Posts Tagged ‘aerospace’

A Change Agent’s Most Important Personal Attribute

Thursday, July 30th, 2009

Today I received a call from a person interested in becoming a Certified Six Sigma Black Belt. Of course, we value him as a customer and he will learn a great deal if he decides to enroll in our Six Sigma training. Among the things he’ll learn are both “hard skills” involving statistics and data analysis techniques, and “soft skills” such as conflict management, team dynamics, and stakeholder analysis. Still, I have my doubts about his chances of becoming a successful Six Sigma Black Belt. He has what I call a “Can’t Do” personality. This is the diametric opposite of the Can Do person. This type of individual looks for reasons why a particular thing can’t be done. How about a project in the sales department? No way, sales people won’t go for it, sales isn’t a process anyway, management won’t let us touch the sales area, etc. etc. etc.

Successful change agents are invariably Can Do people. To be sure they spend a lot of time planning to avoid obstacles, but when they encounter the inevitable obstacle, they don’t shrink from the challenge. They found ways over, under, around, or through the obstacle. They are not to be stopped. They are relentless pursuers of change.

I once had the opportunity to work with a major aerospace client to study the success factors for their Six Sigma Black Belts. We reviewed the histories of a number of Black Belts who had success levels that varied from poor to excellent. After coming up with a list of the factors that seemed to have an impact on success we went through an exercise to determine the importance weights. Using the Analytic Hierarchical Process (AHP) the Six Sigma Champion, Master Black Belts, and me came up with the weights shown in Figure 1.

Figure 1-Black Belt Success Factor Weights

Figure 1-Black Belt Success Factor Weights

The weights are, of course, subjective and only approximate. You may feel free to modify them if you feel strongly that they’re incorrect. Better yet, you may want to identify your own set of criteria and weights. The important thing is to determine the criteria and then develop a method of evaluating candidates on each criterion. The sum of the candidate’s criterion score times the criterion weight will give you an overall numerical assessment that can be useful in sorting out those candidates with high potential from those less likely to succeed as Black Belts. Of course, the numerical assessment is not the only input into the selection decision, but it is a very useful one.

You may be surprised to see the low weight given to math skills. The rationale is that Black Belts will receive 200 hours of training, much of it focused on the practical application of statistical techniques using computer software and requiring very little actual mathematics. Software automates the analysis, making math skills less necessary. The mathematical theory underlying a technique is not discussed beyond the level necessary to help the Black Belt properly apply the tool. Black Belts who need help with a particular tool have access to Master Black Belts, other Black Belts, consultants, professors, and a wealth of other resources. Most statistical techniques used in Six Sigma are relatively straightforward and often graphical; spotting obvious errors is usually not too difficult for trained Black Belts. Projects seldom fail due to a lack of mathematical expertise. In contrast, the Black Belt will often have to rely on his or her own abilities to deal with the obstacles to change they will inevitably encounter. Failure to overcome the obstacle will often spell failure of the entire project.

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Why Six Sigma is an All-Or-None Proposition

Monday, July 27th, 2009

A “toe in the water” approach won’t always tell you if six sigma will work.

Recently some prospective clients asked me for a demonstration project to help them determine if six sigma would be a good idea at their company. I advised them against it. Such a demonstration only shows management’s lack of commitment to the success of six sigma. Although philosophical issues are important, there are more concrete problems with such “toe in the water” projects. In particular, major quality improvements can sometimes yield little or no bottom-line cost impact. The result of such projects is to convince management that six sigma adds cost without adding value. This belief is, of course, totally wrong. But it’s also a logical result of the demonstration approach itself.

For example, Sam was a six sigma enthusiast. He’d studied its use at several major companies and was convinced that it would save his company, which we’ll call Acme, millions of dollars. The hype had also caught the attention of the senior leadership at Sam’s company. But before diving headlong into six sigma, they wanted Sam to conduct a demonstration project to see if the savings reported by the press could actually be obtained at Acme.

The company’s main product was a complex assembly, which Acme sold to a large aerospace customer. The assembly- manufacturing process was in statistical control and producing an average of 10 defects per assembly. With management’s support, Sam documented the cost of noncompliance to be about $1,000 per assembly. After months of diligent effort, Sam’s six sigma team was able to redesign the process. To their delight, they were able to reduce the number of defects per assembly by a full 50 percent, from 10 defects per assembly to five.

Management was also interested in the project. But the accounting department had carefully monitored the costs for the assemblies, and to everyone’s surprise, accounting found only a minuscule 0.7-percent cost savings.

Based on these results, leadership’s conclusion was simple: Quality doesn’t pay. The company won’t pursue six sigma any further.

Did accounting make a mistake? In a word, no. The problem arose because Sam measured quality as defects. The truth is that most costs are incurred because of defectives rather than because of defects. (Thanks to Mikel Harry of the Six Sigma Academy for this insight.) A defective is a unit of product or service that contains one or more defects. Whether a unit contains one defect or several is irrelevant. Customers generally react to defective units by returning them for warranty repair, refunds or other options. Internally, defective units must be identified through costly inspection and then routed through equally costly rework processes, or else scrapped entirely. A unit with one defect costs nearly as much as one with several.

Mathematically, the Poisson distribution describes the relationship between defects and defectives. The equation for the Poisson distribution is

In the equation, x represents the number of defects in the sample, and P(x) means the probability of finding x defects. For example, P(1) is the probability of finding one defect. The symbol μ is the average number of defects per unit of product or service. For Sam’s project, the average assembly had 10 defects before six sigma was applied, so μ = 10. The efforts of the six sigma team reduced the average number of defects per assembly to 5, for a 50 percent improvement in quality.

Let’s plug these numbers into the equation and see what happens. Because we are interested in the probability of an assembly being defect-free, we want to know P(0) for each of the two quality levels. Before six sigma, with μ = 10 we get

In other words, there were virtually no defect-free circuit assemblies before applying six sigma methodologies. After applying six sigma, the probability of getting a defect-free assembly at Acme was

Thus, a 50-percent improvement in the quality level as measured in defects produces only a 0.7-percent improvement in the number of defect-free circuit assemblies. A complete graph of this relationship is shown in Figure 1.

Figure 1-Quality Improvement vs. Cost Savings

The real cost-reduction benefits only start to appear when quality reaches very high levels. This relationship explains the commonly observed phenomenon of quality programs not paying off in the short term. Only when companies stick with it long enough to begin to approach six sigma quality levels do they get the desired results. Too often, “toe in the water” projects scare companies out of the pool before they even start to swim.

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