Michael Schember

Special Guest Blog Post written by Chris Cunnane with InterSystems   In a world defined by rapid market shifts, volatile supply chains, and unpredictable customer behavior, traditional forecasting methods often fall short. Relying primarily on historical data is no longer enough. To stay competitive, organizations are increasingly turning to demand sensing and forecasting, an approach that blends real-time data, advanced analytics, and AI to anticipate demand more accurately and respond faster to change. This shift is not limited to retail or manufacturing. Demand sensing is transforming how organizations across industries plan operations, allocate resources, and improve service levels. Below, we explore key industry use cases where demand sensing is delivering measurable value, and why businesses should care.   Why Demand Sensing Matters Demand sensing moves beyond static historical trends. It incorporates current, high-velocity data signals such as sales transactions, weather patterns, logistics feeds, economic indicators, and even social sentiment to generate short-term demand forecasts that reflect real-world conditions. The benefit is clear. Organizations gain better visibility and responsiveness across procurement, production, inventory, and distribution. Instead of reacting to outdated forecasts, they can make timely decisions that reduce costs, prevent stockouts, and improve customer satisfaction. FMCG, CPG, Retail, & E-Commerce Fast-moving…

Demand can change in an instant. A flash promotion takes off faster than expected. An influencer post sends a niche SKU into the stratosphere. A heat wave flips seasonal demand on its head. And suddenly, your forecast—carefully crafted weeks ago—feels like yesterday’s weather report. This is where demand signal management earns its keep. Instead of reacting too late or overreacting too early, it helps supply chain teams interpret what’s really happening and respond with confidence. For retailers and consumer brands navigating constant demand volatility, it’s the difference between chasing noise and acting on insight.   The problem: demand forecasts lag reality Traditional forecasting has a fundamental challenge: timing. Forecasts are built on historical patterns and planning cadences that move at a measured pace, but demand levels today won’t wait for the next planning cycle. Modern demand shocks often arrive without warning: a promotional discount goes viral, a competitor runs out of stock, weather drives sudden regional swings, or a trend outpaces merchandising plans. In these moments, planners face a familiar tension. React too slowly, and shelves go empty. React too aggressively, and you’re stuck with excess inventory once the spike fades.   What demand signals are (and what they aren’t)…