In the midst of ongoing fears of a global mining slowdown,Australia’s natural resources industry must rapidly upscale the sophistication of its data modelling if it wants to remain competitive on a global scale.
Rigorous data analysis and forecasting methodologies offer miners the opportunity to dramatically improve risk management; and achieve optimal efficiencies across the entire pit-to-port process. However, the industry must first recognise the need to refresh their existing technologies to gain more accurate and meaningful insights into their operations.
Earlier this year, MathWorks Australia published a case study detailing how real options analysis – a forecasting methodology which draws on techniques from the financial sector – could be applied to gauge potential impacts of iron ore prices on the future value of a mine. The same approach, however, can be applied to a range of mining processes and aspects going far beyond commodity prices.
That promises industry leaders a hitherto-untapped source of vast competitive advantage, and a way to better prepare for the risk of a protracted slowdown –if they can muster sufficient support from within their ranks.
Assessing the Options
Real options analysis uses historical data to fashion forecasts of scenarios that may play out in the future. These are probabilities, not predictions – they serve to identify how a change in one area is likely to result in flow-on changes to others. Unsurprisingly, the methodology is used to quantify and plan for risk (particularly in the financial sector),allowing business leaders to develop more effective responses to address each potential scenario.
In our own mining economics study, for example, we used MathWorks’ MATLAB tool to model two different scenarios: one in which iron ore prices remain stable, and one in which they experience a steady decline. Using price data from previous years, we were able to extrapolate where and when mines will stay profitable in each of the scenarios – potentially allowing investors to better plan which and how many mines they fund. We weren’t predicting the future, but instead were helping putative investors gain greater insight into the risks of different situations.
Real options analysis isn’t only applicable to spot prices or financial markets. In fact, it can be applied to any process which generates regular, reliable data – including almost every stage from pit to port in the mining process. Natural resources players who successfully embrace this methodology stand to significantly boost their productivity and profits through greater insight into the risks and opportunities that may affect them and their competitors alike.
New Resources Needed
A growing number of miners are adopting rigorous data modelling within their businesses – but there’s still a long way to go.
Many natural resources firms who we speak to are still working off spreadsheets and tables of average values, which are not only cumbersome to operate but are far less precise than dedicated modelling tools. Even the most advanced miners tend to have departments or divisions which lag behind the rest in their adoption of methodologies like real options analysis.
As global competition intensifies and demand continues to fluctuate, Australian miners will need to focus on greater efficiency and productivity if they want to maintain their profits. And as pressure to optimise existing resources grows, miners will be increasingly attentive to how they manage risk and return. Rigorous data modelling can help with this significantly, but only if the industry acts now to embrace it throughout how they operate and do business.
Miners must first establish basic guidelines and processes to how they collect and control the quality of relevant data. Some of this –such as spot prices or contract volumes – will be readily available, but new modes of data collection may be necessary when it comes to more process-oriented aspects (like transport times, for example). Once the data’sin place, it needs to be run through modelling software, which requires technical and analytical skills which might not be native to the business – potentially prompting new hires or relationships with third-party modelling firms.
A few years ago, Australia’s natural resources industry could afford to roll with the punches of the market. That luxury is no longer available to many miners, and efficiency is fast becoming order of the day for even the bigger players in town. By applying modelling techniques to data from the past, the industry can more accurately evaluate – and thus plan for –different scenarios in the future. Those who don’t will struggle to compete with smarter, more agile firms who know how to use their data effectively. This is a risk you don’t need a model to predict.