The increasing complexity of modern industrial systems and the demand for higher productivity have driven the need for more advanced maintenance strategies. Traditional predictive maintenance (PdM) has improved failure forecasting, yet it struggles to integrate maintenance planning, production scheduling, and quality control into a unified framework. Prescriptive maintenance (PsM) addresses these limitations by leveraging artificial intelligence (AI), machine learning (ML), and IoT-driven analytics to not only predict failures but also recommend optimal maintenance actions, thereby reducing downtime and enhancing operational efficiency.
This study investigates the role of PsM in optimizing production efficiency, focusing on its impact on resource allocation, asset longevity, and decision automation. A systematic review of recent advancements highlights PsM’s ability to enhance real-time maintenance strategies while identifying key challenges such as cybersecurity risks, data integration issues, and industry-specific implementation barriers.
To provide a structured analysis, this article includes an introduction outlining key concepts, followed by a discussion on the study’s significance. A literature review synthesizes previous research, while the research problem identifies existing gaps. The proposed methodology is then presented, leading to a discussion of results and implications. By bridging the gap between predictive insights and autonomous maintenance, PsM is shaping the future of intelligent, resilient manufacturing ecosystems.