Nong, Setshaba2024-07-302024-07-302023Nong, Setshaba. (2023). Use of Artificial Intelligence, Machine Learning and Autonomous Technologies in Mining Industry, South Africa [Master’s dissertation, University of the Witwatersrand, Johannesburg]. WireDSpace. https://hdl.handle.net/10539/39900https://hdl.handle.net/10539/39900A research report submitted in partial fulfillment of the requirements for the degree of Master of Management in the field of Digital Business to the Faculty of Commerce, Law and Management, Wits Business School, University of the Witwatersrand, Johannesburg, 2023The mining industry plays a convincing role globally in driving various industries and contributing to economic prosperity. Locally, South Africa is known for having some of the largest minerals reserves in the world, although it is burdened with challenges inhibiting its progress and competitiveness. It is, however, expected that with application of AI, ML and AT will be able to revolutionise the industry, changing its fortunes, which will increase its competitiveness globally in the process attract investment and contribute to its longevity. As a result of these benefits, this research sought to investigate implication of AI, ML, AT technologies implementation in the mining industry of South Africa. The technologies are considered novel, especially in the mining industry, making employing qualitative study appropriate to assess how the implementation is received by the industry including perceptions and its potential impacts. Key findings of the study indicate that these technologies have the capacity to change the trajectory of the South African mining industry by dealing with issues of safety, costs, labour and efficiency. There is also an opportunity to pursue additional resources locked in pillars, by depth and dangerous working conditions due to geological complexities. However, capital costs, the nature of narrow tabular ore bodies and variability of various conditions are found to be some of the inhibiting factors for implementations of these technologies. As a result, there is no mine that has implemented any of these technologies as a primary means of production. This research will measure current perceptions of industry stakeholders and insights, role of government, mining companies, and equipment manufacturing response. The research highlight areas of impact and challenges that will contribute to strategy development in the process contributing to its sustainability. It is important to consider application of theory of constraint which is a detailed analysis which can assist mining companies in identification of inherent challenges so as to be able to respond appropriately with solutions offered by AI, ML and ATen© 2023 University of the Witwatersrand, Johannesburg. All rights reserved. The copyright in this work vests in the University of the Witwatersrand, Johannesburg. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of University of the Witwatersrand, Johannesburg.Artificial IntelligenceMachine LearningBig DataAutonomous Vehicles and Autonomous technologyMining IndustryMineralsUCTDDissertationUniversity of the Witwatersrand, Johannesburg