Improved Maintenance by Leveraging Reporting, Data Analytics and Machine Learning Capabilities

CERN is since many years using a commercial Enterprise Asset Management (EAM) system for managing its maintenance of physical equipment installed in both the accelerator complex and in the technical infrastructures. These tools and practices have greatly evolved over time and the EAM system is today used by more than 40 CERN groups, covering close to 3 million of individual traced equipment. When using an EAM system, a large amount of data is captured via its different user interfaces and system integrations and if used correctly, this data can generate important insights about the equipment and their maintenance. This paper will showcase how the current reporting and analysis tools at CERN can be used to improve maintenance at CERN. It will also give a short overview of when and how more advanced machine learning algorithms can help in more complex use cases.