Application of Total Cost of Ownership Driven Methodology for Predictive Maintenance Implementation in the Food Industry

The Industry 4.0 has boosted technological advancements leading to the development of predictive maintenance solutions in the manufacturing sector. In this scenario, companies are dealing with complex decision-making problems involving investments in technological solutions and data analytics modelling implementation. Therefore, there is a need for strategic guidance for defining the best investments options through a technical-economic approach based on system modelling and lifecycle perspective. This paper presents the implementation within a relevant Italian food company of a methodology developed to evaluate predictive maintenance implementation scenarios based on alternative condition monitoring solutions, under the lenses of Total Cost of Ownership. Technical systemic performances are evaluated through Monte Carlo simulation based on the Reliability Block Diagram (RBD) model of the system. The results provide concrete evidence of effective applicability of the methodology guiding decision-makers toward a solution for improving technical system performances and reducing lifecycle costs.