THE DEVELOPMENT OF AN ACTION PRIORITY MATRIX AND TECHNOLOGY ROADMAP FOR THE IMPLEMENTATION OF DATA-DRIVEN AND MACHINE-LEARNING-BASED PREDICTIVE MAINTENANCE IN THE SOUTH AFRICAN RAILWAY INDUSTRY

Abstract

In improving railways for the future, artificial intelligence and machine learning were identified as top-priority technology systems that enable data-driven methods and predictive maintenance. A local survey using semi-structured interviews showed that the railway industry lags behind in adopting and implementing data-driven and machine-learning methods for predictive maintenance. Insights from international studies were found to be relevant in South Africa. Other implementation barriers were identified in the socio-economic and socio-political areas of South Africa. An action priority matrix and technology roadmap was developed to guide the South African railway industry towards the implementation of data-driven and machine learning-based predictive maintenance. The action priority matrix was developed by using a two-round Delphi technique to rank the prioritisation of the required activities. The research showed the importance of considering insights from both international studies and the local context when adopting and implementing technology systems to improve business objectives.