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: , , , ,

Jun 2 2018

Data Mining/Machine-Learning Tools In Manufacturing

This elaborates on the section on Analyzing The Data of the previous post. For a list of tools used for  “data mining” or “machine-learning,” I researched, for each one, who invented it, when it was invented, for what purpose, and what applications it has had in manufacturing, and summarized my findings in the table below.

I am, however, not satisfied with the level of applications I found and would like to crowdsource more. If you have made these or other tools useful in your own manufacturing environment, please share whatever information you can about your applications in the survey that follows.

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By • Data science • 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: , , ,

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: , , , ,

Nov 22 2016

If Talk Of Probability Makes Your Eyes Glaze Over…

Few terms cause manufacturing professionals’ eyes to glaze over like “probability.” They perceive it as a complicated theory without much relevance to their work. It is nowhere to be found in the Japanese literature on production systems and supply chains, or in the American literature on Lean. Among influential American thinkers on manufacturing, Deming was the only one to focus on it, albeit implicitly, when he made “Knowledge of Variation” one of the four components of his System of Profound Knowledge (SoPK).

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By • Data science • 1 • Tags: , , , ,

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