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.
In die verbetering van spoorweë vir die toekoms, is kunsmatige intelligensie en masjienleer geïdentifiseer as top-prioriteit tegnologiestelsels wat data-gedrewe metodes en voorspellende instandhouding moontlik maak. 'n Plaaslike opname wat semi-gestruktureerde onderhoude gebruik het, het getoon dat die spoorwegbedryf agterbly met die aanvaarding en implementering van datagedrewe- en masjienleermetodes vir voorspellende instandhouding. Daar is gevind dat insigte uit internasionale studies relevant is in Suid-Afrika. Ander implementeringshindernisse is in die sosio-ekonomiese en sosio-politieke gebiede van Suid-Afrika geïdentifiseer. ’n Aksieprioriteitmatriks en tegnologie-padkaart is ontwikkel om die Suid-Afrikaanse spoorwegbedryf te lei tot die implementering van datagedrewe- en masjienleer-gebaseerde voorspellende instandhouding. Die aksieprioriteitmatriks is ontwikkel deur 'n twee-rondte Delphi-tegniek te gebruik om die prioritisering van die vereiste aktiwiteite te rangskik. Die navorsing het getoon hoe belangrik dit is om insigte uit beide internasionale studies en die plaaslike konteks in ag te neem wanneer tegnologiestelsels aangeneem en geïmplementeer word om besigheidsdoelwitte te verbeter.
