Always-on demand forecasting changes the infrastructure assumptions behind planning AI

A Japanese manufacturing analysis argues that lower-cost, specialized AI inference infrastructure could allow high-frequency applications such as demand forecasting, quality inspection, anomaly detection and maintenance support to run continuously inside industrial operations. Demand forecasting should therefore be evaluated not only through model accuracy but also through compute availability, latency, energy cost, refresh cadence, resilience and fallback. A governed architecture should define which demand signals justify real-time or intraday refresh, minimum forecast-improvement thresholds, compute and energy budgets, service-level requirements, fallback models and whether higher refresh frequency actually changes replenishment, production or allocation decisions.