Unlocking Retail Success: Empowering Decision-Making with Advanced Sales Forecast Models

Authors

  • Geovanne Oliveira Alves University of Pernambuco, Caruaru, PE, Brazil https://orcid.org/0000-0002-2084-5516
  • Alexandre Magno Andrade Maciel University of Pernambuco, Recife, PE, Brazil https://orcid.org/0000-0003-4348-9291
  • Jorge Cavalcanti Barbosa Fonseca University of Pernambuco, Caruaru, PE, Brazil
  • Erika Carlos Medeiros University of Pernambuco, Caruaru, PE, Brazil https://orcid.org/0000-0003-2506-7116
  • Patricia Cristina Moser University of Pernambuco, Caruaru, PE, Brazil
  • Rômulo César Dias de Andrade University of Pernambuco, Caruaru, PE, Brazil
  • Fernando Ferreira de Carvalho University of Pernambuco, Caruaru, PE, Brazil and Federal Institute of Science and Technology of Pernambuco, Recife, PE, Brazil
  • Fernando Pontual de Souza Leão Junior University of Pernambuco, Caruaru, PE, Brazil
  • Marco Antônio de Oliveira Domingues Federal Institute of Science and Technology of Pernambuco, Recife, PE, Brazil https://orcid.org/0000-0002-7579-3485

DOI:

https://doi.org/10.14738/abr.126.17093

Keywords:

Machine Learning, Physical Retail, Sales Forecasting, Shopping Mall

Abstract

The gross revenue indicator contributes to the understanding of the company’s situation, and generating sales revenue forecasts is a strategy that helps the manager in directing the business. This work aims to develop a set of Machine Learning (ML) models to forecast sales in physical retail. Methodology – To carry out this work, a methodology was proposed to create, compare and evaluate ML models. Findings – When analyzing the forecast scenarios, it was observed that Hourly forecasts performed better than Day forecasts. We highlight the LIGHTGBM model, which presented the best scores in the F1-score metric with 82.95%, 79.26% and 76.53% scenario representing one hour, two hours and three hours ahead, respectively. Value – It is expected that the forecast models will help managers to find insights to support the operational decisions of physical retail contributing to carry out actions to optimize companies’ processes.

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Published

2024-06-13

How to Cite

Alves, G. O., Maciel, A. M. A., Fonseca, J. C. B., Medeiros, E. C., Moser, P. C., de Andrade, R. C. D., … Domingues, M. A. de O. (2024). Unlocking Retail Success: Empowering Decision-Making with Advanced Sales Forecast Models. Archives of Business Research, 12(6), 12–44. https://doi.org/10.14738/abr.126.17093

Issue

Vol. 12 No. 6 (2024): Archives of Business Research

Section

Articles

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Copyright (c) 2024 Geovanne Oliveira Alves, Alexandre Magno Andrade Maciel, Jorge Cavalcanti Barbosa Fonseca, Erika Carlos Medeiros, Patricia Cristina Moser, Rômulo César Dias de Andrade, Fernando Ferreira de Carvalho, Fernando Pontual de Souza Leão Junior, Marco Antônio de Oliveira Domingues

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