Uncertainty Quantification in Global PGM Production using Stochastic and Machine Learning Forecasting Algorithms

Researcher: Kelly Langa, University of the Witwatersrand, Johannesburg
Supervisor: Prof. G Nwaila, University of the Witwatersrand, Johannesburg

Uncertainty Quantification in Global PGM Production using Stochastic and Machine Learning Forecasting Algorithms.  The applications of platinum group metals (PGMs) are innumerable and stretch across multiple industries due to their mechanical and chemical catalytic properties.  The introduction of Industry 4.0 and the many challenges associated with mining along with changing world economic systems is causing massive changes in the supply chain of PGMs.  This has consequently led to erratic supply patterns and deficit of PGMs. The growing uncertainty in global PGM production raises a need for the development of robust and efficient data driven PGM production forecasting methods. Accurate production forecasting is indispensable for mitigating potential supply chain disruptions and strategic planning. The advances in technology has brought about new advanced data analysis techniques which presents us with the opportunity to apply them to the most sophisticated of tasks.