Unlocking Retail Success: Empowering Decision-Making with Advanced Sales Forecast Models
DOI:
https://doi.org/10.14738/abr.126.17093Keywords:
Machine Learning, Physical Retail, Sales Forecasting, Shopping MallAbstract
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.
Downloads
Published
How to Cite
Issue
Section
License
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
This work is licensed under a Creative Commons Attribution 4.0 International License.