Holistic and scalable smart optimization framework for prescriptive maintenance

Prescriptive maintenance (PsM) is a proactive approach enabled by the Internet of Things (IoT), asset health prognostics, and prescriptive analytics that aims to optimize maintenance by prescribing a course of action. For complex systems operating in dynamic operations, traditional maintenance practices based on reactive or preventive approaches often result in inefficiencies, high costs, and unexpected equipment failures. In this context, a new PsM-based optimization framework is required to process all sources of information available with the associated uncertainties, manage a large number of system states, and recommend possible maintenance actions. Therefore, the purpose of this paper is to demonstrate that maintenance efficiency and effectiveness can be improved by implementing a prescriptive maintenance framework that provides an optimal course of action and is extensible across industries and assets of different technological maturities. To achieve these objectives and demonstrate the novelty of this work, a thorough review of the existing literature on prescriptive maintenance is presented. The work involves the design of a smart optimization framework for prescriptive maintenance and its verification on Brazilian regional airline operations and São Paulo state health system real-life scenarios. The framework will offer practical guidance and insights for practitioners, enabling them to make informed decisions related to maintenance management, reducing costs, and improving equipment performance with implications for various industries, paving the way for more proactive and efficient maintenance practices in digitalization and prescriptive analytics.