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Conference Paper: A big data approach for worker’s performance evaluation in IoT-enabled manufacturing shopfloors
Title | A big data approach for worker’s performance evaluation in IoT-enabled manufacturing shopfloors |
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Authors | |
Keywords | IoT-enabled manufacturing Big data analytics Performance evaluation |
Issue Date | 2021 |
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 | 54th CIRP Conference on Manufacturing Systems (CMS) 2021: Towards Digitalized Manufacturing 4.0, Virtual Conference, 22-24 September 2021. In Procedia CIRP, 2021, v. 104, p. 271-275 How to Cite? |
Abstract | Internet of things (IoT) and Radio Frequency Identification (RFID) technologies are gradually adopted in manufacturing recently. With the aid of them, numerous data is generated from daily manufacturing operations. Big data analytics is used in locating deficiencies and thus improving the productivity of a manufacturing shopfloor. Many studies have also examined the effect of “Blue Monday” and “post-lunch slump” on worker’s performance. This paper provides a big data approach on analyzing worker’s performance with the data collected from a manufacturing shopfloor. By evaluating the worker’s performance at different time periods, a better decision can be arranged for improving overall productivity. |
Description | Digital Twins & Internet of Things and Simulation (DT_IoT) Session S2.7 - no. PROCIR-D-20-00449 |
Persistent Identifier | http://hdl.handle.net/10722/309131 |
ISSN | 2023 SCImago Journal Rankings: 0.563 |
DC Field | Value | Language |
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dc.contributor.author | Sang, NC | - |
dc.contributor.author | Lok, YW | - |
dc.contributor.author | Zhong, RR | - |
dc.date.accessioned | 2021-12-14T01:40:58Z | - |
dc.date.available | 2021-12-14T01:40:58Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | 54th CIRP Conference on Manufacturing Systems (CMS) 2021: Towards Digitalized Manufacturing 4.0, Virtual Conference, 22-24 September 2021. In Procedia CIRP, 2021, v. 104, p. 271-275 | - |
dc.identifier.issn | 2212-8271 | - |
dc.identifier.uri | http://hdl.handle.net/10722/309131 | - |
dc.description | Digital Twins & Internet of Things and Simulation (DT_IoT) Session S2.7 - no. PROCIR-D-20-00449 | - |
dc.description.abstract | Internet of things (IoT) and Radio Frequency Identification (RFID) technologies are gradually adopted in manufacturing recently. With the aid of them, numerous data is generated from daily manufacturing operations. Big data analytics is used in locating deficiencies and thus improving the productivity of a manufacturing shopfloor. Many studies have also examined the effect of “Blue Monday” and “post-lunch slump” on worker’s performance. This paper provides a big data approach on analyzing worker’s performance with the data collected from a manufacturing shopfloor. By evaluating the worker’s performance at different time periods, a better decision can be arranged for improving overall productivity. | - |
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 | 54th CIRP Conference on Manufacturing Systems (CMS) 2021 | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | IoT-enabled manufacturing | - |
dc.subject | Big data analytics | - |
dc.subject | Performance evaluation | - |
dc.title | A big data approach for worker’s performance evaluation in IoT-enabled manufacturing shopfloors | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Zhong, RR: zhongzry@hku.hk | - |
dc.identifier.authority | Zhong, RR=rp02116 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1016/j.procir.2021.11.046 | - |
dc.identifier.scopus | eid_2-s2.0-85121606283 | - |
dc.identifier.hkuros | 330738 | - |
dc.identifier.volume | 104 | - |
dc.identifier.spage | 271 | - |
dc.identifier.epage | 275 | - |
dc.publisher.place | Netherlands | - |