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Tuesday, June 11, 2013

27A Managing in the Presence of Variation

I cannot overemphasize the importance of learning how to understand variation and how to manage in its presence. Brian Joiner said it best in his training course (Copyright Oriel Incorporated, formerly Joiner Associates, 2009). “When people don’t understand variation:
        They see trends where there are no trends and miss trends where there are trends
        They blame, or credit, others for things over which the others have no control
        They can’t understand the past or plan for the future properly
        Their ability to manage or lead is impaired”
Managers can learn to manage in the presence of variation if they do three things:
        Learn appropriate statistical methods; as described in the Memory Jogger or similar book
        Ensure workers are trained in & use appropriate problem solving and statistical methods
        Learn to think statistically
This lecture addresses learning statistical methods, learning to think statistically and discusses three experiments that are valuable to learning about managing in the presence of variation
Learning Statistical Methods
To achieve the increased organizational effectiveness promised by this course it is necessary to train everyone in the organization in the basic problem solving tools and statistical methods covered in the Memory Jogger. Workers and managers must become familiar with and use flow charts of their processes, check sheets to gather data on their processes, Pareto charts, cause and effect diagrams (fishbone diagrams), run charts, histograms, scatter diagrams and control charts.
Self-study of the Memory Jogger book or a similar book that is written for self-study is one way of learning appropriate statistical methods. In my experience the best way to learn these techniques is to train teams that have common ownership of processes. The team picks a problem in one of the team member’s processes that they think needs improving. A trainer, well versed in these methods then teaches several teams at a time by teaching a technique and then letting the teams put the technique into practice for the problem they have selected. It takes about 50 hours spread over about three months for a team to work through learning the techniques, gathering data, analyzing the data and evaluating the success of its process improvement efforts.
It typically costs several thousand dollars per team, in addition to the cost of the team’s time, for such training. However, the cost savings resulting from the process improvements conducted as part of the training typically saves five to ten times the cost of the training within about a year. This claim is based on documented savings of over $20 million by about 300 such team training efforts over several years in the late 1980s. These teams were from several types of organizations including manufacturing, health care, and civil government services.
Using Statistical Methods
After teams are trained they are ready to be empowered to have control over their process within some boundaries that must be determined for each organization. Typically trained and empowered teams do not have to be encouraged to take control of their processes. Most are eager to fix problems that bother them by making their work more difficult or increasing their work load. These are also the problems that reduce the effectiveness of the team’s processes. As mentioned earlier, it is important to monitor empowered process improvement teams so that workers are not too heavily involved in process improvement at the expense of getting normal work done. In organizations of more than about 40 people it is prudent to designate a person skilled in statistical process control techniques to monitor all process improvement work. This person should ensure that workers are collecting data on the workers’ processes, preparing control charts and solving special cause variation to bring their processes into stable control. Only then should improvement activities be initiated to reduce variation and/or change the mean of a controlled parameter.
 It’s good practice, where it makes sense, to have workers post their control charts where they are visible to the workers and to managers. Remember, managers are workers and are also responsible for processes. Sometimes managers should have control charts for their processes and these should be visible to others except where the charts involve private data relating to people. Having control charts visible to all reinforces the intent to manage on the basis of data rather than someone’s guesses or intuition.
Financial and productivity related data should be available to all as is necessary for evaluating process improvements. Providing such data also helps build and maintain trust in management. Workers are trusted with trade secrets that are far more valuable than typical financial data. Denying them access to financial information prevents them from accurately calculating the cost savings from their process improvement actions and tends to build distrust of management.
A quick search of the web shows that there are numerous vendors offering software packages to assist with generating the subject charts and diagrams. I think it’s a better learning experience to have workers learn how to generate the products by hand before having access to software. The software isn’t really necessary and not having used the commercial products I can’t attest to their utility or cost effectiveness. Therefore I recommend students learn without the help of commercial software and then try a commercial product and determine for themselves if it is a good investment. It may be that such products save time and result in fewer errors so that they pay for themselves over time. I would caution the student that if the software automates most of the data collection and processing to observe carefully to learn whether using such automated tools reduces the ownership workers have in the control of their processes. If they feel the software is being imposed on them and their processes by management it may demotivate them. Of course a wise approach is to let the workers decide if such tools are helpful and cost effective.
After workers are trained, empowered and monitored properly they should take responsibility for fixing special cause variation without involving managers. Knowledge workers can also take responsibility for improving their processes, i.e. reducing common cause variation, without having to get permission from or involving managers. This frees managers from many of the daily crises that take time away from maintaining and improving their own processes. Workers controlling their processes effectively are the basis for the claim in the introduction to this course that if a manager practices the methods taught here there are fewer crises requiring management attention and therefore more time to work on important long term problems.
Learning to Think Statistically
Many books on leadership advise their readers to trust their intuition in making decisions. I wholeheartedly agree with this advice. Being effective often requires making decisions with limited data. In my experience decisions based on available data plus intuition are correct most of the time and the benefits gained from timely decisions outweigh the costs of the few times mistakes are made. I believe that the quality of decisions based on intuition can be improved by learning to think statistically. Thinking statistically means using available data, your experience and your intuition to make judgments based on probability and statistics in situations where statistics apply.
One objective of learning to think statistically is to no longer spend any time explaining obvious common cause variation or asking others to explain common cause variation. Such mistakes are common in analyzing and discussing financial data and productivity data. I have had to sit through or read through countless examples of someone explaining why this months’ expenses for something are up by x% or this months’ sales missed the forecast by y% when the common cause variation in the parameters under discussion was greater than x or y%. It should be obvious to the student at this point that such discussions are a complete waste of time and frustrating to those who have learned to think statistically. Explanations are only called for if a parameter exceeds an agreed upon control limit. Feeing one from such time wasters makes time available for process improvement, growing the organization, working with customers and other effective work.
Weekly or monthly reports are a typical place where seemingly learned discussions of common cause variation are popular. That is another reason why I never liked weekly reports. If you are required to write such reports make sure you are not wasting both your time and your supervisors’ time by discussing common cause variation unless it is in the context of a process improvement action.
Learning to think statistically takes practice. Try to recognize common and special cause variation even when you don’t have a control chart available. Often your experience and intuition are sufficient. This is a useful skill in daily life but should never be a substitute for managing work processes on the basis of data. A good way to practice is by reading or listening to news reports. Think about reported incidents and assess whether you think they are due to special or common cause. An example is a report that some people are concerned because they believe there is a high incidence of “x” in their community. The “x” might be cancer, crime or some similar undesirable event. The fact that the community is concerned is newsworthy, whether or not the concern is justified depends on whether the high incidence of “x” is special or common cause variation. Typically insufficient data is reported to enable an accurate decision. In such cases make an educated guess for the practice.
Try assigning probabilities to events and assigning relative importance to reported events based on your knowledge of the statistics related to the event. An understanding of the statistics of normal distributions applied to limited data given in news reports is often sufficient to make a determination of common or special cause variation with good probability of being correct. You soon find that the newsworthiness of an event often is not proportional to the relative importance of the event compared to other similar events. That is ok for the news media; their first priority is to interest their audience. It’s usually up to the audience to put events into proper context and statistical thinking is essential to achieving a good understanding of news events.
As you learn to think statistically you begin to look at work data more carefully. You do not jump to conclusions without collecting and examining data to determine whether something is common or special cause. You stop wasting time looking for explanations of common cause variation and hopefully go to work improving the processes under your control. You take appropriate actions and stop taking inappropriate actions in the presence of variation.
Appropriate actions to take for processes (the system) that exhibit variation are summarized in the chart shown in figure 19.

Figure 19 Appropriate actions in response to variation.
Brian Joiner, cited above, also has a great summary of “Consequences of Inappropriate Management Actions (i.e. violations of the rules summarized in figure 19):
        Wasted time and energy
        More variation in the system
        Loss of productivity
        Loss of confidence in the manager
        Problems continue”
As shown in figure 19 the system should not be adjusted in the presence of common cause variation. This is called tampering by W. Edwards Deming and just makes the variation worse. If special cause variation is present then you must “Look for the Difference”, i.e. look for the reason that the variation in question is not within the control limits. There is usually some anomaly that accounts for the special cause variation and this anomaly must be corrected so that out of control limits variation doesn’t continue. It is possible that the system has changed and therefore needs adjustment as indicated in column two. However, do not adjust the system if it has not changed as that would be an inappropriate action. The best training example of the results of inappropriate actions is W. Edwards Deming’s famous funnel experiment. If the student has access to the Deming video tapes I strongly recommend watching the tape on the funnel experiment. If that tape isn’t available an excellent alternative is available thanks to Dr. Yonatan Reshef, of the School of Business at University of Alberta. It’s discussed in the first exercise for this lecture.
After you have learned statistical methods, learned to think statistically, trained your workers and empowered them your organization will take fewer inappropriate actions and more appropriate actions and the organization’s effectiveness will increase.

Exercise 1

The Funnel Experiment

Go to the web site and study the funnel experiment. Dr. Reshef provides the rules and has a demonstration that you can download and work through yourself. Please take the time to work through the exercise. It is important to engrain in your mind the principles associated with inappropriate actions. If you have difficulties getting clear results from Dr Reshef’s demonstration you can see the results of a computer simulation of the funnel experiment at Click on funnel experiment in the contents list on this web page.
The objective of the exercise is to learn the difference between tampering (some call it tinkering) and true process improvement. All workers that you plan to empower to control their own processes should work through the funnel experiment as part of their training.
After studying the funnel experiment listen carefully to politicians in the news. As they recommend actions consider whether the recommended actions are tampering or sound process improvements. As you become more expert at statistical thinking you will notice that many politicians recommend actions that sound good to their constituents; often independent of whether the recommended actions are appropriate for the variation that precipitated their recommendation. Also, listen to other managers and your superiors as they suggest responses to problems. Try to assess if their suggested responses are sound process improvements or a form of tampering. These exercises help engrain the teachings of the funnel experiment in your mind.

Exercise 2

The Red Bead Experiment

Another of Deming’s famous experiments is the red bead experiment. You can learn about the red bead experiment at by reading the article by Manjari Raman. This article provides a clear definition of the experiment and a concise summary of the teachings of the red bead experiment. There is additional useful information at but I strongly recommend that you buy Dr. Deming’s video for your organization. It is available at
Observing the red bead experiment carefully or participating in the experiment is a powerful learning experience. Watching the behavior of participants is an amazing demonstration of the human nature that we encounter every day in our work. Workers try to do the impossible when bosses demand it even though the workers know that they cannot succeed. And we have all seen bosses who demand the impossible from workers in a system that is incapable of enabling the workers to achieve what they have been asked to do. Some trainers recommend that managers and their workers jointly do the red bead experiment and discuss it together as a step on the way to changing the behavior in their organization. I think that it is sufficient to watch the experiment but I think it is very important for the student to watch it, not just read about it. After viewing and perhaps discussing the red bead experiment with others, the student is likely to be less enthusiastic about arbitrary goals and management exhortations or slogans. Also it’s likely that the student will develop a more favorable assessment of the willingness of most workers to attempt to do whatever management requests. These likely changes help make the student a more effective manager.
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