Logo des Repositoriums
 

Application Fields and Research Gaps of Process Mining in Manufacturing Companies

dc.contributor.authorDreher, Simon
dc.contributor.authorReimann, Peter
dc.contributor.authorGröger, Christoph
dc.contributor.editorReussner, Ralf H.
dc.contributor.editorKoziolek, Anne
dc.contributor.editorHeinrich, Robert
dc.date.accessioned2021-01-27T13:34:01Z
dc.date.available2021-01-27T13:34:01Z
dc.date.issued2021
dc.description.abstractTo survive in global competition with increasing cost pressure, manufacturing companies must continuously optimize their manufacturing-related processes. Thereby, process mining constitutes an important data-driven approach to gain a profound understanding of the actual processes and to identify optimization potentials by applying data mining and machine learning techniques on event data. However, there is little knowledge about the feasibility and usefulness of process mining specifically in manufacturing companies. Hence, this paper provides an overview of potential applications of process mining for the analysis of manufacturing-related processes. We conduct a systematic literature review, classify relevant articles according to the Supply-Chain-Operations-Reference-Model (SCOR-model), identify research gaps, such as domain-specific challenges regarding unstructured, cascaded and non-linear processes or heterogeneous data sources, and give practitioners inspiration which manufacturing-related processes can be analyzed by process mining techniques.en
dc.identifier.doi10.18420/inf2020_55
dc.identifier.isbn978-3-88579-701-2
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/34765
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofINFORMATIK 2020
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-307
dc.subjectProcess Mining
dc.subjectApplication
dc.subjectProduction
dc.subjectManufacturing
dc.subjectSCOR
dc.subjectLiterature Review
dc.titleApplication Fields and Research Gaps of Process Mining in Manufacturing Companiesen
gi.citation.endPage634
gi.citation.startPage621
gi.conference.date28. September - 2. Oktober 2020
gi.conference.locationKarlsruhe
gi.conference.sessiontitle6. Workshop zum Stand

Dateien

Originalbündel
1 - 1 von 1
Lade...
Vorschaubild
Name:
C7-3.pdf
Größe:
143.39 KB
Format:
Adobe Portable Document Format