Fashion Inventory Management and Demand Forecasting Using Gradient Boosted Trees

Author(s)	Mr. N.M Shahane, Asma Fatima, Shreya Karode, Nikita Jadhav, Shrushti Jadhav
Country	India
Abstract	Efficient inventory management and accurate demand forecasting are persistent challenges in the fashion retail industry. Traditional forecasting methods often fail to account for the complex interplay between product attributes, and customer preferences, leading to issues such as overstocking, stockouts, and missed sales opportunities. These inefficiencies increase operational costs and negatively impact customer satisfaction. This project presents a machine learning–based framework to predict monthly product demand by analyzing historical sales data and product attributes. The proposed system applies Gradient Boosted Trees (LightGBM) to predict demand based on product attributes including type,color, and category. By forecasting sales volumes at the product and attribute level, the approach provides retailers with actionable insights to recognize high-demand products, optimize inventory levels, and improve decision-making. The results demonstrate enhanced predictive accuracy compared to conventional methods, enabling more efficient and customer-centric inventory management.
Keywords	Inventory Management, Demand Forecasting, Machine Learning, Light- GBM, Feature Engineering, Retail Analytics, Product Attribute Analysis, Sales Prediction
Published In	Volume 7, Issue 2, March-April 2026
Published On	2026-04-21

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