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Mathematical Problems in Engineering
Volume 2010, Article ID 579010, 14 pages
Research Article

Forecasting of Sporadic Demand Patterns with Seasonality and Trend Components: An Empirical Comparison between Holt-Winters and (S)ARIMA Methods

1Department of Engineering Sciences and Methods, University of Modena and Reggio Emilia, via Amendola 2, Padiglione Morselli, Reggio Emilia 42100 , Italy
2Department of Management and Engineering, University of Padua, Stradella San Nicola 3, Vicenza 36100, Italy

Received 19 March 2010; Accepted 16 June 2010

Academic Editor: Carlo Cattani

Copyright © 2010 Rita Gamberini et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Items with irregular and sporadic demand profiles are frequently tackled by companies, given the necessity of proposing wider and wider mix, along with characteristics of specific market fields (i.e., when spare parts are manufactured and sold). Furthermore, a new company entering into the market is featured by irregular customers' orders. Hence, consistent efforts are spent with the aim of correctly forecasting and managing irregular and sporadic products demand. In this paper, the problem of correctly forecasting customers' orders is analyzed by empirically comparing existing forecasting techniques. The case of items with irregular demand profiles, coupled with seasonality and trend components, is investigated. Specifically, forecasting methods (i.e., Holt-Winters approach and (S)ARIMA) available for items with seasonality and trend components are empirically analyzed and tested in the case of data coming from the industrial field and characterized by intermittence. Hence, in the conclusions section, well-performing approaches are addressed.