News

Apr 14 2020

From Pandemic Disruption To Global Supply Chain Recovery | David Simchi-Levi | INFORMS

David Simchi-Levi has been a contributor to supply chain management for more than 20 years and I have two of the books he co-authored on my shelf, Designing and Managing the Supply Chain, a textbook for business and industrial engineering students, and The Logic of Logistics, for readers who care about the math behind the formulas and algorithms. So, when he announced a webinar on supply chain recovery after a pandemic, I signed up to listen. The full webinar is now available on Youtube, including a couple of minutes of dead silence in the middle while he was reading audience questions. The slide set is also available in PDF, and it includes an appendix with descriptions of algorithms not discussed in the webinar.

Michel Baudin‘s comments: As the COVID-19 pandemic is a currently unfolding catastrophe, the webinar devotes a large opening section to admiring the problem. This section is as of April 8, 2020. If the webinar were held, say, in August, 2020, this section would require an update. Simchi-Levi then goes on to describe a Risk Exposure Model that he and his team co-developed with Ford in the early 2010s based on the experience of supply chain recovery after the Fukushima earthquake or the Thailand floods of 2011. It is less connected to the latest news than the introduction.

These events disrupted, in particular, the supply of black paint to Ford, so that its customers could buy a car in any color as long as it was not black. They were major disasters at the time but, in retrospect, look small when compared with COVID-19. Simchi-Levi then uses the Risk Exposure Model as the basis for a series of recommendations to manufacturers.

Continue reading…

By • Press clippings • 2 • Tags: , ,

Apr 6 2020

Tracking COVID-19 | D. Wheeler, A. Pfadt, K. Whyte | QualityDigest

Projected COVID-19 Fatalities in the US

“While the spread of Covid-19 was effectively suppressed for two short periods, each time it rebounded. Another aspect of the Covid-19 pandemic is illustrated by this graph—declines in new cases of Covid-19 lag behind the interventions. Accordingly, these data cannot be used to establish cause-and-effect relationships.

In addition, deaths from Covid-19 lag even further behind. On March 27 there had been a cumulative total of 9 deaths from Covid-19 cases in all of Westchester County. Three days later this total had climbed to 19 deaths. The next day, March 31, there had been a total of 25 deaths from this outbreak. All of this suggests that while non-pharmaceutical interventions can be used to mitigate, or even suppress, the Covid-19 pandemic, these interventions have to be maintained until pharmaceutical interventions become available.”

Source: QualityDigest

Michel Baudin‘s comments: An informative, well-researched piece on the current tragedy. It’s not about manufacturing but it’s about the context in which we’ll have to practice it for the next two years.

#covid16, #coronavirus

By • Press clippings • 0 • Tags: ,

Mar 24 2020

A Sobering But Remarkable Chart | Josh Katz and Margot Sanger-Katz | New York Times

Click picture to enlarge as needed.

 

“As the coronavirus pandemic unfolds, people are dying around the world. But the trajectories of cases and deaths differ by country.”

Source: The New York Times

Michel Baudin‘s comments: On two prior occasions, I pointed out remarkable graphics in the New York Times:

This one, however, is remarkably rich, yet readable thanks to details like labeling the lines with the name of the country, as opposed to having the names in a legend block. The author, Josh Katz. is a graphics editor for the newspaper and his co-author, Margot Sanger-Katz, a writer on health care.

Continue reading…

By • Press clippings • 0 • Tags:

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

By • Press clippings • 1 • Tags:

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…

By • Press clippings • 2 • Tags: , ,

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

By • Press clippings • 1 • Tags: , , ,

«< 3 4 5 6 7 >»