The Forecast Dividend: How Retail Leaders Turn Demand Forecasting into Margin

Retail leaders have invested heavily in AI forecasting, and many are still waiting to see the return show up in margin. Every buy, allocation, and markdown starts from a forecast, yet forecasting is usually judged on error rates while the decisions built on it go unmeasured. A forecast can score well on MAPE and still leave one store short, another overstocked, and a promotion discounted deeper than demand required. The value of forecasting lives in those decisions, and conventional measurement rarely looks there.

This paper introduces decision accuracy, the Impact Analytics framework that scores every forecast by the outcome of the decision it informs, benchmarks results against the retailer's own baseline, and reports progress in quarterly business reviews. It also explains how DemandSmart, the Impact Analytics AI-native demand planning and forecasting solution delivers those outcomes across allocation and replenishment, pricing and promotion, and assortment and planning.

What you'll learn

• Why error rates such as MAPE and WAPE stop short of the P&L, and what they leave leadership unable to see
• How decision accuracy gives allocation and replenishment, pricing and promotion, and assortment and planning their own scorecards, each with its own cost of error
• Why a good accuracy level depends on the decision, the level of detail, and the planning horizon of the forecast
• How the forecasting engine selects the best-performing model for every product, store, and season, while handling lost sales, promotions, and new product launches natively
• What the path from a documented baseline to a planning team managing by exception looks like