Machine Learning Application for Sales Forecasting and Inventory Optimization in Wholesale Trade - Kakkad, Pujan Shailesh (International Journal of Basic and Applied Sciences, 2025)
This work explores the use of machine learning in optimizing inventory management processes, using the case study of Vadodara, Gujarat, the largest wholesaler of plumbing and heating products in the Czech Republic and Slovakia. The work combines financial analysis with predictive analytics to identify opportunities for cost reduction and inventory optimization. A linear regression model was applied to historical sales data of corded and cordless power tools to forecast future demand. The findings suggest a clear trend of increasing sales of cordless tools and stagnating demand for corded tools. Based on this, inventory turnover was analyzed, and an ABC analysis was conducted to identify high-priority stock. Recommendations were made to rebalance inventory investments, enhance stock efficiency, and implement strategic purchasing decisions. The application of machine learning in this context demonstrated measurable benefits in predicting sales patterns and improving inventory planning, ultimately contributing to better cost management.
Keywords: Machine Learning; Inventory Optimization; Predictive Analytics; Linear Regression; ABC Analysis; Demand Forecasting