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As of this writing (mid-July 2008) gasoline at my local Chevron station is selling at $4.57 per gallon; rows of giant SUVs sit unsold at the local car dealers; my home energy bill for month of June was $527; airlines are parking jets, dropping routes, and charging passengers for checking bags and seat selection; politicians argue over drilling offshore, building new nuclear power plants, and installing wind farms off the scenic shores of famous politicians’ homes; and, somewhere, Al Gore is smiling.
He may be smiling, but there are a whole of lot of unhappy people in the United States: people who can’t afford to heat or cool their homes, who are having a hard time buying $4.57-a-gallon gasoline, and who are losing their jobs because of the high cost of energy.
The solution? It’s easy. Drill for more oil. No, wait, that’s bad for the environment. Build nuclear power plants. No, wait, that’s too dangerous. Build windmills. No, there isn’t sufficient transmission capacity to get the power from where the wind blows to where the people live. Solar? Too expensive to produce enough power at present. Biofuels? Ugh, at present their water consumption and transportation costs are too high and their energy output too low. In addition, growing corn for ethanol has contributed to rising food prices. Hydrogen? Not ready yet.
The quality and process improvement professions tend to rely heavily on statistical information. The very science of quality control can be said to have begun with Walter A. Shewhart’s development of the control chart and discovery of the concepts of special cause and common cause variation. But few would argue with the statement that there is a downside, and a dark side, to statistics. I hereby present a few examples of good, bad, and ugly statistical usage.
The gold standard for modeling the future in a business environment is the designed experiment. Design of experiments (DOE) is a well-developed approach to planning and executing controlled manipulations.
Somewhat less respectable are models derived from historical data. It makes sense to utilize as much of this information as possible, but caution is required. Problems you may encounter are:
• Measurement error . Historical data are often recorded by untrained people, or the precision required for day-to-day use of the data may be wide compared to what you need for modeling. Errors that aren’t important in the data’s original use may wreak havoc on your model-building activity.
• Range restriction. Operational systems are deliberately controlled to minimize the effect of system variation on results, meaning that the allowance for variation of system parameters is very small. It is very possible that the response we are modeling will not be affected by variation of inputs in this range, but that doesn’t mean that the responses wouldn’t change if the inputs were varied over a larger range. The result is a model that gives misleading results by excluding important parameters.
If any clause in ISO 9001 has increased in importance since the release of the standard’s 2000 edition, it must be subclause 7.4 on purchasing. Not that the relative importance of the words has changed, but rather purchasing and outsourcing have become much more common and important in our day-to-day business. So the relatively small subclause on controlling purchasing may be much more important now than it was back in 2000. (I addressed outsourced processes in my May column, “Is a Controlled QMS Possible?” More about them later.)
The requirements described in ISO 9001’s subclause 7.4.1 on the purchasing process permit an organization to decide the “type and extent of control” to be used for purchasing. The organization’s selection of controls should be based on the effect of the purchased material or services on the product realization processes and on the finished products delivered by the organization to its customers. If purchased materials or services have little effect (e.g., a threaded fastener that’s used inside a noncritical subassembly), then minimal control is needed.
I‘ve been doing quite a bit of home improvement recently--installing new flooring, painting, etc. I’ve got a few sore muscles but the sense of pride in my achievement (plus the money I saved by doing it myself) makes it all worthwhile.
I have to admit that I’ve never been very handy, and I’ve never really gotten the thrill that some people find from “doing it myself.” My brother genuinely enjoys home improvement and could probably build an exact replica of the Taj Mahal given enough time. Usually my home improvements stem from the need to save money.
This current project--replacing wall-to-wall carpeting with wood flooring--was something that I never thought I would (or could) do. But after some words of encouragement from friends (thanks, Jeff) and getting quotes of $3,000 to install flooring in just one room, I thought it was worth the try. I researched the products, bought the necessary tools, watched a bunch of videos on installation (God bless, You Tube), and took the plunge. Three days later my room was done, it looks terrific, and I saved about $2,500.
Last month we reviewed how Ford Motor Co.’s lean concepts were slowly phased out of the organization. The concepts, however, weren’t lost: Toyota realized their potential and improved upon them.
Toyota’s chief of production, Taiichi Ohno, embraced Ford’s concepts wholeheartedly. He applied them to machining operations and then to other areas of production. As a result, the Toyota Production System (TPS) was born in the 1960s and nurtured through the 1970s.
The real test of the TPS came in 1984, when Toyota and General Motors formed a joint venture called New United Manufacturing Inc. to build a car sharing designs, assembly processes, suppliers, and people. Although the venture’s performance didn’t meet expectations, the lean concept began there and spread to other U.S. and international organizations.
Meanwhile, IBM started focusing on process improvement. Since the late 1970s it had benchmarked its international internal operations and gathered best practices from Japan, Germany, and the United States. These best-practice approaches were called “process compatibility.” They focused on streamlining all support processes and used tools such as flowcharting, as-is process mapping, and value engineering.
The following personal stories concern vehicles produced by the automaker that invented lean and is world-famous for its efficient manufacturing operations:
• My old SUV’s bright headlights don’t work. When I hit the switch for the brights, the headlights turn off completely. This will cost me $400 to fix because it requires replacing an entire steering wheel subassembly.
• My wife’s car needs a filter replaced; it’s routine maintenance. It will cost several hundred dollars because it requires extensive disassembly to get to the filter, which has to be done by going through the glove compartment.
• I once lost the key to my car. It was one of those keys with security features, and it opened the doors and trunk. It cost me nearly $300 to replace the key. (This was four years ago, when the dollar was still worth something.) I was told that if I lost my remaining key, it would cost me $3,000 to get a new key because the car’s computer security hardware would need to be replaced. As you might imagine, this would require extensive disassembly.
A couple of months back I was watching “Are You Smarter Than a Fifth Grader?”--one of the more enjoyable game shows in recent memory. The premise is that the contestants should be able to answer the questions, since it’s stuff we learned during or prior to the fifth grade--nothing deceptively clever or arcane; just facts and information, history, science, grammar, and current geography. The questions get harder as you move from the first to the fifth grade, with correspondingly higher monetary prizes.
This night the contestant made it to the $1-million level. If you win at this point, you get the million; if you lose you go back to a measly $25,000 and you have to face the camera and admit that you’re not smarter than a fifth grader.
The question was, “Who was the longest reigning monarch of England?” Well, I knew this answer hands down. Oh, how I wished I was on that stage. I’d become a millionaire. I would be smarter than a fifth grader. I was absolutely sure of it. Except… I was dead wrong. I would have lost thousands of dollars and I would have had to make the humiliating admission as to my diminished level of smartness.
By the time you read this, the new version of ISO 9001 should be out. ISO 9001:2008 is the result of years of work by an international team of volunteer experts. These dedicated men and women gave up hundreds of hours of their time and traveled to locations around the world, usually at their own expense, to revise the standard.
The revision process began almost as soon as the year 2000 version of the standard was published. In fact, work on the next revision of the standard--slated for the year 2015--has already begun.
Most people have three primary questions about the new standard:
• What’s new?
• When does it take effect?
• How long do I have to transition to the new standard?
One of the major causes of TQM and Six Sigma failures is selecting the wrong project. This selection is probably one of the most important decisions that management can make to support the improvement process.
There are many approaches that can be used to select projects. They range from management intuition to complex analyses of how the processes affect business opportunities. I will show you a weighted selection approach that is effective, using a health care example.
In this approach each opportunity is evaluated in a number of parameters. For example:
A) Changeability = 2 points
B) Reduce cost = 2 points
C) Decrease mortality = 5 points
D) Improve patient care = 4 points
E) Improve staff morale = 3 points
F) Reduce wait time = 1 point
Each of these parameters is weighted by a point score from one to five. A rating of one indicates that it’s low priority, and a rating of five indicates that it’s very high priority.
