data science

QCtrackingToyota1950sArtOfLean2

Jul 16 2019

Updating the 7 tools of QC

A conversation with Franck Vermet about problem-solving tools for factory operators caused me to revisit the 7 tools of QC from 50 years ago and ponder how they should be updated with current data science.

Data Science for Operators, as a book, remains to be written. If you google this phrase today, what comes up is training courses offering to “change your career” by attending a “data science bootcamp.” TIBCO Spotfire has “Workflow Operators” but these are programs, not people.

So the following are tentative answers to questions that haven’t been asked before.

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DeustoLectureFeature

Jun 20 2019

The Use of Data to Improve Manufacturing | Luis Mauleón | Empresa XXI

Luis Mauleón

This is the translation of article recently published by Asenta’s Partner-Director Luis Mauleón in Empresa XXI, summarizing the key points of my lecture last month at the Faculty of Engineering of Deusto University in Bilbao, as part of a series of events organized by Asenta Management Consultants.

Practical lessons from Michel Baudin

In an environment with an overabundance of data, it is a paradox that we still have difficulties to finding simple answers to simple questions.

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Jun 2 2018

Data Mining/Machine-Learning Tools In Manufacturing

This elaborates on the section on Analyzing The Data of the previous post. For a list of tools used for  “data mining” or “machine-learning,” I researched, for each one, who invented it, when it was invented, for what purpose, and what applications it has had in manufacturing, and summarized my findings in the table below.

I am, however, not satisfied with the level of applications I found and would like to crowdsource more. If you have made these or other tools useful in your own manufacturing environment, please share whatever information you can about your applications in the survey that follows.

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May 23 2018

Using Data Science To Improve Manufacturing

If you google “data-science + manufacturing,” what comes back is recycled hype about the factory of the future. The same vision has been painted before and hasn’t come to pass, yet we are expected to believe that it will this time and that it will be a “4th industrial revolution.” Whether it’s true or not, this happy talk is no help in today’s factories. “Data science” covers real advances in the art of working with data, and the more relevant question is what it can do to improve existing operations.

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Dec 10 2017

Is SPC Obsolete? (Revisited)

Six years ago, one of the first posts in this blog — Is SPC Obsolete? — started a spirited discussion with 122 comments. Reflecting on it, however, I find that the participants, including myself, missed the mark in many ways:

  1. My own post and comments were too long on what is wrong with SPC, as taught to this day, and too short on alternatives. Here, I am attempting to remedy this by presenting two techniques, induction trees and naive Bayes, that I think should be taught as part of anything reasonably called statistical process control. I conclude with what I think are the cultural reasons why they are ignored.
  2. The discussions were too narrowly focused on control charts. While the Wikipedia article on SPC is only about control charts, other authors, like Douglas Montgomery or Jack B. Revelle, see it as including other tools, such scatterplots, Pareto charts, and histograms, topics that none of the discussion participants said anything about. Even among control charts, there was undue emphasis on just one kind, the XmR chart, that Don Wheeler thinks is all you need to understand variation.
  3. Many of the contributors resorted to the argument of authority, saying that an approach must be right because of who said so, as opposed to what it says. With all due respect to Shewhart, Deming, and Juran, we are not going to solve today’s quality problems by parsing their words. If they were still around, perhaps they would chime in and exhort quality professionals to apply their own judgment instead.

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Oct 23 2017

There Is More To Data Than Just Numbers

Don Wheeler’s Understanding Variation starts with a chapter entitled “Data are random and miscellaneous” that contains no discussion of any part of its title. Implicit in Wheeler’s book, however, is the view that data consists of tables of numbers, representing either measured variables — lengths, weights, densities,… — or event occurrence counts — defective units, defects, machine failures,…

Many times, I have quoted computer scientist Don Knuth on this subject, saying that data is “the stuff that’s input or output,” meaning anything that can be read or written, and it includes much more than tables of numbers. The data we work with today includes, for example, the following:

  • Unstructured text, like 25,000 incident reports written by maintenance techs all over the world in their versions of English about problems with jet engines, or thousands of product reviews posted by consumers on e-commerce sites
  • Images, like photographs of visual defects on products, or electron-microscope images of integrated circuits.
  • Videos recordings of operations.
  • …

Analyzing data about a manufacturing process today means extracting information from all sources. The state of the art, based on automatic data acquisition and databases includes analytical techniques that were unthinkable in Shewhart’s day, known under the labels of data science, data mining or machine learning.

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