Structured Process Mining in Maintenance Performance Analysis: A Case Study in the Offshore Oil and Gas Industry

• Published in: 2023 9th International Symposium on System Security, Safety, and Reliability (ISSSR) pp. 306 - 313
• Main Authors: Ge, Jingrui, Sigsgaard, Kristoffer Wernblad, Mortensen, Niels Henrik, Hansen, Kasper Barslund, Agergaard, Julie Krogh
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2023
• Subjects: big data; Correlation; Data mining; decision support; Maintenance engineering; Natural gas industry; Oils; operations management; Performance analysis; performance measurement; process analysis; process mining; Safety
• ISSN: 2835-2823
• DOI: 10.1109/ISSSR58837.2023.00053

Maintenance plays an important role in production-related industries. As the need to better understand maintenance performance at the process level continues to grow, the application of process mining techniques to maintenance performance measurement is emerging. This paper presents a structured process mining approach for analyzing maintenance process performance. This approach enables the identification of potential root causes by enhancing maintenance event logs with contextual information. Multi-level filtering rules are introduced to facilitate the scoping of process mining. A case study was conducted using empirical data from the offshore oil and gas industry. The results show that maintenance process bottlenecks and conformance issues can be identified by comparing the benchmark process to typical process deviations (skipping, self-loop, and back loop). The quantification of process deviation impacts and cause-effect correlation can support maintenance managers in discovering potential root causes of performance issues and taking action for continuous improvement.