic. However, reverse logistics is shown to be an underexplored aspect of modeling research. The research subject of risk management is also narrowly studied, as only 0.049% of works within reverse logistics relate to this subject. Concerning the analysis of the scope of the research to present approaches to modeling risk management in ELV recycling, two studies were identified, including the studies by Ayvaz et al. and Zhang et al. [31,32]. Further analysis of these works proved that there is a research gap in this topic as these works did not consider risk management in ELV recycling. Considering the above, the following research questions have been identified, RQ1–RQ3, described in the Introduction section. This paper aims at answering these research questions. The research methodology is presented in Figure 3.

With reference to Figure 3, the research steps include the literature review and brainstorming to identify the risk factors in the ELV recycling system; a survey for risk factors assessment by experts; risk factors evaluation using fuzzy logic; risk rating and analysis using the incenter centroid method. In Section 3, the results are presented, including details of the steps/approach adopted.

3. Results

3.1. Identification of Risk Factors

There is a suggested two-step approach consisting of a literature review and brainstorming to identify the risk factors.

Risk is a concept that has been studied in business, engineering, social sciences, and information and communication technology (ICT) areas [33,34,35,36,37,38]. Risk is the likelihood of an uncommon event, which has adverse effects detrimental to an organization [34]. Risks depend on the probability of event occurrence, the number of possible consequences, the significance of such implications [34,39], and the route that leads to the event [38]. More specifically, supply chain risk is any potential variation of consequences affecting the decrease in value added at any activity cell in a chain, where the outcome is explained through the quantity and quality of goods at any location and time in a supply chain flow [40]. Several risks have been studied and identified in the traditional forward supply chain of goods [41].

Reverse logistics is returning products from customers to capture their values or ensure appropriate disposal [42]. In this study, ELV reprocessing or ELV recycling is considered a reverse logistics supply chain since it includes most, if not all, of the activities of a reverse supply chain, including collection, sorting, transportation, reprocessing or recycling, etc. Therefore, several risks and uncertainties associated with the timing, returned product quality, produced quantities of ELV products, and various returns have been identified and associated as some of the critical risks involved in the ELV reverse supply chain [43]. Furthermore, risks involving consumer behavior and preferences, estimating asset value, the cost of operations, and decision-making for product returns along the reverse supply chain are also discussed.

The management of ELV involves collecting and sorting ELV materials for reprocessing. Therefore, an elaborate and sustainable reverse supply chain is required to sustain the ELV reprocessing business (see Figure 1). Although many performance measures and supply network risk tools appropriate for traditional supply chains have been developed, these existing measures and tools are inadequate for use in the reverse supply chain [41]. Risk analysis in the reverse supply chain for handling ELVs has yet to be measured, and there has been no adequate previous research on their management, which justifies research on this topic as a research gap identified.

One of the most significant challenges of the reverse supply chain is the uncertainty associated with product returns, which makes forecasting and planning returned products challenging. Supply chain risk falls into various categories according to its impact on the organization and business environment [24,40,44]. Moreover, risks can be organized while considering the levels of risk sources [45,46]. Risk sources include organizational, environmental, or supply-chain-related variables that might not be certainly anticipated and may impact the supply chain’s outcome variables [47]. In a study by Kleindorfer and Saad, supply chain risks were categorized into two classes, including risks associated with the coordination of supply and demand, and those associated with disruptions to normal activities [48]. In another study by Christopher and Peck, they classified risks into five groups: (1) internal to the company: (i) control; (ii) process; (2) external to the company but internal to the supply network: (i) demand; (ii) supply; and (3) external to the supply network: (i) environmental [49]. According to Ritchie and Brindley, seven sources of risks were identified, including industry characteristics, environmental characteristics, supply chain members, the organization’s strategy, the configuration of the supply chain, problem-specific variables, and decision-making units [38]. Cucchiella et al., in their study, identified and categorized risks based on the sources of uncertainty considering the following risk categories: (a) supplier quality, (b) available capacity, (c) manufacturing yield, (d) internal organization, (e) information delays, (f) competitor action, (g) political environment, (h) stochastic cost, and (i) customs regulations and price fluctuations [50]. Gaonkar et al. categorized risks per event leading to risk—disaster, deviation, and disruption [51].

Based on the various identified risk classes in the supply chain from the literature review and ICT-related risks, which are well-developed, the authors held a brainstorming session with experts to systematically identify and apply some of the risk factors ($F_{ij}$) to the reverse supply chain relevant to the recycling of ELVs. In each risk class (j), specific risk drivers (i) that are likely to influence the outcome of risk impact have been identified and tabulated. Although the list may not be conclusive, it presents a guide to the research question of risk identification and the effects of each on the development of the ELV risk management framework, as pointed out in RQ1. The result of this research stage is a list of ELV recycling risk factors, presented in

Table 2. ELV recycling risk classes, risk drivers, and risk factors.

Risk Class (j)	Specific Risk Drivers (i)	Risk Factor ID (Fij)	Reference
Managerial Risk, R1	Management/country’s inattention to ELV recycling	F11	[27,47,52]
	Lack of ELV return policies	F21
	Lack of understanding of the strategic importance of ELV reprocessing	F31
	Lack of conflict management in the ELV chain	F41
	Unclear decision-making process in ELV activities	F51
	No standardized processes and procedures	F61
Collection and Transport Risk, R2	Disruptions of collection and transportation of ELV materials due to poor network coordination	F12	[28,52,53,54,55,56]
	Delays due to high-capacity utilization, insufficient transport infrastructure, etc.	F22
	Unidentified and unauthorized ELV/parts returns	F32
	Risk of hazardous ELV material	F42
	Inability to satisfy ELV owner compensation demands	F52
	Customers’ loss of confidence in the ELV recycling process	F62
	Lack of use of ELV decision support systems	F72
	Disagreement over the condition, status, and value of returned ELV parts/materials	F82
Information Technology (IT) Systems Risk, R3	IT infrastructure breakdown	F13	[52,57,58,59]
	Task complexity due to extent of networking and data requirements	F23
	Lack of use of IT in ELV activities	F33
	System incompatibility with new ELV IT solutions	F43
	Lack of key IT technical personnel	F53
	Technological discontinuity or obsolescence	F63
	Inability to fulfill reprocessing activities due to incorrect/insufficient ELV data	F73
	ELV information/data loss	F83
	Lack of updated ELV information in the database	F93
ELVsInventory Risk, R4	Lack of capacity to handle ELV return volumes	F14	[44,60,61]
	Loss/damage of ELV materials’ value in storage/during transportation	F24
	Poor ELV return volume forecasts	F34
	Unknown total costs of ELV recycling operations	F44
	Lengthy ELV reprocessing and disposal cycle time	F54
Financial Risk, R5	Unknown total costs of ELV reprocessing	F15	[62,63,64,65,66]
	Lack of proper planning and budgeting for ELV recycling	F25
	Hidden ELV reprocessing costs	F35
	Financial constraints of the company	F45
	Increased costs of services (labor, facilities)	F55
	High costs of ELV owners’ compensation	F65
	High costs of ELV recycling or disposal	F75
Environmental Risk, R6	ELV pollution measurement challenges	F16	[67,68,69,70,71,72,73]
	Lack of adequate corporate social responsibility	F26
	Lack of adequate environmental guidelines on ELV reprocessing	F36
	Noncompliance with governmental/legal guidelines	F46
	Resistance from the local community	F56
	Risk of hazardous material leakages	F66
	Lack of technology, expertise, and/or experience in ELV recycling	F76
Partners’ Relationships Risk, R7	Inadequate terms and ambiguous contracts between ELV supply chain partners	F17	[74,75,76,77,78,79]
	ELV supply chain partners’ poor service quality	F27
	Lack of transparent information sharing among ELV recycling partners	F37
	Disagreement over conditions and value of ELV returns/warranties	F47
	Timeliness of response among ELV partners	F57
	Loss of confidence among ELV partners	F67
	High demand from ELV owners/partners	F77
Outsourcing Risk, R8	Inadequate terms and conditions of ELV collection and reprocessing contract	F18	[80,81,82,83,84,85]
	Third-/fourth-party logistics partners’ poor service quality	F28
	Outsourcing partners with a lack of ELV experience and expertise	F38
	Unknown outsourcing hidden costs	F48
	Loss of privacy and intellectual property	F58
	Inflexibility of partners toward changes	F68
	Lack of transparency and information sharing	F78
	Lack of financial stability to deliver services	F88
	Lack of third-party service providers’ top management level involvement in risk assessment	F98
Legal Risk, R9	Different global rules and regulations in handling and reprocessing ELVs	F19	[86,87,88,89]
	Uncertainty about the legal environment	F29
	Fear of loss of privacy and intellectual property	F39
	Risk of hazardous material effects	F49
	Cost of legal expertise	F59
	Changing company/partner policies	F69
Time Management Risk, R10	No proper follow-ups	F1,10	[59,63]
	Not paying attention to details at the starting stages	F2,10
	Deadlines not met	F3,10
	Transport delays	F4,10
	Less manpower	F5,10
	ELV return order processing delays	F6,10
Culture Risk, R11	Resistance to applying ELV recycling technology	F1,11	[39,90,91,92,93]
	Language barriers	F2,11
	Different customs and cultures	F3,11
	Resistance to change	F4,11

.

The effectiveness of risk management is achieved by the critical review of the risk management framework, which includes identifying, assessing, and managing risks. A hierarchical framework has been proposed in this study to facilitate the process of risk identification in ELV reprocessing practices. Eleven potential risk classes, including managerial risks, collection and transport risks, IT system risks, financial risks, legal risks, inventory risks, environmental risks, relationship risks, outsourcing risks, culture risks, and time management risks, have been identified and associated with ELV reprocessing systems from the review of past supply chain logistics literature, as shown in Table 2. The identified risks were then assessed, as described in Section 3.2.

3.2. Risk Factor Assessment

The next stage of this study was conducted with the use of an online survey. The objective of this stage was to obtain information about the occurrences and impacts of the identified risk factors included in the research by expert decision-makers (DMs), who have experience in ELV recycling. The survey was developed with the aim of collecting information on possible risks encountered while performing the ELV recycling activities, their likelihood of occurrence, and the impact of each risk class.

The research was conducted between 1 and 30 March 2022, where an online survey with the use of Google Forms was prepared, consisting of 10 questions and 157 sub-questions. In the survey, open and closed questions were used to obtain the subjective and objective opinions of experts and DMs. Twenty-five experts and DMs were carefully picked from academia and industry after considering their years of experience in research and practice related to ELV recycling. After the experts’ selection, they were invited via email to take part in the research survey. From the survey, eleven valuable responses were gathered (44% responsiveness rate). A total of nine DMs from academia and two from industry provided their judgment in linguistic terms based on the fuzzy linguistic scales provided in the survey questionnaire. The demographics of the respondents included Malaysia (2), Poland (2), Japan (1), Romania (2), the United Kingdom (1), Kenya (2), and Cameroon (1). The sampling of the experts was considered sufficient for this study considering the limitations associated with the use of online surveys in qualitative studies. Both the data sets have been separately collected from the group of DMs. The list of questions selected for the survey at this stage of the study is described in Table S4. Two sets of linguistic data were collected from the expert group on the assessment of the likelihood of the occurrence and impact of risk for each of the risk factors. Therefore, this stage of the study responded to RQ2.

3.3. Fuzzy Sets Approach

In order to assess the risk factors and rate their level of importance (as pointed out in RQ2), fuzzy logic has been used in the third stage of this study (Figure 2). The linguistic information obtained from the survey has been transformed into appropriate trapezoidal fuzzy numbers, also known as the linguistic scale, as shown in

Table 3. Linguistic classification of risk factor grades [94].

Likelihood of Occurrence	The Impact of Risk	Trapezoidal Fuzzy Numbers (TrFNs)
Very Rare (VR)	Very Low (VL)	(0, 0.1, 0.2, 0.3)
Rare (R)	Low (L)	(0.1, 0.2, 0.3, 0.4)
Often (O)	Moderate (M)	(0.3, 0.4, 0.5, 0.6)
Frequent (F)	Serious (S)	(0.5, 0.6, 0.7, 0.8)
Very Frequent (VF)	Critical (C)	(0.7, 0.8, 0.9, 1.0)

.

Various researchers use fuzzy linguistic scales to carry out subjective assessments in various fuzzy-based decision-making problems [94,95]. However, the type of membership function corresponding to a fuzzy number representing a particular linguistic variable has to be selected following the user’s needs. A commonly used trapezoidal membership function has been found to be satisfactory for this application [95]. Table 3 presents the linguistic variables and corresponding fuzzy number representations used to assess all the risk sources considered in the proposed study. During this risk assessment process, a five-member fuzzy linguistic scale was adopted from a study by Xia et al. [95]. The study assumed that risk was a function of two parameters: the likelihood of occurrence and the impact of risk. Thus, linguistic variables such as Very Rare (VR), Rare (R), Often (O), Frequent (F), and Very Frequent (VF) rate the likelihood of occurrence (of risk).

Similarly, Very Low (VL), Low (L), Moderate (M), Serious (S), and Critical (C) were utilized to rate the impact of the risks. Consequently, Table S1 presents the likelihood of various risk-influencing factors assigned by DMs, while Table S2 shows the effects of the corresponding influencing risk factors. Thus, the activities described in Section 3.2 and 3.3 are in response to RQ2.

3.4. Risk Rating and Analysis Using the “Incenter Centroid Method”

Regarding RQ3, in the last stage, the objective was to rank the risk factors, which was achieved with the use of the incenter centroid method. The incenter centroid method was first proposed by Thorani et al., in which they presented a ranking method for ordering fuzzy numbers using the orthocenter of centroids [96,97]. They provided a formulation for computing the equivalent crisp score against a particular fuzzy number. This concept was later utilized in our previous study [98], to develop a fuzzy decision support tool for ranking risk factors in a reverse logistics supply chain, with the help of computed crisp scores. In this study, we used the fuzzy analysis tool to rank and categorize the risks involved in ELV recycling systems, as shown in Table S3. The graphical representation of risk ratings (crisp scores) in relation to various risk-influencing factors is illustrated in Figure 4.

From Figure 4, it was observed that such factors as ELV pollution measurement challenges (F1,6), lack of ELV return policies (F2,1), uncertainty about the legal environment (F2,9), the risk of hazardous ELV material (F4,2), the unknown total costs of ELV recycling operations (F1,5), noncompliance with governmental/legal guidelines (F4,6), financial constraints of the company (F4,5), and a lack of adequate environmental guidelines on ELV reprocessing (F3,6) impose amplified adverse impacts on the performance and efficiency of ELV recycling activities. The above results have been shown in Table S1. Therefore, the rating for each identified risk driver was computed by the summation of their corresponding risk ratings of influencing factors or classes. Moreover, the overall risk extent can be determined by adding all influencing classes’ risk ratings.

The percentage of the contribution of all individual perceived risks is shown in Figure 5.

With reference to Figure 5, it can be observed that IT systems risk (in red) has the highest rating (0.936) which can impose a considerable impact on overall ELV recycling. Moreover, its percentage of contribution is about 12.95% of the overall ELV performance risk. The inability to fulfill reprocessing activities due to incorrect/insufficient ELV data as well as a lack of updated ELV information in the database or data loss concerning the particular ELV material has considerable implications on estimating the status and availability of ELV waste for proper planning and control of the ELV supply chain. Therefore, there is a need for companies to invest in efficient ELV IT systems. On the other hand, culture risk (green) possesses the lowest risk in the ELV recycling supply chain with a 3.65% contribution. Therefore, it has no serious implications for the efficiency of the ELV supply chain. As a consequence, it can be seen that the risk with a high contribution value is a major source that requires the management of its influencing factors.

Furthermore, the study mapped all the ELV risk factors into five zones: Very Low, Low, Medium-Low, Moderate, Serious, and Critical (Figure 6). The risk mapping gives a graphical visualization of all crisps at a glance, indicating each risk level to enable a quick action plan by the risk management team leaders, risk owners, risk committee, decision team, etc., to identify and manage the risks successfully. The likelihood factor (L) multiplied by the impact factor (I), and the consequence of those two trapezoidal fuzzy numbers in terms of crisp scores, becomes the risk rating.

Table 4. Risk rating (crisp) values for the linguistic risk parametric scale.

Likelihood of Occurrence (L)	Impact of Risk (I)	Fuzzy Risk Rating (LxI)	Risk Rating (crisp)
Very Rare (VR)	Very Low (VL)	(0.00, 0.01, 0.04, 0.09;1)	0.0108
Rare (R)	Low (L)	(0.01, 0.04, 0.09, 0.16; 1)	0.0256
Often (O)	Moderate (M)	(0.09, 0.16, 0.25, 0.36; 1)	0.0797
Frequent (F)	Serious (S)	(0.25, 0.36, 0.49, 0.64; 1)	0.1678
Very Frequent (VF)	Critical (C)	(0.49, 0.64, 0.81, 1.00; 1)	0.2900

presents risk rating (crisp) values for the linguistic risk parametric scale with reference to Table 3.

The incenter centroid method was used to calculate the crisp values, with 0.1800 being the highest risk rating assigned to a particular risk class and the risks ascribed to different risk categories (0–5) corresponding to a specific risk rating (crisp ranges) within a range of (0–0.1800), as shown in Table 4. Figure 6 shows the risk mapping, where all the considered risk classes have been mapped into five zones, Very Low, Low, Medium-Low, Moderate, Serious, and Critical, for ease of visualization. Risks that fall into the “red zone” are considered serious and will be marked for urgent and immediate investigations for an urgent action plan, as recommended by the risk management team.

4. Discussion

The recycling of ELVs in developed and emerging countries faces considerable challenges and risks [2]. Through risk assessment and analysis, various potential risks may be identified and well-managed. The absence of ELV recycling policies and laws in some of these countries is also a major setback causing environmental pollution and resource loss from ELVs.

Considering the results of the study, it can be seen that the risk drivers, including the risk of hazardous ELV materials (F4,2) and the unknown total costs of ELV reprocessing (F1,5), are highly rated and, consequently, should be carefully monitored, controlled, and managed to improve the effectiveness of ELV reprocessing. On the other hand, risk drivers including fear of the loss of privacy and intellectual property (F5,8), language barriers (F2,11), and different customs and cultures (F3,11) present low ratings.

The major outcome of the study is the ranking and mapping of risk factors in ELV recycling systems, which may be successfully monitored, controlled, and managed by the risk management team lead, risk owner, risk committee, and decision team.

In this study, the proposed methodology has been considered a generic one, but the presented risk model and the fuzzy analysis results must be treated as company-specific. Each company has its risk knowledge and experience with respect to particular risk sources as well as risk-influencing factors and may have different risk attitudes. Therefore, this study could serve as a guide to managers on how the detailed procedure can be utilized in practice rather than a universal solution for risk management in ELV recycling. In order to validate the proposed fuzzy analysis approach, the eleven experts with years of experience in ELV recycling practice were interviewed to confirm the validity of the proposed process with respect to (a) the applicability of the proposed risk assessment process for ELV reprocessing exercise, (b) the benefits of operating the proposed risk assessment steps, (c) the completeness of identified risk factors for ELV supply chain systems, and (d) importance to the strategic planning of ELV reverse logistics networks. They all confirmed positively the above questions after a precise examination of the theory and operational steps used.

The research results presented in this study may be considered taking into account the following limitations, namely: (i) the consideration of fuel-based ELVs, (ii) the subjective opinion about the importance of particular risk factors of selected decision-makers, and (iii) the brainstorming session used in order to identify the risk factors and decision-maker (expert) selection. The study allows the authors to identify the risk factors occurring in fuel-based ELV recycling systems to fill in the research gaps within this topic. Future research should consider hybrid and electric vehicles (EVs) with regard to their treatment and reprocessing options when they reach the end of their useful life [99].

5. Conclusions

Effective ELV supply chain risk management necessitates a reliable risk assessment, risk treatment planning, and subsequent implementation. The proposed risk assessment methodology has appeared to be more practical and reliable than traditional statistical methods since it utilizes the experts’ risk perceptions in a subjective way rather than an objective way, especially where there is a lack of sufficient quantitative data on ELV risks, as in our case.

In this study, the fuzzy set theory was embedded in the risk assessment process that helps to quantify risk ratings, where experts’ subjective judgments have evaluated both the risk impacts and the likelihood of occurrence. The developed hierarchical risk structure can easily model perceived ELV risks and their influencing factors. The proposed methodology not only assesses the overall risk in the ELV recycling supply chain but its concept and procedure can also be implemented to evaluate risks in different industries. The applicability of the proposed methodology was tested by conducting a questionnaire survey of supply managers and experts from various established companies.

The unique research contribution relates to identifying critical risk dimensions (effects) and their influencing factors (causes) concerning the reverse logistics of ELV materials for reprocessing. The research presents a unique integrated hierarchical risk assessment model for providing a framework for risk management in the ELV supply chain using fuzzy set theory. Using the incenter centroid method for the crisp representation of a fuzzy number improved the reliability of decision-making by giving comparative results. Further, the systematic and logical categorization of various risk dimensions followed by an action plan for risk mitigation is helpful for the practical use of the tool by ELV recycling managers.

As a further step, the findings and models presented in this study can be utilized in empirical analysis to develop a real-time ELV risk analysis tool to support decision-makers. Hence, decision-makers from the ELV recycling sector could assess and forecast the risk preference levels and the likelihood of occurrence and prepare the action plan, taking into consideration the priorities of the particular risk factors. It is presumed that this will help ELV recycling managers and companies to identify the potential risks and help them improve their recycling businesses. The results can be validated by implementing the proposed approach in various ELV business models to ascertain its applicability and sensitivity to changes in risk factor drivers.