Demand Sensing

Point-of-sale and promotional data forecast demand shifts to improve accuracy and reduce lost sales.

Business impact

• Forecast accuracy increased to 92% — More precise demand predictions reduce stockouts and overstock situations
• Lost sales reduced by 30% — Better sensing of demand prevents missed sales opportunities
• Service levels increased to 98.6% — Higher forecast accuracy ensures product availability for customers
• Product obsolescence reduced by 30% — Accurate demand sensing lowers excess inventory and waste
• Net ROI increased by 6-8% — Optimized inventory and promotions improve financial returns
• Demand planners workload reduced by 50% — Automation frees planners to focus on higher-value tasks

Data requirements

• Point-of-sale (POS) data (Structured) — Provides real-time sales signals to detect demand shifts
• Promotional event data (Structured) — Captures timing and impact of marketing campaigns on demand
• Market signals and social media (Text) — Offers early indicators of changing consumer behavior
• Inventory and supply chain data (Structured) — Supports alignment of demand forecasts with stock levels

AI methods and techniques

• Predictive AI — Models demand patterns and forecasts future sales based on historical and real-time data
• Agentic AI — Autonomously adjusts forecasts and inventory recommendations in response to market changes

AI models and model families

GPT-4o, Claude, Llama

Industries

Real-world evidence

2 documented case studies on record.

Companies using this: Danone, Devoteam.