Factory eco-efficiency modelling: data granularity and performance indicators

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dc.contributor.authorDavé, Aanand
dc.contributor.authorBall, Peter
dc.contributor.authorSalonitis, Konstantinos
dc.date.accessioned2017-03-24T09:17:11Z
dc.date.available2017-03-24T09:17:11Z
dc.date.issued2017-03-20
dc.description.abstractEco-efficiency is becoming an increasingly important performance measure. Currently manufacturers rely on reactive methods such as auditing for assessment. There are still significant theoretical and practical barriers including a lack of knowledge regarding the selection and composition of appropriate data granularities, model quality to improve decision making, and split incentives between facilities and manufacturing asset management. The purpose of this paper is to show the application of an eco-efficiency modelling framework in the case of a fast-moving consumer goods factory. The framework composes resource and production data. These are analysed with respect to three data granularity factors, asset subdivision, time-step, and resource magnitude. Modelling is used to represent asset eco-efficiency across available subdivisions using performance indicators.en_UK
dc.identifier.citationAanand Davé, Peter Ball, Konstantinos Salonitis, Factory Eco-Efficiency Modelling: Data Granularity and Performance Indicators, Procedia Manufacturing, Volume 8, 2017, pp479-486en_UK
dc.identifier.issn2351-9789
dc.identifier.urihttp://dx.doi.org/10.1016/j.promfg.2017.02.061
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/11646
dc.language.isoenen_UK
dc.publisherElaevieren_UK
dc.subjectFactoryen_UK
dc.subjectEco-efficiencyen_UK
dc.subjectModellingen_UK
dc.subjectData Granularity Frameworken_UK
dc.subjectData Compositionen_UK
dc.subjectSimulationen_UK
dc.titleFactory eco-efficiency modelling: data granularity and performance indicatorsen_UK
dc.typeArticleen_UK

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