TY - GEN
T1 - Structured Process Mining in Maintenance Performance Analysis: A Case Study in the Offshore Oil and Gas Industry
AU - Ge, Jingrui
AU - Sigsgaard, Kristoffer Wernblad
AU - Mortensen, Niels Henrik
AU - Hansen, Kasper Barslund
AU - Agergaard, Julie Krogh
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Process mining
KW - Performance measurement
KW - Process analysis
KW - Big data
KW - Decision support
KW - Operations management
U2 - 10.1109/ISSSR58837.2023.00053
DO - 10.1109/ISSSR58837.2023.00053
M3 - Article in proceedings
T3 - International Symposium on System Security, Safety, and Reliability
SP - 306
EP - 313
BT - 9th International Symposium on System Security, Safety, and Reliability
PB - IEEE
T2 - 9th International Symposium on System Security, Safety, and Reliability
Y2 - 10 June 2023 through 11 June 2023
ER -