Abstract
Maintenance management has improved over the years through the adoption of new technologies. These include introduction of programmable logic control (PLC) programming, portable computers, intranet, local area network (LAN), web-based technologies, and more recently, Fourth Industrial Revolution (4IR) technologies. When combined with engineering expertise and scientific principles, these innovations provide powerful opportunities to enhance maintenance practices. The study undertook a through literature review to establish factors that are influencing effective maintenance management using 4IR technology within facilities management context. Five factors were explored: large data processing, decision-making systems, engineering support, agile maintenance strategy, and 4IR technology adoption. These factors assisted in developing questionnaire which was administered to experienced facilities management professionals at the University of Johannesburg (UJ) as well as external service providers rendering engineering services and maintenance to UJ.
A mixed-method research design is adopted. The first phase was the questionnaire which was grounded on the theoretical framework derived from the factors established through research. Statistical analyses were employed to assess the validity and reliability of the data. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the effect sizes of all five factors were determined, all of which were found to have positive effect towards effective maintenance management. However, only three factors achieved statistical significance, the other two: large data processing capability, and engineering support were found to be statistically insignificant according to the selected sample. Further investigation revealed that this could be due to organisational structure, unmodeled mediating variables, or indirect effect. These findings align with existing literature that raw data alone is insufficient; it needs to be contextualised and interpreted through engineering expertise to yield meaningful insights. It was further demonstrated that decision-making functions as organisational capability that directly influences maintenance effectiveness, rather than merely as a downstream process of technological inputs.
A proposed Industry 4.0-enabled maintenance management framework was then developed, incorporating live data and engineering support as enablers for a closed-loop decision-making system into a conventional input-output maintenance management. The second phase validated the framework by applying it to a commonly used equipment in facilities management. The results were positive, resulted in an improved service level performance score (SLPS), Just-in-Time (JIT) maintenance, and improved equipment remaining useful life (RUL) – 13% higher than the OEM’s recommendations. The research demonstrated that integrating large data processing, decision-making systems, engineering support, agile maintenance strategy, and 4IR technology can drive paradigm shift in maintenance management.
The study concludes by revisiting the identified gaps, the research objectives, research contributions, future research and recommendation, and concluding remarks.