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  • Petropoulos_2016_another look at estimators for intermittent demand

    Rights statement: This is the author’s version of a work that was accepted for publication in International Journal of Production Economics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Production Economics, ??, ?, 2016 DOI: 10.1016/j.ijpe.2016.04.017

    Accepted author manuscript, 283 KB, PDF document

    Available under license: CC BY: Creative Commons Attribution 4.0 International License

  • Petropoulos 2016 Another look at estimators for intermittent demand

    Final published version, 692 KB, PDF document

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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Another look at estimators for intermittent demand

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>11/2016
<mark>Journal</mark>International Journal of Production Economics
Issue numberPart A
Volume181
Number of pages8
Pages (from-to)154-161
Publication statusPublished
Early online date21/04/16
Original languageEnglish

Abstract

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 8,000 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.

Bibliographic note

This is the author’s version of a work that was accepted for publication in International Journal of Production Economics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Production Economics, 181, Part A, 2016 DOI: 10.1016/j.ijpe.2016.04.017