May 6 2016
Bodo Wiegand heads Germany’s Lean Management Institute. In his latest newsletter, on Wiegand’s Watch, he discusses the need for managers to go beyond the role of troubleshooter and become leaders.
May 5 2016
“Revered for decades as one of the world’s most innovative companies, 3M lost its innovative mojo when it began using Six Sigma to try to improve its operational efficiency. James McNerney, the CEO named in 2000, was a Jack Welch protégé from GE. He introduced the Six Sigma discipline as soon as he took the helm of the firm, streamlining work processes, eliminating 10% of the workforce, and earning praise (initially) from Wall Street, as operating margins grew from 17% in 2001 to 23% by 2005.
But when McNerney tried to apply the Six Sigma discipline to 3M’s research and development processes it led to a dramatic fall-off in the number of innovative products developed by the company during those years.”
Sourced through Scoop.it from: www.linkedin.com
Michel Baudin‘s comments:
Don Peppers describes “eliminating 10% of the work force” as part of implementing the “Six Sigma discipline,” but I don’t recall seeing anything on that subject when learning about Six Sigma.
May 2 2016
“Here are three ways Big Data is helping manufacturers think bigger than ever before:
Michel Baudin‘s comments:
Manufacturers already collect data by the gigabyte, including metadata, plans and schedules, status, and history. It’s not big data. It’s tiny when compared to the daily terabytes generated by transactions on Amazon or eBay, but it is still ample fodder for analysis, that is woefully underutilized.
The current databases contain information about trends, cyclical variations, product mix, and quality issues that most manufacturers do not currently extract. In such a context, I see an effort at improving analytics on existing data as a more relevant challenge than multiplying the quantity of collected data.
See on Scoop.it – lean manufacturing
May 1 2016
Before we entered the Tesla plant, tour guide Adam Slusser ran through the stipulations of the Non-Disclosure Agreement (NDA) we had just signed: we couldn’t take pictures, draw sketches, or even post accounts of the visit on social media. To honor this NDA, I will therefore limit myself to quoting what others have already posted elsewhere.
Apr 12 2016
Most of the work we do today involves interactions with machines. It is true not only in manufacturing but in many other business processes. The machinist works with machining centers, the pilot with an airplane, the surgeon with a laparoscopy robot, the engineer with a variety of computer systems,…, not to mention the automatic appliances that relieve us of household chores. In fact, I think that being good at working with machines is so essential that I wrote a book about it. For the short version, see the following A3/tabloid infographic. To enlarge it, click on the picture, and then on “View full size” in the bottom right-hand corner.
May 8 2016
Introduction to R for Excel Users | Thomas Hopper | R-bloggers
“…The quality of our decisions in an industrial environment depends strongly on the quality of our analyses of data. Excel, a tool designed for simple financial analyses, is often used for data analysis simply because it’s the tool at hand, provided by corporate IT departments who are not trained in data science.
Unfortunately, Excel is a very poor tool for data analysis and its use results in incomplete and inaccurate analyses, which in turn result in incorrect or, at best, suboptimal business decisions. In a highly competitive, global business environment, using the right tools can make the difference between a business’ survival and failure. Alternatives to Excel exist that lead to clearer thinking and better decisions. The free software R is one of the best of these…”
Sourced through Scoop.it from: www.r-bloggers.com
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By Michel Baudin • Blog clippings 2 • Tags: data science, Excel, Manufacturing, R