High risk and high reward outlines are built-in to the metals and mining industry. Projects are typically on a multi-billion-dollar scale, with staged layers of complexity that everyone brings their own challenges. How can executives and decision-makers during this field effectively manage the threats of failure and set-backs and make truly informed decisions? Often, decisions are made supported subjective experience and expert opinions. While these elements have value, taken alone they're insufficient for handling the challenges of recent mining: delays, budget overruns, and questions of safety. Quantitative, systematic approaches to decision-making and risk management are necessary. Here are just a couple of recommendations on how such strategies are often wont to improve the 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 affect particularly high costs. Take the instance of Met-Mex Peñoles—the world’s largest refiner of silver, and Mexico’s largest refiner of gold. Met-Mex wanted to avoid high pilot program costs and high numbers of the trial. Naturally, any pilot program testing innovations involving silver or gold are costly, as any value that's lost or ruined within the process carries a high price with it. Thus, they developed a quantitative method for process optimization that might hamper on the necessity for multiple trials runs on the metals themselves.
To do this, Met-Mex uses a Six Sigma Design of Experiments model in Microsoft Excel that comes with a Monte Carlo duplicate to make simulated trial runs of the newest manufacturing processes. This enables engineers to simulate changes in process design and answer difficult questions without actually running expensive trials of the method. The corporate utilizes actual data from past pilot projects as inputs to the recent mathematical model, alongside precise specifications and tolerances of its manufacturing devices, assorted physical operations, random processing errors, and price analyses. Precise pieces of knowledge help create a more accurate distribution of outcomes.
Pinpoint the foremost Pernicious Risks with Sensitivity Analysis
In these simulated test runs, Met-Mex Peñoles also wanted how to spot which of the various differing types of variables had the best influence on the result of the simulated test run.
Thus, they turned to a computational technique referred to as “What- If” sensitivity analysis to pinpoint which variables have the foremost impact.