Eltirus founder Steve Franklin writes on how the industry has never had greater access to data than we do now and yet, in many cases, quarry operators either don’t use it at all or don’t make full use of it – why is this and what can be done about it?
Any piece of data generally represents something that has happened (or is going to happen) in the physical universe. Sometimes they denote the quantity of something e.g., 200 litres of oil, and in other cases, they denote a quantity relative to another quantity, e.g., 50 litres of fuel consumed in one hour.
If you think about it, you notice there is something very important that helps us understand what these numbers mean and that is their context. By this I mean that if I spoke to you about 200 units of liquid without mentioning the units involved (litres, millilitres, gallons etc.) you would have no real understanding of what I was talking about. The moment I mention that the quantity is 200 litres, most people would be able to get a sense of what that looked like, if it was a lot or a little and what its relevance was to the discussion.
The context of data is as important as the data itself if we are to truly make any sense of it.
A practical example
Some years ago, I was asked to conduct a load and haul review of a large quarry. The site had a mixed fleet of 50-60 tonne payload trucks of varying ages and manufacture.
In discussions with the Operations Manager, he noted that he received a report from the dealer of one of the truck brands each month. The report showed the trucks were generally carrying a 50-52 tonne payload. I asked him if he was happy with this result. His answer was interesting: “They are 50 tonne trucks, so that seems about right.”
As many of you will know, two things have happened in relation to truck payloads. The first is that the rated payload of the original Cat 773 (45.4 tonnes) has risen to 56 tonnes for the current Cat 773G, a 23 per cent increase over time.
Secondly, whilst we tend to think of truck payloads in terms of the numbers shown above, the reality is that actual payload is a function of Gross Vehicle Mass, minus Tare. In other words, it depends on what you fit to the truck – e.g., body liners, fire suppression etc. all take away from the payload the truck is actually rated to carry (based on configuration).
In the actual example considered, the corrected payload for the trucks under consideration was actually 58 tonnes (they were 60 tonne class trucks with heavy body liners), meaning that the trucks were underloaded by some 11-14 per cent each haul cycle.
Making the most of data
You can immediately see, that if we had some way to show not only the actual data, but what the data should be, we would have a much easier way to grasp it – even if it was a simple as writing say, 52/58.
You could of course say it was 7 per cent underloaded, however this still doesn’t really help unless you know what the correct payload is as you wouldn’t know how much to correct by.
If we extend our example further and say that the underload is 6 tonnes, we could then take steps to work out if the loading tool operator needed to put an additional pass on the truck or improve their bucket fill factor to perform the task in the same number of passes.
Note of course, that we don’t know why the number is out of range – there could be many reasons from poor fragmentation, machine health or lack of operator skill.
The point I am making is that a number, representing an activity on the site, compared to an assessment of what that number should be, can be used as a guide to investigate non-optimum performance.
A further point is that this type of approach helps to determine where management focus is required. By this I mean that if four out of five indicators are within range of their target numbers, the fifth is where a smart manager would put their attention. By this approach, a busy manager can manage by exception.
The broader context
Whilst a target (as noted above) can help us better understand what is happening in our business, sometimes this is not enough. By way of example, we recently conducted a review of a site where there was significant haul trucks waiting at the primary crusher hopper.
It was considered “significant” because it was higher than the figure that had been determined as acceptable, however the problem was that to understand the waiting time, we had to understand not only what was happening with the trucks, but also what was happening with the fixed plant.
Whilst we had access to the truck data, the fixed plant data was not easy to access and so the only way it could be resolved was to sit in the control room and note what was happening with both fixed and mobile plant (and who’s got time for that?).
The reality, of course, is that busy supervisors and managers don’t have time to sit for days looking at truck movements and crusher performance and so the opportunity for improvement is lost.
What we need is a broader context, that is, the ability to not only see a datum in relation to a target, but to then see data from different systems and how these inter-relate.
Sounds simple, doesn’t it? The reality, however, is that this approach is nowhere near as simple as it should be for three reasons:
There are few (if any) systems that are agnostic (can communicate with any other vendors systems).
Vendors won’t open their systems to external parties (often to keep the customer completely within their ecosystem).
Security and access concerns from IT departments (valid but needs to be balanced with operational requirements).
Bring it together
We have been broadly researching in this area for some two years and investigating not only what our clients are looking for but how these issues can be resolved.
We expect to see systems that can both securely and broadly extract data out of other systems for contextualisation and decision making, and use this to form the basis of data analytical approaches, such as machine learning and Artificial Intelligence (AI). An example is the Eltirus Enable system. Such a system would work something like the infographic shown above.
In this example, we see the integration of inputs from a range of different API’s, spreadsheets and manual data entry through forms on ruggedised tablets, validated and brought together with plans and budgets to provide a contextualisation of the data, ultimately resulting in actional data and improved outcomes.
Note that the range of inputs is growing rapidly, particularly as we see more and more environmental type sensor systems (wind, dust, moisture, noise etc) and an ever-increasing number of performance systems such as the Orica FRAGTrack fragmentation analysis system, Propeller DirtMate, etc.
One thing worth noting is the need for validation of data. Once you start looking at the same data from different sources, the need for this becomes even more apparent.
By way of a simple example, fixed plant output minus sales should equal the stocks produced in the period. However, for a variety of reasons, it rarely does.
As a last note, you will see that I have deliberately identified “Plans” and “Budgets” in the infographic. I have done so to ensure that there is a clear understanding that it is not enough to just have a budget by which performance is to be judged.
There also needs to be a plan as to “how” the budget is going to be enacted. In essence, I am talking about having both financial and non-financial metrics to measure against.
For example, if we said that the production of concrete aggregate was going to be 0.75 million tonnes this year, you would need to have a sense of where the material would come from in the quarry, what stripping and development might be required, the crushing yield for this material type and the equipment and resources available.
From this we could then create a plan that detailed exactly how we were going to move this material in the most efficient way and the metrics we would use to measure actual performance (and correct where necessary).
If we say that we need to improve our business, whether it be in terms of maintaining/improving margin during an inflationary period or working more broadly towards decarbonisation of our businesses, technology is a vital part of those processes.
Likewise, if we are to attract new people to our industry, we need to show that we are a savvy, technical and contributing industry – the old ways won’t cut it.
To make the most of that technology, we need to become more effective at collecting and making sense of the data these systems produce and attracting and training the next generation who can make the most of it for us – the future is bright! •
This article was contributed by Eltirus founder Steve Franklin.