Inventory Management: Machine Learning Predicts Demand, Reducing Excess Stock by up to 20%
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V4I2P108Keywords:
Inventory Management, Machine Learning, Demand Forecasting, Supply Chain Optimization, Excess Stock Reduction, Predictive Analytics, Stockout Prevention, Inventory Turnover, Time-Series Forecasting, Smart Supply ChainAbstract
Efficient inventory management is a cornerstone of successful supply chain operations, directly influencing both customer satisfaction and organizational profitability. However, traditional inventory forecasting methods typically reliant on static historical data and linear trend analysis often fall short in accurately predicting fluctuating market demands, leading to either overstocking or stockouts. In recent years, machine learning (ML) has emerged as a transformative tool for demand forecasting, offering adaptive, data-driven solutions capable of identifying complex patterns and nonlinear relationships in large datasets. This paper explores the application of various ML algorithms including Random Forest, Long Short-Term Memory (LSTM) networks, and XGBoost in predicting future product demand with the aim of reducing excess stock levels by up to 20%. A comprehensive methodology involving the use of time-series sales data, promotional calendars, and external economic indicators was employed to train and evaluate these models. Empirical results demonstrate that ML-powered demand forecasting significantly outperforms traditional models in terms of forecast accuracy, inventory turnover ratio, and reduction in excess inventory. Case study evidence from a retail chain implementation further validates these findings, showing measurable cost savings and operational improvements post-ML adoption. The study concludes that integrating ML models into inventory management systems can not only optimize stock levels but also increase agility and resilience in supply chain operations. Future research is recommended in the direction of hybrid modeling approaches and real-time inventory monitoring using IoT data streams to further enhance predictive performance and operational scalability
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