Whilst we, as civilians, only ever see defence activities when they appear on the news or are the subject to budget challenges, we mostly don't appreciate the complexity that lies under the surface of operating one of the leading defence forces.

The programmes that produce the defence equipment used by todays countries are the result of global collaboration and cutting edge technology developed over decades back to when many of us (speaking for myself) were children.

By the time they are introduced into service, their technology is in many ways already becoming out of date, and the engineering skill and experience that went into their production is an increasing rarity.

As with any complex system, things go wrong, need fixing, or replacing... so how do we do that?

All components have a 'service life', signalling when they need replacement, maintenance or overhaul. They can't keep going forever, but at the same time, we can't keep buying a new engine.

So, we need to work out how worn out they are, what needs replacing, how we replace the 'bits', how much this will all cost and how quickly we can get the 'thing' (in this case a EuroFighter Engine) back into service as soon as possible. The clock is ticking....

Thing is, those engineers who developed the engine and have all this 'inherent' knowledge are retiring, onto other projects or generally unavailable. But we need that knowledge to keep the fleet airborne.

How do we distill years of engineering experience; combine this with product data, real world flight information, updated cost and spares availability and performance information to produce a plan and schedule for maintenance, overhaul and turnaround of something as complex as a modern fighter jet engine?

It's not just about availability.. some of this data is coming in on paper, or email due to the reality of field operations.

As part of a number of tactical projects over a number of years, we worked with the engineering teams at Rolls-Royce Defence to first understand all the source of information necessary to maintain the EJ200.

Once defined, our task was to re-engineer n approach to allow processing of the information in a consistent, coordinated and robust manner such that no data was lost and we started the process of maintenance with an up-to-date, accurate model of the 'truth' (related to an engine).

Next we had to mitigate the loss of knowledge from the engineering team(s) through a series of interviews, digitisation and ultimately the development of a logic module which included Machine Learning to 'advise' the final solution on how to 'maintain' an engine.

The next stage involved establishing connections to other systems and sources of information which would provide the constituent information to inform the model.

Finally, we wrapped all this into a low-code, engineering platform which allowed the defence engineering maintenance teams to discern order from complexity, determining a baseline / status of the engine and use actual information and data to establish a plan for maintenance, cost, schedule and return to service of the EJ200 engine.