Fundamentals

Demand Sensing

Definition

Demand sensing uses near-real-time signals such as point-of-sale data, channel inventory, orders, and external data to detect what demand is doing right now, sharpening short-term forecasts beyond what historical models can see.

In Practice

Traditional forecasting projects history forward and updates weekly or monthly; demand sensing corrects that projection daily using downstream signals: retail POS scans, distributor withdrawals, e-commerce traffic, weather, and events. The technique mainly improves the short horizon, roughly zero to eight weeks, where such signals carry real information, and vendors commonly report short-term error reductions of 20 to 40 percent.

The operational payoff is faster, smaller corrections. If sensing shows a promotion selling through 50 percent faster than planned, replenishment quantities adjust within days instead of after the month-end forecast review, preventing both the stockout and the panicked expedite. Because sensing works from actual consumption rather than distorted channel orders, it also damps the bullwhip effect.

A CPG company supplying a major retailer illustrates the mechanics: daily POS feeds by store reveal a regional heatwave lifting sports-drink sales midweek, and deployment plans shift stock toward affected DCs before the weekend, while the statistical monthly forecast would have noticed a month later.

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