Spare Parts Demand Forecasting and Inventory Optimization for Indian Automobile Dealers...

Objective: To develop an accurate demand forecasting model and inventory planning solution for after-sales automobile spare parts to minimize stockouts and overstocking at Indian dealerships.

Problem & Context: Indian automobile dealerships face significant challenges in managing after-sales spare parts, given unpredictable demand, a vast product range, and variable lead times from manufacturers. Inefficient inventory management often leads to stockouts or excess inventory, affecting customer satisfaction and operational costs.

Approach/Methodology: The team will analyze historical sales data, seasonal trends, and service patterns from dealership management systems. Time series forecasting (ARIMA, Prophet) and machine learning models will be developed to predict demand, followed by optimization algorithms for inventory planning. Market insights and interviews with supply chain managers will inform the approach.

Deliverables & Analysis: Deliverables include a demand forecasting model, an inventory optimization plan, a dashboard (Tableau/Power BI) for visualization, and a detailed report with actionable recommendations. The team will validate results with real or simulated dealership data and provide scenario analyses for different demand conditions.

Business Impact & Decision: The solution will help dealerships and OEMs achieve optimal inventory levels, reduce working capital locked in spare parts, and improve after-sales service quality—enabling better data-driven decisions in spare parts procurement and stocking.