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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

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

  • Petropoulos 2016 Another look at estimators for intermittent demand

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Another look at estimators for intermittent demand

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Another look at estimators for intermittent demand. / Petropoulos, Fotios; Kourentzes, Nikolaos; Nikolopoulos, Konstantinos.
In: International Journal of Production Economics, Vol. 181 , No. Part A, 11.2016, p. 154-161.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Petropoulos, F, Kourentzes, N & Nikolopoulos, K 2016, 'Another look at estimators for intermittent demand', International Journal of Production Economics, vol. 181 , no. Part A, pp. 154-161. https://doi.org/10.1016/j.ijpe.2016.04.017

APA

Petropoulos, F., Kourentzes, N., & Nikolopoulos, K. (2016). Another look at estimators for intermittent demand. International Journal of Production Economics, 181 (Part A), 154-161. https://doi.org/10.1016/j.ijpe.2016.04.017

Vancouver

Petropoulos F, Kourentzes N, Nikolopoulos K. Another look at estimators for intermittent demand. International Journal of Production Economics. 2016 Nov;181 (Part A):154-161. Epub 2016 Apr 21. doi: 10.1016/j.ijpe.2016.04.017

Author

Petropoulos, Fotios ; Kourentzes, Nikolaos ; Nikolopoulos, Konstantinos. / Another look at estimators for intermittent demand. In: International Journal of Production Economics. 2016 ; Vol. 181 , No. Part A. pp. 154-161.

Bibtex

@article{a604761bde7d4c87843ebc34305b1482,
title = "Another look at estimators for intermittent demand",
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.",
keywords = "Intermittent Demand, Forecasting, Temporal aggregation, Time series decomposition",
author = "Fotios Petropoulos and Nikolaos Kourentzes and Konstantinos Nikolopoulos",
note = "This is the author{\textquoteright}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",
year = "2016",
month = nov,
doi = "10.1016/j.ijpe.2016.04.017",
language = "English",
volume = "181 ",
pages = "154--161",
journal = "International Journal of Production Economics",
issn = "0925-5273",
publisher = "Elsevier Science B.V.",
number = "Part A",

}

RIS

TY - JOUR

T1 - Another look at estimators for intermittent demand

AU - Petropoulos, Fotios

AU - Kourentzes, Nikolaos

AU - Nikolopoulos, Konstantinos

N1 - 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

PY - 2016/11

Y1 - 2016/11

N2 - 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.

AB - 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.

KW - Intermittent Demand

KW - Forecasting

KW - Temporal aggregation

KW - Time series decomposition

U2 - 10.1016/j.ijpe.2016.04.017

DO - 10.1016/j.ijpe.2016.04.017

M3 - Journal article

VL - 181

SP - 154

EP - 161

JO - International Journal of Production Economics

JF - International Journal of Production Economics

SN - 0925-5273

IS - Part A

ER -