University of Witwatersrand Uses @RISK to Give Mining Engineering Students Practical Financial Analysis Experience
- Industry: Academic, Mining
- Product(s): @RISK
- Application: Mining Engineering
The University of Witwatersrand is using Palisade’s @RISK to offer mining engineering students practical guidance and financial analysis experience, as well as conduct engineering departmental research on topical issues such as the impact of South African mineral resource royalty tax on cut-off grades for Witwatersrand gold deposits.
It’s very important to give students a real-world feel of how mining operations are managed financially – before they head out into the professional world... @RISK is an essential tool for both our undergraduate and post graduate programmes. In any one year, we have anywhere between 700 – 900 students in the School of Mining Engineering. All our students are exposed to Monte Carlo simulation via @RISK.Professor RCA Minnitt, School of Mining Engineering, University of Witswatersrand
The School of Mining Engineering, within the Faculty of Engineering and the Built Environment at Johannesburg’s University of Witwatersrand in South Africa is recognised as one of the top mining engineering schools in the world. Mining engineers play a key role in the planning, exploitation, and excavation of mineral resources that are plentiful in South Africa.
In conjunction with the South African mining industry, the School has developed comprehensive programmes of undergraduate and post graduate courses designed to equip students with proficiencies and knowledge that will be required in their professional lives spanning the entire spectrum of the discipline – from technical subjects for specialist skills in mining, mineral resource management and evaluation and rock engineering; through to management competencies in evaluation techniques and fundamental mineral economic principles.
Uses @RISK for scholastic analysis
Graduates of the mining engineering courses develop careers in the higher echelons of mine management as consultant engineers, specialists and senior executives of mining houses. The School is using @RISK to give students training in and knowledge of Monte Carlo simulation.
“It’s very important to give students a real-world feel of how mining operations are managed financially – before they head out into the professional world,” explains Professor RCA Minnitt at the School of Mining Engineering in the University at of the Witswatersrand. “An understanding of Monte Carlo simulation is absolutely essential. Without this discipline’s knowledge and use, robust and credible financial analysis is not possible. @RISK is an essential tool for both our undergraduate and post graduate programmes. In any one year, we have anywhere between 700 – 900 students in the School of Mining Engineering. All our students are exposed to Monte Carlo simulation via @RISK.”
Mine Financial Evaluation is a key part of the Mine Engineering degree at both undergraduate and post graduate levels. Within this module, financial risk is a key focus area. “We want to give students the opportunity to practically undertake financial risk analysis so that they are able to gain experience in identifying the optimal solution as part of an investment procedure. This helps them think about the concepts of risk and variability,” adds Clinton Birch, Senior Lecturer who runs the Mine Financial Evaluation at the School of Mining Engineering at the University. “By using @RISK, we have made a static discussion on Monte Carlo simulation into a hands-on learning exercise.”
In addition, this year, the School is introducing the use of Monte Carlo simulation to consider the financial risk of their mine design project, an assignment that is part of the engineering courses. This is an important addition to the curriculum as it requires students to apply all the components of mine engineering to the project. Students, split into groups of 15, will create a Cash Flow and apply Monte Carlo simulation using @RISK to illustrate that a Discounted Cash Flow (i.e. value of the anticipated revenue stream from an investment on any given date) does not reflect a single figure for the Net Present Value (i.e. the difference between the present values of the future cash flows from an investment and the amount of investment), but that there is a range of possibilities because of the uncertainty associated with each of the input variables such as capital and operating costs, mining grades, and volumes.
The students will mostly use Normal, Pert and Triangular distributions for areas such as production, exchange rates, costs and production grades. These are relevant to minerals and mining industries for financial optimisation analysis.
University uses @RISK for engineering departmental research
Mining is a major driving force behind the South African economy and so the department uses @RISK to undertake numerous improvement-led, forward-looking research projects on topical mining related issues.
For example, taxation of mining companies is a burgeoning issue in South Africa. Mining companies are required to pay a royalty tax, which is applied to total mineral sales income. In fact, even if a mining operation makes a loss, mineral resource tax still has to be paid. Consequently, companies need to consider this as a cost in their cut-off calculations. The department conducted a study to establish the impact of mineral resource royalty taxes on cut-off grades for narrow, tabular gold deposits in the Witwatersrand Goldfields of the region.
The cut-off grade determines what portion of the mineral deposit can be mined economically. It takes into account the forecast price of the commodity, the expected mine recovery factor, the cost to mine the ore as well as the fixed costs for the mine. As long as the grade is higher than the break-even grade in a particular block being mined, the block will be mined profitably.
Using @RISK, the department developed a model to calculate the break-even cut-off grade value of the ore body. The study was conducted in two parts. The first part selected a single mine and ran the block list (gold grades in grams/ ton or centimeter grams/tonne; the channel width; stoping width and area of the blocks) through @RISK to determine the optimal cut-off grade to ensure either maximum profit or NPV.
The result of this exercise showed that the impact of the mineral resource royalty tax is greater on mines that are higher profitability if just considering the differences in cut-off grade. The largest impact on gross sales (due to reducing the life-of-mine) however was noted when the profitability of the mine was less resulting in mineral resource royalty tax rates between 1.5-3%.
The second exercise compared seven currently operating gold mines. The mines have different grade-tonnage curves the effect of mineral resource royalty tax differs for each one. Changes in cut-off grades varied from 0.2g/t to 1.2g/t. The increased direct revenue payable to the State in most cases was largely offset by reduced taxation opportunities due to less minable reserves and reduced mine life. The impact was generally greater when the mines were optimised for profit rather than NPV, which already reduces the life-of-mine significantly due to high-grading the mine.
The study showed that while the tax is meant to protect marginal mines from early closure, mines with higher profitability are negatively affected and the additional costs can reduce the life significantly.
“@RISK is very well suited for Monte Carlo simulation in academia,” says Birch. “We value it greatly as a modelling training tool to give our students a head start in their professional careers. But we are also using it for broader mining industry related research, which is an important activity for the department.”
“The approach that Palisade took with us was fantastic. The company went out of its way to demonstrate how best we could use @RISK for scholastic modelling. In addition, the software is easy to use and the input variables are clearly defined. The best thing is that @RISK offers a point and click approach, eliminating hours of complicated analyses. If anyone was to ask me which solution to use for Monte Carlo simulation in academia, my recommendation without a doubt would be @RISK,” Professor Minnitt concluded.