NS Tech

IT Leaders: Williams F1 CIO Graeme Hackland on the real worth of AR, AI and 3D printing

Graeme Hackland spent 16 and a half years at Enstone working for the F1 team through the eras of Benetton, Renault and Lotus, before becoming chief information officer (CIO) of the Williams F1 Team back in January 2014.

His lengthy stint at Enstone enabled him to gain a huge amount of experience, with the know-how of an F1 team’s network and IT infrastructure, improving of car performance, as well as the management of security, budget, governance and staff. His role at Williams F1 encompasses all of this but also extends to another side of the business; as an engineering company, Williams F1 commercialises many of the concepts that it comes up with in F1.

“Because the F1 cars are hybrid, we have developed energy systems which we are now applying into other motor sports, for example, and we’re working with hospitals, helping to transport babies,” he says.

The latter project, dubbed Babypod 20, is a collaboration between Advanced Healthcare Technology and Williams Advanced Engineering. Together they made a ‘super cot’ that can withstand a g-force of 20 in a crash, and that also – at least according to those who made it – comes at a fraction of the cost of an incubator.

“Even though F1 is our main mission, it’s nice to see the technology we use in F1 being used to help the rest of the world – I like that aspect of what we do,” says Hackland.

Hackland is constantly looking at what technologies can be transferred out of F1 into the business, and data analytics is one of the key areas that F1 teams have advanced capabilities in.

“F1 cars had a data logger back in 1979, so it’s something we’re good at and we’re trying to apply that into other projects,” he says, adding that he is currently looking at how the business can benefit from the use of the Internet of Things (IoT) and sensors.

Fuelling human performance with data

About 18 months ago, Williams F1 hired a human performance specialist with the aim of getting the team’s pit crew up the leaderboard. The specialist had to consider the technology, personnel, fitness and training required to be the very best, going beyond the traditional F1 considerations of design and engineering. They would be equipped with data to make these decisions.

“We’re all marching towards machine learning and AI, while augmented intelligence is a trend we’ve started to use. Our first step will be to make humans make decisions, giving them better data, more quickly at the right time,” Hackland says.

As well as data, the company is looking at how it can automate and link everything from aerodynamics, physical or virtual models, through to design and engineering and create a loop of feedback.

“For example, if it’s Australia in our first race of the year, and we’re using new parts for the first time and something goes wrong with the car, the problem is making three more of those to cover races after Australia as it takes time after the race to investigate what went wrong and feed it back,” he says.

This is where technology could step in to lend a helping hand, Hackland believes.

“There is potential for a chief mechanic to put an AR headset on to look at the car at the same time as a designer and work together to locate the fault. Then the designer can use a CAD workstation to change the drawing,” he suggests, adding that machine learning could also help the machine itself to stop manufacturing the part automatically.

But Williams F1 doesn’t want to stop there, it wants to get to a point where it has digital manufacturing capability at the track.

“It would be very tough to manufacture parts and get them to the track within a day, and flying parts out means huge costs, whereas digital capability at the track would help us print it over night and stick it on the car,” he says.

“So 3D printing, people may think it is all hype but I think there is good potential there for the future,” he says.

Similarly, despite the hype and scaremongering surrounding AI, Hackland says that many AI engines and the data quality within them are good enough for business use.

“I think it’s the humans that are more of an issue with their reluctance,” he claims.

But Hackland has a way of tackling this. He suggests asking engineers what the most mundane repetitive task that they hate doing is, because then they will be happy for the organisation to take that task away and automate it.

“We think there is an opportunity to put in a whole load of data steams such as voice, weather, tyre information, competitor analysis… and give the team access to better data much more quickly,” he says. “Eventually, AI could make the decision and call a driver over for pit-stop, but I don’t think engineers are quite ready for that.”