Integrating operational and human factors to predict daily productivity of warehouse employees using extreme gradient boosting.
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| Title: | Integrating operational and human factors to predict daily productivity of warehouse employees using extreme gradient boosting. |
|---|---|
| Authors: | Falkenberg, Sven F.1 sven.falkenberg@whu.edu, Spinler, Stefan1 |
| Source: | International Journal of Production Research. Dec2023, Vol. 61 Issue 24, p8654-8673. 20p. |
| Subjects: | Standard deviations, Labor productivity, Workforce planning |
| Abstract: | The majority of warehouse expenses is driven by labour cost. Therefore, efficient management of labour resources is required. To do so, workforce planning is used to match the workforce capacity with the incoming workload. While doing so, it is often wrongly assumed that each worker has the same and constant capacity or performance. Addressing this, we build a model to predict the employee-based productivity of newly hired warehouse personnel that will support workforce planning by incorporating multiple data sources. To this end,wedevelop a framework to identify relevant variables in four categories: warehouse, operator, shift and product. We demonstrate that Extreme Gradient Boosting, using these variables may reduce the root mean squared error of the prediction by more than 50%. A comprehensive scenario analysis shows that improving productivity predictions translates into substantial cost savings. Furthermore, a sensitivity analysis identifies which variable categories should be favoured in the data collection process to achieve the best prediction results. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 174187706 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Integrating operational and human factors to predict daily productivity of warehouse employees using extreme gradient boosting. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Falkenberg%2C+Sven+F%2E%22">Falkenberg, Sven F.</searchLink><relatesTo>1</relatesTo><i> sven.falkenberg@whu.edu</i><br /><searchLink fieldCode="AR" term="%22Spinler%2C+Stefan%22">Spinler, Stefan</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Dec2023, Vol. 61 Issue 24, p8654-8673. 20p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink><br /><searchLink fieldCode="DE" term="%22Labor+productivity%22">Labor productivity</searchLink><br /><searchLink fieldCode="DE" term="%22Workforce+planning%22">Workforce planning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The majority of warehouse expenses is driven by labour cost. Therefore, efficient management of labour resources is required. To do so, workforce planning is used to match the workforce capacity with the incoming workload. While doing so, it is often wrongly assumed that each worker has the same and constant capacity or performance. Addressing this, we build a model to predict the employee-based productivity of newly hired warehouse personnel that will support workforce planning by incorporating multiple data sources. To this end,wedevelop a framework to identify relevant variables in four categories: warehouse, operator, shift and product. We demonstrate that Extreme Gradient Boosting, using these variables may reduce the root mean squared error of the prediction by more than 50%. A comprehensive scenario analysis shows that improving productivity predictions translates into substantial cost savings. Furthermore, a sensitivity analysis identifies which variable categories should be favoured in the data collection process to achieve the best prediction results. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=174187706 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/00207543.2022.2159563 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 8654 Subjects: – SubjectFull: Standard deviations Type: general – SubjectFull: Labor productivity Type: general – SubjectFull: Workforce planning Type: general Titles: – TitleFull: Integrating operational and human factors to predict daily productivity of warehouse employees using extreme gradient boosting. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Falkenberg, Sven F. – PersonEntity: Name: NameFull: Spinler, Stefan IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 12 Text: Dec2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 61 – Type: issue Value: 24 Titles: – TitleFull: International Journal of Production Research Type: main |
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