Transition paths for condition-based maintenance-drivensmart services

Authors Henk Akkermans, Rob Basten, Quan Zhu, Luk Van Wassenhove
Published in Journal of Operations Management
Publication date 2024
Research groups Process Innovation and Information Systems
Type Article


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.

On this publication contributed

  • Quan Zhu
    • Researcher
    • Research group: Process Innovation and Information Systems

Language English
Published in Journal of Operations Management
Year and volume 2024 1
Key words digitization, maintenance, smart services, system dynamics, technology management
Digital Object Identifier 10.1002/joom.1295

Process Innovation and Information Systems