- Friday 17 May 2019
- Professor Aris A. Syntetos; Chair in Operational Research and Operations Management, The Panalpina Chaired Professor of Manufacturing and Logistics; Cardiff University
Remanufacturing operations rely upon accurate forecasts of demand and returned items. Return timing and quantity forecasts help estimate net demand (demand minus returns) requirements. Based on a unique dataset of serialized transactional issues and returns from the Excelitas Group and one of their defense contractors, Qioptiq, we assess the empirical performance of some key methods in the area of returns rate forecasting. We extend their application (for net demand forecasting), by considering that demand is also subject to uncertainty and thus needs to be forecast. Information on remanufacturing operations costs allows for an evaluation of the inventory implications of such forecasts under various settings. We find that serialization accounts for considerable forecast accuracy benefits, and that the accuracy of demand forecasts is as important as that of returns. Further, we show how the combined returns and demand forecast uncertainty affects the inventory performance. Finally, we identify opportunities for further improvements for the operations of Excelitas, and for remanufacturing operations in general.
Keywords: Remanufacturing; Forecasting; Simulation; Empirical data.