Category Archives: Manufacturing

Manufacturing Outlook and History In the USA and Globally

I write primarily about management improvement on this blog – which makes sense given the title. In the very early days I had more on investing, economic data, science, engineering and travel. Then I created three new blogs (Curious Cat Investment and Economics Blog, Curious Cat Science and Engineering Blog, Curious Cat Travel Photos blog) and that made this blog more focused.

Even so the lines of what fits where can be a bit fuzzy and I continue to write about manufacturing, and health care, with a focus on economic data, occasionally. And that is what I am doing today while touching on management related to manufacturing a bit.

As I have written before the story of manufacturing in the USA, and globally, is greatly increased quality of processes and output as well as greatly improved productivity over the last few decades. Manufacturing output also increased, including in the USA, as I have written consistently for a decade now. For example: (Top 10 Countries for Manufacturing Production from 1980 to 2010.

Still many people have the notion that USA manufacturing has been declining, which hasn’t been true, and certainly isn’t true now (the last couple of years have been especially strong and even the general public seems to realize the idea of the USA losing manufacturing is a myth).

Chart of Manufacturing Output fro 1992 to 2012 - USA, China, Japan and Germany

Based on data from the UN. See my blog post on my economics for more details on the data (to be posted next week).

The chart is impressive and illustrates the point I have been hammering home for years. The USA manufacturing base is growing and far from crumbling (job losses are deceiving as they are global and not an indication of a USA manufacturing decline). China’s manufacturing growth is incredible. China and the USA are far away the top 2 manufacturing countries. Japan and Germany make out the top 4 before a large gap which then is followed by a group of countries that are very close (Korea is 5th with less than half the production of Germany).

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George Box

I would most likely not exist if it were not for George Box. My father took a course from George while my father was a student at Princeton. George agreed to start the Statistics Department at the University of Wisconsin – Madison, and my father followed him to Madison, to be the first PhD student. Dad graduated, and the next year was a professor there, where he and George remained for the rest of their careers.

George died today, he was born in 1919. He recently completed An Accidental Statistician: The Life and Memories of George E. P. Box which is an excellent book that captures his great ability to tell stories. It is a wonderful read for anyone interested in statistics and management improvement or just great stories of an interesting life.

photo of George EP Box

George Box by Brent Nicastro.

George Box was a fantastic statistician. I am not the person to judge, but from what I have read one of the handful of most important applied statisticians of the last 100 years. His contributions are enormous. Several well know statistical methods are known by his name, including:

George was elected a member of the American Academy of Arts and Sciences in 1974 and a Fellow of the Royal Society in 1979. He also served as president of the American Statistics Association in 1978. George is also an honorary member of ASQ.

George was a very kind, caring and fun person. He was a gifted storyteller and writer. He had the ability to present ideas so they were easy to comprehend and appreciate. While his writing was great, seeing him in person added so much more. Growing up I was able to enjoy his stories often, at our house or his. The last time I was in Madison, my brother and I visited with him and again listened to his marvelous stories about Carl Pearson, Ronald Fisher and so much more. He was one those special people that made you very happy whenever you were near him.

George Box, Stuart Hunter and Bill Hunter (my father) wrote what has become a classic text for experimenters in scientific and business circles, Statistics for Experimenters. I am biased but I think this is acknowledged as one of (if not the) most important books on design of experiments.

George also wrote other classic books: Time series analysis: Forecasting and control (1979, with Gwilym Jenkins) and Bayesian inference in statistical analysis. (1973, with George C. Tiao).

George Box and Bill Hunter co-founded the Center for Quality and Productivity Improvement at the University of Wisconsin-Madison in 1984. The Center develops, advances and communicates quality improvement methods and ideas.

The Box Medal for Outstanding Contributions to Industrial Statistics recognizes development and the application of statistical methods in European business and industry in his honor.

All models are wrong but some are useful” is likely his most famous quote. More quotes By George Box

A few selected articles and reports by George Box

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Special Cause Signal Isn’t Proof A Special Cause Exists

One of my pet peeves is when people say that a point outside the control limits is a special cause. It is not. It is an indication that it likely a special cause exists, and that special cause thinking is the correct strategy to use to seek improvement. But that doesn’t mean there definitely was a special cause – it could be a false signal.

This post relies on an understand of control charts and common and special causes (review these links if you need some additional context).

Similarly, a result that doesn’t signal a special cause (inside the control limits without raising some other flag, say a run of continually increasing points) does not mean a special cause is not present.

The reason control charts are useful is to help us maximize our effectiveness. We are biased toward using special cause thinking when it is not the most effective approach. So the control chart is a good way to keep us focused on common cause thinking for improvement. It is also very useful in flagging when it is time to immediately start using special cause thinking (since timing is key to effective special cause thinking).

However, if there is result that is close to the control limit (but inside – so no special cause is indicated) and the person that works on the process everyday thinks, I noticed x (some special cause) earlier, they should not just ignore that. It very well could be a special cause that, because of other common cause variation, resulted in a data point that didn’t quite reach the special cause signal. Where the dot happened to land (just above or just below the control limit – does not determine if a special cause existed).

The signal is just to help us systemically make the best choice of common cause or special cause thinking. The signal does not define whether a special cause (an assignable cause) exists of not. The control chart tool helps guide us to use the correct type of improvement strategy (common cause or special cause). But it is just a signaling device, it isn’t some arbiter of whether a special cause actually exists.

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Leading Manufacturing Countries from 2000 to 2010: China, USA…

chart showing leading manufacturing countries output from 2000-2010

Chart of manufacturing production by the top 10 manufacturing countries (2000 to 2010). The chart was created by the Curious Cat Economics Blog. You may use the chart with attribution. All data is shown in 2010 USD (United States Dollar).

Over the years I have been posting data on the manufacturing output of leading countries. In 2010 China finally overtook the USA to becoming the leading manufacturer (long after you would have thought listening to many news sources and political leaders). In a previous post on the Curious Cat Economics Blog I looked at the output of the top 10 manufacturing countries with a focus on 1980 to 2010.

In 1995 the USA was actually very close to losing the lead to Japan (though you wouldn’t think it looking at the recent data). I believe China will be different, I believe China is going to build on their lead. There has been some talk for several years of manufacturing moving out of China seeking lower cost countries. The data doesn’t support any decline in Chinese manufacturing (or significant moves away from China toward other South-East Asian countries). Indonesia has grown quickly (and is the largest SE Asian manufacturing country), but their total manufacturing output is less than China grew by per year for the last 5 years.

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Marketplace Looks at the Apple Economy

Marketplace looks at the Apple economy in China. Marketplace is an excellent source of actual journalism; rare in the post Bill Moyers days, sadly.

A look inside a Foxconn factory

The first misconception I had about Foxconn’s Longhua facility in the city of Shenzhen was that I’ve always called it a ‘factory’ — technically, it is. But after you enter the gates and walk around, you quickly realize that it’s also a city — 240,000 people work here. Nearly 50,000 of them live on campus in shared dorm rooms. There’s a main drag lined on both sides with fast-food restaurants, banks, cafes, grocery stores, a wedding photo shop, and an automated library. There are basketball courts, tennis courts, a gym, two enormous swimming pools, and a bright green astroturf soccer stadium smack-dab in the middle of campus. There’s a radio station — Voice of Foxconn — and a television news station. Longhua even has its own fire department, located right on main street. This is not what comes to mind when you think “Chinese factory.”

Yet it is: as you walk beyond the civic center of Longhua, the buildings begin to change.

From a management perspective there is a great deal to be desired in Apple’s manufacturing practices. The economic perspective however, for me, provides a much different picture than those in rich countries (USA, Europe, Singapore, Japan…) often feel.

The jobs provide workers a chance to earn what for them is a great deal of money. Yes the conditions are harsh – I wouldn’t want to have to work there. But I am pretty sure I would not be happier, if I lived in China, and everything else remained the same in China except now all the Apple products were made in Singapore, USA and Spain.

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Looking at Auto Manaufacturing in the USA

America’s Dirty War Against Manufacturing

Bob Lutz, the former head of GM, says it was neither uncompetitive wages nor unions that drove the Big Three into decline. It was a management with its eye focused on the bottom line and the short term.

That sentiment should be familiar to students of Deming (it is one of Deming’s 7 deadly diseases). It is sad that this bad management practices, short-term thinking, continues to do harm several decades later. Hopefully we can do better in the next few decades.

retiree health care and pensions — burdens that are borne by society, not manufacturing plants, in every other advanced country. That disparity, the result of policy decisions made in Washington rather than wages negotiated by the United Auto Workers, was the source of most of the labor-cost advantage enjoyed by foreign companies.

The excessive health care costs in the USA, another of Deming’s 7 deadly diseases, has continued to get worse every year since he classified it as one. The damage that the failed health care system in the USA does to the USA is enormous.

Related: Manufacturing Skills Gap or Management Skills Gap?Manufacturing in the USA, and Why Organizations Often Don’tBig Failed Three, Meet the Enlightened Eight

Manufacturing Skills Gap or Management Skills Gap?

I stumble across articles discussing the problem of manufacturers having difficulty finding workers with the skills they need (in the USA largely, but elsewhere too) somewhat regularly. While it is true that companies have this problem, I think looking at the problem in that way might not be the most insightful view. Is the problem just that potential workers don’t having the right skills or the result of a long term management skills gap?

To me, the current manufacturing skills gap results directly from short term thinking and disrespect for workers practiced by those with management skills shortages over the last few decades. Those leading the manufacturing firms have shown they will flee the USA with the latest change in the wind, chasing short term bonuses and faulty spreadsheet thinking. Expecting people to spend lots of time and money to develop skills that would be valuable for the long term at manufacturing firms given this management skills shortage feels like putting the blame in the wrong place to me.

Why should workers tie their futures to short term thinking managers practicing disrespect for people? Especially when those managers seem to just find ways to blame everyone else for their problems. As once again they do in blaming potential workers for their hiring problem. The actions taken based on the collective management skill shortage in the manufacturing industry over the last few decades has contributed greatly to the current state.

If managers had all been managing like Toyota managers for the last 30 years I don’t think the manufacturing skill gap would be significant. The management skill gap is more important than the manufacturing skill gap in my opinion. To some extent the manufacturing skill gap could still exist, market are in a constant state of flux, so gaps appear. But if their wasn’t such a large management skill gap it would be a minor issue, I believe.

That still leaves companies today having to deal with the current marketplace to try and find skilled workers. But I think instead of seeing the problem as solely a supplier issue (our suppliers can’t provide us what we need) manufacturing firms would be better served to look at their past, and current, management skills gap and fix that problem. They have control over that problem. And fixing that will provide a much more solid long term management base to cope and prosper in the marketplace.

Another management issue may well be the hiring process itself. As I have written about many times, the recruitment process is highly inefficient and ineffective. When you see workers as long term partners the exact skills they have today are much less significant than their ability to meet the organizations needs over the long term. In general, information technology recruiting has the worst case of focusing on silly skills that are really not important to hiring the right people, but this also can affect manufacturing hiring.

Related: IT Talent Shortage, or Management Failure?Dee Hock on HiringManufacturing Jobs Increasing for First Time Since 1998 in the USA (Sept 2010)Building a Great Workforcemanufacturing jobs have been declining globally (including China) for 2 decadesImproving the Recruitment Process

6 New Kiva Loan to Manafacturing Entrepreneurs

I have been a big fan of Kiva for quite some time, and have written about it previously: Kiva – Giving Entrepreneurs an Opportunity to Succeed, Thanksgiving: Micro-financing Entrepreneurs. I made 6 new loans today to manufacturing entrepreneurs in the USA (and Mexico); Tajikistan; Nicaragua; Armenia; and 2 in El Salvador. The webcast above shows Armen Tsaghikyan in Armenia. It does seem like his process maybe could use a benefit from a bit of application of lean manufacturing ideas.

It is great to be able to help out people whether it is providing useful information (like I hope my web site and blog do) or a small loan of capital that allows some capital improvements. Many of the loans through Kiva amount to providing a loan to get additional supplies (often they have very limited capital). But my favorite loans are those that allow for purchases of new equipment that will make them more efficient.

It is easy to help out yourself; you can loan as a little as $25. The 10 members of the Curious Cat team have made 292 loans for a total of $12,000. Comment with the link to your Kiva page and I will add a link on Curious Cat Kivans.

Related: Kiva Fellows Blog: Nepalese Entrepreneur SuccessMore Kiva Entrepreneur Loans: Kenya, Honduras, Armenia…100th Entrepreneur Loan

Touring Factories on Vacation When I Was Young

Growing up, occasionally, a family vacation would include a factory tour related to my Dad’s work. He was providing some management or engineering consulting and took the opportunity to check in on progress and visit the gemba. Here is a photo from one of those tours (in Nigeria, I think). My brother and Mom are visible in the photo.

The tours (which were not a very common occurrence) were quite enjoyable and interesting. Though I really didn’t like how noisy the factories were. Seeing all the machines and vast scale of the systems was quite a change of pace and added some excitement to the vacations (that often were already pretty exciting). I remember we also visited some factories in Kenya (in between seeing the game parks).

photo of factory tour with my family when I was a kid

Factory in Nigeria (I think) that my family toured


On this tour we found a bit of visual management showing which side of a crate should be on the top.
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Factorial Designed Experiment Aim

Multivariate experiments are a very powerful management tool to learn and improve performance. Experiments in general, and designed factorial experiments in particular, are dramatically underused by managers. A question on LinkedIn asks?

When doing a DOE we select factors with levels to induce purposely changes in the response variable. Do we want the response variable to move within the specs of the customers? Or it doesn’t matter since we are learning about the process?

The aim needs to consider what you are trying to learn, costs and potential rewards. Weighing the various factors will determine if you want to aim to keep results within specification or can try options that are likely to return results that are outside of specs.

If the effort was looking for breakthrough improvement and costs of running experiments that might produce results outside of spec were low then specs wouldn’t matter much. If the costs of running experiments are very high (compared with expectations of results) then you may well want to try designed experiment values that you anticipate will still produce results within specs.

There are various ways costs come into play. Here I am mainly looking at the costs as (costs – revenue). For example the case where if the results are withing spec and can be used the costs (net costs, including revenue) of the experiment run are substantially lower.
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