Analysis of models for IoT-driven predictive maintenance under constraints in the case of the biopharmaceutical industry

Following the advent of the Industry 4.0 concept twelve years ago, the widespread use of digitalisation, connected objects and massive databases is revolutionising industrial processes. The data generated by production equipment is becoming more diversified and can be used in areas such as maintenance, leading to an evolution in practices that were previously exclusively based on corrective maintenance towards condition-based or even predictive maintenance. Such evolution entails enhancing maintenance plans centred on schedules established in accordance with manufacturers’ recommendations in favour of active listening and monitoring of equipment, enabling maintenance to be carried out with maximum precision, i.e. using the maximum amount of information available from the monitored equipment, while minimising the risk of failure. These evolutions in maintenance practices, otherwise known as IoT (Internet of Things) based maintenance or “IOT-driven maintenance” are described in the scientific literature for most manufacturing industries. The purpose of this paper is to highlight the lag in scientific research in the biopharmaceutical sector compared to the manufacturing industry in general, as well as the use of IoT-based methods in the biopharmaceutical sector. This literature review has enabled us to identify a significant gap in IoT-based maintenance research, particularly in the biopharmaceutical sector. Therefore, there is a significant opportunity to make a new and relevant scientific contribution to this specific field.