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

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.

KEYWORDS:

• Behavioural operations management
• decision support systems
• human–computer interaction
• human factors
• predictive maintenance
• Industry 5.0

Acknowledgements

We are grateful to the six experts for their participation and invaluable contributions to our study. This research was approved by the Ethical Review Board of the Eindhoven University of Technology, reference number ERB2021IEIS31a.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.

Additional information

Funding

This research is funded by Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) Grant number NWA.1160.18.238.

Notes on contributors

Bas van Oudenhoven

Bas van Oudenhoven is currently pursuing a PhD degree at the Eindhoven University of Technology, where he is a researcher in the Human Performance Management group, School of Industrial Engineering. He is active in the behavioural operations management field, with a research focus on the effective use of decision-support tools in the area of Predictive Maintenance. He is a member of the consortium PrimaVera (Predictive maintenance for Very effective asset management), for which he collaborates with various high-tech public and private companies.

Philippe Van de Calseyde

Philippe Van de Calseyde is an Assistant Professor of organisational behaviour at the Eindhoven University of Technology (TU/e). His background is mainly in the areas of judgment and decision-making. Philippe’s research focuses on understanding how situational- and cognitive factors influence people’s judgments and decisions. Specific interests include how people respond to the decision-speed of others, interpersonal trust, human cooperation, negotiations, and the role of emotions in decision-making. In testing these relationships, he mostly conducts experiments and field studies.

Rob Basten

Rob Basten is an Associate Professor at the Eindhoven University of Technology (TU/e), where he is primarily occupied with after sales services for high-tech equipment. He is especially interested in using new technologies to improve services. For example, 3D printing of spare parts on location and using condition monitoring information to perform just in time maintenance. He is further active in behavioural operations management, trying to understand how people can use decision support systems in such a way that they actually improve decisions and add value. Many of his research projects are interdisciplinary and performed in cooperation with high-tech industry.

Evangelia Demerouti

Evangelia Demerouti is a Full Professor at Eindhoven University of Technology (TU/e) and Distinguished Visiting Professor at the University of Johannesburg. Her research focuses on the processes enabling performance, including the effects of work characteristics, individual job strategies (including job crafting and decision-making), occupational wellbeing, and work-life balance. She studied psychology at the University of Crete (Greece) and obtained her PhD in Work and Organisational Psychology (cum laude, 1999) from the Carl von Ossietzky Universität Oldenburg (Germany). She has published over 200 national and international papers and book chapters and is associate editor of the Journal of Occupational Health Psychology.