Moving towards data-driven decision-making in maintenance

Traditionally, in the maintenance industry, maintenance efficiency is limited by the capability of the experts making the decision. However, the advancement of digital technologies made it possible to improve the effectiveness and efficiency of maintenance activities by adding insight from the data to expert assessment. The opportunity provided by data for decision making made the companies to shift towards a new type of maintenance strategy called data-driven maintenance. Despite of opportunities, data and analytical tools' companies are still struggling to fully harness data asset to improve maintenance activities because of data-centric challenges. Hence, the main objective of this dissertation is to identify and mitigate those challenges that limit organizational decision-making capabilities to improve maintenance effectiveness. In this dissertation, firstly, quantitative and descriptive analyses of case studies in Finnish Multinational Manufacturing Companies have been carried out to identify key data-centric challenges. The study identified Data Quality, Interoperability, and Data extraction as key challenges. Furthermore, each of the identified challenges have been investigated through one or more original publications. The main results achieved in this dissertation are methods and frameworks to i) assess and compare data quality of maintenance  reporting procedure ii) two-level interoperability framework for inter-system interoperability iii) data discovery methodology to extract data for Extract, Transform and Load process. The applicability and validity of each of the proposed methodologies and framework has been validated through one or multiple use cases. For validation, three different tools namely, MRQA Dashboard, Open-messaging Middleware, and Data Model Logger have been developed to tackle each of the identified data-centric challenges.