Separate uncertainty from choice
A demand forecast does not tell a planner how much to order. That choice also depends on lead times, minimum order quantities, service commitments and the cost of holding stock. Begin by stating the action a team must take and the constraints that make it difficult. Forecast accuracy is one input; it is not the business objective by itself.
Compare feasible alternatives
Use scenarios to expose the cost of being wrong in either direction. Compare policies against the same constraints and objectives, and retain the assumptions behind each recommendation. An explanation should identify which resource or commitment limits the result, rather than merely provide a fluent summary of a number.
Measure the decision, not just the model
Validate recommendations alongside existing planning before changing execution. Measure service, margin, inventory and exceptions against an agreed baseline. Keep prediction error as a diagnostic, but judge the engagement by the quality and consequences of the operational decisions it supports.
A worked example
Illustrative assumptions for discussion, not a customer case or a performance claim.
Suppose weekly demand is 100 units, lead time is two weeks, and available stock is 180 units with no inbound orders. Expected lead-time demand is 200 units, leaving a 20-unit gap before any safety buffer. If the minimum purchase is 50 units, buying 20 is infeasible. A planner should compare buying 50, expediting existing supply or reallocating stock, including cash, lead time and customer priority. A better forecast alone cannot resolve that choice.
Questions to take into a pilot
Which action will change, and who owns it? Which constraints cannot be relaxed? How will stockouts, excess inventory and expedited orders be recorded? Test candidate policies on the same historical period, then run a shadow comparison before enabling execution. Record when a recommendation was rejected and why; those exceptions often reveal missing operating constraints.