Forecast Inaccuracy
Unreliable demand predictions led to stock issues and revenue loss.
Poor Regional Insights
Lack of dealer-level data hindered region-specific strategies.
Data
Delays
Slow ERP sync caused outdated forecasts and planning lags.
Inventory Misalignment
Unclear demand disrupted inventory and production planning.
Reactive Decision-Making
Limited analytics made trend response slow and ineffective.
AI Forecast Engine
Uses XGBoost, CatBoost, and LSTM models to deliver accurate, multi-level demand forecasts.
Granular Forecasting
Predicts demand by product, dealer, fuel type, and region on daily to monthly scales.
Scenario Simulation
Dashboards simulate events like schemes or seasonality to guide real-time planning.
Real-time ERP and dealer data integration ensures timely, adaptive forecasting.
Strategic Analytics
Tracks anomalies, seasonality, and trends to enable forward-looking decisions.
Power BI Dashboards
Visual tools simplify complex forecasts into clear, actionable business insights.
95%+ Prediction Accuracy
Achieved high forecasting precision across products during the pilot phase, boosting planning confidence.
Improved Inventory Turnover
Real-time demand insights led to a 30–40% uplift in inventory rotation and production efficiency.
Reduced Stockouts and Carrying Costs
Better alignment of supply with demand minimized excess inventory and product unavailability.
Hyperlocal Forecasting Precision
Enabled accurate state-, region-, and dealer-level forecasts, supporting targeted strategies.
Faster Response to Market Shifts
Agile planning tools supported quicker decision-making for campaigns and seasonal spikes.
Enhanced Dealer and Customer Satisfaction
Optimized allocation ensured better product availability, boosting trust and loyalty.
