Blog posts on evidence based management

Posts selected fromManagement Blog - Engineering Blog - Investing Blog and other blogs

• Why Do People Fail to Adopt Better Management Methods?

  It is confusing to know that better methods exist but to see those better methods being ignored.  It seems that if there were better ways to manage, people would adopt those methods.  But this just isn't the case; sometimes better methods will be adopted but often they won't.  People can be very attached to the way things have always been done.  Or they can just be uncomfortable with the prospect of trying something new.

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• Visible Data

  Take the time to find the important measures and then don’t keep data hidden in some drawer or computer file out of people’s view and therefore out of mind. Post the important data for everyone to see. Review the data as changes are made and see that the changes had the desired result. Update the measures when appropriate...

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• Lessons on Competition from Mother Nature

  Too often I see simplistic thinking used to accept that the results were good so what we did was wise or the results were bad so what we did was unwise. Sometimes those conclusions have merit. Sometimes they don’t. The results matter but understanding the nature of those results is important. Was it due to luck (did our company due well because the overall market boomed and we were taken along for the ride). Was it due to taking risks that happened to work out well now but is likely to result in bad results in the future? Is it just random variation that we attribute to good luck?

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• Lean Thinking and Management

  I agree that we should acknowledge the paucity of success stories for improving the practice of management. The failures of management are not minor. The problems are large and the successes seem limited. The biggest thing I think we need to learn from this is that improving management is not easy. The concepts may seem simple but most of us can look around and see much more Dilbert Boss behavior than lean thinking behavior. And the gap between those two types of behavior seems to rise as you go “up” the organization chart.

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• The Importance of Critical Thinking and Challenging Assumptions

  Critical thinking is important to applying management improvement methods effectively. It is important to know when decisions are based on evidence and when decisions are not based on evidence. It can be fine to base some decisions on principles that are not subject to rational criticism. But it is important to understand the thought process that is taken to make each decision. If we are not clear on the basis (evidence or opinion regardless of evidence) we cannot be as effective in targeting our efforts to evaluate the results and continually improve the processes in our organizations.

  ...

  Changing the culture to one that values understanding and learning takes time. That process must be done with an understanding of psychology and the challenges of getting people to evaluate decisions. Creating a culture where it is expected that people think about the evidence and are comfortable explaining and defending the reasoning behind decisions is extremely important.

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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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• 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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• 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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• Operational Excellence

  Saying the organization is focuses on new principles (partnering, lean, etc.) is not the same as applying those principles with the great success that Toyota does. That difference is huge and is driving many companies to outsource and try to dramatically cut costs. Reducing costs should be the outcome of improving efficiency.

  Toyota is successful manufacturing in the USA.

  ...

  I would agree that many companies don’t understand the critical importance of management excellence. Rather than take the difficult path to lead real change in their organization they focus on simple cost cutting measures (though usually not cutting executive salaries which have grown dramatically and are excessive in the USA compared to the rest of the world). That won’t work.

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• Theory of Knowledge

  When managing many fail to predict when attempting to test improvement ideas through what should be experiments (often they are just changes without verification the change produced a desired effect, any learning or study of the results of the change). Without prediction learning is much less (if there is any at all) than it would be with such prediction.

  ...

  With, even a fairly simple understanding of the theory of knowledge the effectiveness of management improvement efforts are greatly increased. This topic is difficult for most to understand, I recommend reading chapter four of the New Economics. 

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• Control Charts in Health Care

  The point of using a control chart, and many of the management improvement tools, are to improve the efficiency and effectiveness of resources spent improving. The trick is not really to improve (that is pretty easy) the trick is to improve quickly and effectively (and in a competitive marketplace to improve more quickly than competitors). Where improvement resources are targeted is critical. In deciding which improvement options to explore it is important to understand the impact on the outcome (in this case the health of the patient).

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• Statistics for Experimenters – Second Edition

  Complete with applications covering the physical, engineering, biological, and social sciences, Statistics for Experimenters is designed for individuals who must use statistical approaches to conduct an experiment, but do not necessarily have formal training in statistics. Experimenters need only a basic understanding of mathematics to master all the statistical methods presented. This text is an essential reference for all researchers and is a highly recommended course book for undergraduate and graduate students.

  This updates the classic text by George Box, William Hunter (my father) and Stu Hunter.

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• Measurement and Data Collection

  I find it useful to ask what will be done with the results of data collection efforts (in order to confirm that the effort is a wise use of resources). If you don’t have an answer for how you will use the data, once you get it, then you probably shouldn’t waste resources collecting it (and I find there is frequently no plan for using the results).

  I have found it helpful to ask: what will you do if the data we collect is 30? What will you do if it is 3? The answer does not need to be some formula, if 30 then x. But rather that the results would be used to help inform a decision process to make improvements (possibly the decision to focus resources in that area). I find, that asking that question often helps reach a better understanding of what data is actually needed, so you then collect better data.

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• Operational Definitions and Data Collection

  Claims are often made about results that only are justified based on unstated assumptions about the real world results that the data are meant to represent.  But those claims are undermined when there is no evidence provided that the assumptions are valid.  Without operational definitions for the data there is a significant risk of making claims about what the data means that are not valid.

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• SPC: History and Understanding

  Setting control limits at 3 standard deviations is a decision based on experience. Shewhart, Deming and others determined it was sensible to take resources to look for a special cause was most effective for results more than 3 standard deviations from the mean – it is not a mathematical conclusion but a empirical conclusion.

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