Blog posts on variation

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• Product and Service Innovation is Driven by Customer Focused Organizations

  At the core of W. Edwards Deming’s philosophy is a focus on delighting customers, allowing everyone to contribute what they have to offer and continually improving.

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  An organization must have a deep focus on customers in order to continually improve the value delivered to those customers and to innovate and create new products and services that delight those customers and future customers that the business can win over.

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• How to Use Data and Avoid Being Mislead by Data

  One of the four areas of Deming’s management system is “understanding variation.” The core principle underlying that concept is using data to improve while understanding what data is and is not telling you.

  The mistakes in interpreting data are very often related to mistaking natural variation in data as meaningful. Combining this with our brains ability to find patterns (even from random data) and confirmation bias this creates problems.

  Data can’t lie, but people can be mislead and they can even mislead themselves by misinterpreting data.

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• How to Improve at Understanding Variation and Using Data to Improve

  Getting better at using data is a bit tricky, so struggling is fairly common.
  Probably the easiest thing to do is to stop reacting to normal variation (caused by the system) as if it were special. This isn’t super easy but it is the easiest step. And it does make a big difference even if it doesn’t seem very exciting.
  
  The idea of actually using data properly provides big benefit but it much trickier. Don Wheeler’s book is a great start. Making predictions and evaluating how those predictions turn out is also valuable. And in doing so often (though not always) it will also spur you to collect data. This process of predicting, figuring out what data to use to help do so (and to evaluate the results) and considering the result of the prediction and how well the predictions overall are working can help.

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• Understanding Data is Often Challenging

  Using data to understand the system and validate our theories and successful improvements is an important part managing well. In some cases it is fairly easy to understand and collect data that provides a clear and accurate measure of what we care about. But getting data that helps can also be very challenging.

  Creating a management system that aims to use data while focusing on continually improving is a great start.

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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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• 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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• 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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• 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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• Managing with Control Charts

  ...if managers mistakenly tamper with a stable process, believing an occurrence is exceptional, they introduce an external cause, which destabilises it. Targets do the same thing.

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• The Customer Knows Best?

  Some management ideas are pretty easy and straight forward. But many management practices require knowledge and judgment to apply them successfully. Easy solutions may be desired, but, often you must choose between easy and effective (hint, I suggest effective is the better target).

  Listening to customers is important but it is not sufficient.

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  People could assume the numbers at Enron proved what Enron was doing was correct. But it did not prove that. Until we start to evaluate data more accurately we will continue to mistakenly see proof where it does not exist.

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• Six Sigma and Process Drift

  Quality Quandaries: Six Sigma, Process Drift, Capability Indices, and Feedback Adjustment by George Box and Alberto Luceno. This article is for the more statistically inclined.

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  Feedback adjustment can be dangerous: tampering. In fact, I would say attempting it is likely to be tampering, unless those doing so are careful and knowledgeable. It might be wise to read Box and Luceno’s book on the topic – Statistical Control: By Monitoring and Feedback Adjustment if you are tempted to try.

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• Common Cause Variation

  Every system has variation. Common cause variation is the variation due to the current system. Dr. Deming increased his estimate of variation due to the system (common cause variation) to 97% (earlier in his life he cited figures around 80%). Special cause variation is that due to some special (not part of the system) cause.

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  To take action against a special cause, that isolated special cause can be examined. Unfortunately that approach (the one we tend to use almost all the time) is the wrong approach for systemic problems (which Deming estimated at 97% of the problems).

  That doesn’t mean it is not possible to improve results by treating all problems as some special event. Examining each failure in isolation is just is not as effective. Instead examine the system that produced those results is the best method.

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• Epidemic of Diagnoses

  It sure seems to me this tendency to “over-diagnois” leads to Tampering. Lets assign a special cause to some instance and then implement a counter-measure (it seems to be “take this drug” is a common one). And just as tampering in the management world the “solutions” then create all sorts of problems.

  For me, when I read:

  But the real problem with the epidemic of diagnoses is that it leads to an epidemic of treatments. Not all treatments have important benefits, but almost all can have harms

  I just think: tampering!

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• “Explaining” Random Variation

  When I was a reporter covering Cisco Systems Inc. in the late 1990s, it was my job to talk to several analysts a day to find out the latest bit of news that might move the networking company’s share price.

  If the stock moved more than 2% on any uptick in volume, I had to write a story explaining why. After dealing with that every day for about three years, I realized the overwhelming majority of analysts had no better clue than I did about what was moving Cisco’s stock.

  The “explanations” you hear from media often are just as useless as horoscopes. A bunch of meaningless words presented in the hopes you don’t realize they are empty words.

  The talking heads (and writers) need to say something. It would be much more useful if they took the time to do some research and put in some thought but they seem to be driven by the need to fill space instead of the need to inform.

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