Forecasting the Physical World: Multi-Signal Causal Demand Sensing in Automotive Batteries

Replacement demand for low-voltage automotive batteries is governed by the physical world—temperature, vehicle age, and point-of-sale behavior. Demand Planners for this product typically rely on conventional time-series forecasting which is structurally blind to these drivers. To help solve this problem, this article introduces Multi-Signal Causal Demand Sensing (MSCDS), a methodology that makes causal drivers explicit and the output decomposable. It integrates vehicle-in-operation, weather, point-of-sale, inventory, and pricing signals into a single interpretable forecasting layer. I discuss how deployment of this model has delivered a high-single-digit gain in forecast accuracy at Clarios, the world’s largest manufacturer of low-voltage automotive batteries, with estimated savings in the tens of millions of dollars across the deployed ...