Data makes the difference
I attended a podcast recording last night for Autocar, where the two regular co-presenters, Steve Cropley and Matt Prior were joined by a special guest, Jonathan Palmer. Jonathan now owns and manages many of the UK racing circuits, a unique and superb driving experience centre, and now new locations in Navarra, Spain and Laon, France. However, having abandoned a medical career, he was also a fairly successful F1 driver for six years with Tyrell, Williams, RAM and Zakspeed as well as being a test driver for McLaren, racing and working with some of the greats like Prost and Senna.
He made a general observation that he thought he did better than his raw talent possibly deserved because he took a more analytical approach to his driving than his peers, perhaps attributable to his medical training. In that period, the best that you might have to guide you in terms of improving your lap times were a few sector times, so it was very much up to the driver to use their experience to assess where there might be opportunities for improvement, and to guide the engineers as to how the car was behaving and what changes might be needed. He cited an example where one well-respected teammate struggled with a new car, judging it to have too much oversteer, whereas Jonathan worked out correctly that the problem was actually the opposite – leading to a totally different solution.
The end-result of this ‘finger in the air’ approach to improving driver and car performance was that the gap between fastest and slowest on the grid could be 20 seconds, whereas at last weekend’s Mexico Grand Prix, this was only 1.5 seconds. Telemetry in the car allows every aspect of performance to be closely monitored in real-time, by the team at the track, and the engineers back at base. The data can also be fed into the incredibly sophisticated simulators that the teams now have in order to try to replicate problems and solutions without the need for the real car to be even fired up. The data also informs future design effort, and designs can be evaluated before any fabrication or machining starts
The end result is that with all the teams taking broadly similar approaches, using the same or similar tools, the level of competitiveness has been compressed across the grid. The slowest cars may not have much of a chance of a podium, but they are not going to get lapped in the first ten laps of the race either. The cars are better designed, more optimised to each circuit, and the drivers understand where they are losing time, and what they need to do in order to cut another few tenths off their lap time. Data is driving better decisions at every stage and we are getting better racing.
If we then consider how this translates into the world of automotive distribution, there are close parallels. Increasingly digitalised customer journeys and end-to-end processing of physical vehicle flows and the aftersales life cycle, plus connected car data all create a hugely valuable resource that can be leveraged for improved decision making. As we move into the era of the ‘software defined vehicle’ there is even a digital twin specific to each VIN which can be viewed as the equivalent of the F1 simulator. (For more on software defined vehicles, ICDP members can access our Autumn Meeting presentation here). This can be combined with other data related to the market as a whole, competitor actions and the economic climate to define the environment within which the measured performance was being achieved – if you like, the equivalent of understanding the weather, track conditions and the fact that a key competitor brought an engine upgrade to the last race.
Armed with this data and the tools and people to interpret it, manufacturers can better plan the volume and mix of products for each market based on historical demand, incoming customer enquiries and actual orders. They will be able to adapt pricing and promotions as needed to achieve the final balance between supply and demand because they will understand price elasticity of different channels at the level of market and model over time and through the product life cycle. They and their dealer network will be able to get much closer to personalised marketing for sales, aftersales and even the much-heralded but rarely-seen digital services. Predictive maintenance becomes more feasible, and the economics of applying this can be seen in advance. Dealers can make better decisions on used car stocking and pricing, and special promotions sent to customers to encourage a switch at a time that marries a suitable new car sales opportunity with a demand for the used car that they are currently driving.
As with the F1 grid, if all manufacturers and dealers were to adopt similar tools and approaches, then the competition will become more intense as the back-markers will find ways to compete more effectively with the leaders, but in reality that is unlikely to happen, and some will fall behind in the data race, only to find that their grid slots have been taken by others. In parallel our ‘F1 grid’ of OEMs and franchised dealer networks will find that the ‘Indycar grid’ of increasingly sophisticated repair networks and parts distributors are competing for the attention of the same customers, and are also using data and data analytics to good effect.
Whatever the outcome for any individual players, I am sure that data (and the ability to exploit it well) will have made the difference.
Image: F1i.com