Stuart Hughes is CIO and chief digital officer at Rolls-Royce, where he leads the team digital teams in the civil aerospace business. Rolls-Royce’s engines are used in fighter jets, business jets and more than 50 percent of long-haul planes.
CIO’s Thor Olavsrud sat down with Hughes at IDG’s Edge Computing Summit to discuss how data collected from airplane engines is enabling Rolls-Royce’s customers to plan and execute better flights.
What follows are edited excerpts of that conversation. For more of Hughes’s insights, watch the full video below.
On how IoT and edge have changed how Rolls-Royce does business:
Our technology enables us to have a commercial model where the airplane owner actually pays per engine flying hour, so only pays for the time that the engine is in use. And in exchange for that payment, Rolls-Royce covers all of the maintenance, all of the servicing and all of the warranty elements. In effect, we sell power by the hour rather than airplane engines in that sense.
On planning and executing a better flight:
A really important part of the innovation that my team in Singapore has been working on really closely with Singapore Airlines [is executing a more fuel-efficient flight].
We’ve created applications for the pilot and for the operations team that help them understand some of the strategies available to them. And it’s a really big win, because if they can choose the right strategies, think about how they take off and how much thrust they’re using, think about the way they climb and think about the angle that they climb at, think about how they use wind better, think about how they basically optimize the engine, then there are two really big benefits. There’s a reduction in fuel, so they’re paying less for fuel, they’re carrying less fuel. And the other thing is, of course, we’re reducing CO2 in the atmosphere.
On personalizing service at the engine level:
A really significant change that happened in the time that I’ve been at Rolls-Royce,… [is] moving from treating everything the same, maybe replacing things that didn’t need replacing, maybe impacting the airlines and the customers by taking the engine off the wing too early, and thinking about it in a much more specific, individualized way.
[I]t allows us to take into effect how the pilot has flown the engine and the environment that it’s flown through and the types of missions or flights, as you might call it, that it’s been through. So, it really enabled us to tailor the maintenance and overhaul to the specific engine rather than the product family.
On changing how IT works:
[A]s an IT department, we’ve adapted. We really lean in to the IoT capabilities, the platform capabilities. So, platform-as-a-service offerings. I think before I arrived, there was a “we don’t want to lock ourselves in” mentality. But to me, you’re almost negating the benefits of the cloud if you’re not going to work with the cloud provider’s features.
I think the second side of it really was a cultural change. So, now the team that works for me, we’re split into product teams that represent the various value chains within the platform itself, and we work with a high level of agility.
And I think finally, and the most important piece is, we all have the same outcome. So, we follow an OKR process (objectives and key results). It’s not given to the team. The team are a big part of helping us set our objectives, helping us define our key results. And that helps us judge our progress on the outcomes we actually deliver rather than the IT tasks or the subsystems or how many deployments we’ve done in a day. Or, God forbid, how agile we are, which some people like to measure. So, for me, really we’ve made a huge change away from specs and formality to product teams, collaboration, iteration, and then just a laser focus on the actual outcome.
On advice for getting started with IoT data:
I think all of the projects that I’ve seen go terribly wrong is where it gets stuck in R&D or it gets stuck in IT with everybody trying and capture every bit of data. And then two years in, someone says, “What are we going to do with the data?” And that’s when someone like me normally walks through the door and says, “You’re doing it all wrong.”
So really think about what the end is, who the customer is, what the use case is, what a successful outcome looks like. Capture that data and then start to iterate from there.
