A Grey System for the Forecasting of Return Product Quantity in Recycling Network

Abstract

Reverse Logistics (RL) has gained much attention in recent years due to economic, social and governmental reasons. For firms, it has become essential to manage the reverse flow of materials in an efficient way to gain competitive advantage. One important aspect of RL is to provide a correct and timely estimation of return waste product quantity. Improved forecast accuracy leads to a better decision making in strategic, tactic and operational areas of an organization. Intrinsic and extrinsic forecasting are some of the well-known and frequently used forecasting techniques to predict return product in RL networks. In this study, we presented a grey forecasting system to predict return waste product quantity in RL network. To the best of our knowledge, this study is the first in return product forecasting literature by using grey system to predict return quantity. Solutions showed that grey forecasting system is very efficient to predict return quantity.