Examination of demand forecasting by time series analysis for auto parts remanufacturing

Abstract

Production planning and control in remanufacturing are more complex than those in traditional manufacturing. Developing a reliable forecasting process is the first step for optimization of the overall planning process. In remanufacturing, forecasting the timing of demands is one of the critical issues. The current article presents the result of examining the effectiveness of demand forecasting by time series analysis in auto parts remanufacturing. Most previous studies on demand forecasting in remanufacturing assume that the time distribution of new product sales are known and that the time distributions of product end-of-life and demands for remanufactured products are calculated by adding the product lifespan period to the time distribution of product sales. However, this assumption is not always correct. For example, independent remanufacturers (IRs) do not always have precise information on the time distribution of new product sales, and in this case, a different approach is needed. Based on this background, this study examined the effectiveness of forecasting by time series analysis that does not need those information. To verify the forecasting accuracy, actual data of an auto parts IR was used. The study used the time series data of the shipments of an actual IR of auto parts for a total of 400 types of remanufactured alternators and starters over a period of 12 years. The method was employed on the initial 11 years of data to project the demand over the final year, and the forecasting results provided an average error of 27.2% relative to the actual shipments made over the forecasted year. The factors degrading the accuracy and the means of improving the results are discussed. Also, the implications of the results and future steps regarding the present study are argued.