Demystifying digital transformation

VROC CEO TREVOR BLOCH DISCUSSES DIGITAL TRANSFORMATION AND HOW TO MAKE IT WORK

The real cost of technological change
The real challenges of implementing AI
The real impacts of data driven insights

Globally, the energy industry is at a crossroads. And increasingly, the question being asked is about the role that technology can play to enable the digital transformation we can all see looming. Most will assert that the movement towards artificial intelligence, industrial big data and predictive maintenance and general digital enablement is important. But many have yet to start the journey down the proverbial rabbit hole.

I founded VROC AI to give companies access and insight into their own data through the implementation of cutting-edge AI technology. But in the years we’ve been in operation, we’ve seen a huge disparity in how digital transformation is being approached. The implications of industrial AI and analytics are many and varied. And lots of the companies we work with are only starting their digitalisation journey. Many don’t know what data they have, where it’s stored or how to access it - and yet they want a tool like VROC to analyse, synthesise and interpret their data. It’s an interesting place to be in, at the forefront of cutting-edge tech, but with so much work to do before our clients are ready to fully embrace and apply it.

AI platforms aren’t really a cost

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, calculated expected benefit was more than $1.2m per year thanks to the reduction of maintenance costs and downtime.

Implementing an AI system is the easy part

It might sound like an overstatement but it’s true - because the real challenge is generating the data lake that any AI system is contingent on. AI is only as good as the data it’s fed. And in many of the cases we see, the proposition of AI doesn’t rest on the ability to integrate with systems but rather the access to clean and usable data. But this is where VROC Digital, our consulting arm, comes in.

Arguably the most important piece is the digital transformation puzzle, where the challenge we address is helping industrial companies quickly identify and visualise information among vast quantities of structured and unstructured data and deliver results to support time-sensitive processes.

Digital transformation teams aren’t the only ones pioneering predictive maintenance technology

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 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.

Predictive analytics isn’t just for Data Scientists anymore

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 an AI enabled predictive analytics software to create incredible results.

Safer and more sustainable

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 and sustainability. By reducing workplace incidents of shutdown through predictively identifying what equipment is going to break, before it does, organisations can take workers out of harm’s way with proactive, predicted maintenance. The sustainability impacts generated by AI are also far reaching and profound especially in the energy industry, when you consider its ability to reduce flaring and therefore its detrimental environmental impacts.

Change management is the hardest part of digital transformation

Change management can be hard at the best of times. Add the perceived threat of robot overlords into the mix and you’re in for a hell of a ride! The first question to ask in this area is, “Are you really ready to implement advanced analytics solutions?” If you believe you are, there needs to be buy in from the top down. The board, executive, and managers all need to believe in the power and potential of AI enablement. The biggest hurdle in this arena is often convincing people that bringing in AI technology won’t make their jobs redundant - rather it will take care of the repetitive and monotonous tasks as well as the ones beyond human cognitive capability. While AI has the potential to be ‘creative’ in a limited capacity, it will likely always require humans to assemble datasets, code the AI and apply the more exclusively human skills of judgement and nuance. When combined, AI and humans can achieve great things.

About VROC AI

VROC is a predictive analytics company dedicated to ensuring that your business can effectively harness artificial intelligence and machine learning technologies to maximise the value of your assets. Our mission is to give you back control over your data and empower you to discover valuable insights for more effective decision making. Shortly after identifying the potential of harnessing access to industrial data, I began a Research & Development project within BLOCHTECH Engineering (the first company I started) to work with existing clients and test the ability to provide value to clients from the data they were already collecting. After a successful product development and commercialisation journey, we began winning consulting jobs while building out the core product. Our first major project was won against competition from IBM, SAS, GE, Amazon, and Google.

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