A revealing yet surprising statistic that emerged during a recent presentation with one of our technology partners is that less than 1% of all data generated by devices within the industrial space is actually used.
There is a feeling, or perhaps inherent fear, among industrial operators that the step to Industry 4.0 requires large-scale overhaul of control and automation infrastructure. But, as this statistic illustrates, it’s in fact mostly the case that the data already exists – our challenge rather is to implement systems that enable us to turn this data into actionable information.
Our belief is that, unless plant equipment is exceptionally antiquated, significantly more value can be extracted from control and automation devices that can deliver an array of advantages to management structures across a ‘Connected Mine’.
And why wouldn’t management bodies want to operate a system that extracts maximum value from existing assets? Why wouldn’t managers want to exploit these assets to i) gain greater predictability of their business performance; ii) make better capital investment decisions, and the ability to rank those choices; and iii) implement the most effective and least adverse cost-cutting initiatives they can, when they need to?
These are pretty critical elements for mining companies.
In order to help mining companies realise the latent advantages their equipment holds, our primary challenge is to overcome the misconception that the transition to Industry 4.0 is an overly daunting task. For many, the level of sophistication, as a mine or plant-wide infrastructure, seems such a complex capital commitment: where does one even start? How does one even begin to implement such an advanced, intelligent device communication architecture? Does the return on investment in the medium term justify such an overhaul of plant processes that, admittedly, are working and giving us what we need right now?
But if less than 1% of data generated by devices within industry is actually being used, the chances are that the challenge is not about new equipment as much as it is about better utilisation of existing equipment through smart engineering. It is about implementing a system that efficiently aggregates all this data to create some level of analytics and usable information, and ensuring this information reaches a level high enough in the organisation so that executive decisions can be made based on empirically accurate, transparent, real-time equipment and process statuses.
Leveraging information for greater predictability
At an operational level, predictive maintenance that averts a shortfall in production or allows contingency plans to be made – such as short-term stockpiling – can mitigate any negative impact and allow better predictability at an organisational level.
For CEOs and shareholders, this means that performance and target guidance can be closer to actual profitability for the period. It allows guidance to be more flexible, both to market factors, and to internal business mechanisms.
Enhanced accuracy as to the predictability of production and profitability is a valuable advantage for any listed company, as real value can be added for the shareholders.
Leveraging information to make better investment decisions
Sound capital investment decisions around where to spend, how much to spend, and how to rank this spend need to be based on accurate information received directly from mining operations. Without accurate, indisputable empirical information derived from processes, there is more guesswork; more subjectivity and anecdotal input; and more luck involved in making these big decisions.
For a diversified miner with a range of operations across a range of minerals and perhaps multiple geographies, it becomes a question of not just where to invest capital, but how to rank capital, to prioritise investments, that becomes critical.
Mine-wide control architectures that quantify the status of systems is the most accurate basis upon which to make sound capital investment decisions.
Leveraging information to ensure better cost-cutting initiatives
Aggregating data into a level of analytics that highlights where costs can be cut while limiting any adverse effects on productivity; the amount they can be cut by; for how long they can be cut; as well as how to rank cost-cutting avenues, minimises guesswork, and ensures companies don’t cut expenses they’d have been better off leaving alone, can limit any adverse effects on productivity.
This kind of analytics is also critical in helping miners become leaner by trimming inefficient or wasteful processes and expenditures and gaining maximum value from their assets.
Conclusion
South Africa’s well developed, mature mining environment means that there is a high level of sophisticated control equipment being used across the industry. Our objective is to help mining companies make the best business decisions they can by utilising the data provided by their control equipment to the fullest extent.
For more information contact Michelle Junius, Rockwell Automation, +27 (0)11 654 9700, [email protected], www.rockwellautomation.co.za
| Tel: | +27 11 654 9700 |
| Email: | [email protected] |
| www: | www.rockwellautomation.co.za |
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