Press clippings

Mar 10 2020

Critical Data Elements and Data Quality | Rupa Mahanti | QualityDigest

“Not everything that can be counted counts. We are currently living in the digital age and are drowning in an ocean of data. Organizations have a large number of data entities and data elements, and a large volume of data corresponding to the same, and they continue to amass more and more data with each passing day. With a large amount of data coming in, it’s important to know what is ‘quality’ data, and what isn’t.”

Source: QualityDigest

Michel Baudin‘s comments: It is refreshing to see an article in QualityDigest on current topics, as opposed to pre-World War II methods. I tried to address this topic myself early on in this blog, in A Management Perspective on Data Quality.

#dataquality

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Feb 15 2020

Whatever became of Six Sigma? | Alfred Kieser | brand eins

 

Alfred Kieser

“TQM and Six Sigma are management fads that obey similar laws to clothing fashions. There are fashion designers who create the trend, and multipliers who disseminate and popularize it. These primarily include business consultants, but also scientists, managers, non-fiction authors or journalists. And there is the customer base that hopes to benefit from going with the fashion without having to think about it or take responsibility.”

Source brand eins

 

Michel Baudin‘s comments: Thanks to Ferdinand Grah for drawing my attention on LinkedIn to this interview of German management thinker Alfred Kieser. The article is in German. In it, Kieser paints a bleak picture of Six Sigma at GE and how former CEO Jack Welch leveraged it to his own benefit while wrecking the organization with rank-and-yank management. As for agility, he sees it as “just as content-free as the Balanced Scorecard.”

Continue reading…

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Feb 5 2020

A 1975 Technology Forecast | M. E. Marchant | AFIPS Proceedings

“A recent Delphi-type forecast of the future of manufacturing carried out by the International Institution for Production Engineering Research (CIRP) resulted in 94 forecast events on which good consensus was obtained. Of these, 24, or over one-fourth, strongly indicated that the computer-integrated automatic factory would be a full-blown reality well before the end of this century. The three key events which summarize this aspect of that forecast are as follows:

  1. By 1980 (median), a computer software system for full automation and optimization of all steps in the manufacturing of a part will be developed and in wide use.
  2. By 1985 (median), full on-line automation and optimization of complete manufacturing plants, controlled by a central computer, will be a reality.
  3. By 1990 (median), more than 50 percent of the machine tools produced will not have a “stand-alone” use, but will be part of a versatile manufacturing system, featuring automatic part handling between stations, and being controlled from a central process computer.”

Source: ACM Digital Library

Michel Baudin‘s comments: Thanks to Torbjørn Netland for digging up this gem, which puts the current claims about Industry 4.0 in perspective.

A Delphi-type forecast is based on questionnaires sent to a panel of experts in multiple rounds. Each expert sees the aggregate results of the previous round and modifies answers to arrive at a consensus. In other words, it’s subjective group-think.

1974 Personal Computer
1974 Altair-8800 computer

Like most of science-fiction, these forecasts both overestimate technology and fail to anticipate its evolution. A “central computer ” was going to do everything.  The paper is from 1975, a year after the first personal computer came out, the Altair 8800. At that time, PLCs and minicomputers like DEC’s PDP-11s were already taking over industrial control functions from mainframes.

The author worked for a machine-tool company, Cincinnati-Milacron, and seems to equate manufacturing with metalworking. Machine tools, today, are primarily used in automotive and aerospace; it is only a fraction of manufacturing as a whole.

#automation, #CIM, #industry4.0, #technologyforecast

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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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Nov 4 2019

Phase Two Charts and Their Probability Limits | Don Wheeler | Quality Digest

“The ability to react to process changes is more important than protecting yourself from occasional false alarms. […] So do not worry so much about straining out the gnats of false alarms that you end up swallowing the camels of undetected process changes.”

Sourced through Quality Digest

Michel Baudin‘s comments:

The people of the Honda plant in Anna, OH, claim to make the best engines in the world. On the floor, there is neither a single control chart nor any engineer trained in SPC.

Of course, we should fact-check their claim. Rankings of engine quality are not readily googleable. The closest I could find is a ranking of engine reliability from 2014 in a UK blog called The Car Expert, based on data from Warranty Direct, a UK provider of extended warranties. According to them, Honda indeed made the most reliable engines:

“Only one in every 344 Honda owners have had engine trouble, with second-placed rival Toyota notching up just 1 in 171.”

According to Anna engineers, their machine tools can hold tolerance ten times tighter than necessary. The few quality problems they do have are due to operators picking the wrong parts in assembly. Control charts in the machine shop would produce nothing but false alarms. With the charts crying wolf, the alarms would lose credibility and nobody would react when a real one hit.

In this kind of situation, Wheeler’s statement can be reversed. The ability to protect yourself against false alarms that send your engineers on wild goose chases is more important than detecting changes that hardly ever happen. You do want to detect changes in the process but control charts are too crude a tool for this purpose.

#SPC, #ControlChart

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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.

Continue reading…

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