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What is happening in lean manufacturing in the world
Curated by Michel Baudin
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Introduction to R for Excel Users | Thomas Hopper | R-bloggers

Introduction to R for Excel Users | Thomas Hopper | R-bloggers | lean manufacturing | Scoop.it

"...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..."

Michel Baudin's insight:
Kudos to Thomas Hopper for writing this guide and for making the complete 87-page PDF file available for download. For over two decades, I limited the analyses offered to my consulting clients to what I could do with Excel, because it was the only tool they had, and I wanted to reproduce my results. 

For the past three years, however, I have been teaching myself R and fully agree with Hopper that it is a much more powerful and reliable tool for analytics. I also agree that it takes time and effort to learn, but it is useful even at a beginner's level of proficiency. 

Many, including Hopper, refer to this gradual learning process as  "steep learning curve," which, strictly speaking, means the opposite: the steeper the learning curve of a skill, the faster you learn it... The main challenge I see for the manufacturing engineers and managers I know is the switch from a spreadsheet to a coding mindset. 

Excel is still preferable for expense reports or project cost justification, and R does not obviate the need for a database management system (DBMS). 
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Leather Accessory Manufacturing in India | YourStory.com | C. Ramalingegowda

Leather Accessory Manufacturing in India | YourStory.com | C. Ramalingegowda | lean manufacturing | Scoop.it

"Torero is an Indian leather manufacturing company that is the exclusive global license holder for Cross brand. Here is their story. It  aligns with Prime Minister Narendra Modi’s ‘Make In India’ campaign. It has nearly $20 million in annual revenues, employs 4000 workers...."

Michel Baudin's insight:

$20M/year for 4,000 workers works out to $5,000 in revenue/worker, out of which you have to pay them, buy materials, run facilities, etc., and eke out a profit. Are these numbers right? Given that the pictures do not show sweatshop conditions, I suspect that some the numbers are off, and that  it is really either $200M/year or 400 workers. 

 

According to the article, the owner/operator of this company is a guest lecturer at MIT's Sloan School of Management, and he has focused on modernizing the company's information technology. 

 

The two pictures of the company's manufacturing operations, however, show no hint of thinking in terms of flow, not much in the way of workstation organization or tooling. And these pictures leave you wondering what the operators' backs feel like after a day of sitting hunched over their work. 

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Big Data – The Antithesis of Lean Thinking | Bill Waddell

Big Data – The Antithesis of Lean Thinking | Bill Waddell | lean manufacturing | Scoop.it

"It’s too bad lean thinking is free.  I suppose that’s not entirely true; a lean transformation actually costs a few bucks for the learning – consultants, books and training.  But it is nothing like the cost of an ERP system, and it pales in comparison to ERP thinking on steroids – ‘Big Data’.  Because the ERP and Big Data providers play in such a high dollar arena they can and do spend a lot on very focused marketing efforts.  IBM, a company that stands to gain quite a bit from Big Data becoming the focus of business management, is providing “software, curriculum, case studies—including guest speakers” to Rensselaer Polytechnic Institute, Fordham, Yale and about 300 other schools.  Too bad those schools aren’t cranking out kids steeped in lean thinking, but there is no one who stands to make a enough money from peddling lean in a position to buy college curriculums on such a scale..."

Michel Baudin's insight:

While I concur with Bill on the irrelevance of "Big Data" in manufacturing, I can't follow him when he says it is a "singularly bad idea" for business in general. 

 

Big Data, per se, is actually not an idea but a phenomenon experienced in companies like Google, Amazon, eBay, Netflix, and others that process clicks, queries and transactions from millions of users, and generatie Terabytes of data every day. This is what Big Data is. Making sense of it is vital to these companies, and its volume requires special technology.

 

Even in a large manufacturing company, specs, orders, production status and history, quality problem reports, etc., add up to Gibabytes of data in total, not Terabytes every day. While it is beyond what you can handle on an Excel spreadsheet, it does not qualify as Big Data and does not require the special technology that ecommerce companies have developed. 

 

I also agree that the hot dog example from the HBR blog is simplistic. To give a less trivial example, assume you are in the business of providing streaming videos, and you discover from your customer data that those who view “Tora, Tora, Tora” also tend to view “The Bridges of Madison County.” That is unexpected and you wonder why. Then you find out that the customers who view both are married couples, form which you infer that the wife demands a chick flick for every aircraft-carrier movie…

 

This is a made-up example, but Ed Frazelle, in Supply Chain Strategy, quotes a real one about on-line ordering patterns for clothing. What kind of garments do customers tend to order together? I have asked that question around, and never met anyone who came up with the right answer, although, once you know it, it makes perfect sense: they order the same garment, in the same size, in different colors. And it is good to know if you are in charge of order picking.

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Three Ways Big Data Helps Manufacturers Think Bigger | Industry Week

Three Ways Big Data Helps Manufacturers Think Bigger | Industry Week | lean manufacturing | Scoop.it
Here are three ways Big Data is helping manufacturers think bigger than ever before:
1. Monitoring Product Quality Proactively
2. Seeing the Future—and Changing It
3. Getting Customers into the Data-Collection Game
Michel Baudin's insight:
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. 
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François Pellerin's curator insight, May 2, 2016 11:10 PM

En Français :

  • Adieu le contrôle statistique de procédé. Bonjour le Big Data
  • Maintenance prédictive
  • Des capteurs dans le produit pour analyser les usages du client
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Misleading Graphics in Global Manufacturing Report from McKinsey

Misleading Graphics in Global Manufacturing Report from McKinsey | lean manufacturing | Scoop.it
Manufacturing remains a critical force in both advanced and developing economies. But the sector has changed, bringing new opportunities and challenges to business leaders and policy makers. A McKinsey Global Institute article.
Michel Baudin's insight:

The bubbles above are intended to represent global market share by gross value added in 2010 in the manufacturing of "global goods for local markets," which includes appliances, automotive, chemicals, and pharmaceuticals.

 

The complete chart is a map of the world with a bubble for each of the top ten countries. From the point of view of graphic art, the chart looks professional; as a means of presenting data, however, it misleads.

 

From the center of the bubbles, you can see that China's share is twice that of Japan, but the bubble is four times larger. This is because its radius is twice that of Japan's bubble.

 

This is a perfect example of Tufte's rule that you should not us a two-dimensional symbol to show one-dimensional data. Market share is one number. If you want to make an accurate graphic comparison of different countries' market shares, use a bar chart.

 

As most readers know where in the world the US, China, and Japan are, you can lose the map of the world. It looks great, but it adds no information.

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