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Publicação
Machine Learning in Digital Retail: Demand Forecasting for Inventory Management in a Sportswear Company
| Nome: | Descrição: | Tamanho: | Formato: | |
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| 1.11 MB | Adobe PDF |
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Orientador(es)
Resumo(s)
Several domains benefit from accurate forecasts, especially within the retail sector, as precise
demand forecasting plays a key role in making informed decisions, such as for inventory
management. The focus of this project is to improve the safety stock process for Always
Available (AA) products in the digital channel in a Sportswear company, by implementing a
machine learning solution to forecast demand units. By providing an accurate forecast, the
project aims to optimize inventory levels, ensuring that sufficient stock is maintained to meet
customer demand without overstocking. This approach focused on the implementation of
tree-based models, specifically Random Forest, XGBoost, and LightGBM, which were
evaluated using two performance metrics: Mean Absolute Error (MAE) and Root Mean
Squared Error (RMSE). Various factors influenced the models' performance, including
promotions, inventory levels, website traffic, conversion rates, etc. All three algorithms
demonstrated similar MAE scores, highlighting their effectiveness in handling complex and
large datasets and showing that tree-based models can provide highly effective solutions in a
retail context. However, LightGBM emerged as the most robust method, achieving the lowest
RMSE and thereby indicating a slightly superior performance. This project demonstrates the
significant benefit that machine learning can bring to the retail sector, driving business growth.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
Palavras-chave
Digital Retail Machine Learning Tree-Based Regression Algorithms Inventory Management SDG 9 - Industry, innovation and infrastructure