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- Publisher Website: 10.1016/j.mfglet.2024.09.011
- Scopus: eid_2-s2.0-85206239150
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Article: Data analytics for working performance analysis in production management
| Title | Data analytics for working performance analysis in production management |
|---|---|
| Authors | |
| Keywords | Data analysis KPI Machine learning RFID |
| Issue Date | 1-Oct-2024 |
| Publisher | Elsevier |
| Citation | Manufacturing Letters, 2024, v. 41, n. Supplement, p. 73-80 How to Cite? |
| Abstract | RFID technology has found widespread application in supply chain processes. Previous research has primarily focused on managing products or stocks, with limited attention given to analysing workers’ performances through RFID data. This paper proposes a model for analysing employee performance using RFID data in production management. Key Performance Indicators (KPIs) are defined and utilised to process, analyse, and visualise the data through various analysis tools to develop the proposed model. Comparisons are conducted to evaluate employee performance between different groups based on the defined KPI. The model is validated by testing an independent data set, demonstrating its effectiveness in analysing existing data. Predictably, the model has the potential to reduce supervisory and associated costs in case-like applications. |
| Persistent Identifier | http://hdl.handle.net/10722/366339 |
| ISSN | 2023 Impact Factor: 1.9 2023 SCImago Journal Rankings: 0.587 |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Wang, Yuxin | - |
| dc.contributor.author | Yang, Yishu | - |
| dc.contributor.author | Zhong, Ray Y | - |
| dc.date.accessioned | 2025-11-25T04:18:50Z | - |
| dc.date.available | 2025-11-25T04:18:50Z | - |
| dc.date.issued | 2024-10-01 | - |
| dc.identifier.citation | Manufacturing Letters, 2024, v. 41, n. Supplement, p. 73-80 | - |
| dc.identifier.issn | 2213-8463 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/366339 | - |
| dc.description.abstract | RFID technology has found widespread application in supply chain processes. Previous research has primarily focused on managing products or stocks, with limited attention given to analysing workers’ performances through RFID data. This paper proposes a model for analysing employee performance using RFID data in production management. Key Performance Indicators (KPIs) are defined and utilised to process, analyse, and visualise the data through various analysis tools to develop the proposed model. Comparisons are conducted to evaluate employee performance between different groups based on the defined KPI. The model is validated by testing an independent data set, demonstrating its effectiveness in analysing existing data. Predictably, the model has the potential to reduce supervisory and associated costs in case-like applications. | - |
| dc.language | eng | - |
| dc.publisher | Elsevier | - |
| dc.relation.ispartof | Manufacturing Letters | - |
| dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
| dc.subject | Data analysis | - |
| dc.subject | KPI | - |
| dc.subject | Machine learning | - |
| dc.subject | RFID | - |
| dc.title | Data analytics for working performance analysis in production management | - |
| dc.type | Article | - |
| dc.identifier.doi | 10.1016/j.mfglet.2024.09.011 | - |
| dc.identifier.scopus | eid_2-s2.0-85206239150 | - |
| dc.identifier.volume | 41 | - |
| dc.identifier.issue | Supplement | - |
| dc.identifier.spage | 73 | - |
| dc.identifier.epage | 80 | - |
| dc.identifier.eissn | 2213-8463 | - |
| dc.identifier.issnl | 2213-8463 | - |
