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Conference Paper: Big data driven decision-making for batch-based production systems
Title | Big data driven decision-making for batch-based production systems |
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Authors | |
Keywords | Big data Economic batch quantity Production plan Sales predict Smart product-service system |
Issue Date | 2019 |
Publisher | Elsevier: 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? |
Abstract | The 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). |
Description | Session 8B SS15: Optimization and Game Theory in Production, Service and Supply Chain Management (OG-PSS) - B - no. PROCIR-D-19-00622 |
Persistent Identifier | http://hdl.handle.net/10722/272403 |
ISSN | 2023 SCImago Journal Rankings: 0.563 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zhang, YH | - |
dc.contributor.author | Zhang, R | - |
dc.contributor.author | Wang, YZ | - |
dc.contributor.author | Guo, HF | - |
dc.contributor.author | Zhong, R | - |
dc.contributor.author | Qu, T | - |
dc.contributor.author | Li, ZW | - |
dc.date.accessioned | 2019-07-20T10:41:39Z | - |
dc.date.available | 2019-07-20T10:41:39Z | - |
dc.date.issued | 2019 | - |
dc.identifier.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 | - |
dc.identifier.issn | 2212-8271 | - |
dc.identifier.uri | http://hdl.handle.net/10722/272403 | - |
dc.description | Session 8B SS15: Optimization and Game Theory in Production, Service and Supply Chain Management (OG-PSS) - B - no. PROCIR-D-19-00622 | - |
dc.description.abstract | The 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.language | eng | - |
dc.publisher | Elsevier: 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.ispartof | Procedia CIRP | - |
dc.relation.ispartof | 11th CIRP Conference on Industrial Product-Service Systems (IPS2), 2019 | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Big data | - |
dc.subject | Economic batch quantity | - |
dc.subject | Production plan | - |
dc.subject | Sales predict | - |
dc.subject | Smart product-service system | - |
dc.title | Big data driven decision-making for batch-based production systems | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Zhong, R: zhongzry@hku.hk | - |
dc.identifier.authority | Zhong, R=rp02116 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1016/j.procir.2019.05.023 | - |
dc.identifier.scopus | eid_2-s2.0-85070542144 | - |
dc.identifier.hkuros | 298842 | - |
dc.identifier.volume | 83 | - |
dc.identifier.spage | 814 | - |
dc.identifier.epage | 818 | - |
dc.identifier.isi | WOS:000568146700142 | - |
dc.publisher.place | Netherlands | - |
dc.identifier.issnl | 2212-8271 | - |