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dc.contributor.advisorNdlovu, K.
dc.contributor.authorPeretorius, Barend Hendrik I.
dc.date.accessioned2022-07-21T09:30:24Z
dc.date.available2022-07-21T09:30:24Z
dc.date.issued2022
dc.identifier.urihttps://orcid.org/0000-0002-2549-1622
dc.identifier.urihttp://hdl.handle.net/10394/39434
dc.descriptionMBA, North-West University, Potchefstroom Campusen_US
dc.description.abstractProduction in the mining industry has substantially declined since 2000. Since 2000, the mining industry's productivity has dropped significantly. From roughly 2004 to 2010, the mining industry's productivity dropped by nearly 40%. Between 2010 and 2018, productivity improved somewhat, approximately 15%. Since 2010, it has steadily gained ground, although, between 2010 and 2018, it only increased by about 15%. According to Canart et al., contemporary productivity figures are still 25% lower than those achieved in 2000. New ways of increasing productivity by operating more effective and efficient mines have come to afore using Artificial Intelligence and Machine Learning. This study aimed to investigate the impact of artificial intelligent systems on productivity at a mine. The researcher used a quantitative method employing questionnaires distributed over a representative sample at a specific mine Impala Platinum Rustenburg Concentrator Plant. The respondents completed the fifty-two questionnaires that were handed out. After the questionnaires were collected from the respondents, The questionnaires were submitted to the North-West University's Statistical department for processing. After the information was processed, it was discussed in Chapter 4 of this study. The impact of AI and ML implementation over the past ten (10) years was perceived as positive. The effect was displayed by the data gathered through the questionnaires. The impact of AI and ML on mining productivity is perceived as positive. However, user-friendliness was perceived as positive by users younger than forty (40) and not user-friendly by users 40 and older. It was recommended that users of AI and ML should be trained on how to use AI and ML. But before using AI and ML, it may be a prerequisite, and policies should be implemented to make training compulsory. Ai and ML can be cost-effective by integrating low code into the system. Low-code is relatively inexpensive.en_US
dc.language.isoenen_US
dc.publisherNorth-West University (South Africa)en_US
dc.subjectArtificial Intelligenceen_US
dc.subjectMachine learningen_US
dc.subjectMining industryen_US
dc.subjectLow Codeen_US
dc.subjectProductivityen_US
dc.subjectImpacten_US
dc.subjectUser Friendlinessen_US
dc.titleInvestigating the impact of artificial intelligent systems on productivity at a mineen_US
dc.typeThesisen_US
dc.description.thesistypeMastersen_US
dc.contributor.researchID35181680 - Ndlovu, N. (Kaizer) (Supervisor)


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