Abstract

Maintenance is an important contributor to reach the intended life-time of capital technical assets. How to maintain these assets is gaining increasing attention and relevance. One of the decisions within maintenance is selecting the right maintenance policy: maintenance policy selection (MPS). A maintenance policy is a policy that dictates which parameter triggers a maintenance action. Within MPS for technical capital assets, our research aims at – but does not limit itself to – naval ships. The aim of the research is twofold: first, to investigate which factors truly play a role for MPS and, second, to develop an MPS method incorporating these factors. For this, we turn to multiple criteria decision making (MCDM) methods and propose the use of the Analytic Hierarchy Process (AHP). First, the criteria that play a role for naval MPS are drawn from the literature and eight interviews in practice. The obtained criteria are structured into a decision hierarchy. Using this hierarchy along with the AHP, we organize three sessions in naval practice to test the approach in industry. Next, to further investigate MPS, we broaden our approach towards ships in general. Using the feedback from the naval sessions, we improve the decision hierarchy and use this hierarchy at six more sessions at six different ship companies. These sessions give insight in the most important criteria and considerations for ship MPS. Following that, we go back to naval ships by using the AHP-based MPS approach in a multi-company session, with participants from various companies within the naval maintenance chain, where participants from four companies are present. Concluding, we have captured the relevant criteria and have shown that not only hard, quantifiable criteria, but also softer criteria should be included in ship MPS. Furthermore, the use of the AHP-based MPS method is not so much in making the actual decision, but in providing a structured way to think about MPS and in facilitating a structured and meaningful discussion. This works best for considering high levels in the system in a strategic way, even up to fleet level.

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