Predictive Maintenance and Prognostics Health Management (PHM) can play a pivotal role in ensuring the optimal performance and an improved efficiency of industrial equipment. These practices encompass a wide range of techniques, expertly tailored to match the specific technology and requirements of the targeted equipment. However, implementing such a sophisticated maintenance strategy, involving intricate methods and advanced techniques, can be cost-intensive and may introduce significant complexities, as it often requires the acquisition of specialized instruments, personnel training, and infrastructure enhancements. Therefore, ensuring that such a substantial investment and considerable efforts are directed toward the right assets is crucial. In this paper, we address this challenge by focusing on equipment prioritization as a fundamental driver of maintenance decisions. In contrast to conventional single-criterion prioritization approaches, we seek to encompass multiple perspectives of the problem by using the Analytic Hierarchy Process (AHP) approach for modelling and structuring it. This includes the modelling of the decision dependencies along with associated equipment assessments. These assessments are centred on three core factors: reliability, production, and cost. Leveraging relevant historical industrial data ensures an accurate contextualization of each asset. A concrete case study from a large-scale production company is used as a practical demonstration of the approach's potential to enhance maintenance decision-support and ensure optimal asset performance across diverse industrial settings. 

Analytic Hierarchy Process (AHP), Criticality Assessment, Decision Support, Equipment Prioritization, Performance Evaluation