The value of product lifecycle management systems (PLMS) is more and more recognised by companies and its use current has enormously increased. It is mainly used during the product design when different roles collaborate for sharing models, take review decisions, and approve or reject preliminary results. Often, companies have a general 'picture' about the processes involving PLMS (who performs an activity, when it is performed, what is done) but this knowledge can be reinforced, improved and modified using process mining. Here the knowledge is extracted from the event logs, and model-aware analytics are generated to evaluate the modelled, known and executed process. The business rules filter the logs and verify the impact on the process mining metrics to minimise the divergences between modelled and actual processes and improve the resulting quality metrics. The results help business users to identify lines of investigation for deviations from expected behaviour and propose improvement measures.

Rules-based process mining to discover PLM system processes

Corallo A.;Lazoi M.;Marra M.
2021

Abstract

The value of product lifecycle management systems (PLMS) is more and more recognised by companies and its use current has enormously increased. It is mainly used during the product design when different roles collaborate for sharing models, take review decisions, and approve or reject preliminary results. Often, companies have a general 'picture' about the processes involving PLMS (who performs an activity, when it is performed, what is done) but this knowledge can be reinforced, improved and modified using process mining. Here the knowledge is extracted from the event logs, and model-aware analytics are generated to evaluate the modelled, known and executed process. The business rules filter the logs and verify the impact on the process mining metrics to minimise the divergences between modelled and actual processes and improve the resulting quality metrics. The results help business users to identify lines of investigation for deviations from expected behaviour and propose improvement measures.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11587/469304
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