A Machine Learning Tool for Conveyor Equipment Device History and Recalls

From PPE for hospital staff to OTC products for everyday public use, the entire healthcare ecosystem requires an reliable performance of healthcare products. In this value chain, Conveyor systems and the conveyor equipment are critical to processes as they are used in high-stakes environments like in hospitals for supporting life-saving missions or transporting the fertilized egg in egg processing or ready-to-eat meals in food manufacturing. These units must operate with precision and reliability to assure the safe transport of sensitive instruments. A little miscalculation may trigger serious injuries to the patients and an expensive recall. From 2003 to 2020, 8.9% of medical devices (approximately 4,889) were recalled in the US on account of death or serious injury, lapses that in their own turn could contribute to further patient area. Such recalls not only impact the patient demographic, but they also put a multi-million dollar liability on insurers, health systems, and device companies. Hence, the demand for smart units that can predict impending failures and actually prevent them from reaching their climax is mounting. For the industry in which conveyor systems represent the primary transportation means for the medical devices, this emergence of Ml-based means for evaluating past histories of the device and future recall risk represents a breakthrough opportunity.