Planning and data design
Define the forecast target and horizon, reconcile calendars and entities, and document how source data limits the interpretation of demand.
AI demand forecasting
Forecast demand for the planning decision ahead.
Build demand forecasts around a defined planning horizon and operational decision. Compare candidate models with practical baselines, account for data limitations and give planners a controlled way to review and adjust the result.
- When it starts
- A scheduled planning cycle or a material change in the information used to estimate demand.
- Who owns the decision
- Demand planners and operational stakeholders who approve inventory or capacity decisions.
- The intended output
- A versioned forecast with evaluation context, uncertainty where supported and recorded planner adjustments.
Workflow design
From historical signals to a reviewed planning input
Observed sales are not always unconstrained demand. The workflow documents what the data represents, evaluates predictions using information available at the time and keeps the planner’s decision separate from the forecast.
- 01
Define the planning unit
Agree product and location granularity, horizon, update cadence and the action the forecast will inform.
Output
A forecast specification and planning baseline
- 02
Prepare historical signals
Align sales, availability and permitted external inputs; document missing periods, stockouts and feature timing.
Output
A time-aligned dataset with quality findings
- 03
Forecast and backtest
Compare candidate methods with simple baselines using historical time windows and relevant error costs.
Output
Forecasts with evaluation and limitation records
- 04
Review and publish
Expose exceptions and planner overrides, then deliver a versioned result to the planning process.
Output
An approved planning input and adjustment history
Controls across the workflow
- Time-aware validation
- Feature availability
- Baseline comparison
- Planner approval
Decisions that shape the scope
- What decision needs a forecast?
- Choose a horizon and level of detail that supports an actual replenishment, staffing or capacity decision. More granular forecasts are not automatically more useful.
- How should stockouts and new products be handled?
- Identify where sales understate demand or history is too limited. Use a documented treatment and expose the limitation rather than interpreting every zero as zero demand.
- Should the forecast trigger an order automatically?
- Forecasting and order execution are separate decisions. Inventory policy, lead times, constraints and planner approval must be defined before adding automated purchasing actions.
Evidence before expansion
Define what better means.
These are proposed evaluation measures, not reported client results. Agree the baseline, sample and acceptance threshold before the pilot, then review the evidence with the workflow owner.
- Forecast error by segment
- Evaluate errors by horizon, product or location using measures appropriate to the demand pattern; compare against the same baseline and time windows.
- Forecast bias
- Check persistent over- or underestimation, including the operational importance of errors in sparse or high-impact demand segments.
- Planner usability
- Review whether the forecast arrives at the required granularity and cadence, and whether exceptions and adjustments are understandable and traceable.
Before you commit
Is this the right engagement?
Planning teams need estimates at the right product, location and time horizon, but historical sales can hide stockouts, promotions and changing demand patterns.
- What we need from you
- Historical sales or demand records, product and location definitions, availability and promotion history, planning horizons and the process consuming forecasts.
- How you accept the work
- Evaluate forecasts across historical time windows and relevant segments, compare with the agreed baseline and test the planner review and publication path with representative exceptions.
- Scope & alternatives
- The use case supplies a planning input, not a guarantee of future demand. Automatic replenishment, pricing changes and broader supply-chain optimization need separately agreed policies and scope.
Capabilities
What the implementation includes
Select the relevant components after reviewing the current process, data and decision ownership.
Baseline and model evaluation
Compare suitable methods using time-aware validation, relevant error measures and segment-level analysis.
Planner workflow integration
Publish versioned forecasts, preserve overrides and monitor whether changing inputs or business conditions justify a model review.
Frequently asked questions
Questions before a pilot
01How much historical data is needed?
How much historical data is needed?
It depends on seasonality, product changes, planning horizon and the frequency of observations. Assess coverage and signal quality before deciding whether the available history supports the intended forecast.
02Does a machine-learning model always outperform a simple forecast?
Does a machine-learning model always outperform a simple forecast?
No. Compare it with practical methods such as seasonal or recent-history baselines under the same validation conditions. Additional complexity needs an observed benefit for the planning task.
03How do you handle promotions and stockouts?
How do you handle promotions and stockouts?
Use reliable event and availability records where available, and document their timing and limitations. These conditions can change the meaning of historical sales and should be visible in evaluation.
04Can planners override a forecast?
Can planners override a forecast?
The workflow can support adjustments with reasons and a retained original prediction. This makes it possible to review the adjustment process instead of losing the evidence of what changed.
05How is this different from predictive analytics?
How is this different from predictive analytics?
Predictive analytics covers a broad range of future-outcome estimates. This use case focuses on demand planning, including product and location grain, planning horizons and the operational review cycle.
Start a conversation
Start with one task worth improving.
Share the current steps, representative inputs and the person who owns the decision. We will help define an implementation scope and an acceptance test.