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
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.”
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.
Elon Musk clearly has a blind spot about manufacturing. Building a giant factory for the first use of a new process does not work, and theoretically it cannot work. Even if it did work, it would be non-competitive. Once a factory is built and machines installed, subsequent new discoveries/knowledge cannot be incorporated, except at the margins.
To reach the 100-megawatt goal, sources indicate that the pilot production line in Fremont would eventually need to yield between 800 to 1,000 high-efficiency Whitney panels per day. But the team was not able to automate the process consistently enough to produce more than dozens of Whitney panels per day, according to people familiar with the matter. Most of the production resulted in “scrap,” they say. “The big problem was simply that they couldn’t scale up the technology to the point where you could run it in a factory,” a source familiar with the development explains.
SC overturns Lexmark’s patent win on used printer cartridges. Since the 17th century, restricting resale has been “against Trade and Traffique.”
Summary: once a product is sold, the original patent holder can’t control how it is subsequently used.
Today’s ruling is a win for many tech companies, with companies like Vizio, Dell, Intel, LG Electronics, HTC, and Western Digital all taking the side of Impression Products. [the winner] …The companies on Lexmark’s side, no surprise, were heavy licensers of patents, including tech giants like Qualcomm, IBM, Nokia, and Dolby. Biotechnology and pharmaceutical groups also supported Lexmark. Those lineups largely mirror industry divisions over Congressional debates around reforming patent laws, with the pro-Impression companies favoring user-friendly changes to patent laws, and the pro-Lexmark companies wanting more changes that favor patent owners.
I often gripe about the Supreme Court’s seeming “go with the big $” jurisprudence. But in this case, there was plenty of corporate power on both sides. And the 7-1 verdict means it was not a close call.