It’s Time to Crash Test Your Business Model With Big Data – Learning How With TESLA
Last week I had the great privilege of attending CERAWeek 2016 in Houston, Texas, USA. Oil ministers, researchers, NGOs, CEOs, US Senators. Even the President of Mexico was there to opine on energy policy and outlook, from economics to technology to politics and back again.
An evening dinner with the Chief Technology Officer (and co-founder) of Tesla, J.B. Straubel, proved the highlight of the conference for me. Not because the cars they make are cool (they really are); not because their battery technology will fundamentally change our lives in the coming decade (it will); rather, because this is a unique point in history where we get to see a mass production car company in the making in the modern era and contrast that with the last generation of auto manufacturers. When was the last major automotive “start up” before Tesla? Answer: 1925.
It is broadly instructive to see how the same industry evolves in one era versus another—my belief is that this gives us insight as to how the whole of the economy will move forward, and the lessons there are invaluable, partly because it is so rare to see such a pure, controlled experiment unfold right before our eyes. What do the successful companies in X industry look like? This is probably the most important question among boardroom conversations across the world today. Let’s see if Tesla’s experience can help us answer it.
Let’s look at just one aspect of Tesla’s operations: crash testing. All car companies must do this, and at present all the major players have elaborate, expensive facilities designed, for the most part, to run vehicles into walls or bash them with objects from all angles and then measure the associated forces and damage. Results are reported to regulators and to the public.
Tesla avoided this expensive route by hiring a large crane and dropping the car to the ground from various heights to achieve precisely the same effect. They used software to make a variety of corrections for the vertical orientation, yet duplicated the crash test that the majors do at a fraction of the cost. In principle, this vignette is indicative of Tesla’s corporate approach: using software, automation, and “light weight” infrastructure to conduct operations, all the while consuming a fraction of the normal human resources. Tesla employs 13,000 people, and has a market cap of $25B. GM employs 215,000 people, and has a market cap of $45B. When you consider that Tesla can scale production by an order of magnitude with a roughly similar labor pool, the labor content numbers create an even larger divergence with the status quo.
I’ll also argue that the software that Tesla used to manage and analyze crash testing didn’t simply displace labor costs and capital—it also added to the inventory of intellectual property that the company is amassing.
Don’t just take my word for it. Forbes Magazine loves how TESLA “a high-end disruptor” is doing business.
Tesla automobiles look and drive much like other cars, use established infrastructure like roads and confine much of their product innovation to only one aspect: the power system. FORBES MAGAZINE
So there you have it. The lesson from Tesla thus far is clear: smart companies use software and automation to reduce the labor content and capital cost of major operations. Tesla reimagined something as banal as crash testing and created a double whammy of value: better operating earnings and greater shareholder value.
How many brick-and-mortar companies could benefit from this same kind of approach? My answer: every single one. There isn’t a company on the planet that is immune to Tesla-like rethinking of its operations and assets. My recommendation is that you go through the thought experiment for yourself at your firm…what is my analog for a crash test center and how could that be reborn as a software and automation-driven ghost of its former self?