How do you account for uncertainty and variability in mining economics and finance?

1 Sources of uncertainty and variability

Uncertainty and variability are inherent in mining, as they arise from geological, technical, environmental, social, market, and political conditions. Some of these factors are stochastic, meaning they are unpredictable and random, while others are deterministic, meaning they are known or can be estimated. For example, ore grades, recoveries, and costs are stochastic, while reserves, production rates, and taxes are deterministic. Uncertainty and variability can affect both the inputs and outputs of your project, creating risks and opportunities.

2 Methods of uncertainty and variability analysis

To account for uncertainty and variability, you need to perform various types of analysis that can help you quantify and communicate the range of possible outcomes and the likelihood of achieving them. Sensitivity analysis can help identify the key drivers and critical assumptions of a project, as well as the break-even points and thresholds. Scenario analysis can assess the impact of alternative strategies, contingencies, and external events on the project, as well as the trade-offs and synergies among them. Monte Carlo simulation generates a large number of possible outcomes by randomly sampling input variables from their probability distributions. This method can estimate the expected value, variance, and confidence intervals of a project, as well as the probability of achieving certain targets or constraints.

3 Tools for uncertainty and variability analysis

To perform uncertainty and variability analysis, you need to use appropriate tools that can handle the complexity and uncertainty of your project data and models. Spreadsheet software allows you to create and manipulate project models using formulas, functions, and data tables, as well as perform basic sensitivity and scenario analysis. Simulation software can be used to create and run project models using graphical interfaces, programming languages, or pre-defined templates; it also enables advanced sensitivity, scenario, and Monte Carlo analysis. Decision support software allows for the integration and analysis of project models using various methods and criteria, plus the optimization, ranking, and selection of the best alternatives for your project. However, each tool may have limitations in terms of data sets, calculations, technical skills, data input, assumptions, and judgment.

4 Benefits of uncertainty and variability analysis

Accounting for uncertainty and variability in mining economics and finance can offer several advantages. It can help to improve project evaluation and management by reducing bias, uncertainty, and errors in estimates and forecasts. Moreover, it can enhance project communication and presentation by providing clear, transparent, and robust information to back up decisions and recommendations. Additionally, accounting for uncertainty can increase project value and performance by identifying and exploiting opportunities, mitigating risks, and adapting to changing conditions.

5 Challenges of uncertainty and variability analysis

Accounting for uncertainty and variability in mining economics and finance has many benefits, however, it can also present some challenges. Collecting reliable and relevant data and information for project variables and parameters is one such challenge. Additionally, choosing and applying the most suitable and feasible methods and tools for project analysis can be difficult. Lastly, interpreting and using the results of the project analysis in a meaningful and actionable way is another hurdle to overcome.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?