Key points are not available for this paper at this time.
The digital transformation of maintenance operations, driven by the fourth industrial revolution and characterized by the widespread adoption of emerging technologies, is essential for enhancing operational efficiency, optimizing asset management, and increasing equipment reliability. Despite the significant benefits, maintenance organizations face numerous challenges in implementing these technologies and ensuring successful transformations, including technological integration complexities, cultural resistance, skill gaps, and aligning digital strategies with broader business objectives, among others. This work addresses the critical gap in structured methodologies for evaluating and guiding digital maturity in Maintenance Digital Transformation (MDT) by proposing a comprehensive maturity assessment model. The model is grounded in nine key maturity dimensions spanning organizational culture, technology & data management, leadership & management aspects, organizational development & change, digital strategy, knowledge & skills, and internal integration. Building on emerging research in this field, data collection and advanced statistical techniques, and feedback from experts in the field, this work adopts a structured and rigorous methodology to (1) identify and test the maturity dimensions and subdimensions driving the success of this transformation, (2) adopt well-established guidelines to develop a structured and comprehensive maturity assessment model allowing organizations to assess their current MDT maturity level, and (3) perform an initial empirical validation of the proposed maturity grid. The resulting maturity grid provides organizations with detailed guidelines on their current digital maturity level and actionable recommendations for advancing to higher stages of maturity. The model offers diagnostic insights and a strategic roadmap to guide maintenance organizations through the multifaceted digital transformation process.
Building similarity graph...
Analyzing shared references across papers
Loading...
Afef Saihi
Khalifa University of Science and Technology
Mohamed Ben-Daya
American University of Sharjah
Moncer Hariga
American University of Sharjah
International Journal of Engineering Business Management
Khalifa University of Science and Technology
American University of Sharjah
Building similarity graph...
Analyzing shared references across papers
Loading...
Saihi et al. (Fri,) studied this question.
synapsesocial.com/papers/6a1237101292a1e50c346fb6 — DOI: https://doi.org/10.1177/18479790261453695