The process is always changing. Always. And in most cases the reduction in variation achieved by eliminating special causes is only a small fraction of the total variation. The real payoff is in reducing variation from common causes. But the mindset of classical, enumerative statistics inhibits this activity. The questioning, probing, exploratory approach combined with analytic tools like the control chart encourage you to find the cause of all of the variation. When evaluating the control chart off-line, forget the control limits. Investigate the chart as a whole, examine the patterns. Relate the data to what the team knows about the process. Place the charts end-to-end on a
24The successes of JIT (just in time) methodologies in industry illustrate the kinds of benefits that are
large table and look for cycles and patterns across several charts.
Brainstorm. Be OH!pen minded. Think!
Thomas Pyzdek, 1992
To succeed in finding ways to improve process performance, just as when searching for assignable causes, knowledge of the process—its activities, intermediate products, people, tools, and work flows—is paramount. It is this knowledge that points you to likely areas to investigate. This is one reason why responsibilities for process control and process improvement cannot be delegated to statisticians and staff scientists. Specialists can help, but responsibilities for improvement and for knowing the subject matter lie with those who own and operate the processes.
Remember that when you introduce improvements, the improvements will change the process. Changes mean instability, and instabilities lead (at least temporarily) to lack of predictability. You will not get the most from further improvement actions until you achieve stability at the new level of performance.
Does this mean that you should be reluctant to introduce changes? Not at all! At least not when you have stable baselines against which to measure the results of your actions. Without stable baselines, though, you may have difficulty determining whether or not your actions have the effects intended. This means that the wisest first step is often to bring the existing process under control. Then, at least, you will know where you are starting from, and you can measure how well the improvements work. Without baselines, you will just be guessing.
6.4
Tools for Finding Root Causes and Solutions
…any sophisticated statistical analysis should always be supplemented with easily understood graphics that can be evaluated by people who can relate the conclusions to the process; people who may lack training in advanced statistical analysis.
Thomas Pyzdek, 1992
As you collect data to investigate assignable causes and potential improvements, you will often face the need to sort through and understand the information you obtain. This involves organizing and summarizing your data and looking for patterns, trends, and relationships. Tools such as scatter diagrams, run charts, cause-and-effect diagrams, histograms, bar charts, and Pareto charts can all help you here. These tools are described briefly below and illustrated in greater detail in the pages that follow.
• Scatter diagrams display empirically observed relationships between two process characteristics. A pattern in the plotted points may suggest that the two factors are associated, perhaps with a cause-effect relationship. When the conditions warrant (i.e., a constant system of chance causes), scatter diagrams are natural precursors to regression analyses that reveal more precise information about interrelationships in the data.
• Run charts are a specialized, time-sequenced form of scatter diagram that can be used to examine data quickly and informally for trends or other patterns that occur over time. They look much like control charts, but without the control limits and center line.
• Cause-and-effect diagrams (also know as Ishikawa charts) allow you to probe for, map, and prioritize a set of factors that are thought to affect a particular process, problem, or outcome. They are especially helpful in eliciting and organizing information from people who work within a process and know what might be causing it to perform the way it does.
• Histograms are displays of empirically observed distributions. They show the frequencies of events that have occurred over a given set of observations and period of time. Histograms can be used to characterize the observed values of almost any product or process attribute. Examples include module size, defect repair time, time between failures, defects found per test or inspection, and daily backlogs. Histograms can be helpful for revealing differences that have taken place across processes, projects, or times.
• Bar charts are similar in many ways to histograms, but they need not be based on measures of continuous variables or frequency counts.
• Pareto charts are a special form of histogram or bar chart. They help focus investigations and solution finding by ranking problems, causes, or actions in terms of their amounts, frequencies of occurrence, or economic consequences.
The sections that follow give brief descriptions of these analytical tools and techniques. More complete illustrations, albeit in nonsoftware settings, can be found in several references [Ishikawa 86, Brassard 88, Brassard 89, Montgomery 96]. We particularly commend the 1986 revised edition of Ishikawa’s Guide to Quality Control [Ishikawa 86]. The Venn diagram in Figure 6-6 shows where the tools described in the following pages fit relative to the fact-finding and analysis activities that lead to identifying root causes and potential solutions [Brassard 88, 89].
flow chart check sheet brainstorming nominal group technique pie chart Pareto chart cause & effect diagram run chart stratification bar graphs histogram scatter diagram process capability analysis force field analysis control chart management presentation checklist Problem Identification Problem Analysis affinity diagram interrelationship digraph regression analysis design of experiments