Title: Rules-based process mining to discover PLM system processes

Authors: Antonia Azzini; Paolo Ceravolo; Angelo Corallo; Ernesto Damiani; Mariangela Lazoi; Manuela Marra

Addresses: Consorzio per il Trasferimento Tecnologico, C2T, Carate Brianza, MB, Italy ' Dipartimento di Informatica, Università degli Studi di Milano, Milano, Italy ' Dipartimento di Ingegneria dell'Innovazione, Università del Salento, Lecce, Italy ' Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, UAE ' Dipartimento di Ingegneria dell'Innovazione, Università del Salento, Lecce, Italy ' Dipartimento di Ingegneria dell'Innovazione, Università del Salento, Lecce, Italy

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.

Keywords: process mining; business process assessment; product lifecycle management; PLM; business rules.

DOI: 10.1504/IJPLM.2021.116209

International Journal of Product Lifecycle Management, 2021 Vol.13 No.2, pp.159 - 185

Received: 06 Aug 2020
Accepted: 22 Dec 2020

Published online: 07 Jul 2021 *

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