Demand sensing

Demand planning teams produce SKU × location × week forecasts on a cadence that ranges from weekly to monthly, mixing statistical models, judgmental overrides, and downstream consensus meetings. Service levels and inventory cost both depend on the result.

Every previous wave of demand-planning software promised a single number. Planners learned to override it. The model lost trust because it could not explain itself; the override process lost trust because it lacked discipline.

• iWhich of your product/location combinations carry enough signal for a model-led forecast, and which are judgmental by nature?
• iiHow does the model surface its reasoning to your planner — weather, events, local effects — in a form they'll actually trust?
• iiiWhat does a healthy override workflow look like for your team, with the override reasoning captured for the next cycle?
• ivHow do we measure success in your inventory turns and service level, not forecast accuracy alone?

We deploy a forecasting agent that ingests POS, weather, events, and the planner's own override history; produces SKU × location × week forecasts with the contributing signals visible; and gives the planner a clean override surface that captures the why. Service level and inventory cost both move.

We have shipped SKU-level forecasts in retail production. We know which silver bullets the demand planner has been pitched before, and we design the pod to earn the planner's override-rights without taking them away.