Armed With The Right Tools, Mining And Metals Projects Have Successful Outcomes
High risk and high reward scenarios are inherent to the metals and mining industry. Projects are typically on a multibillion dollar scale, with staged layers of complexity that each brings their own challenges. How can executives and decision-makers in this field effectively manage the threats of failure and set-backs and make truly informed decisions? Often, decisions are made based on subjective experience and expert opinions. While these elements have value,taken alone they are insufficient for handling the challenges of modern mining: delays, budget overruns, and safety issues. Quantitative, systematic approaches to decision-making and risk management are necessary. Here are just a few tips on how such strategies can be used to improve management of mining and metals projects.
Take New Projects on a Test Run with Simulation Models
Pilot projects in any industry are pricy, but metal refineries deal with particularly high costs. Take the example of MetMex Penoles the world’s largest refiner of silver,and Mexico's largest refiner of gold. MetMex wanted to avoid high pilot project costs and high numbers of trial runs. Naturally, any pilot project testing innovations involving silver or gold are costly, as any precious metal that is lost or ruined in the process carries a high price with it. Thus, they developed a quantitative method for process optimization that would cut down on the need for multiple trial runs on the metals themselves.
To do this, MetMex uses a Six Sigma Design of Experiments model in Microsoft Excel that incorporates Monte Carlo simulation to create simulated trial runs of new manufacturing processes. This allows engineers to simulate changes in process design and answer difficult questions without actually running expensive trials of the process. The company uses actual data from previous pilot projects as inputs to this mathematical model, along with precise specifications and tolerances of its manufacturing equipment, assorted physical operations, random processing errors, and cost analyses. Precise pieces of data help create a more accurate distribution of outcomes.
Pinpoint the Most Pernicious Risks with Sensitivity Analysis
In these simulated test runs, MetMex Penoles also wanted a way to identify which of the many different types of variables had the greatest
Quantify Project Potential with Monte Carlo-based Real Options Analysis
Among the most important decisions mining companies make is which new projects to invest in. There are always multiple choices for where to open new mines,and companies must compare and evaluate different potential projects in the face of highly uncertain information. A bad decision could waste billions.
One effective method for evaluating investment options in a portfolio is real option analysis. The process assigns a value to various investment options while considering risk. Unfortunately, many frameworks rely on highly technical mathematical and economic analyses of investment options and portfolio construction, limiting the technique’s potential.
But it doesn’t have to be that way. Simplified real options frameworks based on the Datar Mathews Method, developed by the Boeing Corporation, have been used by companies such as mining giant Anglo American. These analyses incorporate exogenous uncertainty as a source of project potential,and endogenous uncertainty as a source of project risk. Monte Carlo simulation is also used to quantify the uncertainty inherent in each project,and provide probabilistic ranges of possible values for their outcomes. Using these techniques, mining companies can get better insights into their capital portfolios than standard discounted cash flow methods can provide.
Use Decision Trees for Complex Engineering Problems
Unfortunately, the mining and metals industry deals with serious safety risks. Mining is considered one of the world’s most dangerous occupations, with accidents often resulting in loss of life. Thus, an analyzing, evaluating, and mitigating safety risk is paramount for decision makers in this field.
One of the most notorious mining accidents occurred on August 5, 2010 in the San Jose mine in northern Chile. The mine collapsed,trapping 33 miners 700 meters below the ground. The Chilean government reached out to dozens of experts to help deal with the crisis, including the engineering consultancy Metaproject.
Manuel Viera, the CEO and managing partner of Metaproject, used a model-based approach to determine the best way to rescue the miners that would subject them to the least risk. Viera used a decision support tool known as a decision tree to map out the various rescue alternatives,both from a technical and economic perspective. Metaproject also factored the key risks into their calculations,such as the risk of landslides,failure of drilling machines, and the physical and mental health of the miners due to prolonged stays below ground.
Using the decision tree approach, Metaproject developed a matrix of statistical results for each rescue option, making it possible to determine, for example, that for some of the drilling options it was feasible to move the miners in two stages, but for others it was not, due to logistical problems.
Thanks in large part to this thorough and quantitative approach, Meta project was able to recommend the best method of saving the 33 miners,and the rescue operation was carried out successfully.
The mining and metals industry stands to gain a great deal from quantitative risk management. Whether in greater savings, improved pilot projects,or the safety of employees, employing mathematically sound tools to any facet of the business in question can help metal and mining firms succeed.