The context
Traditional planning tools were buckling under market complexity. The manufacturer needed a platform that could scale with its ambitions — granular, daily predictions by product, dealer and geography to reduce stock-outs, optimise production and lift customer satisfaction.
What was holding them back
Unreliable demand predictions caused stock issues and lost revenue.
No dealer-level data to shape region-specific strategies.
Slow ERP sync left forecasts outdated and plans lagging.
Unclear demand disrupted inventory and production planning.
Limited analytics made responses to market trends slow.
What they set out to achieve
- Granular forecasting by product, dealer and region
- Region-based planning optimisation
- Real-time ERP and dealer data integration
- Simulation tools for schemes and seasonality
- A self-learning AI engine that improves over time
What we built
AI forecast engine
XGBoost, CatBoost and LSTM models deliver accurate multi-level demand forecasts.
Granular forecasting
Demand by product, dealer, fuel type and region — daily to monthly.
Scenario simulation
Test schemes, promotions and seasonality before committing.
Live data sync
Real-time ERP and dealer data keeps every forecast current.
Strategic analytics
Anomaly, seasonality and trend tracking for forward-looking decisions.
Power BI dashboards
Complex forecasts turned into clear, actionable insight.
The results
- Hyperlocal forecasting precision at state, region and dealer level
- Faster response to campaigns and seasonal demand spikes
- Better product availability, building dealer and customer trust