Data science

RanchoSanAntonioPlacard

May 6 2020

The Math of COVID-19, And Factories

Whether we like it or not, the past months have given us a crash course in epidemiology. COVID-19 has taken terms like reproduction number, herd immunity, social distancing, or flattening the curve from research literature to daily news and instructions for visitors to California State Parks.

We are in the middle of a pandemic we have partially tamed by putting the economy in a coma. This pandemic has already killed more Americans in two months than the Vietnam war in 20 years and we are facing the unprecedented challenge of restarting factories in this context.

Among the many things to learn in a hurry, are what epidemiologist Adam Kucharski calls the rules of contagion, as they apply to the people who work in a factory and its surrounding community.

Quality control is the closest most of us in Manufacturing ever get to serious statistics/data science. It’s not the same domain as epidemiology, and there is little crossover in tools or methods. This is to share what I have just learned about this topic. I welcome any comment that might correct misconceptions on my part or otherwise enlighten us.

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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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Mar 12 2019

More About the Math of the Process Behavior Chart

In statistics on time series with “moving” in their name, each value is correlated with past and future neighbors — that is, the series is autocorrelated. It affects the way you can use these statistics to detect anomalies and issue alarms.

The moving range in the XmR chart is a case in point. Its autocorrelation in the moving range chart is self-inflicted. It is autocorrelated by construction, regardless of whether the raw data themselves are.

Some raw data are autocorrelated. For example, when you issue a replenishment order for a part by pulling a Kanban from a bin, you are assuming that the demand for a coming period to match that of the period that just elapsed, with minor fluctuations. Implicitly, you are leveraging the autocorrelation of the part consumption across periods.

On the other hand, if a physical characteristic of a manufactured part is the sum of a constant and noise, then the noises are independent, and therefore uncorrelated. Taking moving ranges introduces an autocorrelation between consecutive values that is absent in the raw data.

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Mar 2 2019

The Math Behind The Process Behavior Chart

Ever since asking Is SPC Obsolete? on this blog almost 6 years ago, multiple sources have told me that the XmR chart is a wonderful and currently useful process behavior chart, universally applicable, a data analysis panacea, requiring no assumption on the structure of the monitored variables. So I dug into it and this what I found.

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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  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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