I wrote a few months ago about the lessons that can be learned from Formula 1 in terms of the use of data, and having watched an extremely boring Japanese Grand Prix yesterday, I return to the same topic today.
For those who don’t watch F1, it is apparently the first F1 race in history held on a permanent circuit (as opposed to the street circuits like Monaco which limit ‘real’ racing) where the drivers in the top six positions at the end were the same as the order in which they lined up on the grid.
In fact down to 10th position, the only change was that Hamilton overtook Hadjar on the track to swap positions seven and eight compared to where they all sat on the grid.
There was a little more action further down rankings, but the audience on the stands might as well have been watching the grass at the side of the track for the outbreak of one of the fires that interrupted free practice and qualifying.
On the positive side, all twenty starters finished the race, and all other than Lance Stroll were unlapped, but I suggest that is part of the same story.
If we go back a few decades, there were always retirements, and the performance gaps between the cars were much larger.
These have been narrowed over the years as the result of the application of data analytics – the point made by Jonathan Palmer that I referred to in my blog last year.
However, I suggest that there is more to it than simply applying data analytics to examining and improving car and driver performance, and that there are multiple parallels and warning signs for automotive distribution.
Until recent years, F1 regulations were relatively light, limited to engine size, weight and some aerodynamic restrictions.
As a result we had six wheelers, fan cars, ground effect, twin chassis, turbo and normally aspirated – together creating huge visual variety and big performance and reliability variations.
Some of the innovations worked, others did not. Over the same period, we had similarly high level regulations for road cars that allowed great flexibility in design and layout.
VW and Skoda did not have any family connection at the time, but they shared rear-mounted engines, whilst NSU (a predecessor company of Audi) had the amazing Ro80 with a Wankel rotary engine. Safety features were a source of competitive differentiation.
Today, both F1 and road car regulations are much more prescriptive, resulting in products that are very similar in concept and execution.
Visual appearance is largely dictated by the need to meet the targets set, many features and performance aspects are driven by the regulations rather than the imagination and preferences of the designers and engineers.
The regulatory impact feeds through into the delivery of the output – whether that is the way in which individual races are scheduled and delivered in F1 or the regulations related to the distribution and operation of cars.
Control fuels in F1 (entirely synthetic from next year), 100% ZEV in Europe from 2035 (with a defined route map to get there).
As I commented before, data analytics and simulation help to drive more robust designs that perform in line with the design objectives and are reliable in service (again dictated durability is required for both F1 and road cars).
In the same way that few race teams suffer ‘did not finish’ results for mechanical reasons, there is no such thing as a ‘bad car’ today. All brands perform similarly and are equally durable.
Moving onto the distribution area, the parallels continue. Attending a F1 race anywhere in the world, the experience is going to be fairly similar.
Support races will be the same, a similar cast list of celebrities will be occupying the VIP Paddock Club, even the post-race interviews will be the same.
In automotive, regulation, global and regional OEM standards and shared perceptions of best practice make automotive retail broadly similar globally.
However, my interest is not so much in the distribution model itself, but in the performance of that model.
As degrees of entrepreneurial freedom reduce through more processes being defined, policed and incentivised (some for good reason such as regulatory compliance), and IT systems are harmonised across dealer groups and franchise networks, this will also drive a tendency to ‘group think’.
That phenomenon brings with it a tendency to avoid critical analysis and challenge. At the same time, where there is critical analysis of data in our increasingly digitalised world, this will also drive a convergence towards similar decisions.
The value of a used car to buy or to sell will be set based on the same parameters, processed using similar algorithms, potentially sat within the same AI application.
As the same or similar retention tools are rolled out across networks for sales and aftersales, the communications to customers will look increasingly similar and where the data is available to more than one provider, will come out at a similar time.
Individuals will be less able to choose to follow their own path, but when they do, will they be able to consistently ‘beat the system’, or will inspired creativity and judgement be defeated by external constraints and the power of data science?
I suspect that there will be occasional moments when entrepreneurial freedom wins, but longer term success will depend on optimising a defined business model more effectively than competitors.
We may be in for a succession of Japanese Grand Prixes in terms of business performances, however unexciting that may be, and the buzz for true entrepreneurs will reduce.