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Matthew Barsalou

Lean

Combining Quality Tools for Effective Problem Solving

Using the classic seven and the seven new

Published: Monday, October 23, 2017 - 11:30

Quality tools can serve many purposes in problem solving. They may be used to assist in decision making, selecting quality improvement projects, and in performing root cause analysis. They provide useful structure to brainstorming sessions, for communicating information, and for sharing ideas with a team. They also help with identifying the optimal option when more than one potential solution is available. Quality tools can also provide assistance in managing a problem-solving or quality improvement project.

Seven classic quality tools

The Classic Seven Quality tools were compiled by Kaoru Ishikawa in his book, Guide to Quality Control (Asian Productivity Organization, 1991). Also known as “The Seven Tools” and “The Seven Quality Tools,” these basic tools should be understood by every quality professional. The Classic Seven Tools were first presented as tools for production employees to use in analyzing their own problems; they are both simple enough for everybody to use, yet powerful enough to tackle complex problems.

The seven tools are:
1. Cause and effect diagrams
2. Scatter diagrams
3. Control charts
4. Histograms
5. Check sheets
6. Pareto charts
7. Flow charts

A cause-and-effect-diagram is used to list potential causes of a problem. It is also known as an Ishikawa diagram or fishbone diagram. Typically, the main branches are the “6Ms,” or man, material, methods, milieu (environment), machine, and measurement. Sub-branches are listed under the main branches with “twigs” containing the potential problem causes. A cause-and-effect diagram can be used to assist when the team is brainstorming, and it can also be used to quickly communicate all potential causes under consideration.


Figure 1:
Cause-and-effect diagram. (Click here for larger image)

A scatter diagram graphically depicts paired data points along an X and Y axis. The scatter diagram can be used to quickly identify potential relationships between paired data points. Figure 2 depicts various potential correlations ranging from no correlation to a strong negative and strong positive correlation. It is important to remember that a strong correlation does not necessarily mean there is a direct relationship between the paired data points; they may be following third, unstudied factor.

Figure 2: Scatter diagram. (Click here for larger image)

Control charts are used to evaluate and monitor the performance of a process (Wheeler 1995). There are many types of control charts available for statistical process control (SPC), and different charts are used deepening on the sample size and the type of data used. An individuals chart is used when the sample size is one. The formulas for an individuals chart are shown in table 1, and an example of an individuals chart for a shaft diameter is shown in figure 3. The data are in a state of statistical control when all values are within the control limits, which contain 99.7 percent of all values.


Table 1: Formulas for center line and control limits when sample size is one


Figure 3: Control chart

Histograms are used to visualize the distribution of data (McClave and Sincich 2009). The y-axis shows the frequency of occurrences, and the x-axis shows the actual measurements. Each bar on a histogram is a bin, and bin size can be determined by taking the square root of the number of items being analyzed. Using a histogram can quickly show if the data are skewed in one direction or another. Figure 4 shows a histogram for data that fit a normal distribution, with half of all values above and below the mean.


Figure 4: Histogram

Check sheets are used for the collection of data (Borror 2009), such as when parts are being inspected. The various failure categories or problems are listed, and a hash mark is placed next to the label when the failure or problem is observed (see figure 5). The data collected in a check sheet can be evaluated using a Pareto chart.


Figure 5: Check sheet

A Pareto chart is used for prioritization by identifying the 20 percent of problems that result in 80 percent of costs (Juran 2005). This can be useful when searching for improvement projects that will deliver the most impact with the least effort. Figure 6 shows a Pareto chart with three out of seven problems accounting for 80 percent of all problems. Those three would be the priority for improvement projects.


Figure 6: Pareto chart

A flowchart is used to gain a better understanding of a process (Brassard 1996). A flowchart may provide a high-level view of a process, such as the one shown in figure 7, or it may be used to detail every individual step in the process. It may be necessary to create a high-level flowchart to identify potential problem areas and then chart the identified areas in detail to identify steps that need further investigation.


Figure 7: Flowchart

 

Seven new management and planning tools

The seven new management and planning tools are based on operations research and were created between 1972 and 1979 by the Japanese Society for Quality Control. They were first translated into English by GOAL/QPC in 1983 (Brassard 1996).

These seven tools are:
1. Affinity diagram
2. Interrelationship diagram
3. Tree diagram
4. Arrow diagram
5. Matrix diagram
6. Prioritization matrix
7. Process decision program chart (PDPC)

An affinity diagram identifies points by logically grouping concepts (ReVelle 2004). Members of a team write down items that they believe are associated with the problem under consideration, and these ideas are then grouped into categories or related points.


Figure 8: Affinity diagram

The interrelationship diagram depicts cause-and-effect relationships between concepts and is created by listing problems on cards (Westcott 2014). These cards are then laid out, and influences are identified with arrows pointing at the items that are being influenced. One item with many arrows originating from it is a cause that has many influences, and much can be achieved by correcting or preventing this problem.


Figure 9: Interrelationship diagram

A tree diagram assists in moving from generalities to the specifics of an issue (Tague 2005). Each level is broken down into more specific components as one moves from left to right in the diagram.


Figure 10: Tree diagram

An arrow diagram is used to identify the order in which steps need to be completed to finish an operation or project on time (Brassard 1996). The individual steps are listed, together with the duration, in the order that they occur. Using an arrow diagram such as the one in figure 11 can show steps that must start on time to prevent a delay in the entire project or operation.


Figure 11: Arrow diagram

The matrix diagram is used to show relations between groups of data (Westcott 2014). The matrix diagram in Figure 12 depicts three suppliers as well as their fulfillment of the three characteristics listed on the left side of the table. In this example, only two suppliers share the characteristic “ISO certification.”


Figure 12: Matrix diagram

The prioritization matrix is used to select the optimal option by assigning weighted values to the characteristics that must be fulfilled, and then assessing the degree to which each option fulfills the requirement (ReVelle 2004). The prioritization matrix in figure 13 is being used to select the best option for a staffing problem.


Figure 13: Prioritization matrix

Process decision program charts (PDPC) map out potential problems in a plan and their solutions (Tague 2005). The example in figure 14 shows the potential problems that could be encountered when conduction employee training, as well as solutions to these problems.


Figure 14: Process decision program chart

 

Example of combining quality tools

Multiple quality tools can be used in succession to address a problem (Barsalou 2015). The tools should be selected based on the intended use, and information from one tool can be used to support a later tool. The first step is to create a detailed problem description that fully describes the problem. In this hypothetical example, the problem description is “coffee in second-floor break room tastes bad to the majority of coffee drinkers; this was first noticed in February 2017.” The hypothetical problem-solving team then creates the flowchart shown in figure 15 to better understand the process.


Figure 15: Flowchart for coffee-making process

The team then brainstorms potential causes of the problem. These ideas come from the team members’ experience with comparable, previous issues as well as technical knowledge and understanding of the process. The ideas are written on note cards, which are grouped into related categories to create an affinity diagram based around the 6Ms that are used for a cause-and-effect diagram (see figure 16).


Figure 16: Affinity diagram for bad-tasting coffee

The affinity diagram is then turned into the cause-and-effect diagram depicted in figure 17. The team can then expand the cause-and-effect diagram if necessary. The cause-and-effect diagram provides a graphical method of communicating the many root-cause hypotheses. This makes it easy to communicate the hypotheses, but it’s not ideal for tracking the evaluation and results.


Figure 17: Cause-and-effect diagram for coffee taste

Cause-and-effect diagram items are then transferred to a worksheet like the one shown in figure 18. The hypotheses are then prioritized so that the most probable causes are the first ones to be investigated. A method of evaluation is then determined, a team member is assigned the evaluation action item, and a target completion date is listed. A summary of evaluation results is then listed, and the conclusions are color-coded to indicate if they are OK, unclear, or potentially the root cause. Unclear items as well as potential root causes should then be investigated further, and OK items are moved from consideration.


Figure 18: Cause-and-effect diagram worksheet for coffee taste. (Click here for larger image)

Figure 19 shows a close up view of the cause-and-effect worksheet. Often, the cause-and-effect diagram item is not clean in how it is related to the problem. In such a situation, it can be expand in the worksheet to turn it into a clearer hypotheses. For example, “Water” in the cause-and-effect diagram can be changed to “Water from the city water system containing chemicals leading to coffee tasting bad” in the worksheet.


Figure 19: Close-up of a cause-and-effect diagram worksheet. (Click here for larger image)

A prioritization matrix can be used to evaluate multiple potential solutions to the problem. In this example, the team has identified three potential solutions: The team can clean and repair the old machine, buy a new machine, or buy an expensive new machine. They want to avoid high costs, but do not want to spend too much time on implementing the solution, and they want something with long-term value. Therefore the prioritization matrix shown in figure 20 is used to find the ideal solution.


Figure 20: Prioritization matrix for improvement options

Conclusion

There is no one right quality tool for every job, so quality tools should be selected based on what must to be accomplished. Information from one tool can be transferred to a different tool to continue the problem-solving process. Actions items resulting from a cause-and-effect diagram should be entered into a tracking list. This assists the team leader in tracking the status of items, makes it easier to ensure action items are completed, and is also useful for reporting the result of action items.

References
1. Barsalou, Matthew A. Root Cause Analysis: A Step-by-Step Guide to Using the Right Tool and the Right Time. Boca Raton, FL: CRC Press, 2015.
2. Borror, Connie M., ed. The Certified Quality Engineer Handbook, Third Edition. Milwaukee, WI: ASQ Quality Press, 2009.
3. Brassard, Michael. The Memory Jogger Plus + Featuring the Seven Management and Planning Tools, First Edition. Salem, NH: GOAL/QPC, 1996.
4. Ishikawa, Kaoru. Guide to Quality Control, Second Edition. (Translated by Asian Productivity Organization.) Tokyo, Japan: Asian Productivity Organization, 1991.
5. Juran, J. M. “The non-Pareto Principle Mea Culpa.” From Stephens, K.S., ed. Juran, Quality, and a Century of Improvement, pp. 185–188. Milwaukee, WI: ASQ Quality Press, 2005.
6. McClave, James T. and Terry Sinich. Statistics, Eleventh Edition. Upper Saddle River, NJ: Pearson Education, 2009.
7. ReVelle, Jack B. Quality Essentials: A Reference Guide from A to Z. Milwaukee, WI: ASQ Quality Press, 2004.
8. Tague, Nancy R. The Quality Toolbox, Second Edition. Milwaukee, WI: ASQ Quality Press, 2005.
9. Westcott, Russell T., ed. The Certified Manager of Quality/Organizational Excellence Handbook, Fourth Edition. Milwaukee, WI: ASQ Quality Press, 2013.
10. Wheeler, Donald J. Advanced Topics in Statistical Process Control: The Power of Shewhart’s Charts. Knoxville, TN: SPC Press, 1995.

Originally presented as a conference paper for the 2016 International Conference on Quality Engineering and Management.

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About The Author

Matthew Barsalou’s picture

Matthew Barsalou

Matthew Barsalou is a statistical problem resolution master black belt at BorgWarner Turbo Systems Engineering GmbH. He is an ASQ-certified Six Sigma Black Belt, quality engineer, and quality technician; a TÜV-certified quality manager, quality management representative, and quality auditor; and a Smarter Solutions-certified lean Six Sigma Master Black Belt. He has a bachelor’s degree in industrial sciences, and master’s degrees in engineering, business administration, and liberal studies with emphasis in international business. Barsalou is author of Root Cause Analysis, Statistics for Six Sigma Black Belts, The ASQ Pocket Guide to Statistics for Six Sigma Black Belts, and The Quality Improvement Field Guide.

Comments

Back to Basics

Great to see Mark getting back to basics and avoiding the material that is irrelevant to process improvement, not to mention avoiding the ridiculous dpmo.  As Dr Deming put it:"The student should avoid passages in books that treat confidence intervals and tests of significance, as such calculations have no application in analytic problems in science and industry." (W. Edwards Deming, Out of the Crisis, page 639.)"Analysis of variance, t- test, confidence intervals, and other statistical techniques taught in the books, however interesting, are inappropriate because they bury the information contained in the order of production." (W. Edwards Deming, Out of the Crisis, page 132.)