A grey system for the forecasting of return product quantity in recycling network
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In recent years, Reverse Logistics (RL) activities has gained much attention in terms of economic, social and governmental reasons. For firms, it has become important to manage the reverse flow of products in an efficient way to obtain competitive advantage. However to design RL network is difficult because of some reasons. Especially, uncertain parameters related to return product quantity, quality and time are main characteristics of RL networks. One of the most important decisions of RL is to provide a correct and timely estimation of return waste product quantity, because it affects many decisions related to RL network design process directly. To predict return product in RL networks, intrinsic and extrinsic forecasting are some of the well-known and frequently used forecasting techniques. In this study, we proposed a grey forecasting system to forecast return product quantity in RL network. The contribution of this study is the first study that presented grey forecasting model for product return quantity in reverse logistics network design literature. Solutions showed that grey forecasting system is very efficient to predict return quantity.