A movie sequel often can be as, if not more, captivating than the original. Take “The Godfather: Part II.” Remember “The Empire Strikes Back”? More recently, what about “The Lord of the Rings: The Two Towers”? Audiences everywhere couldn’t wait to get inside theaters on opening night to see what happened to the Corleone family, Luke Skywalker, and Frodo and Sam.
Essentially, a sequel builds on the original, continuing a journey with familiar characters and settings, developing ideas and unveiling more insight. In that spirit, we asked a supporting cast of QP contributors to help us write the script for the sequel to our January 2009 feature on Kaoru Ishikawa’s original seven quality tools.
Back then, we featured snapshots of each of the “old seven”—cause and effect diagrams, check sheets, control charts, histograms, Pareto analysis, scatter plots and stratification—offering the basics on what you need to understand about them and how they are used. This month, we’re throwing the spotlight on the seven management and planning tools, often referred to as the seven new quality tools: affinity diagrams, arrow diagrams, matrix data analysis, matrix diagrams, process decision program charts, relations diagrams and tree diagrams.
A team from the Union of Japanese Scientists and Engineers (JUSE) first collected these tools in 1976 to promote ways to innovate, communicate, and plan major and complex projects. At the time, some of the tools weren’t necessarily new, but their grouping and promotion were.
Not to spoil the ending, but 36 years later, there’s nothing new or groundbreaking in this re-release of the newer seven. But we think this collection of articles about these new tools does what JUSE set out to do when it devised the collection of seven: promote ways to innovate, communicate and plan.
As noted in the original installment, our cast of contributors could have provided much, much more on each tool. Many of the tools include additional resources at the end of each article if you want to learn more.
You can also visit QP’s archives to access the original article (“Building From the Basics,” January 2009), as well as other articles on basic tools. ASQ’s website, too, has plenty of resources and publications (www.asq.org/books-and-publications.html) to help you learn about the basics of quality.
The affinity diagram is a visual tool that allows an individual or a team to group a large number of ideas, issues, observations or items into categories for further analysis. The tool groups the ideas in a way that allows those with natural relationships or relevance to be placed together in the same group or category.1
The affinity diagram partners well with the brainstorming tool to organize many ideas and issues. The tool also provides an opportunity to creatively identify categories of team observations or input. Often, it helps to overcome team paralysis by offering a step-by-step way to organize multitudes of options. Groups can use affinity diagrams:
- As the next step in organizing the output of a brainstorming event into relevant themes or categories for analysis.
- To actively involve stakeholders in the specifics of a situation in which their understanding, experience, knowledge and support is required.
- As a vehicle for breakthrough thinking and creative association.
- To further analyze data, ideas or observations for eventual hypothesis testing, prioritization and decision making.2
To build an affinity diagram, clearly state the issue being explored. Gain consensus among group members on the issue statement, and brainstorm ideas related to the issue under consideration.
Write one idea each on a sticky note. Make sure the words are in large-enough print to be seen at least five feet away. Randomly place the notes on a board, wall or flip chart so they are visible to the whole group. Figure 1 shows an example of a list created by a group that was brainstorming its organization’s community partners.
As a group, cluster the ideas into categories or themes suggested by the content in relation to the issue being explored. Figure 2 shows how the list of community partners can be organized through an affinity diagram.
If an idea logically fits within more than one thematic category, reproduce the note so it can be posted in all relevant areas. Sometimes, it may be necessary to isolate ideas that do not naturally fit into the categories identified by the group. These one-offs may provide valuable insight into additional analysis later.
Next, create an affinity card (or header card) for each group with a short statement describing the entire group of ideas. Review the resulting cluster themes for consensus.
Two additional techniques can be used to encourage creativity among team members:
- Allow no speaking among team members during the affinity categorization of the sticky notes. All associations of one idea with another should be done in silence.
- Require team members to use their nondominant hand to move the sticky notes around during categorization. In other words, a right-handed person should only use his or her left hand when moving ideas around the board, wall or flip chart. This simple exercise will encourage team members to be more deliberate and pay more attention to the decisions and moves they make.
By using affinity diagrams, a group can move away from idea paralysis and start its journey of exploring an issue or finding solutions to problems.
—Grace L. Duffy
- John E. Bauer, Grace L. Duffy and Russell T. Westcott, The Quality Improvement Handbook, second edition, ASQ Quality Press, 2006.
- Ron Bialek, Grace L. Duffy and John W. Moran, The Public Health Quality Improvement Handbook, ASQ Quality Press, 2009.
The arrow diagram—also known as activity diagram, network diagram, activity chart, node diagram or critical path method chart—is used to illustrate the order of activities of a process or project. A basic example is shown in Figure 3.
The arrow diagram can be simple and straightforward, but over time its use has evolved to that of organizing and monitoring complex projects and situations. In the 1950s, two project management techniques—the program evaluation review technique (PERT) and the critical path method (CPM)—propelled the development of the arrow diagram to the next level.
The U.S Navy developed the techniques between 1956 and 1958 while developing its Polaris nuclear submarine. E.I. Du Pont de Nemours Co., planning to construct major chemical plants in United States, also used these methods to plan, schedule and control its projects.
With PERT and CPM, the arrow diagram can include very specific scheduling and monitoring tasks by infusing additional information and details about each activity within a sometimes complex process or project being defined. Table 1 summarizes PERT and CPM terminology. Figure 4 is an example of applying PERT and CPM to selecting a supplier and signing a purchasing agreement.
The critical path, marked in red on the arrow diagram in Figure 4, includes activities that should be conducted without delay because they are critical to meeting the scheduled end date. All other activities can be conducted using a more flexible schedule.
The creation of a purchasing agreement template, for example, can start anytime between the first and the ninth day of the project, and it can take more than the anticipated three days if an early start day was chosen.
The example illustrates how the arrow diagram helps to balance project resources and identify activities that are critical for the completion of the project on time.
- Berger, Roger W., The Certified Quality Engineer Handbook,ASQ Quality Press, 2006.
- Heagney, Joseph, Fundamentals of Project Management, AMACOM Division of American Management Association, 2011.
- Levy, Ferdinand K., Gerald L. Thompson and Jerome D. Wies, “The ABCs of the Critical Path Method,” featured in Managing Projects and Programs,Harvard Business School Press, 1989.
- Miller, Robert W., “How to Plan and Control With PERT,” which appeared in “Managing Projects and Programs,” Harvard Business Review,reprint series No. 10811, Harvard Business School Press, 1989.
- Wilcox, William H., and James J. O’Brien, “How to Win Campaigns,” National Civic Review, Vol. 56, No. 5, 1967, pp. 265-269.
Matrix Data Analysis
Many of the seven new quality tools packaged and promoted by JUSE are referred to by names different from what JUSE originally called them, but only one has actually been modified through the years: matrix data analysis.
In its original form, matrix data analysis was heavy on mathematics. Sometimes, it has been replaced on this list by the similar prioritization matrix. There is very little reference material on matrix data analysis itself, but I have compiled the limited information and developed an example.
In more complex industrial problems, data are not necessarily one dimensional. Often, we get into analyzing data that have many possibilities. For example, automobiles are built with several features targeting different consumer demographics. Different demographic groups may react differently because the features and preferences vary. Younger consumers may pay more attention to design style, while older consumers place greater emphasis on stability and safety of the design. Similarly, there may be preference discrepencies between men and women in terms of color and comfort.
To analyze this data, the traditional seven quality tools may not be adequate. The matrix data analysis method can be used to analyze the data arranged in matrix format. For example, you may want to analyze the customer responses to several attributes of a new product to form a smaller number of uncorrelated variables that are easier to interpret.
The matrix diagram arranges items in a column and row format, with the degree of correlation entered into the relevant columns using symbols or numerical values. This idea appears similar to the relationship matrix tool. In the matrix data analysis, however, the correlation coefficient is used to identify the relationship instead of symbols.
One type of matrix data analysis is principal component analysis. This technique is used in multivariate analysis. Principal component analysis is a selective measurement technique in which the representative characteristics can be mathematically calculated. Principal components analysis can reduce your data and avoid multicollinearity, or a situation in which you have too many predictors relative to the number of observations. Principal components analysis often can uncover unsuspected relationships, allowing you to interpret data in a new way.
For the automobile example, 100 potential customers (both genders of various ages in urban and rural areas) were asked to score five automobile features. A score of one was the lowest preference score, and 10 was the highest. The following steps were used to construct and analyze data using matrix data analysis:
- The scores were averaged and each data item was arranged in row and column format, as shown in Table 2.
- The correlation coefficient matrix was calculated for each observed group. The example in Table 3 is the sex and age of the observed groups.
- The characteristic values and vectors using the correlation matrix were calculated, as shown in Table 4. In our example, the first principal component has variance 7.607 (equal to the largest eigenvalue) and accounts for 0.634 (63.4%) of the total variation in the data. The second principal component (variance 3.608) accounts for 0.301 (30.1%) of the total data variation. The third principal component (variance 0.652) accounts for 0.054 (5.4%) of the total data variation. The first two principal components with variances equal to the eigenvalues greater than one represent 0.935 (93.5%) of the total variability, suggesting the first two principal components adequately explain the variation in the data.
- The degree of preference for each feature by demographics was reviewed. Such value expresses the degree of preference. The value of characteristics’ vector changes from positive to negative in accordance with age for men and women (Figure 5). General preference affected by demography, age and sex are calculated. This is graphically represented by the score plot and biplot in Figures 6 and 7. The score plot graphs the second principal component scores versus the first principal component scores. As in this example, if the first two components account for most of the variance in the data, you can use the score plot to assess the data structure and detect clusters, outliers and trends. For examples with multiple variables, the plot may reveal groupings of points, which may indicate two or more separate distributions in the data.
The biplot overlays the score and loading (preference, importance) plots of the first two principal components. The second principal component scores are plotted versus the first principal component scores. The loadings for these two principal components are plotted on the same graph.
The plot may reveal groupings of points, which may indicate two or more separate distributions in the data. This may be evident with an example that uses hundreds of features. Only five features of the matrix data analysis technique have been mentioned for illustration purposes.
If the data follow a normal distribution and no outliers are present, the points are randomly distributed around zero. In the score plot diagram, the generally preferred features appear as you move right along the horizontal axis, and features that are not preferred move to the left.
With the exception of the youngest age group, the biplot (Figure 7) seems to indicate that rural and urban men have the same preferences, rural and urban women share the same preferences, and those under 35 years old share the same preferences—regardless of gender.
This tool can be used to analyze market data, new product introduction and for narrowing down root cause analysis. Relationships among defects and their causes, location of defect occurrence or process step can be analyzed using the tool.
- Brassard, Michael, The Memory Jogger Plus+, Goal/QPC Inc., 1989.
- Domb, E.R., “7 New Tools,” Quality Digest,December 1994.
- Minitab, “Meet Minitab 16″ software documentation, www.minitab.com/en-us/products/minitab/documentation.aspx?langtype=1033.
- Quality Council of Indiana, Certified Manager of Quality Primer, 2010, pp. vi-19.
- Shigeru, Mizuno, Management for Quality Improvement: The Seven New QC Tools,Productivity Press, 1988.
- Tetsuichi, Asaka, and Ozeki Kazuo, Handbook of Quality Tools: The Japanese Approach, Productivity Press, 1990.
Matrix data analysis is used to analyze and understand a large number of data arranged in rows and columns using computer-aided tools with advanced mathematics. The prioritization matrix is a simple, easy-to-use L-shaped tool to narrow down options through a step-by-step approach applying selection of criteria, assigning weights and arriving at a conclusion through basic mathematics.
The prioritization matrix is useful when multiple criteria (typically three to six) and multiple options (typically five to 10) are involved in making a final selection. Situations include selecting improvement projects, products and service offerings. The matrix even can be used effectively for personal and social applications such as deciding on a school, choosing a financial institution or purchasing property.
There are other variations to this method—analytical criteria method, consensus criteria method and combination ID matrix—that can be applied in different situations. Let’s examine the analytical criteria method for using the prioritization matrix and narrow this process to three steps:
- Form a team. Assemble a team of stakeholders with influence and interest in the decision at hand. Identify a well-trained facilitator. The team should be briefed on ground rules. Team members should be open-minded about the weighting and scoring process by not manipulating calculations in their favor to align with a previous decision. Actual data are encouraged to use for comparisons where available.
- Select criteria. If there is not already a predefined set of criteria, use brainstorming to generate a list and narrow down the criteria to a manageable number. For business applications, these criteria also may come from management, customer and market conditions.
- Weight criteria. To obtain criteria weighting, the criteria are compared with each other and scored on relative importance. The scoring rules are as follows:
- If the criteria in a row are equally important as in a column, assign a score of one.
- If the row is significantly more important than the column, assign a score of five.
- If the row is much more significantly important than the column, assign a score of 10.
- If the row is less important than the column, assign a score of 1/5 (0.2).
- If the row is much less important as the column, assign a score of 1/10 (0.1).
- Consider an organization selecting quality information system software. A cross-functional team was chosen, and criteria were identified as initial cost, flexibility for customization, scalability as the business grows, integration with existing data sources, ability to migrate legacy data into the new system and ease of use.
The criteria were compared against one another for relative importance. The team was educated that initial cost was only the purchase price and the cost of concurrent licenses. The team scored relative importance and calculated weights (Online Table 1).
Online Table 1
After this activity, interestingly to the team, initial cost did not make it to most weighted criteria, which the team found noteworthy. Understandably, usability carried the most weight (Online Table 1). If the system is not easy to use, it does not matter how inexpensive or how convenient other criteria are. The users simply would not use the system.
Also, it made a lot of sense to the team that the ability to integrate with data sources carried the next important weight (Online Table 1). This is probably due to the team’s proactive decision to reduce data duplication errors and ensure the ability to make quick decisions without workarounds or manually populating data into the system.
Flexibility was the next important score, as it allows IT to customize workflows for future new applications (Online Table 1).
This did not mean that the team did not care about cost. Weighting heavily on initial cost would have resulted in selecting a low-cost product. However, the overall cost involved in customizing, entering data through manual sources and scaling up would have increased the maintenance cost significantly over the lifetime. This is why having the right team composition and being open minded in scoring is important to the outcome.
We have three options: vendors A, B and C. The vendors are compared against one another using the same scoring rules noted for the top three weighted criteria: usability (Online Table 2), integration (Online Table 3) and flexibility (Online Table 4).
Online Table 2
Online Table 3
Online Table 4
Finally, the corresponding criteria weights are multiplied with the vendor weights to select a vendor that meets the most important criterions (Online Table 5): vendor C. The team agrees with the decision.
Online Table 5
- Brassard, Michael, The Memory Jogger Plus +,Goal/QPC Inc., 1989.
- Tague, Nancy R., The Quality Toolbox, second edition, ASQ Quality Press, 2004.
See the following resources for formal definitions and more information on the terms mentioned in this sidebar.
- Stark, Philip B.,”Glossary of Statistical Terms,” University of California Berkley, http://statistics.berkeley.edu/~stark/SticiGui/Text/gloss.htm#e.
- Pohlmann, John T., “Factory Analysis Glossary,” Department of Educational Psychology and Special Education, Southern Illinois University, www.siuc.edu/~epse1/pohlmann/factglos.
- Palmer, Michael, “A Glossary of Ordination-Related Terms,” Oklahoma State University, http://ordination.okstate.edu/glossary.htm.
- Darlington, Richard B., “Factor Analysis,” Cornell University, www.psych.cornell.edu/Darlington/factor.htm.
Knowing how to visually present data is absolutely critical in today’s workplace, especially when you consider that visual representation of data is the only way you will reach some individuals. Matrix diagrams can be used to show the relationship between two, three or four groups of information.
There is a fundamental need for matrix diagram users to be familiar with data. To get started, you must determine ahead of time where the comparisons are going to be. The tool can be an excellent way to compare customers, associates in a call center, departments and processes, for example. The entities being compared are typically listed across the page (x axis). Features or aspects for comparison are listed going down the page (y axis).
Suppose a back-office processing area is scanning forms into a system. When you look at productivity data, you can quickly see there are two groups with distinctly different productivity.
List the names of the associates across the page, keeping in mind the total number of associates. If the number is small, you can list all the associates. If the number is large, you may need to create a composite employee, showing how typical high and low performers appear.
Brainstorm potential areas that participants say they think might be at the root of driving performance. The output of this activity would be listed down the page. Start filling in the matrix with the data you have. You will then have something to show management about how employees differ in performance and what may be causing the performances to be different.
One particular matrix diagram my organization uses on a regular basis is the 2 x 2 matrix (Figure 8). When we conduct workshops with clients, invariably a point is reached at which we identify potential solutions. The 2 x 2 matrix helps the business partner differentiate the solutions.
This differentiation is centered on the effort required to implement a given solution and what the potential impact would be. Solutions are placed on the grid in response to the evaluation of the solution against a predefined set of criteria that defines effort and impact.
Potential solutions falling into the red block—high impact and low effort—are the targeted solutions. These are typically labeled as quick hits. Solutions falling into the teal block—high effort and low impact—are prioritized lower on the list because more resources are required to implement a solution that will make less of an impact.
Using the two extremes as an example, the matrix diagram of impact and effort shows the relationship between the solutions by their positions on the grid. Using the predefined criteria enables a group to use this differentiation to make business decisions regarding which solutions to pursue.
Matrix diagrams are a simple yet powerful means of understanding data. Perhaps more importantly, they are an effective way to convey information to decision makers.
- ASQ, “Seven New Management and Planning Tools—Matrix Diagram,”http://asq.org/learn-about-quality/new-management-planning-tools/overview/matrix-diagram.html.
Check out the article that inspired this month’s sequel. “Building From the Basics” appeared in the January 2009 edition of QP and continues to receive rave reviews from readers. You can find the open-access article at http://asq.org/quality-progress/2009/01/basic-quality/building-from-the-basics.html. Share the link with colleagues through email, Twitter or Facebook. Also find templates for most of the seven basic tools at ASQ’s Quality Tools & Templates corner of its website at http://asq.org/learn-about-quality/tools-templates.html.
Process Decision Program Charts
The process decision program chart (PDPC) is an excellent tool for what can be called project risk management. Risk management involves looking ahead proactively during planning to identify potential future problems. PDPC provides a structure to identify what can go wrong and then plan what to do when the wrong things happen.
PDPC is a visual tool that combines and builds on elements of several other techniques. It may enhance a tree diagram in which an objective and one or two levels of activities or tasks already have been defined. It has some characteristics of failure mode and effects analysis, such as the identification of risks, consequences and mitigations.
The PDPC also can be described as a graphical version of the good project management practice of proactively identifying issues, risks and assumptions. Therefore, a PDPC is a nice tool to place into a project plan or charter.
Many levels of planning could benefit from the PDPC. The top level is strategic planning, for which the PDPC could be used to help select key initiatives or programs from several alternatives. A second level is program planning. “Program” means a portfolio of projects or a group of interrelated activities with specific endpoints.
A PDPC can be used to help select the projects or approaches that are most likely to succeed and weed out those that are not feasible because of high risk or unavailability of resources. After a specific project has been chosen, the PDPC can be used in its most basic form for detailed contingency analysis within the scope of project planning.
Figure 9 is a simplified example of a PDPC with information from program and project planning. The initiative of training quality engineers was chosen as a way to support the strategy of improving product quality in design. Alternative projects addressing different methods to deliver the training were evaluated. The concept of using internal resources was chosen as the preferred strategy.
Then, within the scope of this project, several risks were identified and mitigations developed during the planning phase. Throughout the process, decisions were made by considering the potential risks at each step and eliminating the activities for which risk mitigations or countermeasures were not considered practical.
Figure 9 also shows the common PDPC practice of denoting impractical countermeasures with an X and practical countermeasures with an O. Identification of countermeasures deemed impractical was based on constraints in the project cost, schedule or logistics.
Ultimately, using the PDPC thought process facilitates project planning, identifies risks and mitigations, and helps secure approval to execute a project based on the best benefit and risk ratio, and likelihood of success. The process decision program chart is, therefore, very deserving of its accurate and descriptive long name.
Many of the authors drew from Nancy R. Tague’s The Quality Toolbox (ASQ Quality Press, 2005) to develop the summaries of the seven management and planning tools. For more about the book and to read a sample chapter, visit http://asq.org/quality-press/display-item/index.html?item=H1224.
A relations diagram is a graphical representation of the relationship between cause and effect or a given outcome, and all the factors that influence or contribute to that outcome. Figure 10 shows an example of a relations diagram, which is a variation of a typical fishbone or cause and effect diagram.
Developing a relations diagram is a structured approach to problem solving. The diagram also can be used to learn more about the problem being addressed, because it can clarify thinking about how various factors are related or contribute to the problem being addressed. After you know these factors, you can address each one, depending on its importance in terms of severity of effect and the cost of addressing it or not.
Developing a relations diagram involves brainstorming and organizing thoughts as explained in the following five steps:
- Identify a group of people—usually no more than five or seven—to participate in developing a relations diagram about a problem. Include people from various departments and people with different perspectives.
- Distribute a clear problem statement to be reviewed at least a day or two in advance of the first meeting so group members have a chance to think about it and come to the meeting somewhat prepared.
- Ask everyone to write on a sticky note one factor they think contributes to the problem. Collect the notes and place them on a wall or a board. Do this a second or third time, or until all factors are covered. These notes do not need to be placed in any order—placing them randomly is fine.
- Write the problem statement on another wall, board or a flip chart. Then, take one of the notes and discuss whether it is a contributing factor and whether the group agrees it is. Place it near the problem statement, and draw an arrow from this note to the problem statement. Repeat the same process with another note (factor). Continue this and a relations diagram will emerge, as shown in Figure 10. If one factor contributes to more than one outcome, you can have arrows starting from a factor leading to several outcomes, as shown in the figure.
- Schedule a relations diagram session for no more than one hour because of the fatigue factor. If necessary, hold more than one session.
Looking at Figure 10, it is clear the lack of management understanding about the link between quality and profitability contributes to many factors leading to poor quality. However, there are no cost-of-quality data available, so the link between quality and profitability cannot be explained.
Therefore, to address poor quality, the first step must be to collect cost-of-quality data for a certain period. After sufficient data are collected and analyzed, a presentation on the subject can be made to management. Ideally, after management understands the link between quality and profitability, it will support quality efforts and also look into the professionalism of the procurement and purchasing function.
Before the relations diagram was developed, the normal tendency for everyone in an organization was to blame poor quality on high operator turnover, poor-quality materials or poor machine maintenance. The relations diagram clarifies what ultimately drives those factors, which in turn helps address the root cause of poor quality.
A tree diagram allows you to detail a conceptual or highlevel goal into more operational tasks to achieve the desired result. The tree diagram starts with one item that branches into two or more branches, each of which branches into two or more, and so on.
Tree diagrams can be used to break down broad categories into finer levels of detail and can be adapted for a wide variety of uses.1 Developing the tree diagram helps move team thinking from generalities to specifics. The tree diagram is a generic tool that can be adapted for a wide variety of purposes:
- Developing logical steps to attain a specific result.
- Conducting a five whys analysis to explore a root cause.
- Communicating to encourage involvement in the development of a jointly supported result.
- Drilling down to more detailed levels of a process flow.
- Graphically representing a hierarchical progression, such as a genealogy or classification scheme.
The structure of the tree diagram represents the hierarchical nature of a structure in a graphical form. It is called a tree structure because the classic representation resembles a tree, even though the chart is generally upside down compared with the shape of an actual tree.
Some quality improvement teams often represent the tree from left to right, with the root at the left and the increasing levels of detail branching out to the right.
Every completed tree diagram has a root or root node, which also can be thought of as the starting node. The lines connecting elements are called branches, and the elements themselves are called nodes. Nodes without children are called leaf nodes, end-nodes or leaves.2
To construct a tree diagram, begin with the root node. Develop a short statement of the goal, issue or item being broken down. Locate the root node either at the top or far left of the diagram. Brainstorm what will take the hierarchy to the next level of detail. For an action plan, this may be the next steps to be taken. For an organization chart, it’s the person who reports to the next level of the organization.
Brainstorm all possible items for each level until is no item or action available at a finer level of description. For a vertical tree, write each idea in a line below the branch. For a horizontal tree, write it to the right of the first statement. Do a “necessary and sufficient”; check. Are all the items at this level necessary for the one on the level above? If all the items at this level were present or accomplished, would they be sufficient for the one on the level above?3
Figure 11 shows an example of a tree diagram that illustrates how to prepare for ASQ’s manager of quality/organizational excellence (CMQ/QE) certification. “Prepare for CMQ/QE”; represents the goal, or root, of the tree diagram, while”Take ASQ review course,”; “Take another course”; and “Self-study from ASQ Body of Knowledge”; are the next level of details, or nodes, that branch from the root. Further activities and descriptions below each of these three nodes continue until options and ideas seem exhausted.
—Grace L. Duffy
Grace L. Duffy is president of Management and Performance Systems in Tavares, FL. She earned a master’s degree in management and information systems from Georgia State University in Atlanta. Duffy is an ASQ fellow and holds ASQ certifications as a quality auditor, manager of quality/organizational excellence and improvement associate.
Scott A. Laman is a senior manager of quality engineering and risk management for Teleflex Inc. in Reading, PA. He earned a master’s degree in chemical engineering from Syracuse University in New York. Scott is an ASQ fellow and is certified as a quality engineer, reliability engineer, manager of quality/organizational excellence, auditor and Six Sigma Black Belt.
Pradip Mehta is the retired director of quality assurance for the Army & Air Force Exchange Service in Dallas. He earned master’s degrees in textile engineering from Lowell Tech in Lowell, MA, and business administration from the University of Dallas in Irving, TX. A certified quality auditor and systems lead auditor, Mehta is an ASQ fellow and the former chair of its diversity committee.
Govind Ramu is a senior manager for global quality systems at SunPower Corp. in San Jose, CA. Ramu is a licensed professional mechanical engineer from Ontario, Canada. An ASQ member since 1998 and an ASQ fellow, Ramu holds six ASQ certifications: manager of quality/organizational excellence, engineer, Six Sigma Black Belt, auditor, software engineer and reliability engineer. He is co-author of The Certified Six Sigma Green Belt Handbook (ASQ Quality Press, 2008).
Natalia Scriabina is a vice president and cofounder of Centauri Business Group Inc. in Waterloo, Ontario. She earned a master’s degree in engineering sciences, automated control of electrical systems from the National Technical University of Ukraine in Kiev. Scriabina is a member of ASQ.
Keith Wagoner is a director of continuous improvement at Lincoln Financial Group in Greensboro, NC. He is a senior member of ASQ and a certified quality engineer.