Information-based industrial maintenance – an ecosystem perspective

Abstract

In industrial maintenance, the increasing amount of data and information makes the management of information flows much more challenging than previously. Data from different sources is another issue, data can be real-time data from sensors or from different software systems. The type of data can vary from structured data to unstructured data. The Internet of Things (IoT) and modern information and communication technology make it possible to collect data easily. The problem is to recognize the relevant data to support the decision-making process and sharing data and information to right parties in right time.

The aim of this thesis is to identify problems and benefits in information management in industrial maintenance. After the identification of problems and benefits, it is possible to create models and methods for improving the management of information in the industrial maintenance ecosystem. The qualitative research method is used in the empirical part of thesis. Surveys and interviews are used in the qualitative data collection.

The thesis concerns the research gap in identifying problems and benefits in information management in the industrial maintenance ecosystem systemically. The need to share data and information has increased significantly in the networked maintenance ecosystem. The key aspects in information management in maintenance are: why, with whom, what, and how to share data. The thesis offers three main solutions to issues in information management in the maintenance ecosystem. First, the SHELO model was developed and tested in this study. It can be used to find the strengths and weaknesses in maintenance and in the maintenance service network. Second, data sharing is found to improve decision making in maintenance by offering the needed information combined from different sources. Thirdly, the findings highlight the importance of the whole maintenance ecosystem in developing maintenance quality.