Elon Musk is known as a first principles thinker when it comes to inventing new things, but he has recently started applying this type of thinking across all the different processes in his company.
In a letter to his employees, he outlined how the spending practices in the company will be reformed:
“Going forward, we will be far more rigorous about expenditures. I have asked the Tesla finance team to comb through every expense worldwide, no matter how small, and cut everything that doesn’t have a strong value justification.”
“All capital or other expenditures above a million dollars, or where a set of related expenses may accumulate to a million dollars over the next 12 months, should be considered on hold until explicitly approved by me. If you are the manager responsible, please make sure you have a detailed, first principles understanding of the supplier quote, including every line item of parts & labor, before we meet.”
He wants to have his managers apply first principles thinking to all the things they do, including how they source different components from suppliers.
In an analysis of how subcontracting works in his company, Musk found out that it is a complicated system that creates a lot of overhead.
“I have been disappointed to discover how many contractor companies are interwoven throughout Tesla. Often, it is like a Russian nesting doll of contractor, subcontractor, sub-subcontractor, etc. before you finally find someone doing actual work. This means a lot of middle-managers adding cost but not doing anything obviously useful. Also, many contracts are essentially open time & materials, not fixed price and duration, which creates an incentive to turn molehills into mountains, as they never want to end the money train.”
What this means is that all his employees, especially the ones in management positions, need to be first principles thinkers, no matter whether they sit in the lab and create new inventions, or sit in front of the computer looking at Excel tables all day.
Should you be a first principles thinker 100% of the time?
First principles thinking is a powerful took which can help you solve problems in new ways, but it can also create problems when you use it indiscriminately. You don’t always need to reinvent the wheel, and the reuse of best practices or thinking in analogies can often be superior in many circumstances.
In fact, this blind adherence to trying to figure out things using first principles thinking can be behind the many challenges that Musk is facing in the production of his new Model 3 Tesla car. This is supposed to be his first mass-produced electric car, but the entire process has been quite difficult with Musk needing to shut down production of the car several times, and the new Tesla Model 3 cars often coming out with different defects.
The first mover advantage that Tesla had with its electric vehicles is rapidly diminishing and many of the traditional car companies are starting to produce their own electric vehicles. Their advantage might be that they are using their proven and tested processes in the manufacturing of these cars, and only innovating incrementally.
This is opposed to the strategy that Elon Musk initially set for the mass production of his new Tesla Model 3. He had tried to redesign the entire process of manufacturing a car in the first place based on his vision of a fully-automated manufacturing plant run by robots. Yet this is proving not to work.
Musk himself even acknowledged his mistake in a recent Tweet:
“Yes, excessive automation at Tesla was a mistake. To be precise, my mistake. Humans are underrated.”
Many industry analysts are saying that Musk ignored old industry wisdom and lessons from the past, which is now hurting him.
There are two things that were his main mistakes:
1) Too much automation, too fast.
2) Agile development processes without a long testing period.
Elon Musk tried to go first principles on the manufacturing process itself and to replace the humans by robots.
This failed and created a mess on the production floor. Apparently this full-scale automation had already been tried by other car manufacturers, but had to be abandoned. So Musk did not learn from history (and did not consider the analogies from other car manufacturers).
Many of the traditional car manufacturers do innovate, but they do it gradually. For example, the Japanese have a very incremental approach towards automation, very unlike the Big Bang model of Elon Musk.
To quote Benny Daniel, who is the Vice President of Consulting on Mobility in Europe for Frost & Sullivan:
“The Japanese style of production is to try and limit automation initially as it is expensive and statistically inversely correlated to quality. The approach is to get the process right first, then bring in the robots, basically the opposite of what Musk did.”
One of the main problems with automation is that the technology is still not there and cannot replicate some of the delicate tasks the same way that humans can.
Toni Sacconaghi and Max Warburton, authors of a report by Bernstein company, summarize the differences between what the humans can do and the robots cannot:
“In final assembly, robots can apply torque consistently—but they don’t detect and account for threads that aren’t straight, bolts that don’t quite fit, fasteners that don’t align or seals that have a defect. Humans are really good at this. Have you wondered why Teslas have wind-noise problems, squeaks and rattles, and bits of trim that fall off? Now you have your answer.”
It will still take quite a few years for the robots to catch up. The machine learning and artificial intelligence is still not there.
According to many experts, the first principles process was not the right one to adopt when trying to mass manufacture the new Tesla Model 3. Instead, what would have worked better is to adopt (use an analogy) the tried and tested processes from the old car manufacturers and then incrementally innovate on those.
It seems that some of the traditional car manufacturers are catching up fast to Tesla and will roll out fully electric vehicles very soon. This is due to the fact that they can leverage their experience and tried and tested know how.
The thinking by analogies process of the traditional manufacturers seems to be winning the day over the first principles thinking approach of Elon Musk, at least in terms of the mass assembly and manufacture of electric cars.
Another problem is that when Elon Musk did use an analogy, he used the wrong one.
Musk initially made his money developing software. When you are developing software, one of the most common methods is agile development. With this type of methodology the aim is to come out with a minimally viable product as quickly as possible and then improve upon it through succeeding waves of development.
This is a good methodology for developing things like software, and also for running your life, but not always so great when manufacturing cars.
Cars manufacturers instead spend a long time on designing prototypes, testing them, and only once they are happy with the result do they move into the production phase.
This type of process ensures that the risk of future mistakes or faulty parts is minimal.
Peter Schwarzenbauer, a member of BMW’s Board of Management described the reasons for this approach:
“We want to get our products right first time. Customers should not expect to receive lots of patches and updates on their vehicles like they do with some other manufacturers, we want to release a product when it is ready.”
However Musk chose to go with the agile process for the manufacture of his electric car. This has not always resulted in the best quality cars.
Teardowns of his car by different car industry testing groups have determined that the Tesla Model 3 is a mix of cutting-edge high-tech parts which are best in class, with some really shoddy parts.
University professor and expert on manufacturing, Roger Bohn, recently explained in a post on his blog why there is such a high rate of errors in the production of Tesla Model 3. It basically comes down to the process:
“Fundamentally, Tesla has a product design and production process that are “not manufacturable.” That is, the product tolerances are considerably tighter than the process variation. The result is that they produce lots of junk that must be scrapped or reworked. They can partially reduce process variation by stopping more often to adjust machines, but this causes downtime and creates “bottlenecks.””
At the moment, it looks like that in his attempt at mass-manufacturing an electric vehicle, Musk used first principles thinking in the wrong place (and some wrong analogies as well). Maybe he will still surprise us, but it seems that many of the traditional car manufacturing companies will catch up and roll out their own electric cars in the near future, negating Tesla’s first mover advantage.
Why? Because they have experience in the mass manufacture of cars and used these analogies when developing their own electric models. This means that you don’t always need to reinvent the wheel. Instead the reuse of best practices and already existing solutions can get you to your destination much faster in many cases.
When to reinvent the wheel (and apply first principles thinking) and when not to?
This brings about the question: When should you use first principles thinking and when analogies? This is a hard question to answer and I already discussed this a bit in my series on first principles thinking.
A lot of times this isn’t always that clear cut. The analogies strategy is the less risky one, but the first principles one has higher rewards.
Musk used first principles thinking in order to come up with an electric car in the first place. What doesn’t seem to be working is the mass manufacture in production mode of this car. The old tried and tested methods seem to be working better there.
If Musk wants to get to the fully automated factory, he will most likely have to get there incrementally.
Sometimes the technology to be truly innovative simply isn’t there…yet. This is probably the case for the full automation of production. Some of the processes there are so complex that we will have to wait until machine learning and AI improves to the point where it can do things just like humans.
You can see that with the example of the Dot.com busts at the end of the 1990s. Many of the ideas that went bust at that time now form the basis of successful companies.
Why didn’t they work then? One of the problems was that they came too early. The technology just wasn’t there (the internet was slow, not widely rolled out…etc.) and the people were still used to their usual ways of doing things. It took Amazon many, many years to actually get profitable.
In order to decide which type of strategy (first principles, analogy, or something else) to adopt, you will have to look at the different factors carefully and then determine which is the more promising course of action. Is the time right for a first principles approach?
There is no perfect strategy and whether to use first principles thinking and innovate radically or instead use analogies and focus on incremental innovation is often a gut decision.
However there are two questions that you can use to determine whether a first principles radical approach can work.
1) Is the need there?
2) Are the tools readily available?
If you take the example of one of the most radical innovations of the past decade, the smartphone, you can see how this works. Even though most consumers did not realize it, Steve Jobs and the people at Apple saw that there was a potential need for a device that combined the functions of a phone, music player, and the internet. They also saw that the tools and technology were there to make this come about.
On the other hand, while many people might see the need to travel to other solar systems, the tools to do that don’t exist and won’t exist for a while. So whatever first principles thinking you do in order to try to solve that problem, it won’t do much good right now.
Even though the first two questions might be answered in the positive, there is a third question which you can pose in order to determine your chances of success with first principles thinking.
3) How complex is the process?
The more complex the process is, the more parts to the system there are, the harder it will be to use first principles thinking and innovate radically straight away. Instead, a more step by step incremental approach will be more likely to succeed.
Radical innovations might arise out of this process, but they will take a while. If they are complex, then some radical innovations will have to come about in an incremental way. Things like charging grids for electric vehicles will take a long time to set up, so the change will be gradual.
Let’s see how the car manufacturing process stacks up against the three questions.
Is the need there? Yes, there is a need to have better car manufacturing processes.
Are the tools readily available? Probably not yet. Some parts of the car manufacturing process are so complex that the current state of development of robotics still hasn’t caught up. It will catch up in the future, but it is not there yet.
How complex is the process? The manufacturing process is really complex with many different parts. In order to change things up, you will need to do it in many sectors, which might not be feasible if you want to do it fast.
A study looked into the processes for radical and incremental innovation. There is no standard process, for either one, but there are some patterns.
The radical process is usually more iterative, and needs the refining of efforts during the development stage.
“Firms are more likely to use more non-linear processes with newer (less incremental) products. Product development for discontinuous products is more of a “learn and probe” process, rather than a linear one.”
This explains why it is so hard to innovate in a manufacturing process quickly. If you want to mass produce cars straight away, you cannot really tinker with the process too much. It requires a more linear approach.
The illustration below shows how the process of coming up with a radical innovation works. It is pretty messy, and you often have to rework solutions and go back a few steps.
That is kind of hard to do in the middle of full production mode. This is also the reason behind the many stoppages at Musk’s factory.
Here, how to set up the problem to be solved will also be very important.
Elon Musk stated that he wants to mass produce electric cars right now. Even if the technology to do this was all there, the process of mass manufacturing cars is so complex that it would be really hard to succeed through the first principles thinking approach. Others have tried and failed.
However, if he had instead set up his goal as mass producing cars in 10 years, then the approach could be different. There you could start applying first principles thinking right away, tinkering with certain parts of the entire systems, finding out what works and discarding the things that you find out don’t work.
Overall, it is still to be seen what will happen with the Tesla Model 3 production. Personally, I think Musk will be successful in rolling it out in full scale, however he will need to go back to the old tried-and-tested car manufacturing strategies. A full-blown automation is still years away.
This shows that in order to be successful you will need to be able to think both in a first principles way, but also have a toolbox of analogies to fall back upon. The key skill will be knowing when to use which strategy.
If you want to read a longer introduction on first principles thinking and a discussion on barriers to thinking in first principles, click below:
Introduction to first principles thinking and barriers to thinking in first principles.
If you want to read more on what types of techniques you can use in order to overcome those barriers and solve problems using first principles, click below:
The techniques for first principles thinking.
If you want to read more on the applications of first principles thinking, then click below:
The applications of first principles thinking.
Also don’t forget the cognitive biases checklist when making a decision:
A checklist to help you prevent cognitive biases when making a decision.
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