Vol. 3, No. 2
1. Gather, analyze, and react to your data on some regular, periodic basis such as every microsecond, every hour, every day, every month, or every year.
2. Gather data that is likely to influence your key indicators. That is, gather causal data.
Deming popularized two assumptions about the world:
1. Everything is one of a kind. Everything varies.
2. Most decisions we make and actions we take are taken to influence the future.
The logical result of the first assumption is that every time we measure something, we can expect it to change, especially over time. If it does not appear to change, our measuring instrument is probably too blunt. Furthermore, sometimes the numbers will get worse and sometimes they will get better. The first trick is to know whether or not to do something about the change. Deming helped us to understand that some changes are significant and some are not. To act on an insignificant change as if it were significant wastes everyone's time, energy, and other resources, and frequently increases tension in the organization. To treat a significant change as if it were of no consequence is to lose an opportunity for improvement.
The second assumption offers a reminder that most decisions are intended to create a better future. We frequently make these decisions based on experience. With little or no experience, one is likely to become a little nervous about the decision. Deming simply suggested building a little experience with the numbers that are important. Once one knows which metrics are consequential, these should be gathered on a regular basis. Data should be gathered at least as frequently as the most-frequently-changing factor that may significantly affect the key metric. Track the data on a run chart, convert it to a control chart when there is enough data and, bingo, you've got experience. You now know not only when to make a decision but what kind of decision to make. There are very few numbers for which this simple little scheme does not work.
Unfortunately, most organizations do not operate this way, instead generating wasted resources, poor performance, and frustrated people. Let me illustrate this phenomenon.
Early in my manufacturing career, I would attend the plant manager's daily production meeting. A key part of the meeting was the review of the previous day's production. The regimen was predictable. Every day, if the production was up from the previous day, there was celebration - pats on the back, thanks, congratulations, even donuts. If, on the other hand, production was down, there were condemnations, threats, and always an order to go out and find out what went wrong. Without an understanding of variation, however, our answers usually meant little.
The workers, however, may have had a more intuitive understanding of variation. One operator, when asked the "what went wrong" question, went to his personal numbered excuse list which he simply rotated through every day for his supervisor. Unfortunately, some people, including the plant manager, took this exercise seriously. The net result, of course, was that about half the time production got better and about half the time production got worse.
The other sad result of this kind of behavior is that when significant changes do occur, they are treated the same as the "daily variation" game. This kind of ignorance of the numbers, sad to say, continues to be all too common in virtually every kind of organization and community.
Although gathering and analyzing key metrics on a regular periodic basis helps one know when to take action and what kind of action to take, it provides inadequate details. This brings us to our second reminder. Gather data, along with the key metrics that may influence the metric. A simple cause and effect analysis may help lead to these process or upstream indicators that are worth gathering, including metrics such as material or information source, equipment, office or plant involved, shift involved, or people involved. The identification of these process indicators should not be taken lightly. Without them, Six Sigma efforts will be much more difficult. If you gather unnecessary data, on the other hand, you waste everyone's effort. Good data gathering takes good resources.
As always, stay in touch. I'm at email@example.com. With regard to this particular article you can also find much more detail in the Total Quality Transformation materials produced by PQ Systems.
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