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What the 15-of-15 result means
The comparison was between an AI forecast and a forecast the customer already relied on. The incumbent forecasts included statistical, consensus, and manual approaches. All 15 AI forecasts used Pecan’s predictive engine; DemandForecast.ai says the comparisons were made on customer data in live deployments, not collected through surveys. The 15 are the demand-forecasting deployments with a measured incumbent baseline in a larger study of more than 40 deployments.
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The result is a within-deployment comparison: in each of the 15 measured cases, the AI forecast had less error than that customer’s incumbent forecast under the metric used for that comparison. It does not mean the forecasts had the same absolute accuracy, or that one company’s result can be ranked directly against another’s. DemandForecast.ai’s October 1, 2026 announcement gives the headline result.
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DemandForecast.ai reports a 14%–56% reduction in forecast error across the 15 deployments, with a company-reported average of 32%. This is the company’s average across its selected cases, not a standardized pooled score independently calculated from common data and metrics.
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The deployments varied substantially: they covered a few hundred to more than 100,000 SKUs, with forecast horizons from eight weeks to 36 months. DemandForecast.ai explains that product mix, intermittent demand, and the forecast level—such as SKU, SKU-location, or category—affect absolute error. The report references MAPE, weighted MAPE, and customer-defined measures. Because these metrics are not interchangeable, the range and average are best read as summaries of reported within-customer comparisons, not as a common scale for ranking deployments.
The company’s 2026 Demand Forecasting Accuracy Report describes the measures and deployment scope.
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What the report does—and does not—establish
A selected, non-random sample
The report includes deployments that reached a benchmark and reported results. It excludes pilots that stalled before benchmarking and companies that chose not to share outcomes. DemandForecast.ai describes the resulting sample as real but not random. The 15-of-15 finding therefore applies to these measured deployments; it does not establish that 100% of AI forecasting projects beat their incumbent forecasts or that the sample represents the wider market.
Company-reported validation
DemandForecast.ai says the team that ran each deployment validated the underlying data. The report does not describe an independent audit or publish customer-level data, so readers cannot independently reproduce the reported aggregate figures from the material provided publicly.
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Forecast error is not the same as business impact
The report also describes operational outcomes, but cautions that forecast accuracy alone does not cause them. Inventory policies, safety stock, reorder decisions, and planner behavior all affect what happens after a forecast is produced.
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Alongside lower forecast error, DemandForecast.ai reports a 70% average reduction in manual planning effort across the 15 deployments. At Mars, the company says approximately 75% of forecast volume qualified as touchless; Mars Vice President of Global Supply Chain Kristen Daihes said the team “can already ‘not touch’ ~75% of the volume.”
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The report also cites overstock reductions of up to 50% and sales lifts of 10% to 25% in one fast-fashion deployment. Those are deployment outcomes, not effects isolated by the forecast-error benchmark, and the report says downstream results depend on customer decisions. At one Nucor site, it reports $4 million to $5 million in annual sales recovered from sales previously lost to stockouts. That figure is likewise a reported site outcome, not proof that forecast accuracy alone produced the recovery.
How to assess a similar AI forecasting claim
A useful evaluation separates the forecast comparison from the business case. Ask for the details that determine whether a benchmark is relevant to your planning environment:
- Baseline: Was the AI compared with the statistical, consensus, or manual forecast actually used by planners?
- Same data and periods: Were both forecasts evaluated against the same customer data and time periods?
- Metric: What error measure and exact definition were used? MAPE, weighted MAPE, and customer-defined measures can produce different results.
- Forecast level and horizon: Was the result measured at SKU, SKU-location, or category level, and over what horizon?
- Included cases: How many deployments reached measurement, and were stalled pilots or unreported outcomes excluded?
- Business outcomes: Are inventory, service, or labor impacts measured separately from forecast error, with relevant policy changes accounted for?
For a comparable benchmark, DemandForecast.ai says a company needs two to three years of SKU-level order or shipment history, the incumbent forecast for the same periods, and an agreed error metric. The report’s description of starting with a proof of concept on a company’s historical data is available on the DemandForecast.ai site.
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