| Title: | Advancements in data analysis for the work-sampling method |
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| Authors: | ID Buchmeister, Borut (Author)
ID Vujica-Herzog, Nataša (Author) |
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| Files: |  algorithms-17-00183.pdf (2,19 MB)
MD5: E65DF51DAF1CB27B79E999385928215C
 https://www.mdpi.com/1999-4893/17/5/183
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FS - Faculty of Mechanical Engineering
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| Abstract: | The work-sampling method makes it possible to gain valuable insights into what is happening in production systems. Work sampling is a process used to estimate the proportion of shift time that workers (or machines) spend on different activities (within productive work or losses). It is estimated based on enough random observations of activities over a selected period. When workplace operations do not have short cycle times or high repetition rates, the use of such a statistical technique is necessary because the labor sampling data can provide information that can be used to set standards. The work-sampling procedure is well standardized, but additional contributions are possible when evaluating the observations. In this paper, we present our contribution to improving the decision-making process based on work-sampling data. We introduce a correlation comparison of the measured hourly shares of all activities in pairs to check whether there are mutual connections or to uncover hidden connections between activities. The results allow for easier decision-making (conclusions) regarding the influence of the selected activities on the triggering of the others. With the additional calculation method, we can uncover behavioral patterns that would have been overlooked with the basic method. This leads to improved efficiency and productivity of the production system. |
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| Keywords: | work sampling, observations, analysis, proportions, correlations, interdependence between activities |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 31.03.2024 |
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| Article acceptance date: | 28.04.2024 |
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| Publication date: | 29.04.2024 |
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| Publisher: | MDPI |
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| Year of publishing: | 2024 |
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| Number of pages: | 17 str. |
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| Numbering: | Vol. 17, iss. 5 |
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| PID: | 20.500.12556/DKUM-88538  |
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| UDC: | 658.5.018.2:005.6 |
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| ISSN on article: | 1999-4893 |
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| COBISS.SI-ID: | 194880259 |
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| DOI: | 10.3390/a17050183 |
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| Copyright: | © 2024 by the authors |
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| Publication date in DKUM: | 09.05.2024 |
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| Views: | 391 |
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| Downloads: | 41 |
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| Metadata: |    |
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| Categories: | Misc.
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