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    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, 225, 2020 DOI: 10.1016/.j.ijpe.2019.107597

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Optimising forecasting models for inventory planning

Research output: Contribution to journalJournal articlepeer-review

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Optimising forecasting models for inventory planning. / Kourentzes, Nikolaos; Trapero, Juan R.; Barrow, Devon.

In: International Journal of Production Economics, Vol. 225, 107597, 01.07.2020.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Kourentzes, N, Trapero, JR & Barrow, D 2020, 'Optimising forecasting models for inventory planning', International Journal of Production Economics, vol. 225, 107597. https://doi.org/10.1016/j.ijpe.2019.107597

APA

Kourentzes, N., Trapero, J. R., & Barrow, D. (2020). Optimising forecasting models for inventory planning. International Journal of Production Economics, 225, [107597]. https://doi.org/10.1016/j.ijpe.2019.107597

Vancouver

Kourentzes N, Trapero JR, Barrow D. Optimising forecasting models for inventory planning. International Journal of Production Economics. 2020 Jul 1;225. 107597. https://doi.org/10.1016/j.ijpe.2019.107597

Author

Kourentzes, Nikolaos ; Trapero, Juan R. ; Barrow, Devon. / Optimising forecasting models for inventory planning. In: International Journal of Production Economics. 2020 ; Vol. 225.

Bibtex

@article{debe74e8f3df457680fc006fa96fa756,
title = "Optimising forecasting models for inventory planning",
abstract = "Inaccurate forecasts can be costly for company operations, in terms of stock-outs and lost sales, or over-stocking, while not meeting service level targets. The forecasting literature, often disjoint from the needs of the forecast users, has focused on providing optimal models in terms of likelihood and various accuracy metrics. However, there is evidence that this does not always lead to better inventory performance, as often the translation between forecast errors and inventory results is not linear. In this study, we consider an approach to parametrising forecasting models by directly considering appropriate inventory metrics and the current inventory policy. We propose a way to combine the competing multiple inventory objectives, i.e. meeting demand, while eliminating excessive stock, and use the resulting cost function to identify inventory optimal parameters for forecasting models. We evaluate the proposed parametrisation against established alternatives and demonstrate its performance on real data. Furthermore, we explore the connection between forecast accuracy and inventory performance and discuss the extent to which the former is an appropriate proxy of the latter.",
keywords = "Forecasting, Inventory management, Optimisation, Likelihood, Simulation",
author = "Nikolaos Kourentzes and Trapero, {Juan R.} and Devon Barrow",
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, 225, 2020 DOI: 10.1016/.j.ijpe.2019.107597",
year = "2020",
month = jul,
day = "1",
doi = "10.1016/j.ijpe.2019.107597",
language = "English",
volume = "225",
journal = "International Journal of Production Economics",
issn = "0925-5273",
publisher = "Elsevier Science B.V.",

}

RIS

TY - JOUR

T1 - Optimising forecasting models for inventory planning

AU - Kourentzes, Nikolaos

AU - Trapero, Juan R.

AU - Barrow, Devon

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, 225, 2020 DOI: 10.1016/.j.ijpe.2019.107597

PY - 2020/7/1

Y1 - 2020/7/1

N2 - Inaccurate forecasts can be costly for company operations, in terms of stock-outs and lost sales, or over-stocking, while not meeting service level targets. The forecasting literature, often disjoint from the needs of the forecast users, has focused on providing optimal models in terms of likelihood and various accuracy metrics. However, there is evidence that this does not always lead to better inventory performance, as often the translation between forecast errors and inventory results is not linear. In this study, we consider an approach to parametrising forecasting models by directly considering appropriate inventory metrics and the current inventory policy. We propose a way to combine the competing multiple inventory objectives, i.e. meeting demand, while eliminating excessive stock, and use the resulting cost function to identify inventory optimal parameters for forecasting models. We evaluate the proposed parametrisation against established alternatives and demonstrate its performance on real data. Furthermore, we explore the connection between forecast accuracy and inventory performance and discuss the extent to which the former is an appropriate proxy of the latter.

AB - Inaccurate forecasts can be costly for company operations, in terms of stock-outs and lost sales, or over-stocking, while not meeting service level targets. The forecasting literature, often disjoint from the needs of the forecast users, has focused on providing optimal models in terms of likelihood and various accuracy metrics. However, there is evidence that this does not always lead to better inventory performance, as often the translation between forecast errors and inventory results is not linear. In this study, we consider an approach to parametrising forecasting models by directly considering appropriate inventory metrics and the current inventory policy. We propose a way to combine the competing multiple inventory objectives, i.e. meeting demand, while eliminating excessive stock, and use the resulting cost function to identify inventory optimal parameters for forecasting models. We evaluate the proposed parametrisation against established alternatives and demonstrate its performance on real data. Furthermore, we explore the connection between forecast accuracy and inventory performance and discuss the extent to which the former is an appropriate proxy of the latter.

KW - Forecasting

KW - Inventory management

KW - Optimisation

KW - Likelihood

KW - Simulation

U2 - 10.1016/j.ijpe.2019.107597

DO - 10.1016/j.ijpe.2019.107597

M3 - Journal article

VL - 225

JO - International Journal of Production Economics

JF - International Journal of Production Economics

SN - 0925-5273

M1 - 107597

ER -