Can predictive analytics stop shutdowns? We think it can.

It's a fact of life that unscheduled breakdowns occur - and that they are bad for business, on all fronts.

Unplanned, reactive maintenance leads to lost productivity, damaged reputations, envorinmental and material waste and increased safety incidents. 

But what if you could predict failures before they happened and plan your maintenance around exactly what your equipment needs, right when it needs it?

Why predictive maintenance?

A crane with 2,500 sensors can create 4 billion data points in just one year.  

This volume is overwhelming - and far more than a spreadsheet and a human mind can handle.

Thousands of human hours are required for to attempt to deliver insights into what these billions of data points are saying – and even then, accurate insights can be hard to generate.

That's where we come in.

With VROC Predict we can analyse billions of data points in minutes and hours and our predictive analytics software can identify what, when and why equipment will fail - long before it does. 

Using big data to predict machine failure

With Artificial Intelligence, Big Data and Machine Learning, VROC harnesses the power of predictive analytics to breathe new life into the field of predictive maintenance. 

No longer limited by manual checks, innacurate estimates of time to failure and intensive investigation by data science teams, we are able to ensure the right information is available at the right time. 

With AI algorithms, billions of data points can be analysed at scale and speed never seen before.

VROC ingested 12 months of data which took reliability engineers 4,000 man hours to identify and in 90 minutes reached the same conclusion - 2000x faster and without any subject matter expertise.

This is the power of AI and predictive analytics - what could it do for your business?

i want to find out more

predictive analytics is your window to the future

AI enabled predictive maintenance is a game changer because it puts your own data back into your hands - in a way that you can properly understand it.
  • Prioritise scarce maintenance resources on items predicted as most likely to breakdown. 
  • Order parts just-in-time, reducing maintenance spares inventories and shelf spoilage. 
  • Reduce costs and damage related to unscheduled breakdowns.
  • Reduce safety incidents and make sure staff are always kept out of harms way.

predicting time to failure

VROC's in-house algorithms analyse information from billions of data points in real time, allowing rapid identification of complex trends and patterns including Time To Failure (TTF).

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