Extracting Key Value Streams Using Process Mining and Machine Learning

Tomidei, L; Sick, N; Deuse, J; Guertler, M

Extracting Key Value Streams Using Process Mining and Machine Learning

Tomidei, L

Sick, N

Deuse, J

Guertler, M

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2023 IEEE Engineering Informatics, 2024, 00, pp. 1-7
Issue Date:: 2024-05-14

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Field	Value	Language
dc.contributor.author	Tomidei, L https://orcid.org/0000-0003-2780-4554
dc.contributor.author	Sick, N https://orcid.org/0000-0002-0713-6918
dc.contributor.author	Deuse, J https://orcid.org/0000-0003-4066-4357
dc.contributor.author	Guertler, M https://orcid.org/0000-0001-7062-6349
dc.date	2023-11-22
dc.date.accessioned	2024-08-07T05:36:07Z
dc.date.available	2024-08-07T05:36:07Z
dc.date.issued	2024-05-14
dc.identifier.citation	2023 IEEE Engineering Informatics, 2024, 00, pp. 1-7
dc.identifier.isbn	979-8-3503-3853-9
dc.identifier.uri	http://hdl.handle.net/10453/180332
dc.description.abstract	In the past decade the complexity of production systems has grown exponentially Manufacturing companies are under the influence of social economic and political trends that urge them to accelerate product development timelines while simultaneously achieving greater flexibility and resource efficiency Amid these challenges manufacturing companies commonly grapple with the task of defining key value streams within their production processes This challenge is especially relevant for high variety low volume businesses where value streams may include a high number of products and components The identification of these key value streams offers a pathway to improve decision making shedding light on the primary sources of complexity that impede operational efficiency Central to this endeavour is a comprehensive grasp of production flows In the context of Industry 4 0 the expanded accessibility of data sourced from factory operations opens doors to novel prospects facilitating a more profound understanding of production flows Following the long standing principles of Production Flow Analysis which have played a pivotal role in defining value streams over the years we present an approach for discerning these value streams by harnessing production data through process mining and machine learning techniques To demonstrate the practical viability of our proposed approach we present a case study that exemplifies its application in real world scenarios
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2023 IEEE Engineering Informatics
dc.relation.ispartof	2023 IEEE Engineering Informatics
dc.relation.isbasedon	10.1109/ieeeconf58110.2023.10520644
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Extracting Key Value Streams Using Process Mining and Machine Learning
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Melbourne, Australia
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	University of Technology Sydney/Strength - CAM - Centre for Advanced Manufacturing
utslib.copyright.status	recently_added	*
dc.date.updated	2024-08-07T05:36:05Z
pubs.finish-date	2023-11-23
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2023-11-22
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

In the past decade the complexity of production systems has grown exponentially Manufacturing companies are under the influence of social economic and political trends that urge them to accelerate product development timelines while simultaneously achieving greater flexibility and resource efficiency Amid these challenges manufacturing companies commonly grapple with the task of defining key value streams within their production processes This challenge is especially relevant for high variety low volume businesses where value streams may include a high number of products and components The identification of these key value streams offers a pathway to improve decision making shedding light on the primary sources of complexity that impede operational efficiency Central to this endeavour is a comprehensive grasp of production flows In the context of Industry 4 0 the expanded accessibility of data sourced from factory operations opens doors to novel prospects facilitating a more profound understanding of production flows Following the long standing principles of Production Flow Analysis which have played a pivotal role in defining value streams over the years we present an approach for discerning these value streams by harnessing production data through process mining and machine learning techniques To demonstrate the practical viability of our proposed approach we present a case study that exemplifies its application in real world scenarios

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/180332