Using AI to avoid bottlenecks in medical supplies

Early detection system helps plan, manage and control resilient value chains


The coronavirus crisis has driven the lesson home: the extraordinary medical emergencies brought on by the pandemic have led to shortages in urgently needed medical supplies. Global trade restrictions and long delivery times for medical equipment have exacerbated the situation – particularly given that hardly any personal protective equipment (such as gloves, protective gowns and masks) is manufactured in Germany.

To avoid such scenarios in the future, our Engineering of Adaptive Systems division EAS is supporting a project team led by KEX Knowledge Exchange AG that is developing an early detection and prediction system for the medical sector. The aim is to identify supply bottlenecks early on, so that suppliers, distribution points and medical facilities can take action in good time. This will ensure the sustainable, cost-effective supply of necessary items and, more generally, make supply chains resilient to market fluctuations.

In the “” AI lighthouse project of the state of North Rhine-Westphalia, we are primarily working on one of the core functionalities: the AI-based early detection system for consolidating volumes and calculating safety stock levels. This is based on a model that forecasts current and future demand for bottleneck products and derives a safe level of inventory from these projections. The system is designed to detect deviations from optimum inventory levels and provide an early warning whenever the current or future risk of shortages is high. This allows medical facilities to respond to the situation in good time.

We are refining AI algorithms for this functionality and training them with various data such as infection rates, capacity utilization and relevant production metrics. These algorithms will support flexible management of safety stocks in distribution centers and warehouses. In addition, we calculate various forecasts required for the products, such as demand, delivery quantity, delivery time or price. And it is essential to develop the right strategy for procuring and maintaining safety stocks. Finally, regular stress tests will be conducted to optimize the early detection system’s resilience. The “” project is sponsored by the German state of North Rhine-Westphalia.