McLaren COO Jonathan Neale on finding racing margins in IoT sensor data
Jonathan Neale, chief operating officer for the McLaren racing team, speaks about finding marginal advantages through sensor data analytics
Although Spanish Formula One racing driver Carlos Sainz Jr didn’t deliver the somewhat elusive drivers’ world championship to McLaren in 2019, there is cautious optimism in the McLaren camp for what the future holds.
The last time its F1 team won the title was back in 2008 with current World Champion Lewis Hamilton, but this year saw a marked improvement. In a sport where the top five cars operate at around a 1.5% product performance differentiation, the margins between winning and losing are slim. So it is no great surprise that technology is front and central to finding that edge.
“Three years ago in Barcelona, Fernando Alonso crashed in testing,” says Jonathan Neale, McLaren’s chief operating officer (COO), speaking at IoT World Congress at Fira Barcelona, in October. “Using the technology and telemetry, we managed to reverse-engineer the incident through simulation and solve the issue.”
For Neale, this is an example of how far the team has come and the role that sensors and simulation modelling can have in identifying and reducing technical error. Neale is an impressive advocate of new technologies and change. He comes across as a realistic, pragmatic thinker, something that came to the fore in 2015 after being grilled by the BBC following a disastrous race in Canada.
He is under no illusion of the ongoing challenges the McLaren team faces to compete. Each team is essentially in three races – the one to get the best driving skills in the car, another one to get the best designers, and a third is in reinventing and building the car on a bi-weekly cycle to meet stringent (and often changing) regulations set down by official racing body, the FIA.
To understand component performance and alert to possible failure, each car is riddled with sensors – more than 300, according to Neale. Given the complexity of F1 cars – McLaren’s website says the car “has over 25,000 separate bits which are at risk of failing during a Grand Prix” – these sensors are key to ensuring optimum performance and identifying problems as they occur.
“We get 10GB of data per car, per race weekend,” says Neale. This is fed back in real time to analysts at the company’s Woking HQ to validate – an essential process in decision-making, from initial car design and set-up, to testing and tactics.
Blizzard of change
Probably the most stressful period of the weekend is the Friday, which Neale calls the “blizzard of change”, the day when the team has to virtually design and build the car, test in simulation, source the 6,000 or so parts that go through the supply chain every week, and then actually build the cars. It’s a process that would be impossible without sensors and the internet of things (IoT).
“IoT for us is not a phrase we really use internally,” says Neale, adding that McLaren has been working towards an IoT-type system for some time. This system includes the electronic control unit (ECU), a “brain” that sits inside each car. Developed out of necessity by its technology arm, McLaren Applied Technologies – a business that posted a £30m turnover for the first six months of this year – its primary purpose was to make F1 car design and development more efficient.
In 2001, when Neale first joined McLaren from BAE Systems, the design and build process was very wasteful, essentially because the driver would tend to reject about 80% of everything that the engineers built into the car. Today, data-driven simulations enable drivers to test the car virtually before it is put together for testing.
“We now do everything virtually first,” says Neale, “and then create a digital master.” For F1, this is now the norm, but in other areas of the business, it’s new territory.
One of McLaren’s other businesses is high-end sports cars. Applying the same F1 process makes sense, but Neale says you can’t always do this, at least rigidly, when other factors, such as consumers, have to be considered.
“The aim is not always to strive for technical excellence – we have to aim for technical engagement,” he says, after revealing that using the F1 model of data modelling and simulations left the car open to minor criticism from motor journalists.
“We had built a car that was so optimal, we had to make it worse to make it a better experience for drivers,” he says.
Sensors driving future change
It was an interesting lesson to learn. The sports car business is currently the company’s biggest earner, but Neale knows that could change – its high-end sports car division saw 45% sales growth in 2018 over 2017, boosted by a buoyant Chinese market.
But Neale is a realist and recognises that the industry is moving quickly towards increased automation and connectivity. Although McLaren is well placed to take advantage given its leading-edge developments in F1, he wants to avoid complacency.
Certainly, McLaren’s Applied Technologies arm is growing, using its knowledge of sensors, data collection and analytics to drive innovation in other industries. As well as being the sole supplier of batteries for Formula E, a class of motorsport that uses only electric cars, the business is also the main supplier of temperature and pressure sensors in F1. It also supplies its ECU to other F1 and Indy Car teams.
“We are moving from engineering services,” says Neale, a nod towards increased digital innovation and development. “As well as connected technologies in other sports such as football and rugby, we are working in healthcare and the public transport sector.”
The company’s work in connected cars is a natural progression. Neale talks about McLaren using its experience with its command and control unit and sensor integration to provide edge compute for fast decision-making in connected cars.
“We need 5G now,” says Neale, claiming that this is the missing connectivity piece that will enable increased capability at the edge. He sees this as an essential development leap, the sort of advance that will create an acceleration in innovation and increased efficiency in operations.
In much the same way that McLaren sees its vehicle dynamics simulator (VDS) enabling car manufacturers and suppliers to develop vehicles more quickly and at reduced cost compared to traditional R&D methods, it sees its knowledge of edge compute and sensor control as fundamental to solving many pressing issues in healthcare and transport.
Read more about data analytics in motorsport
- How data drives wins at Mercedes-AMG Petronas Motorsport.
- How data analytics helped Lewis Hamilton win the Formula One drivers’ championship.
- How F1 and others are moving beyond descriptive analytics.
It is working in connected rail, again using its F1 sensor, data processing and predictive analysis experiences to improve train utilisation, reliability and efficiency. It is also applying similar thinking in the hard-pressed healthcare industry, which is hungry for technology solutions to increase services while reducing costs.
“We have experience in using sensors on drivers, so we can use the same process to detect stress, for example,” says Neale, although McLaren’s involvement goes much deeper. F1 driver monitoring is crucial and technical, so the business is applying its learnings and processes based around four key pillars – movement, nutrition, psychology and recovery – to patients.
It is working with pharmaceutical company GSK to personalise care and medicine and has been instrumental in providing sensors and data analytics for a recent obesity and diabetes trial with StowHealth and University Campus Suffolk.
This is a great example of how F1 innovation is already filtering into other use cases and providing intelligence that was not previously possible. Across industries, the technology is enabling predictions, a situation that Neale says has led to change in how the business is thinking.
“On-condition maintenance and no longer on-schedule maintenance,” he says, referring to the idea that the organisation can now focus on outcomes rather than the nuts and bolts of making things happen. It’s an example of how worlds are now colliding. Modelling and mapping unstructured and structured data and using machine learning to do the heavy lifting on the data are becoming common denominators across industries.
McLaren is not the only team in this race, but it’s difficult to bet against its F1-driven R&D infrastructure. Successful businesses have been built on less, but you get a sense that McLaren is determined to use the tech to capture that winning feeling both on and off the track.