A requirement-driven approach for competency-based collaboration in industrial data science projects

Syberg, M; West, N; Schwenken, J; Adams, R; Deuse, J

A requirement-driven approach for competency-based collaboration in industrial data science projects

Syberg, M West, N Schwenken, J Adams, R Deuse, J

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Publisher:: UNIV POLITECNICA VALENCIA, EDITORIAL UPV
Publication Type:: Journal Article
Citation:: International Journal of Production Management and Engineering, 2024, 12, (1), pp. 79-90
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Syberg, M
dc.contributor.author	West, N
dc.contributor.author	Schwenken, J
dc.contributor.author	Adams, R
dc.contributor.author	Deuse, J https://orcid.org/0000-0003-4066-4357
dc.date.accessioned	2025-02-04T00:26:54Z
dc.date.available	2025-02-04T00:26:54Z
dc.date.issued	2024-01-01
dc.identifier.citation	International Journal of Production Management and Engineering, 2024, 12, (1), pp. 79-90
dc.identifier.issn	2340-5317
dc.identifier.issn	2340-4876
dc.identifier.uri	http://hdl.handle.net/10453/184898
dc.description.abstract	The digitization of learning resources has led to an increase in specialized collaboration platforms across various fields, including the need for manufacturing companies to develop and maintain expertise in Industrial Data Science (IDS). This paper presents an approach to integrating collaborative and competency-based needs specific to industrial data analytics into a functional collaboration platform. We define the unique requirements of IDS projects and translate them into platform features. These features are then implemented and tested in an online platform within a research project, validating their effectiveness in a dynamic value network setting. The platform’s primary innovation lies in its tailored design for IDS project practitioners from diverse domains, ensuring sustainable integration of data analytics in industrial settings. The initial version of this collaborative platform is currently accessible online and undergoing validation.
dc.language	English
dc.publisher	UNIV POLITECNICA VALENCIA, EDITORIAL UPV
dc.relation.ispartof	International Journal of Production Management and Engineering
dc.relation.isbasedon	10.4995/ijpme.2024.19123
dc.rights	info:eu-repo/semantics/openAccess
dc.title	A requirement-driven approach for competency-based collaboration in industrial data science projects
dc.type	Journal Article
utslib.citation.volume	12
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/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Advanced Manufacturing (CAM)
utslib.copyright.status	open_access	*
dc.date.updated	2025-02-04T00:26:53Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	12
utslib.citation.issue	1

Abstract:

The digitization of learning resources has led to an increase in specialized collaboration platforms across various fields, including the need for manufacturing companies to develop and maintain expertise in Industrial Data Science (IDS). This paper presents an approach to integrating collaborative and competency-based needs specific to industrial data analytics into a functional collaboration platform. We define the unique requirements of IDS projects and translate them into platform features. These features are then implemented and tested in an online platform within a research project, validating their effectiveness in a dynamic value network setting. The platform’s primary innovation lies in its tailored design for IDS project practitioners from diverse domains, ensuring sustainable integration of data analytics in industrial settings. The initial version of this collaborative platform is currently accessible online and undergoing validation.

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http://hdl.handle.net/10453/184898