EventsThe 3rd International Electronic Conference on Machines and Applications
Published
This submission belongs to the session S1. Automation and Control Systems of the event The 3rd International Electronic Conference on Machines and Applications
Published date
07 May, 2026
Academic Editor
author-avatarJames Lam
Citation
Olusegun Aanuoluwapo Oguntona, Simphiwe Shangase, Enablers of Intelligent Mining Systems: Evidence from South Africa’s Hard Rock Mining Industry, in Proceedings of The 3rd International Electronic Conference on Machines and Applications, 12 May–14 May 2026, MDPI: Basel, Switzerland
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Enablers of Intelligent Mining Systems: Evidence from South Africa’s Hard Rock Mining Industry

1. Department of Built Environment, Faculty of Engineering, Built Environment and Information Technology, Walter Sisulu University, South Africa, South Africa
2. Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa, South Africa
Abstract

The mining sector is undergoing a profound digital transformation driven by the integration of intelligent mining systems that enhance operational efficiency, safety, productivity, and ultimately sustainability. Despite the growing global adoption of smart mining technologies, their uptake within South Africa’s hard rock mining industry remains uneven and underexplored. This study investigates the underlying factors enabling the adoption of intelligent mining systems in South Africa’s hard rock mining industry using exploratory factor analysis. Employing a quantitative research design, data were collected through a structured questionnaire administered to actively practising mining professionals in South Africa. An exploratory factor analysis was employed to uncover the latent structures underlying the observed adoption variables. The findings reveal three distinct, statistically robust enabler clusters that collectively shape intelligent mining adoption. These include continuous awareness and knowledge development, enabling regulatory framework and public acceptance, and governmental incentive and support. The extracted factor structure demonstrates strong internal consistency and explanatory power, providing empirical evidence of the multifaceted nature of intelligent mining adoption in a developing nation context. This study contributes to the smart mining and Industry 4.0 body of knowledge by shifting the focus from barriers to actionable enablers, offering a nuanced understanding of the conditions necessary for successful digital transformation in the hard rock mining space. The results provide valuable insights for mining firms, policymakers, and technology providers seeking to accelerate the deployment of intelligent mining systems in South Africa and comparable global mining jurisdictions

Keywords
Hard rock mining
Health and safety
Intelligent mining systems
Smart mining
Technology adoption
Poster
IECMA Poster-Oguntona et al 2026.pdf
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