Mar 9 2026
This is a primer for engineers and operations managers who are hazy about concepts such as the time value of money, discounted cash flow (DCF), net present value (NPV), or internal rate of return (IRR). It is meant to level the playing field for them when dealing with MBAs to whom it’s “Business 101.”
Dec 22 2025
When Katie Anderson, Olivier Larue and I met last Thursday, what did we discuss? We started with the pros and cons of self-publishing versus going through established publishing houses. Then we talked about the challenges and opportunities of providing books in various media. We discussed Olivier’s two new books and, finally, our kids. Katie has the youngest ones; I have the oldest.
Dec 13 2025
The article below was generated by Gemini Deep Research, now based on Gemini 3, in response to the following prompt:
“Gather all the stuff you can about Michel Baudin and tell me what are the strengths and weaknesses of his work.”
The inspiration was Bob Emiliani, who has done the same. It is as complete and accurate as you might expect from a human journalist, and better than anything I have seen from other AI tools. In particular, it doesn’t credit me with books I haven’t written, degrees I don’t have, jobs I never held, or the wrong ethnic background. Still, it’s not perfect, and I have picked a few nits, mostly with numbers:
- In Section 2.1, it confuses the time I worked in research with the period during which my results were published. In 1981-1986, I worked as an engineer in semiconductors, while my papers based on earlier work made their way through peer-review.
- In 2.4, it says I have lived in the US for 31 years. It’s actually 45 years.
- In 2.4, it reduces the influence of the various cultures I have immersed myself in to a single phrase for each, which is nonsense.
- In 3, it says I have written four books, and then includes paragraphs discussing each of my five books.
- Section 3 describes all of my books as being in the “nuts and bolts” series. In fact, it is only three of them: Lean Assembly (2002), Lean Logistics (2005) and Working with Machines (2007). The other two, Manufacturing Systems Analysis (1990) and Introduction to Manufacturing (2023) are not part of this series.
Section 7.2 lists the weaknesses of my work. The first is that my work “is not for the faint of heart. It is dense, mathematical, and dry.” Each of my books aims to serve the needs of a specific audience, from developers of production planning & scheduling systems to designers and managers of assembly operations, supply chain managers, designers and managers of processes involving people working with machines, or students of industrial engineering and operations management. I haven’t yet written one for C-suite executives, but my next collaboration with Torbjørn Netland might be suitable for them.
There is much more math in my blog posts than in my books or in my consulting practice. There is a constituency for it, in professionals who are unhappy applying formulas handed to them that they don’t understand. To my surprise, the math I include in my posts elicits reactions, comments, and sometimes corrections. One reader on LinkedIn once commented that I “make the esoteric understandable.” As for being dry, I try not to be, by including many real examples and illustrations.
The second weakness is my “grumpy expert” persona. When participating in forums on social media, you want respectful but spirited debates to engage readers. Calling people names or attributing motives turns readers off, but challenging claims and ideas is productive. When they are inconsistent with my experience, I ask for elaboration, clarification, and corroboration. People with valid points provide them, and maybe I should thank them more profusely for it.
The third is “niche focus.” My niche is manufacturing, which accounts for ~10% of GDP in the US, the UK, France, and Italy, and ~20% in Germany and Japan. It’s kept me busy. When I am done solving manufacturing problems, I may look into healthcare, software, or service industries. It will never happen.
But enough of my reactions to the output from Gemini Deep Research. Here is what it says:
Nov 7 2025
To use in-process measurements for quality control 100 years ago, Walter Shewhart proposed the \bar{X}-\sigma charts. It entailed arranging workpieces in rational subgroups, summarizing measurements by subgroup into means and standard deviations, charting both, and checking new values against control limits.
When Harold Dodge tried to implement the \bar{X}-\sigma charts at Western Electric, the engineers balked at calculating sample standard deviations with paper, pencils, and slide rules. To gain acceptance, Dodge let them use sample ranges and plot them in R charts instead. While easier to understand and to use daily, sample ranges are mathematically more complex and more sensitive to extreme values than standard deviations.
The SPC literature glosses over the motivation for the R chart and its math. It provides recipes for using these charts, but no explanation. We shouldn’t ask manufacturing professionals to use a tool without explaining its purpose and its theory. This is what this post is trying to remedy.
Like all control charts, the R chart uses limits calculated for the Gaussian distribution. As no simple formula is available for the R chart, setting control limits for it requires numerical approximations that must have consumed months for human computers in 1924. Today, you can replicate them instantaneously with software. These calculations reveal that the \pm 3\sigma limits in the books for the range chart do not actually encompass the 99.73% of the distribution that they do in \bar{X} charts.
The R chart was an ingenious workaround to technical and human constraints of the 1920s that no longer exist. Today, rather than blindly applying these tools, we must draw inspiration from their inventors and develop solutions to meet the process capability challenges we are actually facing.
Oct 21 2025
In 1995, Peter Drucker conceded that Management By Objectives (MBO) was not “the great cure for management inefficiency” he had believed when he coined the term 41 years earlier. In the meantime, the technique had contributed massively to the decline of American industry by turning managers into metrics gamers.
On 10/7/2025, The Conversation published an article by Aurélien Rouquet, reassuring us that Management by objectives is not a Nazi invention, contrary to what historian Johan Chapoutot claims. Rouquet attributes its paternity to Alfred P. Sloan, the head of General Motors who made it the most powerful company in the world by the end of World War II.
Rouquet’s article also includes a link to another article, dated 9/2/2024, where George Kassar asserts in the title that At 70, management by objectives remains unsurpassed. The most surprising thing, for an article on such a subject, is that it does not cite any company whose performance has been improved by MBO. And the author seems to ignore the existence of an approach that has surpassed MBO for decades, the Hoshin Kanri, which perhaps has the misfortune of coming from Japan.
Apr 3 2026
Deviating Standard Deviations
This basic concept deserves revisiting. The following is from a blog post from 2022 hosted by a supplier of statistical software intended to explain the meaning of some notations in plain, simple terms:
The author calls two different things by the same name. If the standard deviation of each variable is 1, how could its expected value be anything else? The confusion within this nonsensical statement is the same we make when we equate the temperature of a soup with a thermometer reading. In our mental model of a bowl of soup, it has a temperature that exists regardless of our ability to measure it, and the thermometer reading is only an estimate of it.
For the purposes of eating soup, confusing the two is harmless, unless the thermometer, poorly calibrated, always gives you an answer that is 15°F off. This is the situation we have with the most commonly used estimator of the standard deviation of a random variable from a small sample. It is biased, and c_4(n) is a correction factor applicable when the random variable is Gaussian.
To describe c_4(N) accurately, we need to dig into probability theory. It is, in fact, the expected value of the estimator S=\sqrt{\frac{1}{N-1}\sum_{i=1}^{N}\left ( X_i -\bar{X} \right )} of the standard deviation from a sample of N independent Gaussian variables \left ( X_1, \dots, X_N \right ) with unit standard deviation, \sigma = 1. This is an accurate statement, but every term in it needs an explanation.
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By Michel Baudin • Technology 0 • Tags: Control Charts, Probability, Quality, Six Sigma, SPC, Standard Deviation, statistics