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Improving the Performance of Popular Supply Chain Forecasting Techniques
Issue number 27
Vol.12 n°4 - 2011 Demand Forecasting and Planning in the SC

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Abstract: This article 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.

Authors: Georgios P. Spithourakis, Fotios Petropoulos, Vassilios Assimakopoulos, Forecasting & Strategy Unit, School of Electrical and Computer Engineering, National Technical University of Athens, Greece Mohamed Zied Babai, BEM Bordeaux Management School, France Konstantinos Nikolopoulos, BEM Bordeaux Management School, France & The Business School, Bangor University, UK

Keywords: forecasting fast-moving demand temporal aggregation in-sample optimization forecasting framework empirical investigation

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