Posts selected fromManagement Blog - Engineering Blog - Investing Blog and other blogs - Unintended Consequences - distorting the system or distorting the data
Using data to understand your processes and improve them is very useful.
But using data often results in unintended consequences. If you don’t have a good understanding on the pressures collecting data will bring to bear on the system you can create pressure for results that damage the delivery of value to customers...
Distorting the system or distorting the data are often the result, instead of the process improvement that is desired and expected. continue reading: Unintended Consequences - distorting the system or distorting the data - Statistical Techniques Allow Management to do a Better Job
For all four groups of people [management, statistical administration, research, front-line workers], the statistical method is more than an array of techniques. It is a mode of thought-sharpened thinking. It helps anyone in the four groups, be he a machine operator or an executive, to make better decisions, and to do his work better, than he could do otherwise.
--- W. Edwards Deming
Those who continue to thinkW. Edwards Deming was focused on the factory floor alone have missed most of what he proposed. Improving the decision making at the executive level was always Deming’s focus. Continual improvement should be a part of everyone’s job but as executives have more authority the impact of improving their performance multiplies, or stifles, the impact of improvement anywhere else in the organization. continue reading: Statistical Techniques Allow Management to do a Better Job - 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. continue reading: How to Improve at Understanding Variation and Using Data to Improve - 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. continue reading: Understanding Data is Often Challenging - Stratify Data to Hone in on Special Causes of Problems
 One strategy to help identify special causes so they can be studied and addressed is to stratify your data.
By stratifying the data you refine your view to make it easier to identify what is causing the problem. Instead of looking at all vehicles and seeking to find the cause they had stratified the data and learned they could exclude looking at most of the processes (those that don’t impact large vehicles). And they then sought to further refine the scope by stratifying the data to further isolate the scope of the investigation. As you refine the scope you can discover what is common just to the population you have isolated by stratifying the data. continue reading: Stratify Data to Hone in on Special Causes of Problems - Using Scientific Knowledge to Drive Policies that Create a Better World
We can’t afford to elect people that don’t have an understanding of how to make wise decisions or how to ensure scientific knowledge forms the basis of policy when it should, such as: overfishing, pollution, global warming, the health care benefits vaccines provide when they are used properly, the dangers of abusing antibiotics, etc..
We need to elect leaders like those that took the steps to have the EPA clean up the incredibly polluted USA. We need to elect leaders that put the policies in place to reduce the overfishing in our waters. We need to elect leaders that care about our country and will learn what they need to from those that know the science. continue reading: Using Scientific Knowledge to Drive Policies that Create a Better World - 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? continue reading: Lessons on Competition from Mother Nature - 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
- distorting the system
- distorting the data or
- 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.
continue reading: Dangers of Forgetting the Proxy Nature of Data - 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… continue reading: Management Improvement History - 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.
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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. continue reading: Theory of Knowledge - 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). continue reading: Control Charts in Health Care - 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. continue reading: Statistics for Experimenters – Second Edition - Traffic Congestion and a Non-Solution
For decades traffic congestion has been a problem in American cities and one that has continued to get worse. The typical proposed solution is to increase the number of roads. The theory behind this solution is not normally stated but, I believe, it amounts to: “if we build more roads then the system will have more capacity which has to decrease congestion.” Unfortunately this theory fails to take into account the past data on the increasing capacity of roads “solution.”
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Ackoff’s solution does require actually changing the system. That is not easy to accomplish. However, if the desire is to reduce congestion the solution is not likely to be to just keep doing what we have been doing (given that it isn’t working). Building more and more capacity doesn’t seem to achieve the desired results. continue reading: Traffic Congestion and a Non-Solution - Targets Distorting the System
 I still remember Dr. Brian Joiner speaking about process improvement and the role of data well over a decade ago. He spoke of 3 ways to improve the figures: distort the data, distort the system and improve the system. Improving the system is the most difficult. continue reading: Targets Distorting the System - W. Edwards Deming’s Seven Deadly Diseases
Seven Deadly Diseases
- Lack of constancy of purpose
- Emphasis on short term profits (Overreaction to short term variation is harmful to long term success. With such focus on relatively unimportant short term results focus on constancy of purpose is next to impossible.)
- Evaluation of performance, merit rating or annual review (see: Performance Without Appraisal: What to do Instead of Performance Appraisals by Peter Scholtes).
- Mobility of top management (too much turnover causes numerous problems)
- Managing by use of visible figures, with little of no consideration of figures that are unknown or unknowable.
continue reading: W. Edwards Deming’s Seven Deadly Diseases
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