4.7 Article

Predictive maintenance for industry 5.0: behavioural inquiries from a work system perspective

Journal

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
Volume 61, Issue 22, Pages 7846-7865

Publisher

TAYLOR & FRANCIS LTD
DOI: 10.1080/00207543.2022.2154403

Keywords

Behavioural operations management; decision support systems; human-computer interaction; human factors; predictive maintenance; Industry 5; 0

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This paper addresses the acceptance issues of Predictive Maintenance (PdM) solutions and explores how the implementation of PdM systems changes the nature of decision-makers' work and affects their acceptance. Through a literature review and interviews with experts, the study identifies trust, control, cognitive resources, and organizational decision-making as factors that facilitate PdM adoption.
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.

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