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  • Boylan Babai IJPE 2016

    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, 181, Part A, 2016 DOI: 10.1016/j.ijpe.2016.04.003

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On the performance of overlapping and non-overlapping temporal demand aggregation approaches

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On the performance of overlapping and non-overlapping temporal demand aggregation approaches. / Boylan, John Edward; Babai, M Zied.

In: International Journal of Production Economics, Vol. 181, No. Part A, 11.2016, p. 136-144.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Boylan, JE & Babai, MZ 2016, 'On the performance of overlapping and non-overlapping temporal demand aggregation approaches', International Journal of Production Economics, vol. 181, no. Part A, pp. 136-144. https://doi.org/10.1016/j.ijpe.2016.04.003

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Boylan, John Edward ; Babai, M Zied. / On the performance of overlapping and non-overlapping temporal demand aggregation approaches. In: International Journal of Production Economics. 2016 ; Vol. 181, No. Part A. pp. 136-144.

Bibtex

@article{23b582e091ac43bd97b5be867d99fc39,
title = "On the performance of overlapping and non-overlapping temporal demand aggregation approaches",
abstract = "Temporal demand aggregation has been shown in the academic literature to be an intuitively appealing and effective approach to deal with demand uncertainty for fast moving and intermittent moving items. There are two different types of temporal aggregation: non-overlapping and overlapping. In the former case, the time series are divided into consecutive non-overlapping buckets of time where the length of the time bucket equals the aggregation level. The latter case is similar to a moving window technique where the window's size is equal to the aggregation level. At each period, the window is moved one step ahead, so the oldest observation is dropped and the newest is included. In a stock-control context, the aggregation level is generally set to equal the lead-time. In this paper, we analytically compare the statistical performance of the two approaches. By means of numerical and empirical investigations, we show that unless the demand history is short, there is a clear advantage of using overlapping blocks instead of the non-overlapping approach. It is also found that the margin of this advantage becomes greater for longer lead-times.",
keywords = "Temporal aggregation, Overlapping, Non-overlapping, Empirical investigation",
author = "Boylan, {John Edward} and Babai, {M Zied}",
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.2016.04.003",
year = "2016",
month = nov,
doi = "10.1016/j.ijpe.2016.04.003",
language = "English",
volume = "181",
pages = "136--144",
journal = "International Journal of Production Economics",
issn = "0925-5273",
publisher = "Elsevier Science B.V.",
number = "Part A",

}

RIS

TY - JOUR

T1 - On the performance of overlapping and non-overlapping temporal demand aggregation approaches

AU - Boylan, John Edward

AU - Babai, M Zied

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.2016.04.003

PY - 2016/11

Y1 - 2016/11

N2 - Temporal demand aggregation has been shown in the academic literature to be an intuitively appealing and effective approach to deal with demand uncertainty for fast moving and intermittent moving items. There are two different types of temporal aggregation: non-overlapping and overlapping. In the former case, the time series are divided into consecutive non-overlapping buckets of time where the length of the time bucket equals the aggregation level. The latter case is similar to a moving window technique where the window's size is equal to the aggregation level. At each period, the window is moved one step ahead, so the oldest observation is dropped and the newest is included. In a stock-control context, the aggregation level is generally set to equal the lead-time. In this paper, we analytically compare the statistical performance of the two approaches. By means of numerical and empirical investigations, we show that unless the demand history is short, there is a clear advantage of using overlapping blocks instead of the non-overlapping approach. It is also found that the margin of this advantage becomes greater for longer lead-times.

AB - Temporal demand aggregation has been shown in the academic literature to be an intuitively appealing and effective approach to deal with demand uncertainty for fast moving and intermittent moving items. There are two different types of temporal aggregation: non-overlapping and overlapping. In the former case, the time series are divided into consecutive non-overlapping buckets of time where the length of the time bucket equals the aggregation level. The latter case is similar to a moving window technique where the window's size is equal to the aggregation level. At each period, the window is moved one step ahead, so the oldest observation is dropped and the newest is included. In a stock-control context, the aggregation level is generally set to equal the lead-time. In this paper, we analytically compare the statistical performance of the two approaches. By means of numerical and empirical investigations, we show that unless the demand history is short, there is a clear advantage of using overlapping blocks instead of the non-overlapping approach. It is also found that the margin of this advantage becomes greater for longer lead-times.

KW - Temporal aggregation

KW - Overlapping

KW - Non-overlapping

KW - Empirical investigation

U2 - 10.1016/j.ijpe.2016.04.003

DO - 10.1016/j.ijpe.2016.04.003

M3 - Journal article

VL - 181

SP - 136

EP - 144

JO - International Journal of Production Economics

JF - International Journal of Production Economics

SN - 0925-5273

IS - Part A

ER -