Emerging risks for the mining industry
We are in an era when the collection and processing of data drives businesses and is widely used to develop enterprise growth strategies. The likes of Google and Amazon have built their business models on their ability to collect and process vast quantities of our personal data to generate prodigious profits, primarily through sales advertising. Organisations such as the now defunct data brokerage firm Cambridge Analytica have developed clever ways to process harvested personal data to shape election campaign strategies, among other things. Clearly, there is huge potential value in data harvesting and a key to success is to know how to process that data to the benefit of the user.
According to McKinsey & Company: “Less than 1% of available data generated by the mining industry is being used.”
McKinsey went on to say that: “By unlocking the value of this data, the (mining) industry could save $100 billion in cumulative maintenance costs by 2025.” 1
If this is true of mine maintenance, how much more could be saved in other parts of the industry by using the data generated more effectively?
Unquestionably the mining industry generates large amounts of data, albeit not on the same scale as Google and Amazon. So the question is: what is the industry doing with all these potential riches?
In the past, the amount of data collected was limited by the ability to store that data and turn it into something useful. A lot of thought was therefore put into (and still is being put into) deciding what data it is really necessary to collect for managing operational processes and reporting. Advances in data storage and processing technology have turned our approach to data collection on its head; it’s now the case that instead of being highly selective, we are in a position to collect as much data as possible.
We can store vast amounts of data safely and cheaply; with continually improving ways to process it, it’s possible that seemingly useless data collected today may be converted to something of real value in the future. Another important factor, not just for the mining industry, is that the miniaturization of electronic components enables us to install data collecting devices in places that were previously difficult or impossible.
Digitization - meeting the need for real time accurate data Traditionally, information from different parts of a mining operation has been used to generate reports which are mostly retrospective and therefore of limited use to shape operational and business decision making. But in an increasingly interconnected and globalised world, industry and commerce need real time access to accurate data, and this is provided by digitization.
New opportunities Digitization is the process of converting data into a computer-readable format. Once data is in a usable format, the ability to collect, store and process that data opens up a vast field of new opportunities. On the operational side these include:
On a broader front, digitization is providing ways to support business decision making as well as driving innovation and process streamlining. Importantly, through the use of interactive whole-of-enterprise modelling, digitization is providing a way to challenge existing business models and to unlock new revenue generating and value-adding opportunities. So how is digitization being used to transform business models?
How data usage is driving change The development of digitization has gone hand in hand with our ability to store immense amounts of data cheaply which, with the help of miniaturization, enables data to be collected in ways that were previously difficult or impossible. Miniaturization, combined with improvements in component and instrumentation robustness and reliability, enable the installation of data gathering devices in places such as rotating machinery as well as electrical and power generating equipment.
Miniaturization enables sophisticated monitoring equipment to be mounted on remote terrestrial, submarine and airborne devices for observance of everything from human activity to environmental degradation. It has also enabled the development of lightweight, wearable personal devices which can be used by underground mine workers to:
It might be said that the challenges of storing and rapidly accessing data have encouraged the development of the digital cloud where entire operating systems and vast amounts of data can be stored. In part this development has been motivated by the desire to not overload personal devices with data and instead provide them with greater functionality as operating devices.
However, for cloud storage to be effective users need a secure and reliable way to access their data, and no one else’s. Unfortunately, as fast as industries have developed to combat cyber malfeasance, so an even larger number of talented amateurs, criminal organisations and even governments have been busily engaged in finding ways to breach that expensively-provided security.
Cloud security? Few organisations have the resources to be truly proactive so endeavours to defeat cyber criminality have to a large extent been reactive. This raises the question of cloud security; while we have been assured that sophisticated encryption and other measures have been implemented to ensure the security of our cloud stored data, it is probably only a matter of time before someone does find a way to defeat those defences to illegally access that data.
Being able to collect and securely store large amounts of data is one thing but it remains just data unless ways are found to turn it into something useful. This need continues to drive the creation of intelligent software and the development of artificial intelligence (AI) applications. These are evolving rapidly and have the ability to create something akin to a digital nervous system, in which all parts of a business are inter-connected and data can travel at high speed around the system to be used wherever needed.
Remote operational centres Digitization and the concept of a digital nervous system have enabled a number of mining companies to build remote operational control centres such as that developed by a major global miner to remotely manage its Western Australian iron ore mining operations. Another example is a Latin American copper mining company which operates a state-of-the-art central control room for its operations at a mine in Chile. Situated about 50km by road from the mining complex in the Andes, mining and mineral processing operations are monitored in real time. Skilled operators sitting in front of TV screens remotely control the movement of ore from draw points located deep inside the orebody, through the underground transport system to the concentrator. Others remotely control ore flow through the mineral processing plant and can adjust process parameters for changes in feed rate, ore characteristics and equipment availability.
Continuing adjustments The constant exchange of key operating data between remote controllers ensures that the operation can be continually adjusted and optimised. For example, if an underground ore drawpoint becomes blocked, the remote operator can move the loader to another drawpoint or even another part of the mine. This move may result in increased loader haul distances and cycle times, which could be compensated by making changes to other loader dispositions elsewhere. Moreover, the move may result in changes in the type and grade of ore going to the plant, requiring plant operating parameters to be adjusted.
Alerting maintenance crews The digital nervous system can also react to equipment breakdowns by alerting maintenance crews, re-directing mobile equipment and re-routing ore flows to minimize production interruption. In a less digitally connected operation, it might take several hours to implement the changes required. With a digital nervous system, the necessary operational adjustments can be made almost immediately.
Reporting in real time A key aspect of the digital nervous system is the ability to report on operating conditions and key performance indicators in real time and online. This is not only useful for operators but also enables operations management to keep track of what’s going on in their area at any time of the day and from almost anywhere. By adding information about, for example, final product shipments, stockpile levels, metal prices, exchange rates and even share prices, senior management up to board level can track the health of the enterprise directly without having to wait for a retrospective monthly report.
Improving transparency and confidence Real time reporting of key performance indicators to regulators and stakeholders could improve transparency and confidence in an enterprise’s management. Moreover, in the case of tailings storage facilities, sharing key retaining wall condition monitoring data with local communities could help promote transparency, build trust and establish a Social Licence to Operate (SLO).
It should be pointed out, however, that sharing information in real time about an operation may challenge conventional wisdom and be seen by some as unnecessarily displaying the company’s dirty laundry. Nevertheless, following recent major tailings dam disasters it has been suggested that local communities would benefit from being kept informed about the dam operator’s efforts to manage dam safety. The digital era has made this possible but providing local communities with dam monitoring data is not a simple panacea for overcoming initial scepticism and building trust; regular consultation and honest face-to-face dialogue will also be needed.
Predictive maintenance In the past, maintenance planning and scheduling have tended to be retrospective and largely based on historical performance and manufacturers’ specified maintenance/service intervals. Digitization has afforded miners with the ability to collect and store data about such issues as equipment breakdown frequency and causes, times and resources to affect repairs, life of components, oil and gas analyses, vibration and temperatures. With access to this information, asset management has moved increasingly towards predictive maintenance, with the result that assets are being used more effectively. As implied in the introduction, only a small part of the data generated from maintenance is being used, so there remains significant scope for improving maintenance and unlocking value that would otherwise be lost.
Autonomous haul trucks Over the last decade very large autonomous haul trucks, mostly in the 280 – 360 ton payload class, have become an economic reality, largely as the result of collaboration between a number of equipment manufacturers and some of the major mining companies. These companies have visions about the mines of the future and have collectively invested hundreds of millions of dollars to develop the technology to enable un-manned equipment to be operated safely and economically.
Now, after over 10 years of prototyping, steady development and full-scale operation, operators of very large autonomous haul trucks are reporting up to 30% increases in tyre life, smoother acceleration and deceleration resulting in less shock loading on transmissions and suspension units and improved fuel efficiency.
Another obvious benefit is that autonomous vehicles do not have to stop for shift changes or for meal, rest and comfort breaks. By removing operators from vehicles, the number of people exposed to the hostile mine environment is reduced and operator error and the consequences of operator fatigue are eliminated. However, some machine-human interface actions will remain inevitable, for example during maintenance, periodic servicing and vehicle recovery. Importantly, because there is no on-board human operator to take corrective action should a control system fail, those systems must be robust, failsafe and have levels of redundancy and reliability approaching that of the aerospace industry.
Single, unified control centre One benefit of digitization that goes beyond purely operational control aspects is in the way enterprises assess their performance and analyse how they operate. At the mine operating level, digitization has facilitated the move to replace several separate control centres (each of which shares only limited amounts of information with the others) with a single, integrated operations control centre.
Having a single, unified control centre which collects and integrates data from all parts of an operation provides line managers and operators with a means to immediately see how a change in one area impacts others. By extending the scope of data collection to include, for example, costs, commodity prices, market trends, competitor production and environmental data, it would be possible to build an interactive holistic enterprise model.
Simulations By adding information on such things as the cost of capital, loan structures, labour productivity data, tax, political information and using intelligent software to interrogate this mass of data, the enterprise model could then be used to carry out simulations. These would test the effect on the business of changes to any one or multiple parameters and so determine the robustness and sustainability of the enterprise. This would also drive changes in the way a business sees itself, how it operates and, importantly, would help identify the need for new employee skills.
As we’ve seen, digitization clearly offers significant benefits to miners but of course it’s also important to understand the real and latent risks involved.
Attitudinal risk Implementing digital solutions and systems requires a mind-set that recognises that the way of working will change. Work practice changes will have implications for skills requirements which will drive recruitment and training efforts. At a fundamental level, individuals will be concerned for their jobs and the prospect of job losses resulting from implementation of digital solutions will almost certainly be resisted by unions.
Negative past experience of cost delays and overruns Past experience of implementing new management systems has not always been good and some enterprises have experienced near crippling delays and cost overruns when transitioning to a new “all bells and whistles” system. It would not be surprising if some C-suite conservatism influences the rate at which digital solutions are introduced into an enterprise. After all, senior managers, whose job it is to protect shareholders returns, will be concerned from past experience that an expensive digital solution may take longer to implement and not deliver the expected benefits in the predicted timeframe. As digital systems become more complex, implementation will become equally complex and the negative experiences of the past can be expected to be repeated in future.
Increasing cyber risk As an organisation becomes increasingly reliant on its ability to collect, process and store electronic data, so cyber risk increases. Cyber risk is not only the risk associated with a malicious intrusion and breach of the enterprise’s data security measures; it also includes risks associated with reliance on software that may not be fully developed before being implemented or is poorly suited to the application. As a simplistic example, imagine a company that introduces a new software package that it expects to simplify payrolling but then discovers employees’ withholding tax or pension contributions are incorrectly calculated or paid to the wrong bank accounts.
Training & up-skilling The digitization era is driving some fundamental changes in the way people work. For example, automation and automatization are replacing skilled and semi-skilled equipment operators. This is all very well in economically developed societies such as in Australia, Europe or North America where alternative employment opportunities exist. However, automation may not be so welcome in less developed countries where a major reason for encouraging mining investment is job creation. Upskilling also means that even in economically developed societies, operators whose work entailed the skilled, hands-on operation of equipment and machinery will have to learn new skills or risk becoming redundant.
As digital systems get larger and more complex, there is also a risk that fewer users will sufficiently understand the systems or the activities they are simulating to recognise when their output is incorrect. The old axiom of “garbage in, garbage out” still applies and there is a possibility that results will not be questioned just because they are computer generated. Enterprise models which are likely to be large and complex, particularly those used to motivate organisational change, should therefore be thoroughly tested and calibrated before being used in earnest.
Subordinating experience There is a risk that digital solutions will subordinate the importance of experience. The use of computers and smart, hand-held devices in the workplace is something that Millennials will adapt to easily; older workers perhaps less so. However, experienced plant operators, engineers and production supervisors still have a role in ensuring digital solutions are practical and do not produce unintended consequences. An experienced production or project manager will have “been there and done that” and will have dealt at first-hand with the sort of practical problems and mistakes that do occur. That experience should be used to assist the computer specialists who develop analytical software, control systems and complex business models to ensure what they do accurately reflects reality. Therefore, until Artificial Intelligence completely takes over our lives, first-hand operating experience will remain a valuable but increasingly scarce commodity.
The threats As with anything that offers benefits, there are also downside risks. Organisations will inevitably become increasingly dependent on being able to safely store and access ever larger amounts of their own or their clients’ data. The consequences of unauthorised access or even loss of access to this data could potentially have business destroying consequences, which is why cyber security is probably the biggest single risk facing a digital data-rich enterprise. There is also a question of cloud security.
Process automation will require employees to be re-trained and new, suitably qualified individuals recruited to fill skills gaps. Automation will almost inevitably result in some positions becoming redundant, with all the associated political and social implications.
Implementation of digital solutions will require visionary leadership and changes in established mindsets. However, there will remain a risk that implementation of new digital systems may prove more complex than anticipated, resulting in cost and schedule overruns.
The adage of “garbage in, garbage out” remains true and there is a risk that as digital systems get bigger and more complex, fewer people in the user’s organisation will understand the system sufficiently to recognise when it’s producing garbage. Until recently, the traditionally conservative mining industry has been rather slow to adopt new digital technologies, with a tendency to rely instead on tried and tested processes. This has been changing and the rate of change is increasing as more digital riches are discovered.
The opportunities The ability to convert data into computer readable format for processing has had an enormous transformative influence on the way the mining industry works and how its future is being shaped. For example, central control rooms are transforming how remote operations are managed.
The creation of what amount to digital nervous systems enables information to be collected and shared in real time right across an enterprise. This is not only a powerful management tool at the operating level but also provides senior management with the ability to log in and see how the business is performing from anywhere that has an internet connection.
A digital nervous system can provide the data to build holistic, whole-of-enterprise business models with the ability to simulate and analyse the effects of changing operational conditions and external influences. The corollary to this is that business models can be stress tested and modified very much more quickly than in the past. Furthermore, the ability to process large amounts of data quickly provides opportunities to identify behavioural trends, be they in operating, maintenance, cost or safety performance. Having identified these behaviours, they can be modified to maximize efficiency or even define new ways of working to unlock previously hidden value.
Don Hunter is a member of the Willis Towers Watson Engineering Risk Management team specifically responsible in the Latin American region, for conducting risk control surveys of clients’ mines and production facilities.
1 https://www.dingo.com/Dingo/media/img/Page%20Hero%20Images/Utilising-Digital-Enablement-for-Increased-Productivity_DINGO-CEO-Paul-Higgins-for-the-Future-of-Mining.pdf.
