Using Process Mining Approach for Machining Operations

dc.authorscopusid 57212212675
dc.authorscopusid 36105855700
dc.contributor.author Altan, Z.
dc.contributor.author Birgün, S.
dc.date.accessioned 2025-01-11T13:04:44Z
dc.date.available 2025-01-11T13:04:44Z
dc.date.issued 2020
dc.department Fenerbahçe University en_US
dc.department-temp Altan Z., Software Engineering Department, Beykent University, Istanbul, Turkey; Birgün S., Industrial Engineering Department, Fenerbahçe University, Istanbul, Turkey en_US
dc.description.abstract In the Industry 4.0 world, both service and manufacturing companies should review their systems and processes, remove any application that causes waste, ensure lean flow and change business models if necessary, in order to fulfill the requirements of this trend. Introducing Industry 4.0 on a problematic system or process might harm it enough to cause the company disappear instead of benefiting it. For applications correctly decided to be built upon a correct system, data flow must be accurate and timely. And at this stage, data amount that increases with process mining and complexity of the big data will be solved and more information will be obtained about real production processes and data. In this study, a prototype is developed using the data of a previously studied manufacturing research. This prototype handles only one phase of the manufacturing process and extracts all the initial possible pathways of this phase through process mining. © 2020, Springer Nature Switzerland AG. en_US
dc.identifier.citation 4
dc.identifier.doi 10.1007/978-3-030-31343-2_40
dc.identifier.endpage 464 en_US
dc.identifier.isbn 978-303031342-5
dc.identifier.isbn 978-981150949-0
dc.identifier.issn 2195-4356
dc.identifier.scopus 2-s2.0-85076225832
dc.identifier.scopusquality Q4
dc.identifier.startpage 452 en_US
dc.identifier.uri https://doi.org/10.1007/978-3-030-31343-2_40
dc.identifier.uri https://hdl.handle.net/20.500.14627/395
dc.identifier.wosquality N/A
dc.language.iso en en_US
dc.publisher Springer Science and Business Media Deutschland GmbH en_US
dc.relation.ispartof Lecture Notes in Mechanical Engineering -- 19th International Symposium for Production Research, ISPR 2019 -- 28 August 2019 through 30 August 2019 -- Vienna -- 233539 en_US
dc.relation.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 5
dc.subject Batch Production en_US
dc.subject Event Logs en_US
dc.subject Manufacturing Process Analysis en_US
dc.subject Process Improvement en_US
dc.subject Process Mining en_US
dc.title Using Process Mining Approach for Machining Operations en_US
dc.type Conference Object en_US
dspace.entity.type Publication

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