The corona.KEX.net project came up as a result of the increasing number of infections during the Corona crisis. The crisis demonstrated that factors beyond the control of healthcare facilities can quickly lead to critical shortages in the supply of medical protective equipment (e.g., respirators, protective clothing, goggles, disinfectants). These factors include global trade restrictions, long delivery times, and low-quality goods. There is a lack of an overarching ecosystem that informs demanders and manufacturers about impending bottleneck situations at an early stage and adapts supply and value chains with foresight.
For this reason, this project aims to develop an AI-based early detection and warning system for medical supply that enables bottlenecks to be identified at an early stage and value chains (upstream suppliers, suppliers, distributors, demanders) to be prepared for them. This should ensure the supply of medical facilities such as hospitals, general practitioners or care facilities and enable cost-efficient action in the event of another pandemic. The early detection system and holistic communication and information flows along the value chain should enable the production and value chain to respond to changes in medical care facilities at short notice. In order to be resilient to strong market fluctuations. A key challenge in the procurement of bottleneck items is ensuring product quality.
Since the traditional suppliers of the demand providers cannot meet the demand in crisis situations, they have to rely on alternative sources of supply. The time-consuming manual inspection of the certificates of each product and supplier is not manageable for the demand carriers. In order to be able to guarantee the quality of the bottleneck products, a semi-automated quality control is required. For this reason, a prototype quality assurance system is to be set up as part of this project. This system enables an automated certificate check of the suppliers and, together with the real test results of a test laboratory, ensures the quality of the bottleneck products supplied to the users.
The inIT is represented by the working group Discrete Systems in the area of industrial image processing. The research tasks relate to the automatic detection of authentic certificates of medical products.