# E-Waste Reverse Supply Chain: A Review and Future Perspectives
## 5 Research Gaps and Potential Research Directions
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. - 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. - Although different forecasting techniques were applied for recycling or remanufacturing products, the study for forecasting returned products until disposition level is quite limited. * 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 [127; 104]. 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. - 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 [131; 94; 132; 133; 8]. To handle uncertain parameters, stochastic approaches have been proposed [134; 135; 104; 114; 89; 115]. 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. - 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. - 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. 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].