Build a forecast handover for an inventory planner

A model file is not a forecast handover. An inventory planner needs to know what each row means, when the forecast was issued, which horizons are included, how uncertainty should be read, which assumptions condition the values, and what happens when inputs fail.

A compact machine-readable packet makes these choices testable and prevents a dashboard label from carrying the entire contract.

A complete teaching packet

The local lab verifies this illustrative record:

from timeseries_cases import handover_case

result = handover_case()
assert result["complete"] is True
assert result["packet"]["grain"] == "sku_region_month"
assert result["packet"]["rollback"] == "last_approved_packet"
print(len(result["required_fields"]))

It prints 12. The required fields are:

This metadata is illustrative and does not claim an active NeuraPath or client forecast.

Add the evidence a planner needs

Attach backtest metrics by horizon and relevant segment, not only an overall score. Include signed bias, interval coverage, data cutoff, model version and baseline comparison. Explain whether a demand target was reconstructed from capacity-limited sales. Record new-item and missing-calendar fallbacks.

Show the forecast in the planner’s decision grain. If orders occur weekly but forecasts are monthly, document the allocation rule. If lead time is six weeks, include horizons that cover it. Translate uncertainty into a planning scenario or policy only after agreeing on understock and holding costs.

Define approval and monitoring

An override should preserve the original model value, new value, reason, owner and timestamp. Reconcile totals after overrides. Set monitoring windows for bias, error, interval coverage and input freshness. Define who can pause a release and what packet downstream systems use during a pause.

The handover should also name limitations: unannounced competitor actions, causal lift and unconstrained demand during stockouts do not appear merely because a forecast exists. A planner can then combine model output with information outside the model’s scope.

The Data Science course treats this bridge from notebook to decision as part of a completed project.

Exercise

Extend the packet with model version, training cutoff, validation evidence URI, schema hash and approval timestamp. Write automated checks for missing horizons, stale promotions, inconsistent units and totals that fail reconciliation.

Continue learning

This article is part of the Forecasting and time-series analysis sequence. Use the neighbouring tasks when you need the prerequisite or the next application.

• Review the prerequisite or neighbouring task in Anomaly detection in a seasonal metric.
• Continue with Document what a sales forecast cannot predict.

Reference: Forecasting: Principles and Practice on judgmental adjustments.