Overstock Prediction Using Machine Learning in Retail Industry

Success in supply-chain relies, in large part, on good stock management. It is quite simple to guess that there will be an increase in demand for a type of product, or rather reluctance over a period of time, but it becomes complicated to know in advance the exact or optimal number of products to or...

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Bibliographic Details
Published in2023 3rd International Conference on Computer, Control and Robotics (ICCCR) pp. 439 - 444
Main Authors Agbemadon, K. Bernard, Couturier, Raphael, Laiymani, David
Format Conference Proceeding
LanguageEnglish
Published IEEE 24.03.2023
Subjects
Computational modeling
deep learning
Demand forecasting
Digital control
Industries
Machine learning
Measurement
overstock prediction
retail industry
Service robots
supply chain
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Summary:Success in supply-chain relies, in large part, on good stock management. It is quite simple to guess that there will be an increase in demand for a type of product, or rather reluctance over a period of time, but it becomes complicated to know in advance the exact or optimal number of products to order to avoid stock-outs and at the same time overstocking. This article shows how transactional data can be used with Machine Learning to forecast demand in the retail industry. To train the machine learning models, a sample of 5,115,472 records of receipt data was obtained from the French branch of one of the largest Belgian supermarket chains's data warehouse. The results revealed that the machine learning models manage to learn the seasonality effects and allow to make better predictions.
DOI:10.1109/ICCCR56747.2023.10194060