Workforce Learning Curves for Human-Based Assembly Operations: A State-of-the-Art Review
In this state-of-the-art review, the authors explore the recent advancements in the topics of learning curve models and their estimation methods for manual operations and processes as well as the data collection and monitoring technologies used for supporting these. This objective is achieved by ans...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/19/9608 |
| _version_ | 1797480720471425024 |
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| author | Carlos Peña David Romero Julieta Noguez |
| author_facet | Carlos Peña David Romero Julieta Noguez |
| author_sort | Carlos Peña |
| collection | DOAJ |
| description | In this state-of-the-art review, the authors explore the recent advancements in the topics of learning curve models and their estimation methods for manual operations and processes as well as the data collection and monitoring technologies used for supporting these. This objective is achieved by answering the following three research questions: (RQ1) What calculation methods for estimating the learning curve of a worker exist in the recent scientific literature? (RQ2) What other usages are manufacturing enterprises giving to the modern learning curve prediction models according to the recent scientific literature? and (RQ3) What data collection and monitoring technologies exist to automatically acquire the data needed to create and continuously update the learning curve of an assembly operator? To do so, the PRISMA methodology for literature reviews was used, only including journal articles and conference papers referencing the topic of manual operations and processes, and to fulfil the criteria of a state-of-the-art review, only the literary corpus generated in the last five years (from 2017 to 2022) was reviewed. The scientific databases where the explorative research was carried out were Scopus and Web of Science. Such research resulted in 11 relevant journal articles and international conference papers, which were first reviewed, synthesized, and then compared. Four estimating methods were found for learning curves, and one recently developed learning curve model was found. As for the data collection and monitoring technologies, six frameworks were found and reviewed. Lastly, in the discussion, different areas of opportunity were found in the current state-of-the-art, mainly by combining the existing learning curve models and their estimation methods and feeding these with modern real-time data collection and monitoring frameworks. |
| first_indexed | 2024-03-09T22:05:09Z |
| format | Article |
| id | doaj.art-0c28c037493841819d8349a9ae94d9e8 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:05:09Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0c28c037493841819d8349a9ae94d9e82023-11-23T19:42:32ZengMDPI AGApplied Sciences2076-34172022-09-011219960810.3390/app12199608Workforce Learning Curves for Human-Based Assembly Operations: A State-of-the-Art ReviewCarlos Peña0David Romero1Julieta Noguez2School of Engineering and Sciences, Tecnológico de Monterrey, Monterrey 64849, MexicoSchool of Engineering and Sciences, Tecnológico de Monterrey, Monterrey 64849, MexicoSchool of Engineering and Sciences, Tecnológico de Monterrey, Monterrey 64849, MexicoIn this state-of-the-art review, the authors explore the recent advancements in the topics of learning curve models and their estimation methods for manual operations and processes as well as the data collection and monitoring technologies used for supporting these. This objective is achieved by answering the following three research questions: (RQ1) What calculation methods for estimating the learning curve of a worker exist in the recent scientific literature? (RQ2) What other usages are manufacturing enterprises giving to the modern learning curve prediction models according to the recent scientific literature? and (RQ3) What data collection and monitoring technologies exist to automatically acquire the data needed to create and continuously update the learning curve of an assembly operator? To do so, the PRISMA methodology for literature reviews was used, only including journal articles and conference papers referencing the topic of manual operations and processes, and to fulfil the criteria of a state-of-the-art review, only the literary corpus generated in the last five years (from 2017 to 2022) was reviewed. The scientific databases where the explorative research was carried out were Scopus and Web of Science. Such research resulted in 11 relevant journal articles and international conference papers, which were first reviewed, synthesized, and then compared. Four estimating methods were found for learning curves, and one recently developed learning curve model was found. As for the data collection and monitoring technologies, six frameworks were found and reviewed. Lastly, in the discussion, different areas of opportunity were found in the current state-of-the-art, mainly by combining the existing learning curve models and their estimation methods and feeding these with modern real-time data collection and monitoring frameworks.https://www.mdpi.com/2076-3417/12/19/9608learning curvesmanual processesmanual assemblymanual operationsmonitoring |
| spellingShingle | Carlos Peña David Romero Julieta Noguez Workforce Learning Curves for Human-Based Assembly Operations: A State-of-the-Art Review Applied Sciences learning curves manual processes manual assembly manual operations monitoring |
| title | Workforce Learning Curves for Human-Based Assembly Operations: A State-of-the-Art Review |
| title_full | Workforce Learning Curves for Human-Based Assembly Operations: A State-of-the-Art Review |
| title_fullStr | Workforce Learning Curves for Human-Based Assembly Operations: A State-of-the-Art Review |
| title_full_unstemmed | Workforce Learning Curves for Human-Based Assembly Operations: A State-of-the-Art Review |
| title_short | Workforce Learning Curves for Human-Based Assembly Operations: A State-of-the-Art Review |
| title_sort | workforce learning curves for human based assembly operations a state of the art review |
| topic | learning curves manual processes manual assembly manual operations monitoring |
| url | https://www.mdpi.com/2076-3417/12/19/9608 |
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