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  • Drug Prices in the USA

    There is the contention that without the ability to overcharge American’s the drug companies won’t invest in Research and Development. This is such a poor argument I can’t believe people can make it without suffering a big blow to any credibility they had. Obviously drug development has a very high cost (creating a high fixed cost) and often drug manufacture has a small marginal cost (though at times the marginal cost is also high). Right now the companies count on the American market to provide funds for much of the research, development, marketing and profits. Then they sell the drugs in other markets only looking to maximize profits looking at marginal costs. Obviously, if they no longer could count on excessive prices in the United States they would have to spread the fixed cost over the rest of the world. The argument that they won’t invest in research and development without excessive costs in America is false. They would adjust their pricing structures around the world based on losing the cash cow of the American consumer. That should be obvious to anyone who even took one economics course.

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  • Taguchi Loss Function

    In practice, I have seen the concept of the Taguchi Loss Function used quite a bit. I have never actually seen any losses quantified and totaled and shown on a graph. I think focusing specifically on who suffers a loss and what that loss could be, can help. I think actually quantifying the losses to society can be daunting. So, while I see the value in framing the concept that way I think to actually get the losses quantified you are best served by starting with those closest to the process and then adding additional loses to those results.

    Second, if you attempt to use the concept to help you manage (as a guide in decision making) the impacts to society are a factor, but, I think the loss to your company, the customer and perhaps the end user are most important. A negative impact to society at large is not going to have the same impact to a decision maker as the same negative impact to the customer. The decision maker will likely be willing to invest more to reduce the loss to a customer than to society at large (and that seems logical and sensible to me).

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  • Management Improvement History

    I do believe we need to improve our practice of Quality (and to do that we need to understand what happened in the past and why it was not more successful). The idea that Design of Experiments (DoE) was at the core of some Quality Movement to me is not at all accurate.


    In my experience only a few Quality professionals today understand what it means and how it should be applied. The idea that it was common place in the 40’s I seriously doubt (though I don’t have first hand knowledge of this). I find it difficult to believe we would have decided to stop using DoE if it was commonly done previously. The understanding I have from those that should know (like George Box and previously my father – Bill Hunter) is that it was not at all common practice and still is not outside of a few industries and even there it is isolated in the domain of a few experts.I do have first hand knowledge of the 80’s and the idea that we did “employee training in problem solving, team activities and just-in-time inventory” well is not even close to accurate. We sent people to training on these things but other than JIT inventory the effectiveness of these efforts were poor (with a few exceptions that really did well).

    “Quality” is not being practiced anywhere close to the level with which I am satisfied with in more than a few organizations. We have huge improvements to make in the practice of DoE, SPC, process improvement, having decisions made by the appropriate level (as close to the issue as possible), leadership, teamwork, data based decision making, the use of basically all the Quality tools, systems thinking, transformation…

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  • Dangers of Forgetting the Proxy Nature of Data

    We use data to act as a proxy for some results of the system. Often people forget that the desired end result is not for the number to be improved but for the situation to be improved. We hope, if the measure improves the situation will have improved. But there are many reasons this may not be the case (one number improving at the expense of other parts of the system, the failure of the number to accurately serve as a proxy, distorting numbers, etc.).

    I find something I learned from Brian Joiner an excellent summary – which I remember as:

    Data (measuring a system) can be improved by

    1. distorting the system
    2. distorting the data or
    3. improving the system (which tends to be more difficult though likely what is desired)
      Brian Joiner’s book, 4th Generation Management is a great book for managers.

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