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Jan 3 2012

Omissions on the ASQ’s History-of-Quality web page

Going backwards in time, the ASQ website’ page on the history of quality ignores Lean Quality in the late 20th century, interchangeable parts technology in the 19th, and the origin of the concept of “quality” in ancient Rome.

Specifically:

  1. The page contains no mention of Lean Quality. Lean manufacturers have outperformed competitors by techniques that are not even listed, such as one-piece flow, successive inspection, mistake-proofing, and others. Shigeo Shingo, who created and documented many of these techniques is not referenced. That the  methods are not statistical does not make them less valuable.
  2. The summary ignores the entire 19th century, which saw the emergence of  interchangeable parts technology, with the blueprints, critical dimensions and tolerances that are the foundation of modern quality.
  3. Quality is a word whose origin is known, as it was coined by Cicero in his Academica in 45 BC. What he meant by it is less clear, but my take on it is that it is the way in which a system is more than the collection of its parts.

The first omission is critical because Lean Quality is the state of the art in quality management. The second is mind boggling: how could a history of quality skip over a massive, decade-long and eventually successful undertaking that was targeting the elimination of variability? The third is a detail.

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By Michel Baudin • History • 1 • Tags: Lean, Quality

A3 sample

Jan 2 2012

What is an A3?

Many discussions of A3 reports in Lean omit one basic fact: A3 is a paper format. In millimeters, a A3 sheet is 297X420, roughly equivalent to 11×17 in inches. It is the size of two A4 sheets side-by-side, and half of an A2 sheet. The A-series of  paper sizes is used all over the world, except in the US…

An A3 report is not just a story on one sheet of paper, but on one sheet of paper of this particular size, which has been found right to tell a manufacturing story with just enough details without turning into a victorian novel.

It can be posted on bulletin boards or above operator workstations. Operator instruction sheets are actually supposed to be on A3 paper.

Size does matter. If you shrink an A3 to A4 or letter size, it is no longer works as an A3, because the print will be too small for viewing on a board. If you show it on a PowerPoint slide, it is not an A3 either, because it does not have the permanence of hardcopy and, unless you have really advanced IT, you cannot annotate it manually.

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By Michel Baudin • Management • 36 • Tags: Lean, Lean manufacturing, Management

Dec 27 2011

IndustryWeek survey on Continuous Improvement

Via Scoop.it – Cellular manufacturing
According to this article, the survey shows that continuous improvement separates the winners from the losers and drives financial gains.  The body of the article, however, contains no information about the survey method. We know neither how many companies responded nor the positions of the people who responded. I assume that survey questionnaires were sent to a selected group of executives, and that some among the recipients opted to answer. I am not sure what such a sample is supposed to represent.

The article says that more respondents with continuous improvement programs expect revenue and income growth >3% in 2012 than respondents without such programs. So it is about what this self-selected sample believes will happen next year. The only statement about actual results is a similar one about cash flow for this year. Based on the article, I fail to see how the survey supports the claims in the title and subtitle.
Via www.industryweek.com

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By Michel Baudin • Press clippings • 0 • Tags: Continuous improvement, Management

spc1

Dec 27 2011

Is SPC obsolete?

In the broadest sense, Statistical Process Control (SPC) is the application of statistical tools to characteristics of materials in order to achieve and maintain process capability. In this broad sense, you couldn’t say that it is obsolete, but common usage is more restrictive.

The semiconductor process engineers who apply statistical design of experiments (DOE) to the same goals don’t describe what they do as SPC. When manufacturing professionals talk about SPC, they usually mean Control Charts, Histograms, Scatter Plots, and other techniques dating back from the 1920s to World War II, and this body of knowledge in the 21st century is definitely obsolete.

Tools like Control Charts or Binomial Probability Paper have impressive theoretical foundations and are designed to work around the information technology of the 1920s. Data was recorded on paper spreadsheets, you looked up statistical parameters in books of tables, and computed with slide rules, adding machines or, in some parts of Asia, abacuses (See Figure 1).

In Control Charts, for example, using ranges instead of standard deviations was a way to simplify calculations. These clever tricks addressed issues we no longer have.

Figure 1. Information technology in the 1920s

Another consideration is the manufacturing technology for which process capability needs to be achieved. Shewhart developed control charts at Western Electric, AT&T’s manufacturing arm and the high technology of the 1920s.

The number of critical parameters and the tolerance requirements of their products have no common measure with those of their descendants in 21st century electronics.

For integrated circuits in particular, the key parameters cannot be measured until testing at the end of a process that takes weeks and hundreds of operations, and the root causes of problems are often more complex interactions between features built at multiple operations than can be understood with the tools of SPC.

In addition, the quantity of data generated is much larger than anything the SPC techniques were meant to handle. If you capture 140 parameters per chip, on 400 chips/wafer and 500 wafers/day, that is 28,000,000 measurements per day. SPC dealt with a trickle of data; in current electronics manufacturing, it comes out of a fire hose, and this is still nothing compared to the daily terabytes generated in e-commerce or internet search  (See Figure 2).

Figure 2. Data, from trickle to flood, 1920 to 2011

What about mature industries? SPC is a form of supervisory control. It is not about telling machines what to do and making sure they do it, but about checking that the output is as expected, detecting deviations or drifts, and triggering human intervention before these anomalies have a chance to damage products.

Since the 1920s, however, lower-level controls embedded in the machines have improved enough to make control charts redundant. The SPC literature recommends measurements over go/no-go checking, because measurements provide richer information, but the tables are turned once process capability is no longer the issue.

The quality problems in machining or fabrication today are generated by discrete events like tool breakage or human error, including picking wrong parts, mistyping machine settings or selecting the wrong process program. The challenge is to detect these incidents and react promptly, and, for this purpose, go/no-go checking with special-purpose gauges is faster and better than taking measurements.

In a nutshell, SPC is yesterday’s statistical technology to solve the problems of yesterday’s manufacturing. It doesn’t have the power to address the problems of today’s high technlogy, and it is unnecessary in mature industries. The reason it is not completely dead is that it has found its way into standards that customers impose on their suppliers, even when they don’t comply themselves. This is why you still see Control Charts posted on hallway walls in so many plants.

But SPC has left a legacy. In many ways,  Six Sigma is SPC 2.0. It has the same goals, with more modern tools and a different implementation approach to address the challenge of bringing statistical thinking to the shop floor.

That TV journalists describe all changes as “significant” reveals how far the vocabulary of statistics has spread; that they use it without qualifiers shows that they don’t know what it means. They might argue that levels of significance would take too long to explain in a newscast, but, if that were the concern, they could save air time by just saying “change.” In fact, they are just using the word to add weight to make the change sound more, well, significant.

In factories, the promoters of SPC, over decades, have not succeeded in getting basic statistical concepts understood in factories. Even in plants that claimed to practice “standard SPC,” I have seen technicians arbitrarily picking parts here and there in a bin and describing it as “random sampling.”

When asking why Shewhart used averages rather than individual measurements on X-bar charts, I have yet to hear anyone answer that averages follow a Bell-shaped distribution even when individual measurements don’t. I have also seen software “solutions” that checked individual measurements against control limits set for averages…

I believe the Black Belt concept in Six Sigma was intended as a solution to this problem. The idea was to give solid statistical training to 1% of the work force and let them be a resource for the remaining 99%.

The Black Belts were not expected to be statisticians at the level of academic specialists, but process engineers with enough knowledge of modern statistics to be effective in achieving process capability where it is a challenge.

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By Michel Baudin • Technology • 127 • Tags: problem-solving, Quality, Six Sigma, SPC

process plant

Dec 25 2011

Work Cells in Process Plants: Virtual or Pretend?

Via Scoop.it – Cellular manufacturing

In the latest issue of the AME’s Target magazine, Peter King explains how he has applied the cell concept in process plants, but his cells are virtual, meaning that their implementation does not involve relocating equipment.   In the case of synthetic rubber at Dupont’s plant in Louisville, KY he reports decreasing scrap and lead time by 28%, and decreasing finished goods inventory by 50%. There is no mention of improvements in Productivity, WIP and Raw Materials inventory, or Space Requirements.

While these improvements are substantial and respectable, they are  not up to cell benchmarks: reductions of 80% to 90% in lead time, inventory and defect rates, with a 30% to 50% increase in productivity, all in 25% to 30% less space. But that cannot be achieved without moving equipment…

If you can’t move the equipment, I prefer to call it managing monuments than implementing cells. Since you can’t get from managing monuments the order-of-magnitude performance boosts that you get from cells, I prefer to keep the distinction in sharp focus rather than blur it by pretending that “virtual cells” are cells.   Where cells apply, they are wonderful, but they are not a panacea. Even in discrete, mechanical manufacturing plants, there are often a few areas like, Heat Treat, Electroplating or Painting, where cells are difficult or impossible for now, and the skill of managing monuments is necessary.
Via www.ame.org

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By Michel Baudin • Press clippings • 2 • Tags: Cellular manufacturing, Lean manufacturing, Monument management

Dec 23 2011

How to achieve a lean transformation

Via Scoop.it – lean manufacturing

The Manufacturing Digital ezine devotes an entire section to Lean, and this is the latest entry. It is more about what needs to be done than how to do it. In the featured picture, the executives look like the marines on Iwojima, but they also seem about  to jump off a cliff.
Via www.manufacturingdigital.com

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By Michel Baudin • Press clippings • 0 • Tags: Lean, Lean implementation, Lean manufacturing, Manufactuting

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