The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Title from PDF of title page (University of Missouri--Columbia, viewed January 26, 2010). Thesis advisor: Dr. Cerry M. Klein. Includes bibliographical references.

Student Number : 9910899R - MSc dissertation - School of Mining - Faculty of Engineering and the Built Environment / The field of risk management has been growing in popularity over the last few years. Risk management is not a new concept but is becoming more important since the release of the Turnbull report. This research reviews all the risk management systems currently available in the mining industry. The focus of this research is from a Mining Economics as well as a Minerals Resource Management perspective. It is the Mineral Resource Managers primary task to ensure that the orebody is extracted in the most optimum method to ensure the maximum return for the shareholder. In order to do that, the Resource Manager needs a good understanding of the ore body as well as the extraction methods and the cost of mining. Recently it has become important to understand the risks around the mining process as well. It was found that the principal risk associated with mining is extracting the orebody sub economically and hence the research focus was on optimisation. Three tools have been designed to facilitate the determination of optimisation. The above three tools have been tested in practice. The first section of research focuses on how risk is defined in the industry. There is also an analysis what a Mining Economist and A Mineral Resource Manager will encounter in terms of risk. The second section covers the Basic Mining Equation (BME) and its uses. The research looks at using stochastic methods to improve optimisation and identifying risk. The @Risk software was used to analyse 5 years of historical data from an existing mine and predicting the future, using the distributions identified in the history. The third section is based on the use of the Cigarette Box Optimiser (CBO), where the cost volume curve and the orebody signature are used to determine optimality in returns. It also looks at various forms of the BME and how it can be used to identify risk. The section also covers quantification of risk from a probability perspective using systems reliability logic. The fourth section centres on the Macro Grid Optimiser (MGO), which considers the spatial differentiation of the orebody and determining the optimality through, an iterative process. The last section analyses risk from a Mining Economics perspective. It considers the use of the ‘S-curve’ to determine risk. The section also includes a high-level shaft infrastructure optimisation exercise. As an overall conclusion, it was found that the biggest risk associated with mining could be to extract the orebody sub economically. Some ore bodies could yield double the return that they intend to extract. In order for that to happen, the extraction program should undergo some form of optimisation. This will ensure that the optimal volume, cut-off, selectivity and efficiencies are met. There is no greater risk than to mine an ore body out without making an optimal profit. We are in mining to make money! Cash is king!