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The implications of industrial AI and analytics are many and varied. Lots of the companies we work with are surprised to learn some of the facts compiled below. So we thought we’d share some of our insight into how artificial intelligence is really impacting and changing industrial practices.
There's a common misconception that predictive maintenance modelling is only relevant for data scientists that already spend their time creating time to failure and root cause analysis models.
But the reality is that there's application for this kind of tech all over a business. With the right kind of user-friendly technology anyone from an operational, on the ground engineer right up to a General Manager should be able to easily and simply use a AI enabled predictive analytics software to create incredible results.
It's not silly to think of new technology as costly. Change can often be as expensive as it is important.
But we've seen time and again that implementing AI enabled tech very quickly causes it to go from a cost to a profit centre very quickly - thanks to its ability to generate outstanding ROI.
Our case studies are full of examples. In just one of them, the rapid identification of the root cause of a problem that was resulting in near constant shutdowns, the calculated expected benefit was more than $1.2m per year thanks to the reduction of maintenance costs and downtime.
Sometimes, the adoption of AI is an operational, on the ground initiative - and we've seen that work just as well as when it's championed by a digital transformation team.
In some cases, it's actually been easier and simpler, because the engineers dealing with the tech are the ones that need to truly understand and experience its benefit.
When a predictive maintenance platform allows an engineer to quickly, accurately and easily identify the root cause of a failure or the likely time to failure of a critical piece of equipment, the motivation to adopt can become much stronger and can ultimately make the entire transformation process much smoother.
" ... [after] the rapid identification of the root cause of a problem that was resulting in near constant shutdowns, the calculated expected benefit was more than $1.2m per year thanks to the reduction of maintenance costs and downtime. "
You probably haven't thought about AI as a form of green tech before. But the sustainability impacts generated by AI are far reaching and profound.
Google, for example is using it to great effect to optimise their data centres, turning to machine learning to further optimize efficiency in it's data centres, after engineers reached what they believed was the limit for how much more they could be improved. Their application of AI has resulted in an astounding 40% reduction in the amount of energy used for cooling data centres.
There's not doubt that with the right approach, AI can be used to positively impact the bottom line as well as the planets sustainability. And while it's been a topic of interest for some time now it's still a fairly unchartered area in the industrial space.
There's no doubt that AI enabled technology, particularly predictive analytics and maintenance can majorly reduce shutdowns, turnarounds and outages. What most haven't considered though is the positive impact this is having on worker safety.
By reducing workplace incidents of shutdown through predictively identifying what equipment is going to break, before it actually does, organisations can take workers out of harm’s way with proactive, predicted maintenance.
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The efficient deployment, continuous retraining of models with live data and monitoring of model accuracy falls under the categorisation called MLOps. As businesses have hundreds and even.
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