Challenge
Most organizations still operate with a deeply flawed objective: forecast accuracy. Statistical models extrapolate past patterns but miss market intelligence - promotions, pipeline shifts, channel fill, competitive actions. Sales overrides attempt to fix this but introduce bias and instability. In CPG, this shows up as promotional over-forecasting. In high-tech, it shows up as demand cliffs after channel stuffing. In pharma, it manifests as poor alignment between prescription demand and distribution inventory. Critically, all errors are treated equally — but they are not. Over-forecasting drives excess and write-offs. Under-forecasting drives lost sales and expediting. These costs are asymmetric, yet most systems ignore that entirely. The result is systemic value leakage: • Excess inventory that eventually turns into write-offs (CPG, pharma expiry risk) • Lost revenue due to stockouts in high-margin segments • Expediting costs in high-tech and automotive • Supplier and plant instability due to demand churn It is not unusual for mid-sized portfolios (few hundred SKUs) to leak $1–5M annually purely due to misaligned forecasting incentives.
Solution
VYAN reframes forecasting as a decision input, not an output. It combines statistical models, commercial inputs, and scenario-based demand signals into a unified decision system. Instead of asking “which forecast is most accurate,” it asks: “Which forecast leads to the lowest economic loss across supply, inventory, and service outcomes?” The system evaluates forecast alternatives through downstream supply chain simulations - capturing cost-to-serve, inventory exposure, lost sales, and operational churn - and selects the forecast that minimizes cost of forecast error.
