Demand Planning & Forecasting Resources

Every demand planning team eventually hits the same wall: they upgrade the forecasting model, add more historical data, tune the algorithm, and accuracy barely moves. That's because forecast error usually isn't a math problem. It's a timing and visibility problem. The signal that would have changed the forecast — a point-of-sale spike, a weather shift, a competitor stockout, a shortened promo window — existed somewhere in the organization well before the forecast was finalized. It just didn't reach the planner in time to matter.

This is the silo problem showing up in a specific and expensive way. Demand sensing lives in one system, S&OP lives in a monthly cadence, and execution happens in real time on the ground. Each function optimizes its own slice, and the handoffs between them are where accuracy actually leaks out. A planner staring at a weekly forecast review has already lost days of reaction time before the number even reaches them, and by the time supply chain or finance sees it, the window to act cheaply has closed. Better statistical models can't fix a structural delay.

Closing that gap means rethinking the cadence and the plumbing, not just the model: getting granular signals to planners continuously instead of on a monthly cycle, giving humans the judgment calls AI shouldn't make alone, and connecting S&OP to what's actually happening downstream. The articles below cover demand sensing, S&OP process design, and the practical mechanics of getting signal to decision-makers while there's still time to act on it.