Improving the Performance of popular Supply Chain forecasting techniques
This paper empirically investigates the extension of the use of an aggregation-disaggregation forecasting approach for intermittent demand (ADIDA) to fast moving demand data, addressing the need of supply chain managers for accurate forecasts. After a brief introduction to the framework and its background, an experiment is set up to examine its performance on data from the M3-Competition. The relevant forecasting methodology and in-sample optimization techniques are described in detail, as well as the core experimental structure and real data. Empirical results of forecasting accuracy performance are presented and discussed, placing further emphasis on the managerial implications of the framework’s being a simple, cost-efficient and universally implementable forecasting method self-improving mechanism. Finally, all conclusions are summarized and guidelines for prospective research are proposed.