Demand Sensing with AI
Improving demand forecasting accuracy by 20-40% using real-time signals—point-of-sale data, social sentiment, weather patterns—that traditional forecasting misses.
The Limitations of Traditional Forecasting
Standard demand forecasting relies on historical sales data to predict future demand. This works reasonably well when demand patterns are stable, but fails in precisely the situations where accurate forecasting matters most: new product launches, seasonal shifts, promotional campaigns, and market disruptions. AI-based demand sensing addresses these limitations by incorporating real-time signals that indicate changing demand before those changes appear in historical sales data. When a promotion launches, AI can sense the resulting demand spike from point-of-sale data within hours, not weeks. When weather patterns shift, AI adjusts forecasts based on historical correlations between weather and sales.
Key Takeaways
- •AI incorporates real-time signals that traditional forecasting misses
- •Demand sensing typically improves forecast accuracy by 20-40% for seasonal and promotional items
- •Integration with point-of-sale systems and external data sources is critical
- •Start with high-volatility SKUs where improved forecasting has the biggest impact