Investigation on Predictive Maintenance Implementation Cost-Efficiency - Li, Jiahong (Cranfield University, 2025)
The manufacturing industry faces significant challenges in maintaining competitiveness, including the need for increased customization, reduced operational costs, improved sustainability, and the integration of advanced technologies. Predictive Maintenance (PdM) has been identified as a potential solution by optimizing equipment reliability and reducing maintenance costs. However, the adoption of PdM has been hindered by high initial costs and a lack of systematic methods to evaluate its cost-benefit. A systematic methodology has been proposed in this research to assist manufacturing industries in evaluating the cost-benefit of PdM implementation, tailored to specific organizational needs and operational conditions. The methodology consists of two key components: the evaluation of the most suitable maintenance strategy and PdM techniques to determine tangible assets and the assessment of intangible assets such as knowledge and technology required for implementation. The outcomes of this research provide manufacturers with a structured approach to assess PdM’s suitability, enabling informed decision-making aligned with Industry 4.0 and 5.0 objectives. The developed tool can be applied at both the manufacturing system level to support specific PdM technology implementations and at the enterprise level to facilitate long-term evaluations. The foundation for future investigations into PdM’s role in, digital transformation, and the broader Industry 5.0 context has also been laid.
Keywords: Predictive Maintenance; Maintenance Strategy; Analytic Hierarchy Process; Predictive Maintenance Technique; Intangible Assets; Intangible Assets Evaluation