What will you do differently if the forecast is right?

What will you do differently if the forecast is right?

A forecast has no business value until it changes a decision. Improving statistical accuracy while keeping inventory, staffing, pricing or capital plans unchanged is an analytical achievement, not an economic one. Forecast design should therefore start with the action and its asymmetric costs.

Define the decision cadence, horizon, lead time and smallest change worth making. Identify the cost of acting too early, too late, too much or too little. A retailer protecting availability may care more about under-forecasting a high-margin item than equally sized over-forecast error; a liquidity decision may be driven by an adverse quantile, not the expected value.

Provide a distribution or scenarios rather than one number. The M5 uncertainty competition required nine quantiles across 42,840 retail series, reflecting the practical need to understand ranges at different products and aggregation levels. Translate those ranges into pre-agreed policies: reorder, reserve capacity, hedge, escalate or wait.

Test the complete decision rule against a simple baseline. Backtesting must respect time, information availability and hierarchy; compare not only forecast error but service, margin, waste, working capital and intervention cost. Track overrides and their reasons so expert judgement can be evaluated rather than either prohibited or accepted without evidence.

After each cycle, distinguish model error from execution failure and from a rational decision under uncertainty. Recalibrate thresholds as economics change. The most useful forecast is not necessarily the most accurate on average�it is the one whose uncertainty is understood and whose signals consistently improve the choices the organisation is prepared to make.