Forecasting returns in reverse logistics using GERT network theory

The objective of this study is to explore a new approach to the forecasting of returns. Here, 'returns' refers to used products which can be sorted into resalable products, remanufacturing-able parts, renewable materials, and otherwise disposable waste. This research establishes a model by adopting the Graphical Evaluation and Review Technique (GERT) network theory combined with Bill of Material (BOM) to forecast returns. By using this model, the probability, the quantity and the expected timing of the returns can be predicted. Additionally, in line with the product BOM, the corresponding scale of reuse, i.e. remanufacturing-able parts and renewable materials, can also be forecasted. A numeric example is provided at the end of the study.