Aug 27 2025

Quality and Me (Part I) — Semiconductors

This is the first of several posts about my personal history with manufacturing quality. While I have never had the word “quality” in my job title, and it has never been my exclusive focus, I can’t name a project I have worked on in the past 44 years that didn’t have a quality dimension.

Controversial views about quality have earned me rebukes from quality professionals, who gave me reading lists. To see the error of my ways, all I had to do was study the complete works of Walter Shewhart, W. Edwards Deming, and Donald J. Wheeler. It never occurred to them that I might be familiar with these authors.

There are also other authors on quality that my contradictors ignored or dismissed, like J.M. Juran, Kaoru Ishikawa, or Douglas Montgomery. I didn’t see them as any less worthy of consideration than the ones they were adamant about.

I don’t think any of these authors intended their works to be scripture. Instead, they aimed to assist their contemporaries in addressing their quality issues with the technical and human resources available. We should do the same today. I recently heard from Sam McPherson of a piece of advice haiku author Matsuo Bashō gave to a painter in 1693: “Do not follow the footsteps of the ancients, seek what they sought instead.” This is what I have been doing.

By Michel Baudin • History 1 • Tags: Quality, semiconductors, yield enhancement

Jun 25 2025

Update on Data Science versus Statistics

Based on the usage of the terms in the literature, I have concluded that statistics has been subsumed under data science. I view statistics as beginning with a dataset and ending with conclusions, while data science starts with sensors and transaction processing, and ends in data products for end users. Kelleher & Tierney’s Data Science views it the same way, and so do tool-specific references like Gromelund’s R for Data Science, or Zumel & Mount’s Practical Data Science with R.

Brad Efron and Trevor Hastie are two prominent statisticians with a different perspective. In the epilogue of their 2016 book, Computer Age Statistical Inference, they describe data science as a subset of statistics that emphasizes algorithms and empirical validation, while inferential statistics focuses on mathematical models and probability theory.

Efron and Hastie’s book is definitely about statistics, as it contains no discussion of data acquisition, cleaning, storage and retrieval, or visualization. I asked Brad Efron about it and he responded: “That definition of data science is fine for its general use in business and industry.” He and Hastie were looking at it from the perspective of researchers in the field.

By Michel Baudin • Data science, Uncategorized 0 • Tags: data science, math, statistics

Jun 17 2025

How One-Piece Flow Improves Quality

There is a phase in the maturation of a manufacturing process where one-piece flow is the key to improving quality. Once the defective rate is low enough, one-piece flow reduces it by up to a factor of 10. The magnitude of the improvement often surprises managers. The cause-and-effect relationship is not obvious, and the literature on manufacturing quality is mute on the subject. We explore it here.

By Michel Baudin • Uncategorized 2 • Tags: Flow, Flow line, One-piece flow, Quality

Dec 28 2024

Using Regression to Improve Quality | Part III — Validating Models

Whether your goal is to identify substitute characteristics or solve a process problem, regression algorithms can produce coefficients for almost any data. However, it doesn’t mean the resulting models are any good.

In machine learning, you divide your data into a training set on which you calculate coefficients and a testing set to check the model’s predictive ability. Testing concerns externally visible results and is not specific to regression.

Validation, on the other hand, is focused on the training set and involves using various regression-specific tools to detect inconsistencies with assumptions. For these purposes, we review methods provided by regression software.

In this post, we explore the meaning and the logic behind the tools provided for this purpose in linear simple and multiple regression in R, with the understanding that similar functions are available from other software and that similar tools exist for other forms of regression.

It is an attempt to clarify the meaning of these numbers and plots and help readers use them. They will be the judges of how successful it is.

The body of the post is about the application of these tools to an example dataset available from Kaggle, with about 30,000 data points. For the curious, some mathematical background is given in the appendix.

Many of the tools are developments from the last 40 years and, therefore, are not covered in the statistics literature from earlier decades.

By Michel Baudin • Data science 0 • Tags: Linear Model, Quality, regression, Validation

Nov 6 2024

Rebuilding Manufacturing in France | Radu Demetrescoux

Radu Demetrescoux has been a manufacturing consultant for 25 years and recently authored a Lean Toolbox (in French) with actionable details on 64 tools. He has seen the French manufacturing sector losing half its factories and is working to rebuild it. This is how he explains what happened and the way forward. It includes an endorsement of our Introduction to Manufacturing as a contribution to this effort!

The Numbers

Between 1995 and 2015, France lost almost half of its factories and a third of its industrial jobs. In French economic statistics, the industry sector encompasses extraction and refining in addition to manufacturing. The share of Industry in GDP has fallen from 35% in 1970 to less than 20% currently. The share of manufacturing in GDP fell to 11% in 2017 compared to 17% in 1995. The objective stated by the government is to quickly increase the share of manufacturing in GDP to 15%.

By Michel Baudin • Personal communications, Uncategorized 0

Sep 8 2024

Using Regression to Improve Quality | Part II – Fitting Models

This is a personal guided tour of regression techniques intended for manufacturing professionals involved with quality. Starting from “historical monuments” like simple linear regression and multiple regression, it goes through “mid-century modern” developments like logistic regression. It ends with newer constructions like bootstrapping, bagging, and MARS. It is limited in scope and depth, because a full coverage would require a book and knowledge of many techniques I have not tried. See the references for more comprehensive coverage.

To fit a regression model to a dataset today, you don’t need to understand the logic, know any formula, or code any algorithm. Any statistical software, starting with electronic spreadsheets, will give you regression coefficients, confidence intervals for them, and, often, tools to assess the model’s fit.

However, treating it as a black box that magically fits curves to data is risky. You won’t understand what you are looking at and will draw mistaken conclusions. You need some idea of the logic behind regression in general or behind specific variants to know when to use them, how to prepare data, and to interpret the outputs.

By Michel Baudin • Data science 0 • Tags: Bagging, Bootstrapping, Kriging, Linear regression, Logistic regression, MARS, Multiple regression, Multivariate regression, Substitute characteristic, True characteristic

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