Start with a consistent baseline
Define the planning unit, calendar, and level of detail before loading data. Weekly units by location answer a different question from monthly revenue by product family. Reconcile historical actuals against the source system and flag missing periods. Keep returns and cancellations visible so they do not silently inflate the demand baseline.
Make assumptions editable and visible
Record why a planner changed growth, lead time, or available capacity. Give each scenario a name and keep it separate from the approved forecast. A percentage change is a useful exploration tool, but it is not a statistical forecasting model. Explain that distinction in the interface so readers do not mistake a simple scenario for a prediction.
Connect the forecast to a decision
Show which items require action when projected demand exceeds capacity or supply. Assign an owner to investigate the constraint rather than presenting a warning with no next step. Compare the planned quantity with available stock and incoming supply using agreed dates. Decide how promotions, discontinued products, and new launches enter the review.
Review accuracy on a schedule
Freeze forecast versions so later actuals can be compared with the estimate available at the time. Track both error and directional bias at useful aggregation levels. Avoid percentage errors that divide by zero for low-volume items. Discuss recurring misses with planners, update assumptions, and keep the explanation alongside each revision.
A planning guide. The right implementation depends on your system and requirements.
Talk through your next step