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Transition paths for condition-based maintenance-driven smart services

Akkermans,Henk
Basten,Rob
Zhu,Q.
Van Wassenhove,Luk N.
Abstract
This research investigates growth inhibitors for smart services driven by condition-based maintenance (CBM). Despite the fast rise of Industry 4.0 technologies, such as smart sensoring, internet of things, and machine learning (ML), smart services have failed to keep pace. Combined, these technologies enable CBM to achieve the lean goal of high reliability and low waste for industrial equipment. Equipment located at customers throughout the world can be monitored and maintained by manufacturers and service providers, but so far industry uptake has been slow. The contributions of this study are twofold. First, it uncovers industry settings that impede the use of equipment failure data needed to train ML algorithms to predict failures and use these predictions to trigger maintenance. These empirical settings, drawn from four global machine equipment manufacturers, include either under- or over-maintenance (i.e., either too much or too little periodic maintenance). Second, formal analysis of a system dynamics model based on these empirical settings reveals a sweet spot of industry settings in which such inhibitors are absent. Companies that fall outside this sweet spot need to follow specific transition paths to reach it. This research discusses these paths, from both a research and practice perspective.
Description
Publisher Copyright: © 2024 The Authors. Journal of Operations Management published by Wiley Periodicals LLC on behalf of Association for Supply Chain Management, Inc.
Date
2024-06
Journal Title
Journal ISSN
Volume Title
Publisher
Research Projects
Organizational Units
Journal Issue
Keywords
digitization, maintenance, smart services, system dynamics, technology management, SDG 9 - Industry, Innovation, and Infrastructure
Citation
Akkermans, H, Basten, R, Zhu, Q & Van Wassenhove, L N 2024, 'Transition paths for condition-based maintenance-driven smart services', Journal of Operations Management, vol. 70, no. 4, pp. 548-567. https://doi.org/10.1002/joom.1295
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