Forecasting of Sporadic Demand Patterns with Seasonality and Trend Components: An Empirical Comparison between Holt‐Winters and (S)ARIMA Methods
Year: 2010
Type: article
Abstract: 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.
DOI: 10.1155/2010/579010
Institutions: University of Modena and Reggio Emilia, University of Padua
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $2,550
OpenAlex ID: W1978784367