Technology

Aug 14 2018

BOMs at Toyota | Discussion on LinkedIn

I shared the BOM rap post with the TPS Principles and Practices group on LinkedIn, in order to tap into the TPS knowledge in this group, and this is what I learned from Todd McCann, Carey Boggess, Jeff Merriss, Hein Winkelaar, and Gary D. Stewart.

My thanks to all for their valuable input on the control of BOMs within Toyota and the uses BOM data is put to. What these responses did not address, however, is the information model for the BOMs, including what items are in and how BOMs are structured at Toyota, as opposed to other companies.

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Aug 1 2018

The BOM Rap

Why bother with a yawn-inducing topic like bills of materials (BOMs)? Bring it up with manufacturing professionals and their eyes glaze over instantly. And the Lean literature is mute on the subject. I even asked Michael Ballé for his input on the subject and he responded that he had none to offer.

Yet everyone involved with assembly agrees that BOMs are at the core of their activity, that BOMs have chronic accuracy issues, that the workarounds to these inaccuracies impair the companies’ ability to update and customize products, and that BOM maintenance hogs resources. Perhaps it’s worth giving the subject some thought and having a conversation about it.

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By • Information Technology • 9 • Tags: , , , , , ,

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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By • Data science • 1 • Tags: , , ,

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  this time 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.

This is not just about reaping tangible benefits today rather than hypothetical ones in the future but also about acquiring skills needed to design new plants and production lines 5 years from now. These publications endow technology with a power to drive innovation that it doesn’t have. It is only a means for people to innovate. Their ability to do so hinges on their mastery of the technology, which is acquired by using it in continuous improvement.

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By • Data science • 7 • Tags: , , , , ,

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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