Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
Industry 4.0 has brought about tremendous changes in equipping machinery and factory setups with sensors and bridging the gap between the digital and the physical world. Process mining has proven to be a valuable tool for analyzing industrial workflows, gathering models, and checking the conformance of executions. However, faults that occur seldom in industrial processes cannot be easily learned by applying machine learning methods. Explicit nominal models can help to close this gap. The given approach shows how nominal product, resource, and process models can be used in a physical twin environment to enhance process mining tasks and related error root cause analysis. In this scenario a model factory serves as physical twin of a real-life factory. The paper concludes with a depiction of a potential proof-of-concept.
Recommended Citation
Emrich, Andreas; Gutermuth, Oliver; Frey, Michael; Fettke, Peter; and Loos, Peter, "Towards a Model Factory Experimentation Environment for Cyber-Physical Twins" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 3.
https://aisel.aisnet.org/hicss-57/st/digital_twins/3
Towards a Model Factory Experimentation Environment for Cyber-Physical Twins
Hilton Hawaiian Village, Honolulu, Hawaii
Industry 4.0 has brought about tremendous changes in equipping machinery and factory setups with sensors and bridging the gap between the digital and the physical world. Process mining has proven to be a valuable tool for analyzing industrial workflows, gathering models, and checking the conformance of executions. However, faults that occur seldom in industrial processes cannot be easily learned by applying machine learning methods. Explicit nominal models can help to close this gap. The given approach shows how nominal product, resource, and process models can be used in a physical twin environment to enhance process mining tasks and related error root cause analysis. In this scenario a model factory serves as physical twin of a real-life factory. The paper concludes with a depiction of a potential proof-of-concept.
https://aisel.aisnet.org/hicss-57/st/digital_twins/3