A novel conceptual framework for predictive maintenance of medical devices in a circular healthcare supply chain

Abstract

The advancement of digital transformation within healthcare systems has prompted major stakeholders to adopt a circular supply chain model. The current linear supply chain model for medical devices raises expenses, produces more medical waste, and pollutes the environment directly. With the widespread use of fourth industrial technologies and the Internet of Things (IoT), there has been a significant increase in the volume of data generated by medical devices. To enhance the circularity of medical equipment, there is growing interest in leveraging predictive maintenance alongside real-time data collection. This paper aims to provide a comprehensive review of predictive maintenance applications in medical device systems. It addresses challenges related to circularity and proposes a novel conceptual framework that utilizes historical machine data to predict Remaining Useful Life (RUL) and evaluate the feasibility of circulation. Furthermore, this paper explores potential gaps and identifies various areas for future research.