Search Results for: #Manufacturing

Nov 17 2018

Manufacturing Today, By The Numbers

[latexpage]

What does manufacturing as a whole look like as a worldwide economic activity? When a company starts up or shuts down a plant, or relocates production from one country to another, it is a data point. It may be a compelling story but it doesn’t tell you the aggregate effect of all manufacturing on a country or the world.

Beyond anecdotes about particular companies or locations, the global data collected by the World Bank and the International Labor Organization confirm that (1) the size of the manufacturing sector is positively correlated with that of the rest of the economy, and (2) the share of manufacturing in advanced economies is holding its own in terms of value added but declining in terms of share of the labor force.

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By • News • 1 • Tags: , , , ,

Oct 12 2018

For Lean Production to Work, a Company Needs to Be All In | Ellen Rosen | The New York Times

 

“For a company in Chesterfield, Mo., it involved something as seemingly simple as attaching a trash can to an employee’s chair. For one in St. Louis, it meant leaving the cover off an electronic temperature controller. For others it’s meant gathering employees from the chief executive on down for what’s known as Kaizen events — based on the Japanese word for continuing improvement. What do these seemingly unconnected efforts have in common? They are approaches to what is known as lean manufacturing — or, more recently, lean production — aimed at streamlining production processes, enhancing employee engagement and increasing profits.”

Source: The New York Times(10/11/2018)

Michel Baudin‘s comments: Thanks to Kevin Hop for drawing my attention to this article. Like him, I do read The New York Times regularly and usually appreciate the quality of its reporting. This article, however, does not measure up. It reflects the conventional wisdom on Lean manufacturing which, if anything, explains why so many implementations fail.

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

Feb 5 2018

Countries Don’t Have Production Systems, Companies Do

Kobe Steel CEO apologizing (10/2017)

“Companies Everywhere Copied Japanese Manufacturing. Now the Model Is Cracking. Concepts celebrated in business publications worldwide have been tarnished by a string of scandals.

Japan’s reputation for flawless manufacturing quality and efficiency transformed the country’s postwar economy, changed business practices worldwide and spawned a library’s worth of management manuals and business advice books. Now, the model is cracking.

Kobe Steel Ltd., Mitsubishi Materials Corp., and Subaru Corp. have all admitted in recent months to manipulating quality inspections, though all say no safety problems emerged. Takata Corp. declared bankruptcy last year after admitting to supplying more than 50 million defective vehicle airbags in the U.S. Mitsubishi Motors Corp. has admitted covering up vehicle faults and falsifying fuel-economy data.”

Sourced from The Wall Street Journal

Michel Baudin‘s comments: What does the Volkswagen diesel emission scandal say about eyeglass lenses and telescopes made by Zeiss or A320s assembled by Airbus in Hamburg? Nothing. Factories for these companies are all located in the same country but a lapse by one is just that, and the Wall Street Journal did not publish articles suggesting that it made a statement about German industry as a whole. When it comes to Japan, however, this is exactly what they are doing with this article, assuming there is such a thing as “Japanese manufacturing,” which is blemished by the misbehavior of any Japanese company.

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By • Press clippings • 11 • Tags: , , ,

Apr 16 2017

An American Student In An iPhone Factory | Business Insider | Kif Leswing

 

“Imagine going to work at 7:30 every night and spending the next 12 hours, including meals and breaks, inside a factory where your only job is to insert a single screw into the back of a smartphone, repeating the task over and over and over again. During the day, you sleep in a shared dorm room, and in the evening, you wake up and start all over again.That’s the routine that Dejian Zeng experienced when he spent six weeks working at an iPhone factory near Shanghai, China, last summer. […]. Unlike many of those workers, Zeng did not need to do the job to earn a living. He’s a grad student at New York University, and he worked at the factory for his summer project.”

Sourced through Business Insider

Michel Baudin‘s comments: Thanks to my colleague Kevin Hop for sending me this rare peek into the life of the people who assemble iPhones by hand in Chinese factories each employing tens of thousands of workers. We need to keep in mind that this is the perspective of Dejian Zeng, an American student who was there for 6 weeks, not someone who works there for a living, but it is still informative.

While his account wouldn’t make anyone want to embrace iPhone assembly as a career choice, it’s not a horror story. The work is dull and repetitive, and there is too much of it, but it’s not described as dirty or dangerous. I have seen worse in poorly ventilated paint shops and machine shops with slippery floors, and not only in China.

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By • Press clippings • 0 • Tags: , ,

Jan 19 2017

Meaningful work for everyone ? Sorry…Lean can’t do that yet ! | John Dennis | LinkedIn

“It is disrespectful to workers for Management to make promises that they cannot deliver on. However there are presently some academics and authors in the Lean community who say that Lean transformation should provide ‘Meaningful Work’ for all workers. This phrase is setting too high an expectation for our workers…that we will not be able to deliver on…”

Sourced through LinkedIn

Michel Baudin‘s comments: I agree. Just Another Car Factory? Lean Production and Its Discontents is a chronicle of the early years of CAMI, a GM-Suzuki joint venture in Canada, which describes labor problems as due to management overselling Lean to production operators. As a manager, it’s one thing to overpromise to your superiors and another to shop floor operators. They don’t react the same way. Superiors reward you for setting “stretch goals,” and punish you if you only commit to what you can deliver. It’s the project game, as it has been played by generations in American managers. With shop floor operators, on the other hand, you lose your credibility and your ability to lead.

There is nothing you can do to turn a job in which you repeat the same 60 seconds of activity 400 times a day into “meaningful work.” You can make it easier and safer, you can mitigate the monotony by rotating operators between stations every two hours, and you can involve operators in Kaizen,… All of this improves both the performance of the production line and the experience of working on it, but it still won’t make working on an assembly line the kind of jobs kids dream of doing when they grow up. Dennis is right to say that overpromising to workers is disrespectful. They can handle the truth.

By • Blog clippings, Uncategorized • 12 • Tags: , , , ,

Jan 3 2017

Probability For Professionals

dice In a previous post, I pointed out that manufacturing professionals’ eyes glaze over when they hear the word “probability.” Even outside manufacturing, most professionals’ idea of probability is that, if you throw a die, you have one chance in six of getting an ace.

2000 years ago, Claudius wrote a book on how to win at dice but the field of inquiry has broadened since, producing results that affect business, technology, science, politics, and everyday life.

In the age of big data, all professionals would benefit from digging deeper and becoming, at least, savvy recipients of probabilistic arguments prepared by others. The analysts themselves need a deeper understanding than their audience.

With the software available today in the broad categories of data science or machine learning, however, they don’t need to master 1,000 pages of math in order to apply probability theory, any more than you need to understand the mechanics of gearboxes to drive a car.

It wasn’t the case in earlier decades, when you needed to learn the math and implement it in your own code. Not only is it now unnecessary, but many new tools have been added to the kit. You still need to learn what the math doesn’t tell you: which tools to apply, when and how, in order to solve your actual problems. It’s no longer about computing, but about figuring out what to compute and acting on the results.

Following are a few examples that illustrate these ideas, and pointers on concepts I have personally found most enlightening on this subject. There is more to come, if there is popular demand.

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By • Data science • 1 • Tags: , , , ,

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