# E-Waste Reverse Supply Chain: A Review and Future Perspectives
## 4 E-Waste Reverse Supply Chains
### Forecasting Product Returns  
The design and planning for RSC are more challenging than forward supply chain due to uncertain factors including quantity, cost, time, and quality of e-waste [68]. Forecasting for the number of product returns is vital for the optimal level of RSC performance since it is a key step for collection, recovery strategies and transportation activities [28; 69]. The design of the RSC network, the layouts of treatment facilities and planning of the treatment processes are significantly affected by the estimation of product returns [70]. Hence, the way forecasts such returns are crucial in RSC, some of the existing forecasting models are presented in Table 5.  
[PERSON], [PERSON] and [PERSON] [69] applied a fuzzy coloured Petri net method to estimate the number of mobile phone returns in different regions in Australia. Input data used in this study include income distribution, demographics, sales data and the lifespan of mobile phones. Their result showed that this method could reach 90% accuracy in forecasting the returns of mobile phones. [PERSON] et al. [71] proposed a model for estimating the quantity of e-waste generation in Brazil. They used a variety of methods such as an average lifetime, the number of sales and stock which can apply for the mature as well as non-mature market products. The study found that it is important to estimate the lifespan of products which are normally based on the understanding of customer's behaviour. To estimate the computer waste produced in Chile, [PERSON] et al. [72] utilized a material flow analysis (MFA) approach. The data of sales and imports were collected from two sources, customs databases and publications. The result showed that LCD monitors and laptops in 2020 would rapidly increase due to the replacement of CRT monitors. The MFA method was also used by [PERSON] and [PERSON] [73] for estimating the amount of e-waste from household generation in Indonesia. They found that the overall e-waste from households produced in the years 2015-2025 will grow to 3.75-4.98 million tonnes. By using different forecasting approaches, [PERSON] et al. [74] proposed a model for estimating computer waste quantities in different regions (i.e., Europe, Asia, Australia, Americas). Seven forecasting techniques used in the study including Bass, Gompertz, logistic, trend model, level model, auto regressive moving average and exponential smoothing. The findings showed that computer waste generation in USA and United Kingdom would rapidly increase in the period 2014-2030 since the lifetime of a computer is decreasing and the consumption is increasing. It requires the need for effective recycling systems and initiatives for reuse or recover. In United States, the future trends of e-waste generation were also conducted by [PERSON] et al. [75]. The factors for forecasting were historical data on sales and the lifetime of electronic products. The decreasing rate and polynomial regression analysis methods were used. The results showed that approximately 1.0835 million units of e-waste of thirteen selected products would be generated in 2025 while mobile phones will account for the highest proportion. In the research of [PERSON] et al. [76], the amount of EoL mobile phones and computers produced in the past and future in Iran was also investigated. This study applied the time-series lifespan and logistic function models. The outflows of obsolete products were firstly estimated through the time-series lifespan approach. At the next step, the logistic function was employed to forecast the values of end-of-life electronic appliances. Finally, the results showed that around 90 million obsolete computers will be generated in 2035 while about 50 million obsolete mobile phones will be generated in 2040 in Iran.