Abstract
This study aimed to evaluate how digital transformation, including smart technology and predictive maintenance, impacts Transnet’s operational efficiency, asset dependability, and cost reduction. The study employed a quantitative research approach and used a purposeful non-probability sampling method. Non-probability sampling aims to gather enough observations to provide rich, detailed data for a comprehensive understanding of the topic. The literature provided the foundation for developing the proposed conceptual framework, which aims to improve operational efficiency, reduce unscheduled downtime, and lower maintenance costs. Key elements, including data collection, predictive analytics, current technologies, and user-friendly maintenance interfaces, are examined in this study.
A closed-ended and Likert scale questionnaire was used to collect data from employees at Transnet. The study's findings show that for Exploratory Factor Analysis (EFA), seven factors were extracted, with two factors each for sections B, D, and E, and one factor for section C. Empirical reliability was chosen for Section B, which focuses on the implementation of maintenance protocols, and for Section E: failure rate. Theoretical reliability was selected for Section C: Implementation of multi-data for predictive maintenance and for Section D: Implementation of technology-based predictive maintenance for uninterrupted train maintenance. Correlation analysis revealed a stronger negative relationship between the independent variable and the dependent variable. Regression analysis indicated a stronger relationship between the implementation of technological predictive maintenance and the failure rate of material availability. This suggests that the introduction of new technologies helps reduce the failure rate.
The study makes a significant contribution to the body of knowledge by helping the organization adopt advanced Industry 4.0 technologies, such as AI, machine learning, and Internet of Things sensors, in a structured way to predict and prevent equipment problems.