Impact of Temporal Aggregation on Stock Control Performance of Intermittent Demand Estimators: Empirical Analysis

M.Z. Babai, Mohammad Ali, Konstantinos Nikolopoulos

Research output: Contribution to journalArticle

35 Citations (Scopus)

Abstract

Intermittent demand is characterized by occasional demand arrivals interspersed by time intervals during which no demand occurs. These demand patterns pose considerable difficulties in terms of forecasting and stock control due to their compound nature, which implies variability both in terms of demand arrivals and demand sizes. An intuitively appealing strategy to deal with such patterns from a forecasting and stock control perspective is to aggregate demand in lower-frequency ‘time buckets’, thereby reducing the presence of zero observations. In this paper, we investigate the impact of forecasting aggregation on the stock control performance of intermittent demand patterns. The benefit of the forecasting aggregation approach is empirically assessed by means of analysis on a large demand dataset from the Royal Air Force (UK). The results show that the aggregation forecasting approach results in higher achieved service levels as compared to the classical forecasting approach. Moreover, when the combined service-cost performance is considered, the results also show that the former approach is more efficient than the latter, especially for high target service levels.
Original languageEnglish
Pages (from-to)713-721
Number of pages9
JournalOmega
Volume40
Issue number6
Early online date12 Oct 2011
DOIs
Publication statusPublished - 2012

Keywords

  • intermittent demand
  • parametric forecasting
  • aggregation
  • stock control
  • empirical investigation

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