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MarcoComuzzi

Comuzzi, Marco
Intelligent Enterprise Lab.
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dc.citation.endPage 103 -
dc.citation.startPage 92 -
dc.citation.title DECISION SUPPORT SYSTEMS -
dc.citation.volume 104 -
dc.contributor.author Cho, Minsu -
dc.contributor.author Song, Minseok -
dc.contributor.author Comuzzi, Marco -
dc.contributor.author Yoo, Sooyoung -
dc.date.accessioned 2023-12-21T21:36:52Z -
dc.date.available 2023-12-21T21:36:52Z -
dc.date.created 2017-10-24 -
dc.date.issued 2017-12 -
dc.description.abstract The management of business processes in modern times is rapidly shifting towards being evidence-based. Business process evaluation indicators tend to focus on process performance only, neglecting the definition of indicators to evaluate other concerns of interest in different phases of the business process lifecycle. Moreover, they usually do not discuss specifically which data must be collected to calculate indicators and whether collecting these data is feasible or not. This paper proposes a business process assessment framework focused on the process redesign lifecycle phase and tightly coupled with process mining as an operational framework to calculate indicators. The framework includes process performance indicators and indicators to assess whether process redesign best practices have been applied and to what extent. Both sets of indicators can be calculated using standard process mining functionality. This, implicitly, also defines what data must be collected during process execution to enable their calculation. The framework is evaluated through case studies and a thorough comparison against other approaches in the literature. -
dc.identifier.bibliographicCitation DECISION SUPPORT SYSTEMS, v.104, pp.92 - 103 -
dc.identifier.doi 10.1016/j.dss.2017.10.004 -
dc.identifier.issn 0167-9236 -
dc.identifier.scopusid 2-s2.0-85033545969 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/22849 -
dc.identifier.url http://www.sciencedirect.com/science/article/pii/S0167923617301823?via%3Dihub -
dc.identifier.wosid 000418224000008 -
dc.language 영어 -
dc.publisher ELSEVIER SCIENCE BV -
dc.title Evaluating the effect of best practices for business process redesign: An evidence-based approach based on process mining techniques -
dc.type Article -
dc.description.isOpenAccess FALSE -
dc.relation.journalWebOfScienceCategory Computer Science, Artificial Intelligence; Computer Science, Information Systems; Operations Research & Management Science -
dc.relation.journalResearchArea Computer Science; Operations Research & Management Science -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordAuthor Process redesign -
dc.subject.keywordAuthor Best practice -
dc.subject.keywordAuthor Process performance indicator -
dc.subject.keywordAuthor Process mining -
dc.subject.keywordAuthor Case study -
dc.subject.keywordAuthor Business process management -
dc.subject.keywordPlus SIMULATION -
dc.subject.keywordPlus SUPPORT -
dc.subject.keywordPlus MODELS -

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