Optimizing Inventory Management: Applying Data Mining to Forecast Demand in Retail

Abstract

The retail industry operates on thin margins, where efficient inventory management is a critical determinant of profitability and customer satisfaction. Traditional inventory control methods often struggle to cope with the volatile and complex demand patterns inherent in the modern retail environment. This paper explores the application of data mining techniques to optimize inventory management by improving demand forecasting accuracy. We leverage a large transactional dataset from a retail company to develop and compare several forecasting models. The study focuses on identifying significant patterns and variables that influence consumer demand, such as seasonality, promotions, and other external factors. Methodologically, we employ time series analysis, regression models, and machine learning algorithms to predict future sales. Specifically, we implement Autoregressive Integrated Moving Average (ARIMA), multiple linear regression, and a Long Short-Term Memory (LSTM) neural network. The performance of these models is rigorously evaluated using standard metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results indicate that the LSTM model outperforms the traditional ARIMA and regression models, demonstrating a superior ability to capture the non-linear and complex dependencies within the sales data. The comparative analysis reveals that while ARIMA provides a solid baseline, the machine learning approaches offer a significant improvement in forecasting precision, leading to a potential reduction in holding costs and stockouts. This research contributes to the existing body of knowledge by providing a practical framework for retailers to implement advanced data-driven forecasting systems. The findings underscore the transformative potential of data mining in shifting inventory management from a reactive to a proactive paradigm, ultimately enhancing operational efficiency and competitive advantage.