Today, inventory management is a key component of retail operations, which is characterised by fluctuating demand, seasonal trends, supply chain reservations, and consumer behaviour. The traditional ways of managing inventories are based on fixed prediction models and manual decision making, which result in overstocking, stock outs and better holding costs. The proposed framework consists of data preprocessing, feature engineering, demand forecasting, stock classification, and inventory optimization techniques, all of which involve the use of machine learning algorithms and Business Intelligence (BI) analytics. Various machine learning models such as time-series forecasting methods and Random Forest and Linear Regression are tested to accurately forecast future demand. For instance, optimised inventory parameters like reorder point, safety stock and economic order quantity (EOQ) are eagerly calculated with predictive insights. Furthermore, Power BI dashboards offer real-time visualization of various key performance indicators (KPIs), stock alerts, sales trends, and supplier performance metrics, aiding in strategic and operational decision-making. The results of the experiments demonstrate the benefits of more accurate forecasting and substantial savings in stock-out, excess inventory and total holding costs when using predictable approaches. The findings confirm how predictive analytics and interactive BI tools improve effective efficiency and improve data-driven decision-making in the retail industry. The results of this research provide an adaptable and effective approach to intelligent retail stock inventory management that connects predictive modeling with business implications.
Business Intelligence, Retail Inventory, Machine Learning, Demand Forecasting.
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