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.
Dec 25 2019
Your Lean Six Sigma Belt Program Is the Problem | Dan Markovitz | Industry Week
“I visited a company a few weeks ago that asks all of their employees to do a green belt project. It’s not mandatory, but completion of a project is part of their annual review. Not surprisingly, the management boasts that nearly everyone does a project. You know how many people do a second project? Less than 5%. This company is doing okay, but they definitely don’t have a culture of continuous improvement.”
Source: Industry Week
Michel Baudin‘s comments: Dan’s article is spot on, except in his assessment of statistical tools. Depending on the company’s situation, none of the ones he lists may be needed. Other tools, like SMED, cell design, mistake-proofing or JKK may be more relevant. Data science is needed in semiconductors and pharmaceuticals but the statistical tools Dan describes as “advanced” are not. ANOVA, regression, and t-tests go back 100 years; Design Of Experiments (DOE), a good 50. As for Ishikawa’s “7 tools of QC” from the 1960s, I have never seen them used as advertised anywhere. They are sorely in need of an update in every respect, from data acquisition to analysis and presentation.
#leansixsigma, #blackbelts, #datascience,#7toolsofqc
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By Michel Baudin • Press clippings • 2 • Tags: 7 tools of QC, Black Belts, data science, Green Belts, Lean Six Sigma