My friend Don Norman wrote an op-ed this weekend calling for an FDA-like testing program before autonomous cars are put on the roads in the US. Clearly, some level of government approval is important. But I see lots of problems with using drug testing (FDA = Food and Drug Administration) as a model.
Here is an excerpt from a recent article about testing problems with Uber cars, which were the ones in the recent fatal accident. After the break, my assessment of how to test such cars before they are allowed on American roads.
Waymo, formerly the self-driving car project of Google, said that in tests on roads in California last year, its cars went an average of nearly 5,600 miles before the driver had to take control from the computer to steer out of trouble. As of March, Uber was struggling to meet its target of 13 miles per “intervention” in Arizona, according to 100 pages of company documents obtained by The New York Times and two people familiar with the company’s operations in the Phoenix area but not permitted to speak publicly about it.Yet Uber’s test drivers were being asked to do more — going on solo runs when they had worked in pairs.And there also was pressure to live up to a goal to offer a driverless car service by the end of the year and to impress top executives.
So Uber car performance was more than 100 times worse than Waymo cars?!
Once again, Tesla demonstrates no understanding of volume manufacturing! Newspaper: “Tesla reworks 40% of its parts.” Tesla response: “But we inspect every car carefully before shipping it!”
Tesla fires back against a CNBC report that cited unnamed employees’ complaints about the electric carmaker cranking out a high number of parts that need to be repaired or replaced. Tesla say…
Source: Tesla: flawed parts report is flawed regarding cars’ quality
But as Deming and others pointed out decades ago, you cannot achieve good final quality by doing lots of inspection. There are many reasons for this, including that inspection/testing is not 100% accurate. The whole field of statistical process control, which eventually morphed into today’s “Six Sigma,” was invented as an alternative to massive inspection.
So if Tesla claims that it can make parts so poorly that 40% need rework, but still have defect-free cars, it is an admission of ignorance. This continues Tesla/Musk’s consistent pattern of not understanding that high volume manufacturing is not just “low volume manufacturing repeated many times.” (See my October post about Tesla’s attempts to ramp up Model 3 production.)
My suggestion for people with a Model 3 on order: don’t expect it to be on time, or have good build quality. Sorry.
Accelerated Learning by Experimentation
In most technologies and most industries, experiments play a central role in organizational learning as a source of knowledge and as a check before changes are implemented. There are four primary types of experiments: controlled, natural, ad-hoc, and evolutionary operation. This paper discusses factors that affect learning by experimentation and how they influence learning rates. In some cases, new ways of experimenting can create an order of magnitude improvement in the rate of learning. On the other hand, some situations are inherently hard to run experiments on, and therefore learning remains slow until basic obstacles are solved. Examples of experimentation are discussed in four domains: product development, manufacturing, consumer marketing, and medical trials.
Keywords: Learning, Experimentation
Full published version: Bohn Accelerated Learning by Experimentation. in Learning Curves: Theory, Models, and Applications edited by Mohamad Y. Jaber, CRC Press, 2011.
Preprint version, through SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1640767.
This is a rewritten version of my 1987 paper about manufacturing. Although I never published that paper, it was very influential in the 1990s, including becoming the (plagiarized) framework of a book and several articles by Stefan Thomke. This book chapter expands and extends the original concepts, including new material from Michael Lapré.
Gina Kolata in the NY Times has been running a good series of articles on fraudulent academic publishing. The basic business model is an unholy alliance between academics looking to enhance their resumes, and quick-buck internet sites. Initially, I thought these sites were enticing naive academics. But many academics are apparently willing participants, suggesting that it’s easy to fool many promotion and award committees.
All but one academic in 10 who won a School of Business and Economics award had published papers in these journals. One had 10 such articles.
It has always been clear that Musk does not understand high-volume manufacturing. Building rockets is very hard, but building 100,000 cars is very hard for a different reason! His predicted ramp rate was absurd. In the last 6 months, I think he has started to realize this.
Tesla has little chance of hitting its 5,000 weekly output during the fourth quarter. The chief reason: Its current production line can’t build vehicles at that rate unless it runs two 10-hour shifts seven days a week, which is
Source: Tesla | Hiccups Threaten to Slow Model 3 Launch | Industry content from WardsAuto
According to the article, Tesla has also deliberately ignored much of the accumulated wisdom about how to ramp in auto production. That might be OK for his second high-volume vehicle.
More details on Tesla’s ramp plans:
Tesla now has 2 choices, both bad:
- Go ahead and start building and shipping as fast as possible. The result will be multiple problems that require expensive hardware recalls.
- Add another 6? months to the schedule to run the as a pilot line for learning, rather than for volume. Expect zero salable output during that period. (As one of the comments said, they can give/sell those cars to employees.)
Added December 27: Tesla “still in manufacturing hell.”
Latest of many articles about Tesla manufacturing problems.
Here is a comment on that article: Musk needs to face up to having made a MAJOR mistake when he skipped some steps in the original manufacturing ramp-up.
He is probably also making another major mistake at present: adding new machines to the manufacturing process, before he has the existing machines working perfectly. This seems logical to people with no manufacturing experience, but it does not work. For one thing, it diverts his key resource, which right now is manufacturing engineers.
TL;DR In Southern California should put PV on houses and buildings that are far from the coast, because coastal areas are cloudy much of the summer. But the actual pattern is the opposite. I estimate a 30% magnitude of loss. Even my employer, UCSD, has engaged in this foolishness in order to appear trendy.
The bumpiness of this graph shows the effects of coastal weather in August.