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DC Field | Value | Language |
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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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