Wielding Occam's razor: Fast and frugal retail forecasting

34 Pages Posted: 26 Mar 2021 Last revised: 7 Sep 2022

See all articles by Fotios Petropoulos

Fotios Petropoulos

School of Management, University of Bath, UK

Yael Grushka-Cockayne

University of Virginia - Darden School of Business

Enno Siemsen

University of Wisconsin - Madison

Evangelos Spiliotis

National Technical University of Athens, Greece

Date Written: September 7, 2022

Abstract

Problem definition: Retail forecasting algorithms have increased in complexity in recent years, particularly those using machine learning. More traditional families of forecasting models, such as exponential smoothing and autoregressive integrated moving averages, have also expanded to contain multiple possible forms and forecasting profiles. Complexity in models, and in model availability, however, come at a cost and do not always offer accuracy or other benefits.

Methodology and results: Using a large scale empirical analysis, we show that one can parsimoniously identify suitable subsets of models without decreasing forecasting accuracy or a reduced ability to estimate forecast uncertainty. We propose a framework that balances forecasting performance with computational cost, resulting in the consideration of only a reduced set of models.

Managerial Implications: We demonstrate that considering a reduced set of models can perform well in retail practice and translate the computational benefits to monetary cost savings and improved environmental impact and offer implications to large retailers.

Keywords: exponential smoothing, ARIMA, big data, suboptimality, computational cost, retail, forecasting, forecast-value-added

Suggested Citation

Petropoulos, Fotios and Grushka-Cockayne, Yael and Siemsen, Enno and Spiliotis, Evangelos, Wielding Occam's razor: Fast and frugal retail forecasting (September 7, 2022). Available at SSRN: https://ssrn.com/abstract=3792565 or http://dx.doi.org/10.2139/ssrn.3792565

Fotios Petropoulos (Contact Author)

School of Management, University of Bath, UK ( email )

Claverton Down
Bath, BA2 7AY
United Kingdom

Yael Grushka-Cockayne

University of Virginia - Darden School of Business ( email )

P.O. Box 6550
Charlottesville, VA 22906-6550
United States

Enno Siemsen

University of Wisconsin - Madison ( email )

716 Langdon Street
Madison, WI 53706-1481
United States

Evangelos Spiliotis

National Technical University of Athens, Greece ( email )

Patision Street 42
Athens, Attiki 10682
Greece

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