More recently, and as part of that continued investment, there has been a drive towards autonomous operations where assets of the future would combine a wide range of digital, data and visualisation technologies with operations in order to deliver an asset that can operate without human intervention. These assets would continually learn, adapt, and improve in order to operate more cleanly, safely and efficiently.
However, that vision must be tempered with the immediate reality of where the industry is at the moment. There is a gradual move away from traditional monitoring modes where we can react to “what’s happening now” to a more predictive mode of operation where we can predict “what happens next” and take more proactive and informed action. This predictive mode of operation is most commonly seen in the maintenance arena but as a mode of operation, predictive automation is applicable across a wide range of scenarios and is a significant step in the journey to autonomous operations.
To this end, the NZTC is looking for ideas that sit firmly within in the predictive mode of operation, where prediction of “what happens next” enables key business process areas/use cases to be automated. A good example of this in action would be spare part management where large and expensive spares stock holding can be eliminated with predictive automation provisioning only the required spares based on previous equipment performance.
It’s solutions like these we are looking for but across a wide range of process areas/use cases.
Key points to consider for meeting this challenge
The predictive automation technology should:
- automate a key process area/use case using the power of predictive analytics
- be applicable to the energy sector which includes oil & gas, renewables, and new energy solutions for the future
- have demonstrable value case in either increasing efficiency, reducing carbon impact and/or reducing risk
- be able to reach a minimum TRL of 6/ideally 7 to a maximum of 8 to allow field trialling in a relevant and/or operational environment