Demand planning
Estimate future demand at an agreed product, location and time resolution.
Predictive Analytics Services
Forecasts and risk scores that support a specific planning decision, with uncertainty, baseline comparisons and feedback from actual outcomes.
DemandUncertainty interval
Tools & technologies
- Python
- Statistical or ML models
- SQL
- Reporting API
Forecasts that fit a planning decision.
Capacity planning
Translate expected workloads into scenarios for staffing or infrastructure.
Risk scoring
Prioritize records for review while preserving an accountable decision process.
A forecast with its assumptions attached.
- Forecasting dataset and pipeline
- Backtesting and baseline report
- Scenario dashboard or API
- Monitoring and refresh schedule
- Forecasting dataset and pipeline
- Backtesting and baseline report
- Scenario dashboard or API
- Monitoring and refresh schedule
Plan next week's stock
Capability Example
Historical sales include promotions, stockouts and seasonal variation.
Illustrative data. Nothing is sent to an external system.
Example workspace1 / 3
Planning history
- Weekly demand
- Promotion dates retained
- Future outcomes excluded
Distinguish observed demand from days when stock was unavailable.
Backtest
- Train on earlier weeks
- Compare seasonal baseline
- Review stockout periods
Evaluate forecasts on later periods using only earlier information.
Planning output
- Next-period estimate
- Uncertainty interval
- Planner adjustment retained
Present an expected range alongside a simple seasonal baseline.
A planning forecast with uncertainty and a record of how it performed against actual demand.
Follow the record through the next step
Agree the horizon and the cost of error.
Decision horizon
Daily replenishment and quarterly capacity planning require different evaluation windows.
Error cost
Define the consequences of over- and under-prediction before selecting a metric.
Questions before we start.
Are forecasts guaranteed?
No. Predictions carry uncertainty, and unusual events can invalidate historical patterns. The interface should make those limits visible.
How is this different from a dashboard?
A descriptive dashboard reports past events. Predictive work estimates future or unknown outcomes and needs backtesting against real observations.
Bring your starting point.
Share the decision horizon, historical records and the cost of an inaccurate estimate.
