A Review of Supply Chain Uncertainty Management in the End-of-Life Vehicle Industry

Abstract

Various uncertainties exist in the End-of-Life Vehicle (ELV) industry, which further complicates the ELV business’s growth. In order to pursue greater progress in the ELV business establishment, a comprehensive analysis of previous ELV studies with regard to the supply chain uncertainty perspective is essential. The objective of this study is aimed at categorising the existing supply chain uncertainty sources according to their end-of-life (EoL) strategies, identify the management approaches adopted to analyse the prominent research trends, and conduct a regional analysis of ELV supply chain studies for the past years, from 2016 until 2022. The content analysis method was used to extract all the essential information from previous research, and an analysis was performed to obtain the latest research trends and identify the relationship between the gathered data. The findings show that the past research focuses on three main supply chain uncertainties, namely, uncertainty in logistics and network facilities (31.8%), production and operations (30.7%), and environmental (25.0%). Furthermore, the regional analysis shows that 83% of the studies were conducted in developing countries over the past years. Lastly, several research gaps were presented to provide researchers with potential directions and the way forward to explore ELV supply chain research from the uncertainty management context.

1. Introduction

As world waste generation is escalating, there is expected to be 2.59 billion tonnes of waste in 2030, and the volume of waste is predicted to reach 3.40 billion tonnes by 2050 [1]. However, despite the increment in the volume of waste generated, the waste return activities are still minimal, especially in low-income countries where the waste return rate is 51% for lower-middle-income countries and 39% for low-income countries [1]. In addition, the waste take-back activities are significantly lower in rural areas than in urban areas since residents in rural areas prefer to openly dump, burn, or compost the waste [1,2,3].

At the same time, 4% of the world’s generated waste is metal [1], and motor vehicles contribute to metal waste since the major parts of a vehicle are made from metal [4]. A motor vehicle comprises various types of ferrous metals, non-ferrous metals, and non-metal elements [5], and when the vehicle malfunctions and can no longer perform, it becomes an End-of-Life Vehicle (ELV) [6,7]. An ELV is a hazardous waste that can lead to environmental pollution, including water, soil, and air pollution [8]. The inefficient waste management system for ELVs causes not only a severe impact on the environment but also on human well-being, livelihood, and industrial growth. The waste management strategy for ELVs is a complex process since the ELVs need to be properly dismantled according to environmental regulations before they can be recycled or remanufactured. Hence, an efficient management system for ELV recovery should be adopted to solve the wrongful disposal of ELVs.

In recent years, there has been a discernible increase in academics’ interest in ELV supply chain research, hence, it is essential to synthesise the findings and knowledge to identify new meaningful areas to be explored. Previously, several review articles discussed supply chain issues in managing the ELV sector, as listed in

Table 1. Previous review articles in relation to ELV supply chain.

Authors	Year	Scope	Period of Analysis
Numfor et al. [9]	2021	SWOT analysis on ELV recycling in developing countries	2004–2020
Karagoz et al. [10]	2020	ELV management	2000–2019
Merkisz-Guranowska [11]	2020	ELV network design	2000–2019
Simićand Dimitrijević [12]	2019	Logistics network design	2013–2019
Simic [13]	2013	Environmental engineering	2003–2012
Current study	2022	Supply chain uncertainty in the ELV industry	2016–2022

. As shown in Table 1, Numfor et al. [9] conducted a strengths, weaknesses, opportunities, and threats (SWOT) analysis for ELV recycling in developing countries. Karagoz et al. [10] reviewed previous works related to ELV management from 2000 to 2019 by categorising the articles into studies on supply chain networks, surveys, production and planning, and regulations. At the same time, they also reviewed the methods used in the previous research focusing on mathematical modelling approaches.

Meanwhile, Merkisz-Guranowska [11] analysed previous research from 2000 to 2019 that discussed supply chain network designs for ELVs and compared the characteristics among the previous works. Next, a review was conducted by Simić and Dimitrijević [12], which compared past studies from 2013 to 2019 concerning logistics in supply chain network design regarding ELV management. Lastly, another review in the ELV field was conducted by Simic [13], which focused on published studies during the period from 2003 to 2012 on environmental engineering for the recycling of ELVs.

While there are review articles covering ELV management issues in various supply chain areas, there has yet to be a detailed examination of the existing EOL strategies to address the multiple supply chain uncertainties in the ELV supply chain literature. In order to address the aforementioned gap, the purpose of this study is to classify the existing ELV supply chain studies according to their EOL strategies, as well as to categorise sources of supply chain uncertainties and uncertainty management strategies, for the years 2016 to 2022. The main contribution of this study is to present a new perspective on the supply chain uncertainty aspect for the ELV sector that emphasises the source of uncertainties and their management strategies, provide the current research trend on the supply chain uncertainties sources in the ELV industry, and deliver an informative regional analysis of ELV studies for the said years.

As such, this review paper adds four dimensions to the body of knowledge. Firstly, it classifies EoL strategies and supply chain uncertainty sources in the ELV industry. This analysis is an aid in comprehending the supply chain uncertainty sources that had been explored in each of the EoL strategies. Secondly, this study identifies and analyses the management mechanisms for each supply chain uncertainty source discovered within the ELV industry. Thirdly, this review paper analyses the recent research trend on the sources of supply chain uncertainty for the years 2016 until 2022. This research trend is crucial for researchers to foresee the research pattern of supply chain uncertainty studies. Finally, this study examined the engagements of ELV studies based on economic status, which analyse the countries that actively research ELV studies and revealed the direction of ELV studies globally. Based on the authors’ best knowledge, no study has been performed on supply chain uncertainty sources and EoL strategies for the ELV industry context in recent years. This analysis is beneficial as the management approaches adopted to tackle these uncertainties in previous studies can be enlightened and conveyed to the advantage of other practitioners in the ELV sector.

This article’s remaining sections are organised as follows: Section 2 discusses the background of the research, including the definition of terminologies in this review. Section 3 presents the methodology adopted in this review. Section 4 displays the classification of EoL strategies, supply chain uncertainty, and their management strategies. The discussion on the findings of this study, including a detailed content analysis of supply chain uncertainties in the ELV industry, is also presented in Section 4. The regional analysis of the respective studies in the literature and the research gaps for future studies is presented in Section 5 and Section 6, respectively, and Section 7 summarises the review with concluding remarks.

2. Research Background

The circular economy and end-of-life (EoL) strategy is seen as a feasible approach for managing ELVs. EoL strategies are implemented to extend a product’s lifespan, reduce waste generation, and prevent the loss of natural resources, for instance, energy and raw materials [9,14], including the waste from ELVs. Previous studies proposed the Ricoh comet diagram that explained the EoL strategies according to their priority and environmental impact, including reuse, product service, remanufacture, recycling, and disposal as shown in Figure 1 [15,16,17]. EoL strategies involve closed-loop supply chains and open-loop supply chains, in which the strategies for reuse, service or maintenance, remanufacturing, and recycling are categorised as closed-loop strategies [15]. Closed-loop EoL strategies are more environmentally friendly as the resources are not wasted; instead, they are processed to be used again [17].

The ELVs are generally treated at the automotive treatment facility (ATF), which serves as a depollution centre to dismantle the vehicle into components before it may be reused, remanufactured, or recycled [18]. Nevertheless, managing supply chain issues in ATF is a big challenge for practitioners in the ELV industry. As the ELV industry in developing countries is still not well established, many supply chain problems have arisen in the ELV industry, including supply uncertainty and the high cost of handling hazardous waste [19,20]. Therefore, optimal supply chain planning is essential to support and sustain the ELV business for the long term. In planning an efficient supply chain system, it is essential to identify the sources of the underlying supply chain uncertainties and the appropriate approaches for managing them.

According to Simangunsong et al. [21], supply chain uncertainty can be interpreted as an ambiguous situation that stimulates positive and adverse reactions in a supply chain system. In other words, uncertainty is the unstable factor that significantly impacts a business or the inadequate knowledge regarding an issue causing ambiguities in a business that may sprout within the company, inside or outside the supply chain [22,23]. Supply chain uncertainty can be separated into two specific areas: the roots of the uncertainty and the uncertainty managing mechanism [21]. In addition, there are two sets of uncertainty managing mechanisms: reducing uncertainty and coping with uncertainty [21]. The ELV industry is still unstable and riddled with several sources of uncertainty; hence, suitable management solutions must be implemented in the ELV supply chain to mitigate these issues.

2.1. Definition of Terminologies

In this section, past studies on the EoL strategies, sources of supply chain uncertainties, and uncertainties management strategies was observed to define the potential themes before conducting content analysis on literature related to ELV supply chain studies. According to De Almeida and Borsato [15], Rose [17], and Tani [16], the main EoL strategies consist of remanufacturing, recycling, and recovery. In contrast, Simangunsong et al. [21], Awudu and Zhang [24], and Peidro et al. [25] found various supply chain uncertainties sources, and the most related sources of supply chain uncertainties in the ELV industry include production and operation, decision intricacy, organisation and human behaviour, ELV supply, logistics and network facilities, and environmental uncertainty. Meanwhile, Simangunsong et al. [21] and Peidro et al. [25] proposed several uncertainty management strategies, which include the reconstruction of chain facilities, evaluation of process efficiency, decision support systems, redesigning regulations, and quantitative methods. Hence, the classification of EoL strategies, supply chain uncertainty sources, and uncertainty management strategies was adopted from these studies. The interpretation of all potential themes is presented in the following sub-sections.

2.1.1. End-of-Life (EoL) Strategies

The EoL strategies commonly adopted in the ELV supply chain include reuse, remanufacture, and recycle. According to De Almeida and Borsato [15], reuse refers to the retention of the end-of-life product (ELP) in its original form and performing the same functions as before. Remanufacturing is grouped with reuse since both EoL strategies maintain the functions of the initial product, even though remanufacturing is integrated with reassembling and rework activities to revive the product [8,15,26]. In contrast, recycling involves the manual removal and dismantling of parts to secure the recyclable materials (ferrous and non-ferrous materials), where the collected material will later be sorted and processed into new materials [8]. On the other hand, recovery refers to those EoL strategies aimed at extending the lifespan of ELPs [6,27,28].

2.1.2. Uncertainty Sources

There are three types of uncertainty sources: company uncertainty, uncertainty within the supply chain, and external influences uncertainty [21]. Company uncertainty refers to a dilemma in the organisation that usually involves uncertainties concerning product characteristics, manufacturing procedures, controls, complex decision making, organisation and human behaviour, and information technology systems. In contrast, the uncertainty that arises from issues within the supply chain includes concerns about customer demand, a significant increase in demand, product supply, channel interactions, forecasting over the horizon, network design, and chain infrastructure. Meanwhile, natural disasters and environmental issues such as policies, government subsidies, and competitors are examples of external influences that cause uncertainty in the supply chain.

Previous studies identified six sources of uncertainty over the last six years. These uncertainty sources include production and operation, decision intricacy, organisation and human behaviour, ELV supply, logistics and network facilities, and environmental uncertainty.

Table 2. Definition of Uncertainty Sources.

Category of Uncertainty Sources	Uncertainty Sources	Definition
Company Uncertainty	Production and Operation	Common production and operation issues related to supply, workforce, machine, and process layout [21].
	Decision Intricacy	Complexity in deciding since the decision involves many issues to be considered [21].
	Organisation and Human Behaviour	Management issues and the attitude of stakeholders in a company [21].
Within-Supply-Chain Uncertainty	ELV Supply	Fluctuating and unpredictable ELV supply [21].
	Logistics and Network Facilities	Transportation issues, infrastructure, facilities, and geographical considerations [21].
External Influences	Environmental	Legislation, economic, and social issues [21].

presents the interpretation of the uncertainty sources identified according to their respective category and elaborate definition.

2.1.3. Uncertainty Management Approaches

Five approaches for managing uncertainty were adopted in previous ELV studies, namely, reconstruction of chain facilities, evaluation of process efficiency, decision support systems, redesigning regulations, and quantitative methods. These uncertainty approaches were categorised as approaches for reducing uncertainty, except for the quantitative methods, which came under uncertainty coping approaches. The interpretation of each uncertainty handling approach can be found in

Table 3. Definitions of Uncertainty Management Approaches.

Category of Uncertainty Management Approach	Uncertainty Management Approach	Definition
Reducing Uncertainty Approach	Reconstruction of chain facilities	Reorganising the supply chain system and facilities, including the main factory, delivery centres, modes of transportation, manufacturing processes, and network partnerships, to meet consumer demands [21]. The reconstruction of supply chains often has far-reaching consequences that affect certain aspects of an organisation [29].
	Evaluation of process efficiency	Measuring the performance of the processes and procedures in a system to identify and diminish the uncertainty [21].
	Decision support system	The approach where a decision-making system is adopted to propose a solution to a complex problem [21,30,31].
	Redesigning regulations	Restructuring of current regulations in handling issues to enhance the stability and efficiency of the supply chain [21].
Uncertainty Coping Approach	Quantitative methods	The usage of operations research techniques, including mathematical modelling, simulation, forecasting, and any other related method, to lessen the influence of a source of uncertainty [21,25].

.

3. Methodology

In general, the methodology in this research consists of a two-step procedure: determining the potential themes for deductive content analysis in the first phase and selecting articles based on the study’s scope in the second phase, followed by data interpretation and data categorisation.

3.1. Phase I- Determination of Potential Themes for Deductive Content Analysis

This review article is inspired by previous reviews conducted in the ELV industry in which this review will identify the EoL strategies, the source of supply chain uncertainties, uncertainty management strategies, and the region where the ELV studies were conducted. Thus, this review seeks to respond to the following research questions:

(1).

RQ1: What are the EoL strategies frequently adopted in the present day to manage ELVs?

(2).

RQ2: What are the sources of supply chain uncertainties in the ELV industry?

(3).

RQ3: What are the uncertainty management strategies employed to manage the ELVs?

(4).

RQ4: Which countries are currently active in solving the ELV supply chain uncertainty?

Content analysis is a method to systematically categorise the subjective interpretations of textual data into specified themes using the coding method [32]. According to Moretti et al. [33], content analysis can be conducted as inductive and deductive. Deductive content analysis is preferred for evaluating a theory, but inductive content analysis is usually applied when previous research is immature, and the subject of knowledge has not been well investigated [34]. For this reason, the research strategy employed in this review article is a deductive content analysis. This approach is selected because the theory of supply chain uncertainty has been broadly discussed in the past literature; however, the study of supply chain uncertainty in the ELV industry is still minimal. In addition, the theme selection for deductive content analysis is based on the definition provided in Table 2, Table 3, and the authors’ knowledge.

3.2. Phase II- Material Selection, Data Interpretation and Categorisation

This section describes the material selection process, data interpretation, and categorisation for this review article. Generally, the review process involved determining the EoL strategies and supply chain uncertainty in previous research on ELVs over the past six years. Initially, the keywords “end-of-life vehicle” and “supply chain” were used to collect the published articles. The search was limited to publications over the last six years, from 2016 to 2022, to analyse the latest trends in supply chain studies in relation to the ELV sector. Immediately after the keywords were entered, the search output from the Google Scholar search engine listed 1480 articles related to ELVs and the supply chain. The search results were then filtered to identify the most relevant research works related to ELV supply chain in which subsequent to the selection process, 88 articles were identified as related to ELV supply chain studies. The articles were gathered using several databases, including Web of Science, ScienceDirect, EmeraldInsight, SpringerLink, SAGE journals, IEEE Xplore, Inderscience, and Google Scholar.

Consequently, articles were examined, and the data interpretation was conducted, where vital information such as the EoL strategies, supply chain uncertainties, uncertainty management strategies, and region of the study conducted were identified. Finally, all information was coded and categorised according to the predetermined themes in stage I. Following the classification of the papers, the results were analysed and discussed in Section 3. The discussion included the research trends for the sources of uncertainty in the ELV industry. Consequently, a regional analysis was conducted to investigate the literature’s relevant studies on the ELV supply chain over the last six years. Finally, a research gap analysis and conclusion were drawn from all the findings to summarise the review.

4. Content Analysis

4.1. Classification of Supply Chain Uncertainty Sources

In the reviewing process adopted, it was found that the past studies have discussed the source of supply chain uncertainty being considered and proposed management strategies to minimise the negative impact on the ELV industry. Therefore, each article’s sources of uncertainty and managing mechanism were classified and presented in

Table 4. Classification of previous studies according to the sources of uncertainty, EoL strategies, and uncertainty management approaches.

Supply Chain Uncertainty	Authors	EoL Strategies			Uncertainty Management Approaches
		Remanufacturing	Recycling	Unspecified	Coping Approach	Reducing Approach
					Quantitative Methods	Evaluation of Process Efficiency	Redesign of Regulation, Policies and procedures	Decision Support System	Reconstruct of Chain Facilities
Decision Intricacy	Yildizbaşi et al. [35]	✓			✓
	Guo et al. [36]	✓			✓
	Zhou et al. [37]		✓		✓
	Zhou et al. [38]		✓		✓
	Simic [19]		✓		✓
ELV Supply	Simic [39]		✓		✓
	Simic [40]		✓		✓
	Simic [41]		✓		✓
Environmental	Sun and Xiao [42]	✓			✓
	Saxena et al. [43]	✓			✓
	Ray et al. [44]	✓			✓
	Abdullah [45]	✓			✓
	Pan and Li [46]		✓			✓
	Gan and Luo [47]		✓		✓
	Chavez and Sharma [48]		✓			✓
	Tang et al. [49]		✓		✓
	Chen et al. [50]		✓			✓
	Yu et al. [51]		✓		✓
	Zhang et al. [52]		✓			✓
	Mohan and Amit [53]		✓		✓
	Li et al. [54]			✓		✓
	Hu and Wen [55]			✓	✓
	Zailani et al. [56]			✓			✓
	Aksoylu and Demiral [57]			✓	✓
	Mohamad-Ali et al. [28]			✓	✓
	Gorji et al. [58]			✓	✓
	Kaviani et al. [59]			✓	✓
	Alamerew and Brissaud [60]			✓	✓
	Khan et al. [61]			✓	✓
	Agrawal et al. [62]			✓	✓
Logistics and Network Facilities	Li et al. [63]	✓			✓
	Govindan et al. [64]	✓			✓
	Alkahtani and Ziout [65]	✓			✓
	Reddy et al. [66]	✓			✓
	Demirel et al. [67]		✓		✓
	Kosacka and Kudelska [68]		✓		✓
	Phuc et al. [69]		✓		✓
	Balcia and Ayvazb [70]		✓		✓
	Shankar et al. [71]		✓		✓
	Sahebjamnia et al. [72]		✓		✓
	Deng et al. [73]		✓		✓
	Forouzanfar et al. [74]		✓		✓
	Kuşakcı et al. [75]		✓		✓
	Xiao et al. [76]		✓		✓
	Zhang et al. [77]		✓		✓
	Langarudi et al. [78]		✓		✓
	Dong et al. [79]		✓		✓
	Al-Quradaghi et al. [80]		✓						✓
	Karagoz et al. [81]		✓		✓
	Wan et al. [82]		✓		✓
	Chaabane et al. [83]		✓					✓
	Medrano-Gómez et al. [84]		✓		✓
	Govindan and Gholizadeh [85]		✓		✓
	Karagoz et al. [86]		✓		✓
	Ayvaz et al. [87]		✓		✓
	Özceylan, et al. [88]			✓	✓
	Sun et al. [89]			✓	✓
	Lin et al. [90]			✓	✓
Organisation and Human Behavioural	Go et al. [91]	✓					✓
	Keivanpour et al. [92]		✓		✓
	Mohan and Amit [93]		✓		✓
Production and Operation	Govindan et al. [94]	✓			✓
	Chakraborty et al. [95]	✓			✓
	Liao et al. [96]	✓			✓
	Tian et al. [97]	✓			✓
	Chakraborty et al. [98]	✓			✓
	Sokić et al. [99]		✓		✓
	Zhou et al. [100]		✓		✓
	Cucchiella et al. [101]		✓		✓
	Xia et al. [102]		✓		✓
	Tian and Chen [103]		✓		✓
	Vulić et al. [104]		✓		✓
	Hao et al. [105]		✓		✓
	Wang et al. [106]		✓		✓
	Yu et al. [107]		✓		✓
	Ling Zhang et al. [108]		✓		✓
	Li et al. [109]		✓		✓
	Petronijević et al. [110]		✓		✓
	Li et al. [111]		✓		✓
	Li et al. [112]		✓						✓
	Wu et al. [113]		✓		✓
	Pourjavad and Mayorga [114]			✓	✓
	Azmi and Tokai [115]			✓	✓
	Ene and Öztürk [116]			✓	✓
	Zhang and Chen [117]			✓	✓
	Zhang and Chen [118]			✓	✓
	Nguyen [119]			✓	✓
	Son et al. [120]			✓	✓

, then elaborated further in the following subsections.

Past research has explored the uncertainties in various EoL strategies, especially regarding the recycling and remanufacturing strategies. Nevertheless, several research papers included in this review article studied the supply chain uncertainties without specifying or focusing on one type of EoL strategy. Usually, these research papers often referred to the EoL strategies as recovery. These research articles are classified as unspecified EoL strategies in Table 4.

4.1.1. Decision Intricacy

Decision intricacy represents the complexity of decision making, where many issues must be considered to make an effective decision. Decision intricacy problems may involve production capacity constraints, transportation system issues, policy issues, and administrative uncertainties [21]. Besides, long-term planning, such as technology adoption, may be an issue in decision making [21]. As shown in Table 4, several papers focused on studies into complex decision-making. Firstly, Zhou et al. [37] and Zhou et al. [38] investigated the uncertainty in the decision-making process with regard to choosing an ELV recycling partner in a different type of industry. Yildizbaşi et al. [35] analysed the uncertainty in deciding on a distribution network system to refurbish ELVs. Simic [19] focused on decision uncertainties related to various supply chain issues in ELV recycling, including ELV allocation, production planning, transportation, and investment management. Furthermore, Guo et al. [36] explored the solution to minimise the total cost to remanufacture the ELV, taking into account carbon taxes and subsidies and the quality of the product in an uncertain market. In brief, the analysis shows that the exploration of uncertainty in complex decision-making for the ELV industry is minimal and could be extended, primarily to manage uncertainty in the ELV industry in developing countries.

4.1.2. ELV Supply

ELV supply uncertainty refers to the inconsistent supply of ELVs due to difficulties in securing supplies or due to transportation problems [21]. All the previous studies in this uncertainty group focused on ELV recycling. Simic [39] studied the uncertainty in allocations for ELV recycling and proposed a mathematical model to reduce the risk of disruption due to supply uncertainty. Besides that, Simic [40] examined the uncertainty in the ELV allocation system for recycling operations and formulated another model to minimise the financial uncertainty. Additionally, Simic [41] conducted another study to manage the ELV supply uncertainty involving multiple ELV facilities, planning the production and inventory of ELV, and maintaining maximum profits in this business under rigid environmental regulations. The findings show that past studies only investigated ELV supply uncertainty in ELV recycling operations, suggesting that more studies are expected to reduce supply uncertainty in other EoL strategies.

4.1.3. Environmental Uncertainty

Environmental uncertainty involves government regulations and political, economic, social, and business competitors [21]. As shown in Table 4, Saxena et al. [43] constructed a supply chain plan for the ELV remanufacturing industry with extra attention to environmental and financial issues. Sun and Xiao [42], Ray et al. [44], and Gorji et al. [58] analysed the competition to repurchase ELVs in the ELV remanufacturing industry, which involved the influence of government subsidies. Other than that, Abdullah [45] investigated the technical, management, and sustainability aspects of ELV remanufacturing to normalise the sustainability index value.

Next, Pan and Li [46], Chavez and Sharma [48], Chen et al. [50], Zhang et al. [52], and Mohan and Amit [53] explored the competition among formal and informal dismantlers in the unregulated ELV recycling market. Moreover, Gan and Luo [47] examined the environmental uncertainty influencing ELV recycling activities. Tang et al. [49] analysed the significance of a reward and penalty system for recycling batteries from electric vehicles concerning the environment, economy, and society. In addition, Yu et al. [51] studied the competition between legal and illegal ELV recyclers with government subsidies and proper ELV regulation policies.

Furthermore, Li et al. [54] investigated the environmental issues in the ELV industry, focusing on ELV recycling and ELV recovery activities. Hu and Wen [55] assessed the environmental uncertainty in the ELV recovery industry and distinguished the factors into three scenarios: the advanced, informal, and ordinary sectors. Additionally, Zailani et al. [56], Kaviani et al. [59], and Agrawal et al. [62] identified the environmental uncertainty that had become a barrier to the implementation of a closed-loop supply chain system for the recovery of ELVs. Aksoylu and Demiral [57] analysed the financial uncertainty in ELV recovery processes focusing on dismantling operations. Mohamad-Ali et al. [28] classified the environmental uncertainties in ELV recovery operations and determined the relationship between the factors. Meanwhile, Alamerew and Brissaud [60] determined the environmental uncertainty in recovering electric vehicle batteries (EVBs). Finally, Khan et al. [61] observed the interaction between original automobile producers and ELV recyclers under the government’s influence.

In summary, past studies focused on external influences in the ELV sector, such as economic issues, government roles, business competition, legislation, environment, and social factors. Given these points, it can be summarised that various studies have been executed in the past six years that covered many different environmental aspects to eliminate environmental uncertainty.

4.1.4. Logistics and Network Facilities

Uncertainty in logistics and network facilities commonly occurred due to geographical issues involving resource collection and manufactured product distribution. Additionally, logistics and network facilities uncertainty may be influenced by the transportation facilities, fleet capacity, and shipping schedule [21]. As shown in Table 4, Li et al. [63] examined the supply chain network to remanufacture electric vehicle batteries. Other than that, Govindan et al. [64] conducted a study to select a third-party logistics provider for ELV remanufacturing companies. Alkahtani and Ziout [65] explored the uncertainties in the closed-loop supply chain network in the ELV remanufacturing industry to remanufacture hydrogen vehicle batteries. Lastly, the most recent research on ELV remanufacturing was conducted by Reddy et al. [66], focusing on developing a closed-loop supply chain model for the remanufacturing ELVs in India, taking into consideration the environment.

In contrast, Demirel et al. [67] studied the uncertainties in network designs in the ELV recycling industry in order to mitigate the uncertainties. Kosacka and Kudelska [68] focused on the uncertainties in relation to network facilities in the ELV recycling industry, precisely the uncertainty regarding the storage of ELVs in dismantling centres. Next, Phuc et al. [69] investigated the uncertainties in closed-loop supply chain network designs involving different types of ELV recycling operations and various ELV parts. Balcia and Ayvazb [70] studied ELV recycling network designs and analysed the uncertainties in the logistics and network facilities. Shankar et al. [71] analysed the uncertainties in the closed-loop supply chain network for ELV recycling operations, specifically on the various ELV transportation plans. Sahebjamnia et al. [72] analysed the uncertainties in the location and allocation problems in the ELV recycling industry. Deng et al. [73] studied the uncertainties in the ELV recycling logistics system and facilities. Meanwhile, Forouzanfar et al. [74] explored the uncertainties intertwined with the location routing inventory for ELV recycling.

Besides that, Kuşakcı et al. [75] studied the uncertainties in the location and allocation of ELVs involving the location of facilities and the flow of materials. Xiao et al. [76] analysed the uncertainties in network designs for ELV recycling operations. Meanwhile, Zhang et al. [77] determined the uncertainties with regard to planning for the transportation of ELVs in the ELV recycling process. Furthermore, Langarudi et al. [78] explored the uncertainty in the reverse logistics network for automotive recycling operations. Dong et al. [79] examined the uncertainties in the closed-loop supply chain before proposing a strategic location for ELV recycling.

Aside from that, Al-Quradaghi et al. [80] proposed minimising the uncertainties by designing an eco-industrial park to improve the ELV flow. Karagoz et al. [81] analysed the uncertainties of selecting locations for authorised disassembly facilities for ELV recycling operations. Wan et al. [82] investigated the uncertainties in ELV recycling network designs under the influence of government subsidies. Meanwhile, Chaabane et al. [83] studied the vehicle routing problem (VRP) for ELV collection systems. Likewise, Medrano-Gómez et al. [84] explored the uncertainties in a reverse logistics network system for tire recycling operations, giving special attention to maximising tire collection.

Additionally, the most recent study on ELV recycling was conducted by Govindan and Gholizadeh [85], with a focus on network designs for ELV recycling operations, taking into consideration recycling technologies and the capacity of facilities. Karagöz et al. [86] proposed an approach to select the best location to build the ELV facilities in Istanbul, Turkey, considering the uncertainties in the ELV supply chain. Finally, Ayvaz et al. [87] investigated the uncertainties associated with the locations, chain facilities, and flows of the ELV recycling industry, emphasising sustainability.

In addition, Özceylan et al. [88] explored the network design uncertainties in the ELV recovery industry related to the flow of materials in the closed-loop supply chain network. Sun et al. [89] analysed the uncertainties associated with the determination of the location of ELV distribution centres for the ELV recovery process. Similarly, Lin et al. [90] examined the uncertainties in relation to location and allocation in the recovery of ELVs to minimise the total cost of selecting the most suitable location for recovery.

To summarise, many research studies investigated the uncertainty related to logistics and network facilities, focusing on minimising the uncertainties in closed-loop supply chain network design. However, since geographical issues are the primary concern of supply chain network design, exploring uncertainty in logistics and network facilities should be extended to other countries.

4.1.5. Organisation and Human Behaviour

Organisational uncertainty mainly occurs due to company politics, which usually involves the top management in the organisation. Meanwhile, human behaviour uncertainty refers to the attitude of stakeholders in the company in dealing with supply chain risks [21]. As shown in Table 4, a few previous studies were with regard to uncertainties in the organisation and human behaviour. For example, Go et al. [91] analysed the uncertainty in human behaviour towards ELV recovery strategies. Likewise, Keivanpour et al. [92] examined the uncertainties associated with the behaviour of vital automotive players in handling ELV recycling problems. On the other hand, Mohan and Amit [93] explored the uncertainty of human behaviour among dismantlers with regard to market barriers in the unregulated ELV recycling market. The analysis shows that minimal investigation of organisation and human behaviour uncertainty can be extended to other issues such as company politics or workforce commitment.

4.1.6. Production and Operations

Production and operation uncertainties may include supply chain issues such as difficulties in acquiring resources for production, irregular production output, machines, faulty equipment, and the efficiency of the processing line or process layout [21]. As presented in Table 4, Govindan et al. [94] identified the uncertainties of production and operations in ELV remanufacturing, which are crucial obstacles in this industry. Besides that, Chakraborty et al. [95] investigated the uncertainties of production and operations in engine remanufacturing. Liao et al. [96] investigated the uncertainties in engine remanufacturing, specifically uncertainties in relation to customer demand and profit acquisition in terms of quality and quantity. Tian et al. [97] explored the uncertainties of production and operations in ELV remanufacturing to determine an appropriate arrangement for remanufacturing operations. Chakraborty et al. [98] also studied the uncertainties that posed a hindrance to ELV remanufacturing operations.

In contrast, Sokić et al. [99], Hao et al. [105], Yu et al. [107], Ling Zhang et al. [108], Li et al. [109], Li et al. [111], and Wu et al. [113] explored the uncertainties associated with the expected volume of ELVs for ELV recycling operations. Zhou et al. [100] investigated the uncertainties in the ELV disassembly layout. Cucchiella et al. [101] studied the uncertainties in the dismantling process for ELV parts, namely, waste printed circuit boards (WPCBs). Xia et al. [102] studied the uncertainties in the ELV disassembly processing line, where both destructive and non-destructive ELV disassembly methods are combined. Tian and Chen [103] examined the uncertainties intertwined with production and operations in the manual dismantling of ELVs. Vulić et al. [104] examined the uncertainties related to recycling technologies in the automotive industry. Wang et al. [106] analysed the efficiency of the production system in a closed-loop supply chain for ELV recycling and focused on the uncertainties related to resource utilisation. Petronijević et al. [110] studied energy generation via the ELV recycling process and discovered the possibility of energy recovery in Serbia. On the other hand, Li et al. [112] investigated inefficiencies in the plant facility structure, specifically for the ELV recycling and disassembly.

In addition, Pourjavad and Mayorga [114] analysed the uncertainties related to determining the best strategies for ELVs based on end-user needs. Azmi and Tokai [115], Ene and Öztürk [116], and Nguyen [119] analysed the uncertainties in relation to the ELV population in later years. Zhang and Chen [117] examined the uncertainties in the ELV disassembly line for the ELV recovery process. Zhang and Chen [118] studied the production and operation uncertainties associated with the selection of ELV dismantling approaches. Lastly, Son et al. [120] explored the solution to reduce the ELV dismantling costs, focusing on minimising dismantling operational cost, inventory cost, scrap cost, and backorders.

In essence, the findings revealed that many previous studies focused on reducing the production and operation uncertainties, covering the ELV volume prediction, the issues in disassembly layout, and resource utilisation. As observed, fewer studies were conducted to minimise the uncertainties related to machine breakdowns, the workforce, and uncertain production outputs.

4.2. Research Trends of Uncertainty Sources

An overview of the sources of uncertainty percentage in past studies is given in Figure 2. As illustrated in Figure 2, most research focused on uncertainties in logistics and network facilities (31.8%). Similarly, many studies were conducted to analyse the uncertainties of production and operations in the ELV industry (30.7%). Besides that, it can be observed that 25.0% of studies explored the issue of environmental uncertainties.

Nevertheless, it can be observed that there were fewer engagements with regard to decision intricacy uncertainty, ELV supply uncertainty, and organisation and human behaviour uncertainty. In other words, 5.7% of previous studies analysed the uncertainty in complex decision making, 3.4% of studies investigated the uncertainty in relation to the supply of ELVs, and another 3.4% of studies examined the uncertainty in relation to organisation and human behaviour. In other words, previous research works focused mainly on the uncertainties of logistics and network facilities, production and operations, and environmental issues. The findings also showed that the uncertainties of complex decision making, ELV supply, and organisation and human behaviour have yet to be fully explored and are promising areas to be considered in future works.

As the analysis continued, the trend of publications for each uncertainty source was observed. Figure 3 shows the research trend for each source of uncertainty over the last six years. However, publications in 2022 were not included in the analysis since they would not have been representative of the publications for the entire year. Therefore, the summary of the publication trends focused more on the articles published from 2016 until 2021.

Studies regarding logistics and network facilities’ uncertainties increased from 2016 to 2017, peaking in 2018 and 2019. This increase is believed to be due to the high complexity of ELV logistics and network facilities caused by product quality, varied recovery methods, and environmental effects [121,122]. However, the latest publication trend shows a steady decline, suggesting these issues no longer receive much attention.

Production and operation uncertainty studies peaked in 2016 and gradually decreased the following year. Although the number of studies on production and operation uncertainty decreased the following year, the publishing trend on this issue is quite linear from 2018 until 2021. At the same time, the total number of published studies on this subject is relatively large (30.7%). These findings suggest that production and operation uncertainty are constantly on trend, even though the number of studies on this issue has slightly decreased in the subsequent years. Since complex ELV recovery techniques and various ELV conditions significantly impact the entire ELV recovery process [19], the need for such research is empirical and continues to be in trend.

On the other hand, environmental uncertainty has a nonlinear pattern and peaked in 2020, indicating that this subject has gained recent interest due to various environmental challenges, including legislation, macroeconomic, social, and corporate competitiveness [21]. Meanwhile, research on ELV supply, decision complexity, and organisational and human behaviour has slowed in recent years. In summary, the researchers’ focus on logistics and network infrastructure is waning, but production and operation issues, and environmental issues continue to garner attention. However, research on decision intricacy, ELV supply and organisation, and human behaviour is sparse, suggesting the need for further discussion and exploration of these issues such that uncertainties in the ELV supply chain could be reduced holistically.

4.3. Management Approaches for Handling Uncertainty

Every type of uncertainty has unique characteristics that can be managed using a suitable approach by the company. The management approaches proposed to minimise supply chain uncertainties are shown in Table 4. The uncertainty management approaches adopted to handle the uncertainties in the ELV supply chain include developing decision support systems, evaluating process efficiency, reconstructing chain facilities, redesigning regulations, and using quantitative techniques. As shown in Figure 4, most research works adopted quantitative methods (88.64%) to deal with the uncertainties. Apart from that, there was a relatively low adoption of process efficiency evaluation, as only 5.68% of the previous studies used this approach to reduce the impact of uncertainty on the ELV supply chain.

Similarly, the adoption of other approaches was minimal (reconstruction of chain facilities, decision support system, and redesigning regulations). Only 2.27% of past studies adopted the redesigning regulations approach, 2.27% of previous studies employed the reconstruction of chain facilities approach, and another 1.14% applied the decision support system approach to diminish the uncertainties. In essence, it can be assumed that quantitative methods were the preferred uncertainty management approaches for handling the supply chain uncertainty. The detailed summary of managing mechanisms applied or proposed in previous research is explained below. These mechanisms are sorted according to their uncertainty sources to address the strategies employed, which will be a helpful reference for future studies in mitigating the uncertainties in the ELV context.

4.3.1. Managing Decision Intricacy

The literature shows that the uncertainty in complex decision-making can be reduced using quantitative methods. In Table 4, all studies on decision intricacy uncertainty adopted quantitative methods to solve the uncertainty. Zhou et al. [37] employed the fuzzy VlseKriterijuska Optimizacija I Komoromisno Resenje (FVIKOR) method to cope with the uncertainties, while Zhou et al. [38] used multi-criteria decision making (MCDM), which is a combination of fuzzy decision making trial and evaluation laboratory (DEMATEL), antientropy weighting (AEW), and FVIKOR to cope with decision intricacy in the ELV recycling problem. Meanwhile, Yildizbaşi et al. [35] formulated a mathematical model using mixed-integer linear programming (MILP), and Simic [19] solved ELV recycling uncertainties using interval-parameter programming (IPP). Guo et al. [36] used nonlinear programming (NLP), which is a fuzzy opportunity constraint programming, to cope with ELV remanufacturing uncertainties and solved the model using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).

4.3.2. Managing ELV Supply Uncertainty

The past studies proposed solutions to inconsistent ELV supply using quantitative methods (Table 4). The method used in these studies was mathematical modelling [39,40,41], namely the IPP method. Quantitative methods are preferred in coping with the ELV supply uncertainty because many considerations are needed to stabilise the ELV supply.

4.3.3. Managing Environmental Uncertainty

External influences are an inevitable factor that affects the whole supply chain. Therefore, various managing approaches can be adopted to reduce environmental uncertainty, whichever suits the situation. As shown in Table 4, some studies employed quantitative methods to cope with environmental uncertainty. Mohamad-Ali et al. [28], Alamerew and Brissaud [60], Mohan and Amit [53] adopted the system dynamics (SD) method to cope with the uncertainties. Several studies employed MCDM techniques such as the combination of DEMATEL and intuitionistic fuzzy set (IFS) method [47], the combination of best-worst method (BWM) and weighted influence nonlinear gauge system (WINGS) [59], fuzzy best–worst multi-criterion (FFBW) [45], and the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) [62]. Additionally, other quantitative methods adopted to cope with uncertainties include the social cost-benefit analysis (CBA) [55], cross-entropy solution (CE) [43], activity-based costing (ABC) [57], and Stackelberg game model (SG) [42,44,49,51,58,61].

Likewise, several studies on environmental uncertainty adopted process efficiency evaluation to reduce the uncertainty. Three of the studies adopted the life cycle assessment (LCA) technique in their studies [50,52,54]. The other studies used the energy analysis [46], and the political, economic, social, technological, legal, and environmental (PESTLE) analysis [48] to reduce environmental uncertainties. Besides, one previous study on environmental uncertainty conducted by [56] proposed redesigning the regulations after analysing the barriers in the ELV industry using structural equation modelling (SEM) to reduce the uncertainty. In brief, there are three uncertainty management strategies used to deal with environmental uncertainty. The strategies include the quantitative method, evaluation of process efficiency, and redesigning the regulation. Since environmental uncertainty refers to various external factors, the solution for the uncertainty differs according to the investigated problem.

4.3.4. Managing Logistics and Network Facility Uncertainty

Logistics and network facilities play a vital role in collecting and delivering ELV supplies. Collecting retired vehicles from end-users without proper logistics management will be challenging. For this reason, many past studies proposed numerous strategies to manage the uncertainty related to logistics and network facilities. Firstly, several studies applied quantitative methods to cope with uncertainties in logistics and network facilities, which include mathematical modelling methods such as MILP [66,67,70,71,72,73,75,76,78,79,84,90], linear programming (LP) [88], fuzzy linear programming (FLP) [69], bi-objective nonlinear integer programming (BONLIP) [74], bi-level programming [89], mixed-integer nonlinear programming (MINLP) [63], multi-objective mixed-integer linear programming (MOMILP) [65], polymorphic uncertain linear programming (PULP) [77], mixed-integer nonlinear programming (MINLP) [82], and multi-objective fuzzy linear programming (MOFLP) [87]. Other than that, several studies adopted the MCDM method to deal with the uncertainties, which include the combination of elimination and choice translating reality (ELECTRE I), Simos procedure, and stochastic multi-criteria acceptability analysis [64], intuitionistic fuzzy combinative distance-based assessment (CODAS) [81], and interval type-2 fuzzy additive ratio assessment (ARAS) [86].

In addition, a study proposed quantitative conceptual frameworks [68] to handle the uncertainties, and another study used the CE algorithm, which is an artificial neural network technique [85], to cope with the uncertainties. In contrast, Chaabane et al. [83] adopted a decision support system to minimise the uncertainties using the MILP method before designing the system interface. Finally, Al-Quradaghi et al. [80] employed a reconstruction of the chain facilities mechanism and proposed a conceptual framework to diminish the uncertainty in the ELV recycling industry. To sum up, the studies investigating the uncertainties in logistics and network facilities mainly employed quantitative methods to handle the uncertainty. Besides that, one study also proposed a decision support system, while another used the reconstruction of chain facilities approach to managing the uncertainty in logistics and network facilities. These findings show that quantitative methods are advisable to manage logistics and network facilities uncertainty.

4.3.5. Managing Organisation and Human Behaviour Uncertainty

Uncertainty related to organisation and human behaviour is undeniable and needs appropriate strategies to diminish the uncertainty. Previous studies regarding the organisation and human behaviour adopted two uncertainty management approaches. One of the studies conducted by Go et al. [91] recommended the redesigning regulations approach to reduce the uncertainty after analysing the current remanufacturing situation using the descriptive analysis method. In contrast, another two studies employed a quantitative method to cope with the uncertainty by applying the SG method [92] and the SD method [93]. Nevertheless, the exploration of the organisation and human behaviour uncertainty in the ELV industry is still minimal, and more studies can be conducted to propose more solutions to reducing the uncertainty regarding the organisation and human behaviour aspects.

4.3.6. Managing Production and Operations Uncertainty

The uncertainty in production and operations could lead to large-scale disruption to the ELV industry if not handled appropriately since production and operations are a crucial stage in ELV recovery. Hence, various managing mechanisms were applied in previous research. Most of the previous studies adopted quantitative techniques as their uncertainty coping mechanism. Based on the findings in Table 4, several studies used MCDM in their studies and applied methods such as the combination of fuzzy analytical hierarchy process (FAHP) and fuzzy TOPSIS (FTOPSIS) [114], FAHP [103], FTOPSIS [95], the combination of FAHP and fuzzy grey relation analysis and technique for order performance by similarity to ideal solution (G-TOPSIS) [97], the mixed method of interval triangular fuzzy numbers type-2 and modified ELECTRE [104], analytical hierarchy process (AHP) [118], and the combination of data envelopment analysis (DEA), TOPSIS, and ELECTRE [106].

Furthermore, another quantitative method to cope with the uncertainty is mathematical modelling, for instance, linear programming [96,100], the combination of Net Present Value (NPV) and Discounted Payback Time (DPBT) [101], and MILP [120]. Additionally, other quantitative methods employed to solve the uncertainties include the combination of interpretive structural modelling (ISM) and analytic network process (ANP) [94], CBA [102], SD method [115], arena simulation [117], fuzzy interpretive structural modelling (FISM) [98], and material flow analysis (MFA) [110]. Other than that, there are several quantitative methods for forecasting that were used to solve the uncertainties, for example, the Weibull distribution function [99,108,109,111,113], grey modelling [116], the combination of grey modelling and exponential smoothing [105], life distribution function [107], and logistic function and Weibull distribution function [119]. Finally, Li et al. [112] suggested reducing the uncertainty of production and operations by reconstructing chain facilities using the system layout design (SLP) method.

In short, the quantitative technique is the prominent mechanism adopted to handle production and operations uncertainty. Besides that, past studies also applied other strategies, such as the reconstruction of chain facilities. Given these points, it can be concluded that many strategies have been introduced to solve various production and operation uncertainties in the ELV industry.

5. Regional Analysis

In this section, the relationship between the research works and the region of the studies was analysed to identify the research interest in relation to the ELV supply chain from a global perspective. Figure 5 shows the distribution of the studies that were conducted according to the country and year of publication. As seen in Figure 5, 19 countries have explored supply chain issues in the ELV industry for the past six years. It can be observed in Figure 5 that China took the lead, whereas Turkey and India also showed interest in exploring ELV supply chain issues.

Additionally, Serbia also showed engagement in the ELV supply chain research, and it could be observed that the engagement from Iran was growing. Meanwhile, an inconsistent trend could be observed in other countries (Malaysia, Canada, France, Taiwan Province of China, Iraq, United States, Mexico, South Korea, Poland, Italy, Vietnam, Brazil, Saudi Arabia, and Qatar), indicating less interest and engagement in ELV studies focusing on the supply chain aspect.

Additional information was obtained regarding the economic status of each related country. According to a world economic report by the United Nations [123], the economies of Canada, France, Italy, and the United States are categorised as major developed economies, Poland is categorised as a developed economy, Serbia is categorised as an economy in transition, while Brazil, China, India, Iran, Malaysia, Mexico, Qatar, Saudi Arabia, Turkey, South Korea, Taiwan Province of China, and Vietnam are categorised as developing economies.

These points are illustrated in Figure 6, which shows the research work engagements in the ELV supply chain according to the economic status of each country. As can be seen, 83% of the research work was conducted in countries with developing economies, 8% was conducted in countries with economies in transition, 8% was conducted in major developed economies, and 1% was conducted in countries with developed economies. Hence, it can be concluded that there was massive participation in research work for ELV studies related to supply chain issues from countries with developing economies over the last six years. In contrast, there was less engagement from major developed economies and countries with economies in transition.

According to Ahmed et al. [8], in 2011, most countries with major developed economies and developed economies had achieved the targeted ELV recycling and recovery rates, which explains why there was less engagement from these countries in ELV supply chain research. Conversely, the growth of the ELV industry in developing countries was still minimal [8,20,124]. However, the findings in Figure 6 suggest that nowadays, developing countries are trying to prove the statement wrong by ensuring the steady development of the ELV industry.

It is essential to realise that the economic status affects the growth of the ELV industry, for instance, technological advancements, investment capital, policies, and government subsidies. The developing countries still lack the technologies to execute EoL strategies on a full scale [28]. Additionally, the slow economic growth in developing countries and the lack of support from the government are also affecting the development of the ELV industry [28,82]. Therefore, many studies are being conducted to propose solutions and ensure that the ELV industry survives despite all the challenges developing countries face.

6. Research Gap Analysis

6.1. EoL Strategies

The EoL strategies can reduce pollution and stimulate a healthier environment. Based on the analysis, it can be observed that for the past few years, most research focused on recycling and remanufacturing ELV instead of other EoL strategies. Besides, several studies did not specify their studies on specific EoL strategies and aimed to reduce supply chain uncertainties for the larger scale of the ELV recovery industry. Only a few studies focus on the ELV dismantling issues, which is the initial step to recycling or remanufacturing ELVs. Nonetheless, the exploration of ELV recycling for energy recovery is still minimal. Thus, it is recommended that investigations on energy recovery from ELV recycling activities be conducted.

It is well known that adopting recovery operations in the automotive industry is one of the approaches to supporting sustainability. A few years back, many ELV recycling problems were discussed to minimise the risks of recycling ELVs. Although the procedure for ELV recycling nowadays can be considered a structured process, it still needs much improvement, particularly for the recycling process in developing countries [19]. Furthermore, ELV recycling operations have indeed been of great assistance in the manufacturing sector, especially during the Covid-19 pandemic, where the short supply of metal ores and scrap materials has caused an upsurge in the price of construction steel [125]. This suggests that the recycling of ELVs is an essential modus operandi for managing the supply of raw materials. Furthermore, 75% of a vehicle is comprised of metal [8], indicating that ELV waste should provide sufficient raw materials for steel production. Nonetheless, the fluctuating ELV supply in ELV recycling operations is alarming. For these reasons, it is predicted that additional studies will be conducted into ELV recycling to reduce the uncertainty related to ELV supply.

Additionally, the exploration of ELV remanufacturing has made slower progress than ELV recycling. Complex disassembly and reassembly procedures were among the main challenges in ELV remanufacturing operations that influenced the research engagement in ELV remanufacturing. Compared to recycling, remanufacturing involves a lot of inspections and complex procedures to extend the lifespan of the automotive parts [26]. Besides, inadequate technologies to remanufacture ELVs efficiently are a significant hindrance in ELV remanufacturing operations in developing countries. Reusing and remanufacturing old parts from ELVs are quite unpopular among customers due to a lack of confidence in recovered automotive spare parts [8,124]. It cannot be denied that consumer awareness is one of the significant issues in sustainability. However, this should not be a stumbling block in executing ELV remanufacturing initiatives. On the bright side, ELV remanufacturing promotes the reduction of environmental impacts, certifies the availability of parts, and lessens the production expenditure [8]. As has been pointed out, the findings showed that no recent study had been conducted in relation to the supply of ELVs for remanufacturing operations. Thus, future explorations should focus on ELV remanufacturing processes and the availability of ELVs, where the ELV recoverability of ELVs should be considered when selecting the supply. Given these points, more studies on supply chain uncertainties need to be conducted to diminish ELV supply, production, and operation uncertainties.

6.2. Sources of Supply Chain Uncertainty

The analysis in this review highlighted the leading supply chain uncertainties that disrupt the ELV industry: logistics and network facilities, production and operation, environmental, decision intricacy, organisation and human behaviour, and ELV supply. The analysis showed that most research focused on solving logistics and network facilities, production and operation uncertainties, and environmental uncertainties in the past few years. However, it can be seen that less engagement could be observed to solve the decision intricacy uncertainties, organisation and human behaviour uncertainties, and ELV supply uncertainties. Therefore, future research on the ELV supply chain is expected to manage these uncertainties, mainly on ELV supply. Studies on ELV supply are expected to assist in minimising the crucial supply issues that significantly impact the ELV recycling and remanufacturing industry [19].

In addition, the stunted growth of the ELV industry is also influenced by the lack of a decision support tool for complex situations in developing countries. Although many past studies proposed numerous solutions for managing complex decision-making for recycling and remanufacturing operations, regional issues such as the location, government influence, and legislation should be considered when making a complex decision. Therefore, it can be assumed that more research will be executed in developing countries to manage complex decision issues while handling the uncertainties in recycling and remanufacturing operations.

Besides, it is possible to extend ELV supply chain studies to include chaos control uncertainty, the uncertainty of information technology systems, the uncertainty related to long-term forecasting, and the uncertainty due to natural disasters [21]. Managing these sources of uncertainty can eliminate the turmoil in the ELV industry so the ELV business can soon flourish. With this in mind, it is expected that future ELV studies will be directed in relation to these sources of uncertainty.

6.3. Approaches for Managing Uncertainty

Based on the analysis, it can be observed that quantitative methods are widely adopted to handle the uncertainties, which suggests that quantitative methods are robust in managing the uncertainties. However, several uncertainties can be handled using other equally essential methods, such as the reconstruction of chain facilities, process efficiency evaluation, decision support system, and redesigning regulations. Beyond those, future research should also implement other uncertainty handling strategies, for instance, lean methodologies, collaborations with other supply chain members, and adoption of information and communication technology (ICT) systems in the supply chain system [21]. For example, adopting lean methodologies in recycling and remanufacturing operations may increase the efficiency of the production system. Similarly, collaborations among supply chain members can minimise the uncertainties in the supply chain system and support a constructive synergy in the chain, whereas the adoption of ICT systems in the ELV sector is highly crucial since the utilisation of real-time integrated monitoring systems in the ELV industry barely exists.

7. Conclusions

The increasing production of vehicles in the local and global markets suggests that a large amount of ELV waste will be generated in the future. Hence, an immediate action plan should be prepared to face this upcoming ELV waste. The adaptation of a closed-loop supply chain system is seen as a crucial step for the collection and processing of ELV waste, either by extending the lifespan of the ELVs (reusing, repairing, reconditioning, refurbishing, or remanufacturing) or converting the ELVs into new materials through recycling [28]. Furthermore, adopting EoL strategies in the ELV industry has reduced the unfavourable disposal of ELVs by means of incineration or landfill dumping [8]. In this article, previous research on the ELV supply chain literature was reviewed, where the content analysis method was adopted to identify the existing supply chain uncertainties and EoL strategies over the last six years. Eighty-eight research articles from the past six years were included in the review process, where crucial data were extracted and analysed to present essential information for researchers of similar interest. Additionally, this paper also conducted a regional analysis and addressed the challenges faced by developing countries in establishing the ELV industry.

The findings from the content analysis revealed that logistics and network facilities, production and operations, and environmental uncertainties were the top three sources of uncertainty in the ELV supply chain that were analysed. On the other hand, fewer investigations were carried out into uncertainties related to decision intricacy, organisation and human behaviour, and ELV supply. Aside from that, the research trend for the sources of uncertainty was presented and discussed in this paper. Furthermore, a regional analysis was presented.

It was found that there was high engagement in ELV chain studies from developing countries over the last six years. Meanwhile, the participation from countries with major developed and developed economies in this field of study was less than 20%. In summary, the analysis showed that developing countries actively explored the uncertainties in the ELV supply chain field, which is believed to be due to the lack of standard ELV regulations to handle ELV in the said economic region. With today’s technological advancements, ELV dismantling plants should improve their operational efficiency and effectiveness by utilising technology to accommodate their business operations to successfully manage the many challenges that arise and become sustainable in today’s volatile markets. The final part of this review has highlighted an analysis of research gaps on supply chain uncertainty sources and the management approaches adopted to face the uncertainties. The analysis also proposed potential directions for future studies to guide researchers with common interests on the way forward for ELV supply chain research.

Nonetheless, this review article has a few limitations, which may be addressed for the future reviews. Firstly, the search results are relatively constrained since “end-of-life vehicle” and “supply chain” are the keywords chosen to collect the previous literature. Adding keywords such as “automotive industry” and “circular economy” in search engines may help the paper collection process while still focusing on the closed-loop supply chain. Secondly, a comprehensive investigation of past studies utilising systematic literature review can be applied to analyse the previous research comprehensively. In conclusion, despite the various imperfection that exist in ELV system, this review analysis can navigate the direction of prominent research within the ELV supply chain’s broader perspective.