How MedTech Leaders Are Using AI to Improve Demand Planning

Accurate demand planning has always been a challenge in healthcare, where patient needs, regulatory changes, and supply disruptions make forecasting uniquely complex. At LogiMed and across the broader MedTech landscape, leading companies are turning to artificial intelligence—not to replace planners, but to augment decision-making with real-time intelligence.

Why Traditional Forecasting Isn’t Enough

Spreadsheet-based planning and even legacy ERP forecasting tools often fall short when conditions shift fast. These models rely on historical data, which doesn’t reflect emerging changes in provider behavior, patient volumes, or external disruption events like regulatory delays or geopolitical risk.

How AI Improves Forecast Accuracy

AI-enabled demand planning can:

• Ingest and process vast, real-time data sets (weather, market signals, epidemiological data)
• Detect anomalies early (e.g., unusual spikes in SKU demand in one region)
• Generate multiple planning scenarios based on different assumptions
• Reduce reliance on subjective overrides and guesswork

Real-World MedTech Use Cases

Top manufacturers have started embedding AI into S&OP processes. One example: a team layered machine learning models on top of their historical usage data to fine-tune forecasts for surgical kits by hospital. Another used AI to identify which SKUs had the highest risk of stockouts based on both supplier performance and demand volatility.

Keys to Success with AI Forecasting

• Start with one product line or region where data quality is reliable
• Involve planners in model design—AI should support their workflow, not replace it
• Set clear thresholds for when to escalate or override AI-generated forecasts
• Monitor accuracy over time and continuously retrain models

Join the Conversation at LogiMed 2026

Want to see how your peers are building smarter demand planning processes? Join the discussion at LogiMed 2026—where AI meets operational strategy in real-time.