E-Waste Reverse Supply Chain: A Review and Future Perspectives
Abstract
:1. Introduction
2. Electronic Waste Issues
3. Reverse Supply Chain
3.1. Definition of Reverse Supply Chain
3.2. Reverse Supply Chain Processes
3.2.1. Product Acquisition
3.2.2. Reverse Logistics
3.2.3. Inspection and Disposition
3.2.4. Refurbish
3.2.5. Redistribution and Sales
3.3. Differences between Forward and Reverse Supply Chains
4. E-Waste Reverse Supply Chains
4.1. Factors of Implementation
4.2. Performance Evaluation and Decision Making
4.3. Forecasting Product Returns
4.4. Research on E-Waste Reverse Supply Chain Network Design Models
5. Research Gaps and Potential Research Directions
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- It is important to establish an integrated approach (combining field trips, literature review, and quantitative data analysis) to manage e-waste in developing countries (i.e., India, Pakistan, Vietnam, the Philippines) since informal recycling practices are common in these countries. Recycling methods normally use larger labour force and low-level technology so a significant number of e-waste components are heading to landfills.
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- There are some challenges in the implementation of e-waste management for developing countries. Firstly, setting up formal processing of e-waste requires significant investment at the initial stage so informal collectors are popular in these countries. Secondly, since the regulations of e-waste management in developed countries are extremely stringent, these countries export their waste to developing countries, such as India, where costs of management are lower and the rules are not as tough [24]. Hence, Extended Producer Responsibility (EPR) is considered as a useful practice for e-waste management. This practice can share the responsibility with companies, consumers, and smaller waste collectors and expand the reach of waste clean-up operations and creates a formal structure for a profitable and efficient e-waste management program. EPR can be implemented well in the long term only if effective monitoring of the collection process and roles of all the stakeholders are clearly defined in an integrated manner. This would be an interesting topic in the future.
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- Implementing proper e-waste management is not only a practical and technical issue but also political and financial aspects. In the future study, decision-makers should align proposed improvements with regional priorities and with a mechanism for monitoring and evaluate changes to a management system.
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- Evaluation of the existing e-waste practices is primary and it leads to prioritizing actions that start by regulating aspects of e-waste. Future work should include legislations of e-waste disposal and product imports.
- Research group 1: factors affected e-waste RSC implementation
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- In the category of factors affecting e-waste RSC implementation, although there are various factors to be considered, such as regulations, top management, environmental issues, financial concerns and etc., consumers’ behaviour is one of the most vital factors which contributes to the success of e-waste RSC implementation. Their return intention is a crucial variable for the prediction of the quantity of e-waste, and as a result, this will become important inputs for electronic firms in implementing the design of their RSC systems. Even though a few studies examined customers’ behaviour and residents’ awareness for returning e-waste issues, their research just focused on some particular products such as mobile phones or computers. Hence, further studies can enhance the behaviour of customers for a variety of electronic products, like household devices or IT and telecommunications equipment.
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- Most studies in this category try to improve the awareness of inhabitants about e-waste by the public media, educational programmes, etc. The roles of government and industry through incentive campaigns to encourage consumers to return their end-of-life products can be an interesting topic in the future.
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- There is a lack of studies focusing on e-waste RSC implementations in rural areas. The residents and local governments in the countryside, especially in developing countries have less awareness of e-waste recycling and environmental protection [46]. Hence, research on factors affected e-waste RSC implementations in rural areas can be conducted in future studies.
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- Extended producer responsibility (EPR) policy aims to manage and treat their end-of-life products. This plays an important role in e-waste RSC implementations at an industry perspective, which can be enhanced to the implementation at the national level. More practical research specifying on EPR implementation for e-waste would be potential future studies.
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- Risk assessment is also an important factor in RSC operation but it has been not examined by existing studies. In future research, risk assessment will be an interesting topic to explore.
- Research group 2: Performance evaluation and decision making
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- E-waste is normally recognized as a complex issue to make a proper decision. The complexity is influenced by various elements such as various sources of collection, the flow of returned products, the numbers of decision-makers. Hence, it requires an appropriate decision to improve the RSC performance which should be based on the collective decision. In this context, focusing on the vagueness of different decision-makers using Fuzzy Technique for Order Performance by Similarity to Ideal Solution (FTOPSIS) and more criteria to interpret their interaction on the RSC performance would be potential topics in future.
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- To comply with governmental regulations and create a competitive advantage, there is a need for electronics industries to collaborate with third-party reverse supply chain providers to effectively manage their returned products [67]. Although some existing studies conducted the evaluation and selection of third-party RSC providers, the limited number of studies focused on multiple attribute evaluation of electronics companies for RSC collaborations. Hence, more research on this issue by considering the vagueness of the decision-making process could be a promising study direction in the future.
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- Another potential topis could be conducting game theory to examine the strategic interactions in the decision-making process among different partners in RSC operations for e-waste.
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- Some recent research has considered three suitable dimensions such as social, economic and environmental aspects. It would be an interesting direction if future studies could integrate the technology dimension into the sustainable RSC system. The applications of radio-frequency identification, the internet of things can implement for inventory management, tracking the flows of materials, the management of recovery information could be a new study direction in this category.
- Research group 3: Forecasting product returns
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- Most existing studies estimate the number of product returns by examining different variables such as data sales, the lifespan of products, material composition, etc. However, other factors like customs and culture, regulations, demographics and consumer’s income might affect the prediction of product returns. There is no study analysing these factors for estimating returned products, which might create potential future research.
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- Previous research estimated the data of weight and e-waste composition based on the literature. However, these data might not accurate or vary over the years due to technological innovations. This might create some level of uncertainty. Further, other data such as lifespan, market share and value of unit which were normally collected and analysed by consumer survey, official government statistics or stakeholders. This also leads to fuzzy or uncertain data. Hence, in such a context, uncertainty analysis could be possible research in future implementation.
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- Although different forecasting techniques were applied for recycling or remanufacturing products, the study for forecasting returned products until disposition level is quite limited.
- Research group 4: e-waste RSC network designs
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- Network design is a strategic issue and has received great attention from researchers. Existing research mainly focused on transportation, fixed, operating, and disposal costs in the total cost of RSC. They ignore risk factors involved RSC operation which has a significant influence on RSC costs [104,127]. Risks can be seen at treatment centres and transportation activities due to a variety of dangerous materials contained in e-waste [128,129,130]. Therefore, integrating risk factors in an RSC network design model can be an interesting topic for future direction.
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- Regarding uncertain issues, most of the existing studies assumed that all parameters in the RSC system are deterministic or known in advanced. However, RSC operation is generally recognized as dynamic in nature with a high degree of uncertain parameters, such as return rate, processing cost, transportation cost, capacity and so on [8,94,131,132,133]. To handle uncertain parameters, stochastic approaches have been proposed [89,104,114,115,134]. Nevertheless, there are two main issues in applying a stochastic method. The first one is that, in some practical cases, there is insufficient historical data to be used for uncertain parameters, so it is difficult to acquire the exact random distributions of these parameters. The second reason is that in most of the existing studies on RSC under uncertainty environment, a stochastic approach is applied through different scenarios for modelling the uncertainty which might result in heavily computational burdens [135]. Therefore, to deal with these issues, the fuzzy approach has emerged as a potential option because it can be flexible to cope with imprecise information and various kinds of uncertainty simultaneously with high computational efficiency [136]. A few studies [137,138,139] used the fuzzy approach in the field of supply chain management. However, current studies on the e-waste RSC model with the fuzzy approach are still in their infancy.
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- Most articles in this group neglect to specify the source of e-waste which normally comes from three main sources: households, industries, government sectors. This factor should be cooperated in the design phase of RSC network to provide better policy management for e-waste in terms of economic and environmental aspects.
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- RSC consists of a set of activities including disassembly, repair and recycling to recover returned products. Selecting partners in the RSC network is crucial to achieving optimal outcomes. In this regard, partner selections can be addressed as a multi-criteria decision-making problem. This step can be implemented prior to considering a network design problem. There is a clear deficit of applying an integrated model (e.g., fuzzy AHP and MILP) which comprises the selection of RSC partners and network design.
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- The number of research which considers a specific product of e-waste RSC network modelling is still limited. Only a few studies examined some specific products such as refrigerators, conditioners [1,97], mobile phones, cameras [123]. More product-oriented research, such as IT products, computers, medical devices, etc., should be investigated in future studies.
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- To solve a large scale of problems, meta-heuristic or heuristic algorithms like Genetic Algorithms, Ant Colony Optimization and Tabu search can be applied for obtaining better computational performance.
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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No. | Category | Examples | Amount (Million Tonnes) |
---|---|---|---|
1 | Large devices | Washing machines, large printing machines, dishwashing machines | 11.8 |
2 | Small devices | Vacuum equipment, radio, toasters, toys, medical devices, video cameras, electronic tools | 12.8 |
3 | Small IT and telecommunications equipment | Telephones, printers, personal computers, pocket calculators | 3.0 |
4 | Temperature exchange equipment | Air conditioners, freezers, refrigerators, heat pump | 7.0 |
5 | Lamps | LED lamps, fluorescent lamps, high intensity discharge lamps | 1.0 |
6 | Screen and monitors | Televisions, monitors, laptops | 6.3 |
Forward Supply Chain | Reverse Supply Chain |
---|---|
Focus on increasing profit and cost minimization | Focus on environmental issues, regulations, profit and cost minimization |
Product demand is quite straightforward to forecast | Returned products are relatively difficult to estimate |
The quantity of product is less variation | The quantity of returned products are highly uncertain |
Conventional marketing techniques can be used | There are some elements requiring complicated marketing |
Processing times and stages are well identified | Processing times and stages are vary based on the quality of returned products |
Products are delivered from one location to other locations | Used products are collected from a lot of locations and then reach to one processing centre |
Speed is one of the main factors in terms of competitive advantage | Speed is not an important element |
Product packaging is standard | Returned product packaging highly varies or lack of packaging |
Product structure is standard | Returned product structure is modified |
Cost estimation is quite easier because of accounting systems | Cost factors are complicated to determine |
Disposition options are rather clear | Disposition alternatives depended on the condition of a returned product |
Inventory management is consistent | Inventory management is chaotic |
Cost implications are quite clear | Cost implications are unclear |
Processes for real-time product tracking are highly visible | Processes for returned product tracking are less visible because of lack of information system infrastructure |
Product life cycle changes are easily managed | Product life cycle changes are difficultly managed |
Models are relatively deterministic | Models are more stochastic |
Key importance to manufacturers | Key importance to end-of-life processors (such as remanufacturers, recyclers) |
Factors | Products or Sector | Country | References |
---|---|---|---|
Internal: strategic consideration, financial issues, management skills and technological issues External: public awareness, environmental regulations, economic evaluation, systems and collaboration | Electronic industry | China | Hung Lau and Wang [22] |
Economic elements, regulations, corporate citizenship and environmental and green issues | End-of-life computers | India | Ravi, Shankar and Tiwari [37] |
Financial needs, environmental requirements, social factors | Electronic industry | Taiwan | Chiou, Chen, Yu and Yeh [38] |
Strategic consideration, environmental awareness, economic concerns and social awareness | Electronic waste | Brazil | Guarnieri, e Silva and Levino [39] |
Regulations, the demand of customers, strategic cost, environmental issues, the quantity and quality, incentives and integration and coordination | End-of-life computers | Australia | Rahman and Subramanian [40] |
The incentive dependency of residents, e-waste concern, residents‘ awareness | Electronic waste | Iran | Jafari, Heydari and Keramati [41] |
Top management concern, strategic partners, the relative cost and performance, the strategy of reducing returned products, revenue from recyclable materials, the speed to put products back to markets | Electronic waste | European nations | Janse, Schuur and de Brito [42] |
Consumer behaviour in returning used products | Mobile phone | India | Dixit and Badgaiyan [43] |
Behaviour, education background, community knowledge, attitudes | Electronic waste | Indonesia | Pandebesie, Indrihastuti, Wilujeng and Warmadewanthi [44] |
Subjective norm, environmental knowledge and consumer perception | E-waste | Nigeria | Nduneseokwu et al. [45] |
Public awareness, enterprise perspective, government policy | E-waste | China | Cao et al. [46] |
Laws and regulations, environmental awareness, public media and educational campaigns | E-waste | Vietnam | Thi Thu Nguyen et al. [47] |
Reference | Main Goal | Modelling Approach/Method | Dimension Concerns | Country |
---|---|---|---|---|
Cucchiella, D’Adamo, Koh and Rosa [15] | Evaluating recovery value of 14 types of e-waste from the recycling process | Literature analysis | Economic | European |
Keh, Rodhain, Meissonier and Llorca [48] | Evaluating the integrated RSC model | IBM case study | Economic, environmental and social | France |
Lin, Wen and Tsai [49] | Determining the priority for e-waste recycling | AHP | Environmental | Taiwan |
Ravi [50] | Evaluating the quality of recycling operation | Multi Attribute Global Inference of Quality (MAGIQ) (a conceptual model) | Environmental and economic | India |
Bereketli, Erol Genevois, Esra Albayrak and Ozyol [51] | Evaluating and selecting of e-waste treatment strategies | A linear programming technique for multidimensional analysis of preference (LINMAP) | Environmental and economic | Turkey |
Liu, Zhong and Wei [52] | Evaluation of e-waste RL capability | Index system and quantitative approaches | - | China |
Ponce-Cueto, Manteca and Carrasco-Gallego [54] | Determining the locations of collection areas | AHP | Economic | Spain |
Tsai and Hung [55] | Optimizing treatment and recycling systems | Activity-based costing (ABC) | Environmental and economic | Taiwan |
Dhib, Addouche, El Mhamdi and Loukil [56] | A compromising strategy for sustainable performance of e-waste management | Entropic approach, Fuzzy set theory | Environmental, economic, and social | Tunisia |
Duygan and Meylan [58] | Providing policy support for e-waste management | Material Flow Analysis and structural analysis | Social, economic, technical and political aspects | Switzerland |
Jayant et al. [59] | Evaluating the selection of RSC Service Providers | Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method | Environmental and economic | India |
Sahu et al. [60] | Recognizing the important enablers for the responsibility of replacing obsolete mobile phones | Decision-Making Trial and Evaluation Laboratory (The Grey-DEMATEL) | Environmental | India |
Tran et al. [61] | Analysing the management of TVs in urban areas | Material Flow Analysis (MFA) | Environmental | Vietnam |
Bahers and Kim [62] | Evaluating the implementation of Extended Producer Responsibility for e-waste activities | Material Flow Analysis | Environmental, economic elements | France |
Agrawal et al. [63] | Analysing critical strategic issues and challenges faced by a real electronics industry | Interviews, Strength, Weaknesses, Opportunities, and Threats (SWOT) | Environmental, economic aspects | India |
Shokohyar and Mansour [64] | Designing the sustainable recovery network for e-waste | AHP and simulation optimization method | Environmental, economic and social aspects | Iran |
De Meester et al. [65] | Optimizing the environmental performance of the e-waste recycling system assisting decision markers | Material Flow Analysis and life cycle assessment | Environmental | Belgium |
Isernia et al. [66] | Evaluating the e-waste collection system | The probability transition matrix | Environmental, economic and social aspects | Italy |
Govindan et al. [67] | Selecting partners for sustainable reverse supply chain collaboration | Complex Proportional Assessment, the best worst method | Environmental, economic and social aspects | India |
Authors | Model Focus | Method | Variables for Forecasting | Product/Equipment | Country/Region |
---|---|---|---|---|---|
Hanafi, Kara and Kaebernick [69] | Estimating mobile phone returns | Fuzzy Coloured Petri Net | Income distribution, demographics, sales data, the lifespan of the electronic equipment | Obsolete mobile phones | Australia |
Araújo et al. [71] | The quantity of e-waste generated | Time-step, consumption and use | An average lifetime, the number of sales and stock | E-waste | Brazil |
Steubing et al. [72] | Evaluating computer waste generation | Material flow analysis | Data on sales and import | Computer waste | Chile |
Andarani and Goto [73] | Forecasting e-waste from households generated | Material flow analysis | The lifespan of the electronic devices, consumption data, consumer’s behaviour | Televisions, washing machines, refrigerators, personal computers, and mobile phones | Indonesia |
Petridis et al. [74] | Forecasting the quantity of computer waste | Different forecasting approaches including Bass, Gompertz, Logistic, Trend model, Level model, Auto Regressive Moving Average and Exponential Smoothing | Data sales, the lifespan of a computer | Computer waste | Europe, Asia, Australia, Americas |
Chang, Assumaning and Abdelwahab [75] | Predicting the amount of e-waste generation | Decreasing rate, polynomial regression analysis | Sales data, Lifespan of the electronic equipment | E-waste of thirteen selected electronic devices | United States |
Rahmani et al. [76] | Estimating the past and future quantity of end-of-life computers and mobile phones generated | Time-series and logistic function | The lifespan of the electronic equipment | End-of-life computers and mobile phones | Iran |
Ikhlayel [77] | Comparing different methods to estimate e-waste generation | Consumption and use, Simple Delay, Time Step, Mass Balance, Approximation 2 | The number of households, the average number of people per household, current and future population, and the sales rate | E-waste of six selected electronic equipment | Jordan |
Polák and Drápalová [78] | Estimating the quantity of obsolete mobile phone generation | Delay | The lifespan of the electronic equipment, data on the import and export | Mobile phones | Czech Republic |
Alavi et al. [79] | Predicting e-waste generation | Consumption and use | The lifetime of the electronic equipment | E-waste | Iran |
Nguyen et al. [80] | The use of large home applications and the estimation of e-waste generation in future | Weibull distribution, the logistic function, and the population balance model | The lifespan of electronic equipment, the family size, and data sales | Five large home appliances | Vietnam |
Kim et al. [81] | E-waste generation | Population balance model, Weibull distribution | The lifespan of electronic equipment, domestic shipments, the number of electronic products owned per household | Eight selected e-waste | South Korea |
Islam and Huda [82] | Estimating e-waste generation, recoverable materials, potential revenue | Holt’s double exponential smoothing and Weibull distribution and Monte Carlo simulation | Stock estimation, dynamic lifespans, the assessment of put-on-market | E-waste | Australia |
Gusukuma and Kahhat [83] | Estimating the flows and the stocks of CRT Televisions in 2018-2025 | Dynamic Material Flow Analysis | Importers, Household users, Business and Public, Intermediaries | Cathode Ray Tube (CRT) Televisions | Peru |
Lau et al. [84] | Estimating disposal outlets | Material flow analysis | Consumer behaviour, private e-waste trader survey | TVs, washing machine, air conditioners, refrigerator, personal computers | Hong Kong |
Abbondanza and Souza [85] | Estimating e-waste generation in Sao Jose dos Campos city | Survey | E-waste lifespan, disposal profiles, estimated population | Sixteen selected types of e-waste | Brazil |
References | Goal | Network Stages | Model | Uncertain Parameters | ||||||
---|---|---|---|---|---|---|---|---|---|---|
CF | DF/SF | RcF | RpF | DpF | SM | PM | ||||
Doan et al. [1] | CM | √ | √ | √ | √ | √ | √ | √ | FMILP | Quantity, shipping, processing, recycling and risk costs, capacity |
Dat et al. [36] | CM | √ | √ | √ | √ | √ | √ | √ | MILP | Not considered |
Kilic, Cebeci and Ayhan [96] | CM | √ | √ | √ | √ | MILP | Not considered | |||
John et al. [97] | PM | √ | √ | √ | √ | √ | √ | √ | MILP | Not considered |
Gomes, Barbosa-Povoa and Novais [101] | CM | √ | √ | √ | √ | √ | √ | MILP | Not considered | |
Linh et al. [104] | CM | √ | √ | √ | √ | √ | √ | √ | MILP | Not considered |
Lieckens and Vandaele [106] | PM | √ | √ | √ | √ | √ | MINLP | Transportation delays, and inventory levels | ||
John, Sridharan and Kumar [108] | PM | √ | √ | √ | √ | √ | √ | √ | MILP | Not considered |
Alumur et al. [109] | PM | √ | √ | √ | √ | √ | √ | MILP | Not considered | |
Krikke, van Harten and Schuur [110] | CM | √ | √ | √ | √ | MILP | Not considered | |||
Shih [111] | CM | √ | √ | √ | √ | MILP | Not considered | |||
Deng and Shao [112] | CM | √ | √ | √ | √ | √ | √ | √ | MILP | Not considered |
Lee and Dong [114] | CM | √ | √ | √ | SMILP | Quantity of returned products | ||||
Ayvaz, Bolat and Aydın [115] | PM | √ | √ | √ | √ | √ | SMILP | Quantity, quality and shipping costs | ||
Demirel and Gökçen [116] | CM | √ | √ | √ | MILP | Not considered | ||||
Srivastava [117] | PM | √ | √ | √ | √ | √ | √ | MILP | Not considered | |
Xianfeng et al. [118] | CM | √ | √ | √ | √ | MILP | Not considered | |||
Grunow and Gobbi [119] | CM | √ | √ | √ | √ | √ | MILP | Not considered | ||
Zhi et al. [120] | CM | √ | √ | √ | MILP | Not considered | ||||
Achillas et al. [121] | CM | √ | √ | √ | √ | √ | √ | MILP | Not considered | |
Alshamsi and Diabat [122] | PM | √ | √ | √ | √ | √ | √ | MILP | Not considered | |
John et al. [123] | PM | √ | √ | √ | √ | √ | √ | √ | MILP | Not considered |
Banguera et al. [124] | PM | √ | √ | √ | √ | √ | MILP | Not considered | ||
Tosarkani and Amin [125] | PM | √ | √ | √ | √ | √ | FANP, MILP | Not considered | ||
Messmann et al. [126] | CM | √ | √ | √ | √ | √ | MILP | Not considered |
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Doan, L.T.T.; Amer, Y.; Lee, S.-H.; Phuc, P.N.K.; Dat, L.Q. E-Waste Reverse Supply Chain: A Review and Future Perspectives. Appl. Sci. 2019, 9, 5195. https://doi.org/10.3390/app9235195
Doan LTT, Amer Y, Lee S-H, Phuc PNK, Dat LQ. E-Waste Reverse Supply Chain: A Review and Future Perspectives. Applied Sciences. 2019; 9(23):5195. https://doi.org/10.3390/app9235195
Chicago/Turabian StyleDoan, Linh Thi Truc, Yousef Amer, Sang-Heon Lee, Phan Nguyen Ky Phuc, and Luu Quoc Dat. 2019. "E-Waste Reverse Supply Chain: A Review and Future Perspectives" Applied Sciences 9, no. 23: 5195. https://doi.org/10.3390/app9235195