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Conference Paper: Big data driven decision-making for batch-based production systems

TitleBig data driven decision-making for batch-based production systems
Authors
Issue Date2019
PublisherElsevier: Creative Commons Attribution Non-Commercial No-Derivatives License. The Journal's web site is located at http://www.elsevier.com/wps/find/journaldescription.cws_home/727717/description
Citation
11th CIRP Conference on Industrial Product-Service Systems (IPS2): Smart Product and Service Twin, Zhuhai and Hong Kong, China, 29-31 May 2019. In Procedia CIRP, 2019, v. 83, p. 814-818 How to Cite?
AbstractThe era of big data has brought new challenges to chemical enterprises. In order to maximize the benefits, enterprises are considering to implement intelligent service technology into traditional production systems to improve the level of intelligence in business. This paper proposes a service framework based on big data driven prediction, which includes information perception layer, information application layer and big data service layer. In this paper, the composition of big data service layer is described in detail, and a sales predicting method based on neural network is introduced. The salability of products is divided, and the qualitative economic production volume mechanism is finally given. Based on the framework, an intelligent service system for enterprises with the characteristics of mass production is implemented. Experimental results show that the big data service framework can support chemical enterprises to make decisions to reduce costs, and provides an effective method for Smart Product Service System (PSS).
DescriptionSession 8B SS15: Optimization and Game Theory in Production, Service and Supply Chain Management (OG-PSS) - B - no. PROCIR-D-19-00622
Persistent Identifierhttp://hdl.handle.net/10722/272403
ISSN

 

DC FieldValueLanguage
dc.contributor.authorZhang, YH-
dc.contributor.authorZhang, R-
dc.contributor.authorWang, YZ-
dc.contributor.authorGuo, HF-
dc.contributor.authorZhong, R-
dc.contributor.authorQu, T-
dc.contributor.authorLi, ZW-
dc.date.accessioned2019-07-20T10:41:39Z-
dc.date.available2019-07-20T10:41:39Z-
dc.date.issued2019-
dc.identifier.citation11th CIRP Conference on Industrial Product-Service Systems (IPS2): Smart Product and Service Twin, Zhuhai and Hong Kong, China, 29-31 May 2019. In Procedia CIRP, 2019, v. 83, p. 814-818-
dc.identifier.issn2212-8271-
dc.identifier.urihttp://hdl.handle.net/10722/272403-
dc.descriptionSession 8B SS15: Optimization and Game Theory in Production, Service and Supply Chain Management (OG-PSS) - B - no. PROCIR-D-19-00622-
dc.description.abstractThe era of big data has brought new challenges to chemical enterprises. In order to maximize the benefits, enterprises are considering to implement intelligent service technology into traditional production systems to improve the level of intelligence in business. This paper proposes a service framework based on big data driven prediction, which includes information perception layer, information application layer and big data service layer. In this paper, the composition of big data service layer is described in detail, and a sales predicting method based on neural network is introduced. The salability of products is divided, and the qualitative economic production volume mechanism is finally given. Based on the framework, an intelligent service system for enterprises with the characteristics of mass production is implemented. Experimental results show that the big data service framework can support chemical enterprises to make decisions to reduce costs, and provides an effective method for Smart Product Service System (PSS).-
dc.languageeng-
dc.publisherElsevier: Creative Commons Attribution Non-Commercial No-Derivatives License. The Journal's web site is located at http://www.elsevier.com/wps/find/journaldescription.cws_home/727717/description-
dc.relation.ispartofProcedia CIRP-
dc.relation.ispartof11th CIRP Conference on Industrial Product-Service Systems (IPS2), 2019-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleBig data driven decision-making for batch-based production systems-
dc.typeConference_Paper-
dc.identifier.emailZhong, R: zhongzry@hku.hk-
dc.identifier.authorityZhong, R=rp02116-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1016/j.procir.2019.05.023-
dc.identifier.hkuros298842-
dc.identifier.volume83-
dc.identifier.spage814-
dc.identifier.epage818-
dc.publisher.placeNetherlands-

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