It seems that in every trade publication and on every industry web site, the pundits are expounding endlessly upon the greatness of the emerging Internet of Things (IoT). It is all over the place and is pitched as the most supreme power known to man. However, what exactly is it? Why would we want to do it at all? Is it just another passing trend or will it fade? It is all rather confusing.
Well, they say that knowledge is power. The more knowledge that you possess the better decisions you can make. All too often in the natural resource industries, decisions are made simply by relying on personal experience and gut instincts. However, divining decisions on these two human attributes can be extremely flawed and lead to dramatic failures. Experience is indeed a great teacher, but personal bias and poor memories can fool experts into thinking that this problem is identical to problems experienced in the past, ergo the same solution will fit.
Intuitively we all know that no two problems are identical so why do we believe that the solution for one would be an ideal fit for another? Sometimes the same solutions fit just enough to derive a positive outcome. But it is not ideal, a perfect fit, nor optimized, and the outcome might have been much better if we drove decisions based upon facts and figures, trends and patterns, and analytical analysis instead of human magic. Our experience and intuition are valuable tools that should contribute to the solution, but they are never perfect and must be properly filtered as a part of the decision process.
Consider a baseball player at bat. Some players have more than a decade of experience and have seen the same pitches from the same pitchers thousands of times before, yet in baseball, a successful and admired batter is consider superior and incredible with a statistical 0.300 batting average. That means getting a hit just 3 out of 10 times. Would you bet the company on a 30% chance of success? Of course not. So, why do we trust our personal experience and intuition to such a high level when they are so flawed?
We need data. Data is the starting point. As data is accumulated and combined, we can derive information. We build a contextual model from the data points and start the process to paint a picture. We need to visualize the problem and understand it better and in context to the variables the problems possesses. We assemble the pieces that answer the questions of: Who? What? When? Where? Once the information is formed and assembled into a framework, we can begin to build even more contextual awareness by asking questions of the information.
Now we can wheel the information around, by considering it from different perspectives, through diverse lenses. We adjust the variables and prioritization of the elements to form new insights. We ask the questions: How can this situation exist? How did we get here? What happened and in what order of operation did it all occur to get us to this point? Asking “How” is a very powerful tool.
At the knowledge stage we combine the primary data with other secondary data sources that help to tell the story unfolding before us. The picture grows and we see a broader landscape view of the problem.
Up to this point, everything that we have done has been reactive to the problem. To get truly on top of problems and make proper and optimized decisions, we need to ask “Why?” When we ask why, we do leverage the past and consider the expertise and experience of experts, but not just one guru, or from the unified group-think of a meeting, we leverage thousands or tens of thousands of experts via analytic reference tools and models, like IBM’s Watson. We access a knowledge base of thousands of experts recorded and documented for reference. This knowledge is clean of bias and devoid of current opinions. It is a vast library of content, history, logs, evidence, analogs, and theory, both proven and unproven. By applying algorithms to consider procedures and formulas that can shed light on an issue, we manage this knowledge base towards statistical outcomes that best fit our current situation.
In this final wisdom stage is when we become proactive and grow towards a harmonized flow of data, information, knowledge and applied wisdom to face the problem at hand. In some cases, we can even get ahead of it, we become proactive. Our contextual level is maximized along with our understanding of the problem.
Okay, so this is all good and interesting, but how does it connect to the IoT?
Well, the IoT is the technology that facilitates the source of data. It is the vast array of end points, composed of sensors, probes, readings, measurements, calculations, and facts and figures that feeds into the process. Data is the proverbial source of the Nile. And, IoT is the means to harvest this data to begin the problem solving process. So, IoT is the first step in the journey from the source of the data of the digital Nile. Just as the mighty Nile River begins as droplets of water that flow into trickles, then to streams, which combine into the river eventually meeting the ocean, so does the flow of data make a similar journey building and aggregating at each stage until it ultimately meets with the sea, meaning the pool of Big Data. The volume of data can be so vast that cloud computing is the best way to manage it.
IBM announced that it is collaborating with ARM to deliver the first unified chip to cloud enterprise class IoT platform. This will allow companies of any size to transform their businesses with embedded smart and connected technologies. Secure connectivity with ARM® mbedTM-enabled devices, will allow companies to gather, analyze and act upon huge quantities of data from all sorts of devices.
So, the Internet of Things is evolving to be a federated network, composed of both centralized and distributed processing, whereby intelligence is pushed to the edge of the network and no longer only resides at the heart of the network topology in the data centre. We run algorithms in real-time to create meaning from data in-flight and data at-rest. This derive data is shared and consumed all along the network chain. With the latest versions of low cost microcontrollers and microcomputers, from manufacturers like ARM, we can now combine data at the networks edge to derive new data, discover new results, uncover new outcomes, instantly at the device being monitored and controlled. The key strategy for IoT is to have intelligence throughout the network topology and leverage compute power ubiquitously over the network fabric. A real-time network helps to speed the decision process. It gives us visibility end to end.
In the mining, oil and gas industries, this means placing sensors and embedding devices in the machinery that is used to extract the resources. It is the first principle of autonomous mining. We can enhance safety, improve efficiency, boost profitability, and greatly improve the process for the extraction and movement of the ores, oil and gas.
By understanding how the equipment is performing, we can prevent break-downs and know that the equipment is functional and operational. We can remotely detect and control parameters to ensure uptime. We can schedule repairs and maintenance for the best time in the production schedule. We can avoid equipment blocking ramps and eliminate work stoppages. But we need visibility into the equipment performance and parameters and that means we need data. The IoT provides this requisite data, and more.
It is not just autonomous mining that needs IoT. We can pull data from a multitude of sources that collaborates with the autonomous equipment. We can download weather data, harvest data from the site related to noise, light, vibration, seismic, tremors, acidity, water, slurry ponds, leach residues from mine tailings, flows, volumes, quality, conveyors, vegetation, and terrain. We can use drones to contribute into the mix and to harvest more broad scope data by overflying the site and collecting multidimensional data from the surface. History files and maintenance logs as well as production demands and resource attributes can contribute data too. Regardless of the data source and type, it is probable that we can automate it is some way using the IoT approach and technology.
Risk is always a consideration and most believe that risks can be reduced with better situational awareness. But creating a tsunami of data can overwhelm a data centre so a holistic approach is required with an end to end upgrade of the systems to process the data and give it meaning. IoT means Big Data and we can not just focus on IoT in isolation of the Big Data challenges and issues.
Spending capital at this time with oil, gas and ore prices suffering would give pause for thought for many operators. Yet, retooling and taking costs out of the operation is best done in down economical times. IoT can and will reduce labour costs so backlash from unions and workers is a risk. While the workforce will undoubtedly be changed, a new type of employee trained in these new IoT technologies will be added to the operations. So, some workers can be retrained for the new roles and responsibilities. IoT is not just about removing workers from the workforce pool, but adding the right talent to the pool to make it all work smarter, faster and better.
The Internet of Things is not an “all or nothing” bet. It can be phased in gradually or in parts of the operations.
New ways to work such as remote mining from the surface or running in site operations off-site are possible too. Semi-automation is feasible so it can be a “crawl, walk, run” approach timed to match cash flows and ROI objectives.
The Internet of Things is real. It is coming and it will make many major and positive impacts on the mining, gas and oil industries. The smart approach may be to take a measured approach and run a proof of concept first before staging any mass roll-outs. Learning and education are still needed. Industry bench-marking and external collaboration are useful ideas to defer risk and avoid mistakes.
Traditional vendor partners are learning too and some are further down the road compared to others. So, it is vital to not get caught up in the smoke and mirrors and develop your own IoT platform based upon practical and useful outcomes. Do not fear IoT, but take a managed and measured approach, so you do not strike out swinging.
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