Promoted content: Predicting an effective maintenance future

Big data is a well-recognised term and concept, with businesses now grasping the importance of its role in improving operations, providing competitive advantage and positively impacting profitability – and this is particularly pertinent in the industrial sphere. With the effective collation of disparate pieces of information about a problem, we can apply artificial intelligence and predict future trends. This concept certainly appears to be the nirvana of solving maintenance issues, providing total visibility of failures before they occur.

But while this all sounds ideal in theory; the operational reality can be very different. Over the last year, I’ve spent time with industrial end users trying to get under the skin of the organisations to understand what the real challenges are and explore key barriers to adoption for businesses when it comes to implementing IIoT technologies.

The challenges are varied, and experiences mixed, but there are some common themes that emerge across the board. A major barrier seems to be that IIoT maintenance solutions are difficult to understand and specify, and confusing and fragmented to purchase. Many focus on futuristic benefits, rather than practical results that can be delivered with pace.  Implementations can be costly and complex to organise and manage – and could take years to fully and successfully implement.  The objectives get blurred and focus shifts to adding new sensors rather than utilising the data which is already available.