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Another look at estimators for intermittent demand
Logistics and Operations Management Section, Cardiff Business School, Cardiff University, United Kingdom.
Department of Management Science, Lancaster University Management School, Lancaster University, United Kingdom.ORCID iD: 0000-0003-0211-5218
Bangor Business School, Bangor University, United Kingdom.
2016 (English)In: International Journal of Production Economics, ISSN 0925-5273, E-ISSN 1873-7579, Vol. 181, p. 154-161Article in journal (Refereed) Published
Abstract [en]

In this paper we focus on forecasting for intermittent demand data. We propose a new aggregation framework for intermittent demand forecasting that performs aggregation over the demand volumes, in contrast to the standard framework that employs temporal (over time) aggregation. To achieve this we construct a transformed time series, the inverse intermittent demand series. The new algorithm is expected to work best on erratic and lumpy demand, as a result of the variance reduction of the non-zero demands. The improvement in forecasting performance is empirically demonstrated through an extensive evaluation in more than 8000 time series of two well-researched spare parts data sets from the automotive and defence sectors. Furthermore, a simulation is performed so as to provide a stock-control evaluation. The proposed framework could find popularity among practitioners given its suitability when dealing with clump sizes. As such it could be used in conjunction with existing popular forecasting methods for intermittent demand as an exception handling mechanism when certain types of demand are observed.

Place, publisher, year, edition, pages
Elsevier, 2016. Vol. 181, p. 154-161
Keywords [en]
Decomposition, Intermittent demand, Temporal aggreation, Variance reduction, Inverse problems, Time series, Defence sector, Exception handling mechanism, Forecasting methods, Forecasting performance, Standard frameworks, Variance reductions, Forecasting
National Category
Probability Theory and Statistics Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:his:diva-18247DOI: 10.1016/j.ijpe.2016.04.017ISI: 000389091000017Scopus ID: 2-s2.0-84966746623OAI: oai:DiVA.org:his-18247DiVA, id: diva2:1402521
Available from: 2020-02-28 Created: 2020-02-28 Last updated: 2020-02-28Bibliographically approved

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Kourentzes, Nikolaos

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf