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A $3M Opportunity Hiding in Plain Sight
Most companies don’t lose money because their forecasts are “inaccurate.”
They lose money because their forecasting decisions are optimized against the wrong objective.
In a recent VYAN value pilot, we identified ~$3M in avoidable business impact across just a subset of SKUs — driven not by focusing simply on forecast error, but by how forecast error translated into inventory, service, cash, and operational instability.
We didn’t achieve this by endlessly fine-tuning models or correcting outliers manually. We achieved it with out of the box optimization of forecasts for Cost of Forecast Error (COFE) — the economic cost of being wrong.
Autonomous Forecast Optimization — Proven on the M5 Benchmark
Most enterprises still rely on planner heroics or constant AI model tuning to keep forecasts afloat. In this edition, we share why neither scales—and how our autonomous forecasting engine proved its value on the M5 benchmark, outperforming the winner on ~60% of SKUs without manual tuning. The focus isn’t accuracy theater—it’s building forecasts businesses can trust.
