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2023 | OriginalPaper | Chapter

Demand Forecasting Using Machine Learning and Deep Learning Approaches in the Retail Industry: A Comparative Study

Authors : Asma ul Husna, Saman Hassanzadeh Amin, Ahmad Ghasempoor

Published in: Industrial Engineering in the Covid-19 Era

Publisher: Springer Nature Switzerland

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Abstract

Demand forecasting is one of the crucial issues in the retail industry in terms of minimizing costs, setting correct inventory levels, optimal use of inventory space, and reducing the out-of-stock problem. Predicting future demand accurately is a challenging task for retailers and wholesalers because of sudden changes in demand levels, lack of historical data, new trends, and seasonal demand spikes. This paper presents a comparative analysis of Machine Learning (ML) and Deep Learning (DL) techniques (i.e., Random Forest, Gradient Boosting Regression, and Long Short-Term Memory) to forecast the product demand, using large amounts of time series historical data. The forecasting models’ performance and accuracy are evaluated by comparing Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results indicate that Random Forest is more efficient and more promising than the other considered techniques in this study due to its prediction accuracy.

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Title: Demand Forecasting Using Machine Learning and Deep Learning Approaches in the Retail Industry: A Comparative Study
Authors: Asma ul Husna
Saman Hassanzadeh Amin
Ahmad Ghasempoor
Publisher: Springer Nature Switzerland
Book: Industrial Engineering in the Covid-19 Era
Print ISBN: 978-3-031-25846-6

Electronic ISBN: 978-3-031-25847-3

Copyright Year: 2023
DOI: https://doi.org/10.1007/978-3-031-25847-3_24

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