Energy efficiency measures and production resources: Towards an integrative classification framework for decision makers

Katic, M; Trianni, A

Energy efficiency measures and production resources: Towards an integrative classification framework for decision makers

Katic, M

Trianni, A

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Industrial Engineering and Engineering Management, 2020, 2020-December, pp. 225-229
Issue Date:: 2020-12-14

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Field	Value	Language
dc.contributor.author	Katic, M https://orcid.org/0000-0001-9066-8036
dc.contributor.author	Trianni, A https://orcid.org/0000-0002-1699-7787
dc.date	2020-12-14
dc.date.accessioned	2022-01-27T06:19:48Z
dc.date.available	2022-01-27T06:19:48Z
dc.date.issued	2020-12-14
dc.identifier.citation	IEEE International Conference on Industrial Engineering and Engineering Management, 2020, 2020-December, pp. 225-229
dc.identifier.isbn	9781538672204
dc.identifier.issn	2157-3611
dc.identifier.issn	2157-362X
dc.identifier.uri	http://hdl.handle.net/10453/153658
dc.description.abstract	Energy Efficiency Measures and Production Resources: Towards an Integrative Classification Framework for Decision MakersThe adoption of energy efficiency measures (EEMs) is a significant area of concern for today's industrial organisations. Whilst literature on this subject has soared in recent decades, there remains a gap in understanding the extent of their impact on an organisation's operations as well as the manner, and whether, they are adopted in the first place. This paper provides a preliminary attempt at addressing these concerns by investing attention into the notion of production resources as a mechanism through which a deeper appreciation of EEM impact on operations could be provided through the development of a generalised decision-making framework. We end this paper with conclusions and areas for further work.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	IEEE International Conference on Industrial Engineering and Engineering Management
dc.relation.ispartof	IEEE International Conference on Industrial Engineering and Engineering Management
dc.relation.isbasedon	10.1109/IEEM45057.2020.9309836
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Energy efficiency measures and production resources: Towards an integrative classification framework for decision makers
dc.type	Conference Proceeding
utslib.citation.volume	2020-December
utslib.location.activity	Singapore, Singapore
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 - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
utslib.copyright.embargo	2023-01-12T00:00:00+1000Z
dc.date.updated	2022-01-27T06:19:46Z
pubs.finish-date	2020-12-17
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2020-12-14
pubs.volume	2020-December
dc.location	Piscataway, USA

Abstract:

Energy Efficiency Measures and Production Resources: Towards an Integrative Classification Framework for Decision MakersThe adoption of energy efficiency measures (EEMs) is a significant area of concern for today's industrial organisations. Whilst literature on this subject has soared in recent decades, there remains a gap in understanding the extent of their impact on an organisation's operations as well as the manner, and whether, they are adopted in the first place. This paper provides a preliminary attempt at addressing these concerns by investing attention into the notion of production resources as a mechanism through which a deeper appreciation of EEM impact on operations could be provided through the development of a generalised decision-making framework. We end this paper with conclusions and areas for further work.

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http://hdl.handle.net/10453/153658