Technology

ChatGPT about "Michel Baudin"

Feb 26 2023

Wrong things ChatGPT says about me

What ChatGPT says about me is generally flattering but inaccurate and inconsistent. Ask multiple times and you get different wrong answers. I find it disturbing that it credits me for things I didn’t do. It also omits some I have done, which makes me wonder to whom ChatGPT attributes them. Here are a few mistakes I found in four successive versions:

  • “Baudin is the author of several books […], including […] Lean Work Design.” Sorry, I never wrote a book called “Lean Work Design.” Instead, I wrote Introduction to Manufacturing (2022), Working with Machines (2007), Lean Logistics (2005), Lean Assembly (2002), and Manufacturing Systems Analysis (1990).
  • “He has over 30 years of experience…” 42 years is the number.
  • “Baudin began his career at Hewlett-Packard…” Nope. I started out at Fairchild. Years later, Hewlett-Packard was a consulting client, but I never was an employee.
  • “his own consulting firm, Baudin Associates.” Wrong. I never had a firm by that name. It was MMTI, and then The Takt Times Group.
  • “He has also served as a professor of Lean manufacturing at the California State University, Chico.” I have never set foot in Chico. I have taught extension courses at UC Berkeley, the University of Dayton, the Hong Kong Productivity Council, and the University of Buckingham, but not Chico State.
  • “Baudin has taught courses […] at several universities, including […] MIT, and Stanford…” No, I never did.
  • “He is currently a professor at Institut Mines-Télécom Business School in France.” No.
  • “Baudin is a graduate of Ecole Centrale Paris.” No, it’s Mines-ParisTech, not Centrale.
  • “He holds a Ph.D. in Mechanical Engineering from the University of California, Berkeley.” I don’t have a Ph.D.
  • “He has worked as a researcher at several institutions, including the Lawrence Berkeley National Laboratory and the French National Institute for Industrial Environment and Risks.” I never worked at either of these places. I did research early in my career, at the Hahn-Meitner Institute in Berlin, Germany, Mines-ParisTech in Fontainebleau, France, and the University of Tokyo, Japan.
  • “Baudin has also served as a consultant for a variety of companies, including Toyota, Ford,…” No. I never did any work for either. In the auto industry, I did some work for Honda, Renault, and GM, but neither Toyota nor Ford.

In other words, #ChatGPT is looser with facts than any journalist I have ever read. 

 

By • Technology • 6 • Tags:

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Nov 7 2022

Analyzing Variation with Histograms, KDE, and the Bootstrap

Assume you have a dataset that is a clean sample of a measured variable. It could be a critical dimension of a product, delivery lead times from a supplier, or environmental characteristics like temperature and humidity. How do you make it talk about the variable’s distribution? This post explores this challenge in the simple case of 1-dimensional data. I have used methods from histograms to KDE and the Bootstrap, varying in vintage from the 1890s to the 1980s:

Timeline of tools used in this post

Other methods were surely invented for the same purpose between 1895 and 1960 or since 1979, that I don’t know about or haven’t used. Readers are welcome to point them out.

The ones discussed here are not black boxes, automatically producing answers from a stream of data. All require a human to tune the settings of the tools. And this human needs to know the back story of the data.

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By • Data science • 2 • Tags: , , ,

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Oct 12 2022

Musings on Large Numbers

Anyone who has taken an introductory course in probability, or even SPC, has heard of the law of large numbers. It’s a powerful result from probability theory, and, perhaps, the most widely used. Wikipedia starts the article on this topic with a statement that is free of any caveat or restrictions:

In probability theory, the law of large numbers is a theorem that describes the result of performing the same experiment a large number of times. According to the law, the average of the results obtained from a large number of trials should be close to the expected value and tends to become closer to the expected value as more trials are performed.

This is how the literature describes it and most professionals understand it. Buried in the fine print within the Wikipedia article, however, you find conditions for this law to apply. First, we discuss the differences between sample averages and expected values, both of which we often call “mean.” Then we consider applications of the law of large numbers in cases ranging from SPC to statistical physics. Finally, we zoom in on a simple case, the Cauchy distribution. It easily emerges from experimental data, and the Law of Large Numbers does not apply to it.

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By • Laws of nature • 1

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Jul 18 2022

The Most Basic Problem in Quality

Two groups of parts are supposed to be identical in quality: they have the same item number and are made to the same specs, at different times in the same production lines, at the same time in different lines, or by different suppliers.

One group may be larger than the other, and both may contain defectives. Is the difference in fraction defectives between the two groups a fluctuation or does it have a cause you need to investigate? It’s as basic a question as it gets, but it’s a real problem, with solutions that aren’t quite as obvious as one might expect. We review several methods that have evolved over the years with information technology.

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By • Data science • 0 • Tags: , , , , ,

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Jun 12 2022

Perspectives On Probability In Operations

The spirited discussions on LinkedIn about whether probabilities are relative frequencies or quantifications of beliefs are guaranteed to baffle practitioners. They come up in threads about manufacturing quality, supply-chain management, and public health, and do not generate much light. Their participants trade barbs without much civility, and without actually exchanging on substance.

The latest one, by Alexander von Felbert, is among the more thoughtful, and therefore unlikely to inspire rants. I do, however, fault it with using words like “aleatory” or “epistemic” that I don’t think are helpful. I am trying to discuss it here in everyday language, and to apply the concepts to numerically specific cases, with an eye to operations.

While there are genuinely great and not-so-great ideas, the root of the most violent disagreements is elsewhere, with individuals generalizing from different experience bases. You may map probability to reality differently depending on whether you are developing drugs in the pharmaceutical industry, enhancing yield in a semiconductor process, or driving down dppms in auto parts. The math doesn’t care as long as you follow its rules, and it doesn’t invalidate other interpretations.

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By • Data science • 0 • Tags: , , ,

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Feb 23 2022

Standards, China, and the Industrial Revolution

[Featured image: a Han dynasty bronze ruler]

As a general principle, in manufacturing, you need to do the work the same way every time if you want the output to be consistent. In some cases, like extracting metals from ores, you need to tweak processes to produce consistent output from raw materials of varying compositions. Then the tweaks themselves must be executed consistently so that the response to a particular variation in ore content is always the same.

Standards are an area where China had a 2,000-year headstart but neither the scientific nor the industrial revolutions occurred there.

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By • Technology • 1 • Tags: , , ,

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