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    Rights statement: This is the author’s version of a work that was accepted for publication in International Journal of Forecasting. 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 Forecasting, 38, 1283-1318, 2022 DOI: 10.1016/j.ijforecast.2019.06.004

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Retail forecasting: Research and practice

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Retail forecasting : Research and practice. / Fildes, Robert; Ma, Shaohui; Kolassa, Stephan.

In: International Journal of Forecasting, Vol. 38, No. 4, 01.12.2022, p. 1283-1318.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Fildes, R, Ma, S & Kolassa, S 2022, 'Retail forecasting: Research and practice', International Journal of Forecasting, vol. 38, no. 4, pp. 1283-1318. https://doi.org/10.1016/j.ijforecast.2019.06.004

APA

Vancouver

Fildes R, Ma S, Kolassa S. Retail forecasting: Research and practice. International Journal of Forecasting. 2022 Dec 1;38(4):1283-1318. Epub 2019 Oct 5. doi: 10.1016/j.ijforecast.2019.06.004

Author

Fildes, Robert ; Ma, Shaohui ; Kolassa, Stephan. / Retail forecasting : Research and practice. In: International Journal of Forecasting. 2022 ; Vol. 38, No. 4. pp. 1283-1318.

Bibtex

@article{1e3b100d653a48aa9028613432770560,
title = "Retail forecasting: Research and practice",
abstract = "This paper reviews the research literature on forecasting retail demand. We begin by introducing the forecasting problems that retailers face, from the strategic to the operational, as sales are aggregated over products to stores and to the company overall. Aggregated forecasting supports strategic decisions on location. Product-level forecasts usually relate to operational decisions at the store level. The factors that influence demand, and in particular promotional information, add considerable complexity, so that forecasters potentially face the dimensionality problem of too many variables and too little data. The paper goes on to evaluate evidence on comparative forecasting accuracy. Although causal models outperform simple benchmarks, adequate evidence on machine learning methods has not yet accumulated. Methods for forecasting new products are examined separately, with little evidence being found on the effectiveness of the various approaches. The paper concludes by describing company forecasting practices, offering conclusions as to both research gaps and barriers to improved practice.",
keywords = "Comparative accuracy, Forecasting practice, Marketing analytics, New products, Product hierarchies, Retail forecasting, Social media data",
author = "Robert Fildes and Shaohui Ma and Stephan Kolassa",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in International Journal of Forecasting. 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 Forecasting, ?, ?, 2020 DOI: 10.1016/j.ijforecast.2019.06.004",
year = "2022",
month = dec,
day = "1",
doi = "10.1016/j.ijforecast.2019.06.004",
language = "English",
volume = "38",
pages = "1283--1318",
journal = "International Journal of Forecasting",
issn = "0169-2070",
publisher = "Elsevier Science B.V.",
number = "4",

}

RIS

TY - JOUR

T1 - Retail forecasting

T2 - Research and practice

AU - Fildes, Robert

AU - Ma, Shaohui

AU - Kolassa, Stephan

N1 - This is the author’s version of a work that was accepted for publication in International Journal of Forecasting. 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 Forecasting, ?, ?, 2020 DOI: 10.1016/j.ijforecast.2019.06.004

PY - 2022/12/1

Y1 - 2022/12/1

N2 - This paper reviews the research literature on forecasting retail demand. We begin by introducing the forecasting problems that retailers face, from the strategic to the operational, as sales are aggregated over products to stores and to the company overall. Aggregated forecasting supports strategic decisions on location. Product-level forecasts usually relate to operational decisions at the store level. The factors that influence demand, and in particular promotional information, add considerable complexity, so that forecasters potentially face the dimensionality problem of too many variables and too little data. The paper goes on to evaluate evidence on comparative forecasting accuracy. Although causal models outperform simple benchmarks, adequate evidence on machine learning methods has not yet accumulated. Methods for forecasting new products are examined separately, with little evidence being found on the effectiveness of the various approaches. The paper concludes by describing company forecasting practices, offering conclusions as to both research gaps and barriers to improved practice.

AB - This paper reviews the research literature on forecasting retail demand. We begin by introducing the forecasting problems that retailers face, from the strategic to the operational, as sales are aggregated over products to stores and to the company overall. Aggregated forecasting supports strategic decisions on location. Product-level forecasts usually relate to operational decisions at the store level. The factors that influence demand, and in particular promotional information, add considerable complexity, so that forecasters potentially face the dimensionality problem of too many variables and too little data. The paper goes on to evaluate evidence on comparative forecasting accuracy. Although causal models outperform simple benchmarks, adequate evidence on machine learning methods has not yet accumulated. Methods for forecasting new products are examined separately, with little evidence being found on the effectiveness of the various approaches. The paper concludes by describing company forecasting practices, offering conclusions as to both research gaps and barriers to improved practice.

KW - Comparative accuracy

KW - Forecasting practice

KW - Marketing analytics

KW - New products

KW - Product hierarchies

KW - Retail forecasting

KW - Social media data

U2 - 10.1016/j.ijforecast.2019.06.004

DO - 10.1016/j.ijforecast.2019.06.004

M3 - Journal article

AN - SCOPUS:85076606968

VL - 38

SP - 1283

EP - 1318

JO - International Journal of Forecasting

JF - International Journal of Forecasting

SN - 0169-2070

IS - 4

ER -