Food Demand Forecasting
Transforming historical meal-order data into actionable planning intelligence to reduce uncertainty in procurement and staffing.
Tableau Analytics
Demand Forecasting
Operations
Python
Demand Forecast vs Actuals
Center-level prediction visualization (Last 14 Days)
Simulation Active
Impact & Results
94%
Forecast Accuracy
Achieved within 5% error margin across major operational centers.
4.5x
Faster Planning
Cut time spent preparing decision inputs for procurement.
Variance Analysis
Daily volatility index across different demand tiers. Highlighting the stabilization after dashboard deployment.
The Approach
- Data Engineering: Process to clean order history and map complex center dimensions dynamically.
- Signal Extraction: Captured trends, weekday behaviors, and deep seasonality down to the specific SKU.
- BI Integration: Embedded complex forecasting outputs into intuitive Tableau dashboards for non-technical managers.
- Risk Flagging: Added anomaly detection zones to warn operations of sudden expected demand shifts.
Why It Matters
Forecast numbers alone are not enough. The key success of this project was translating raw statistical outputs into targeted planning actions for procurement teams.
- Bridged the gap between Data Science and Operations.
- Created a proactive, rather than reactive, supply chain culture.
- Built scalable logic applicable to multiple geographic regions.