Forecasting Sales and Service Demand for Automobile Dealerships Using Dashboard Analytics

Objective: To develop an interactive dashboard that accurately forecasts sales and service demand for automobile dealerships in India, enabling data-driven decision making.

Problem & Context: Indian automobile dealerships face unpredictability in sales and service demand due to fluctuating market conditions, local competition, and seasonal effects. Traditional forecasting methods often fail to capture dynamic trends, resulting in inventory mismatch and underutilized service capacity.

Approach & Methodology: The project will utilize historical dealership data, regional market trends, and external factors (e.g., festivals, policy changes) to build predictive models. The team will combine statistical analysis and machine learning, integrating business logic specific to Indian dealerships.

Deliverables & Analysis: The student team will deliver a user-friendly dashboard (using Power BI/Tableau) that presents real-time forecasts, key sales/service metrics, and actionable insights. The analysis will include model performance, scenario simulation, and recommendations for inventory and staffing.

Business Impact: The solution will support dealership managers in optimizing inventory, planning workforce, and maximizing revenue opportunities by anticipating customer demand. It informs decisions on promotions, procurement, and resource allocation, tailored to Indian market realities.