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Data-driven inventory management in fashion retail: a machine learning approach to demand forecasting
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Resumo(s)
This study develops a machine learning model to enhance inventory management at the “Kilt”
clothing store by accurately predicting annual product sales, preventing overstock, and
maximizing profitability. Analyzing historical sales data from 2009 to 2023, I evaluated
multiple regression models, identifying the Random Forest as the most effective. The model
forecasts 2024 profits to reach 2.2 million, significantly higher than 2023’s 1.4 million. By
leveraging past sales data and advanced predictive modeling, the study provides strategic
insights to optimize inventory decisions and improve overall store profitability.
Descrição
Palavras-chave
Fashion retail Machine learning Demand forecasting Data analytics Data-driven decision making