Predictive maintenance for industry 5.0: behavioural inquiries from a work system perspective.
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| Title: | Predictive maintenance for industry 5.0: behavioural inquiries from a work system perspective. |
|---|---|
| Authors: | van Oudenhoven, Bas1 (AUTHOR) b.v.oudenhoven@tue.nl, Van de Calseyde, Philippe1 (AUTHOR), Basten, Rob1 (AUTHOR), Demerouti, Evangelia1 (AUTHOR) |
| Source: | International Journal of Production Research. Nov2023, Vol. 61 Issue 22, p7846-7865. 20p. 1 Illustration, 1 Diagram, 2 Charts. |
| Subjects: | Product management software, Decision support systems, Trust |
| Abstract: | Predictive Maintenance (PdM) solutions assist decision-makers by predicting equipment health and scheduling maintenance actions, but their implementation in industry remains problematic. Specifically, prior research repeatedly indicates that decision-makers often refuse to adopt the data-driven, system-generated advice in their working procedures. In this paper, we address these acceptance issues by studying how PdM implementation changes the nature of decision-makers' work and how these changes affect their acceptance of PdM systems. We build on the human-centric Smith-Carayon Work System model to synthesise literature from research areas where system acceptance has been explored in more detail. Consequently, we expand the maintenance literature by investigating the human-, task-, and organisational characteristics of PdM implementation. Following the literature review, we distil ten propositions regarding decision-making behaviour in PdM settings. Next, we verify each proposition's relevance through in-depth interviews with experts from both academia and industry. Based on the propositions and interviews, we identify four factors that facilitate PdM adoption: trust between decision-maker and model (maker), control in the decision-making process, availability of sufficient cognitive resources, and proper organisational allocation of decision-making. Our results contribute to a fundamental understanding of acceptance behaviour in a PdM context and provide recommendations to increase the effectiveness of PdM implementations. [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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 172441288 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Predictive maintenance for industry 5.0: behavioural inquiries from a work system perspective. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22van+Oudenhoven%2C+Bas%22">van Oudenhoven, Bas</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> b.v.oudenhoven@tue.nl</i><br /><searchLink fieldCode="AR" term="%22Van+de+Calseyde%2C+Philippe%22">Van de Calseyde, Philippe</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Basten%2C+Rob%22">Basten, Rob</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Demerouti%2C+Evangelia%22">Demerouti, Evangelia</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Nov2023, Vol. 61 Issue 22, p7846-7865. 20p. 1 Illustration, 1 Diagram, 2 Charts. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Product+management+software%22">Product management software</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+support+systems%22">Decision support systems</searchLink><br /><searchLink fieldCode="DE" term="%22Trust%22">Trust</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Predictive Maintenance (PdM) solutions assist decision-makers by predicting equipment health and scheduling maintenance actions, but their implementation in industry remains problematic. Specifically, prior research repeatedly indicates that decision-makers often refuse to adopt the data-driven, system-generated advice in their working procedures. In this paper, we address these acceptance issues by studying how PdM implementation changes the nature of decision-makers' work and how these changes affect their acceptance of PdM systems. We build on the human-centric Smith-Carayon Work System model to synthesise literature from research areas where system acceptance has been explored in more detail. Consequently, we expand the maintenance literature by investigating the human-, task-, and organisational characteristics of PdM implementation. Following the literature review, we distil ten propositions regarding decision-making behaviour in PdM settings. Next, we verify each proposition's relevance through in-depth interviews with experts from both academia and industry. Based on the propositions and interviews, we identify four factors that facilitate PdM adoption: trust between decision-maker and model (maker), control in the decision-making process, availability of sufficient cognitive resources, and proper organisational allocation of decision-making. Our results contribute to a fundamental understanding of acceptance behaviour in a PdM context and provide recommendations to increase the effectiveness of PdM implementations. [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=172441288 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/00207543.2022.2154403 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 7846 Subjects: – SubjectFull: Product management software Type: general – SubjectFull: Decision support systems Type: general – SubjectFull: Trust Type: general Titles: – TitleFull: Predictive maintenance for industry 5.0: behavioural inquiries from a work system perspective. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: van Oudenhoven, Bas – PersonEntity: Name: NameFull: Van de Calseyde, Philippe – PersonEntity: Name: NameFull: Basten, Rob – PersonEntity: Name: NameFull: Demerouti, Evangelia IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 11 Text: Nov2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 61 – Type: issue Value: 22 Titles: – TitleFull: International Journal of Production Research Type: main |
| ResultId | 1 |
