A number of companies utilise end-of-use products (i.e. cores) for remanufacturing or recycling. An adequate supply of cores is needed for such activities. Establishing a purchasing policy for cores, over a finite planning horizon, requires multi-step ahead forecasts. Such forecasts are complicated by the fact that the number of cores in any future period depends upon previous sales and recent returns of the product. Distributed lag models have been used to capture this dependency for single-period ahead forecasts. We develop an approach to use distributed lag models to make multi-period ahead forecasts of net demand (i.e. demand minus returns), and investigate the cost implications, at a prescribed service level, of using such forecasts to purchase cores on a rolling horizon basis. Our results indicate that the effects of errors in the sales forecasts are negligible if sales follow an autoregressive pattern but are substantial when sales are more random. Dynamic estimation of the parameters in a rolling horizon environment yielded the most cost savings at high prescribed service levels (i.e. >0.95). Collectively, our results demonstrate the conditions in which companies can best leverage the dynamic nature of distributed lag models to reduce the acquisition costs over a finite horizon.
Available at: http://works.bepress.com/toyin-clottey/4/