Biography and Professional Vita - Dr. Mikel Harry

Now we have for you the Biography and Professional Vita of Dr. Mikel Harry who is regarded as the Father of Six Sigma, Please go through the Biography of this Great Man.

Mikel J. Harry

Ph.D., Arizona State University, 1984
M.A., Ball State University, 1981
B.S., Ball State University, 1973

Dr. Harry has been widely recognized and cited in many publications as the principle architect of Six Sigma and the world's leading authority within this field. His most recent book entitled Six Sigma: The Management Strategy Revolutionizing the World's Top Corporations has been on the "best seller list" of the Wall Street Journal, Business Week, and Amazon.com. He has consulted to many of the world's top CEOs and has been a featured guest on such television programs as the NBC show "Power Lunch." In addition, he has been distinguished by Arizona State University with the 2002 Engineering Excellence Award for superb achievements in the engineering profession and notable contributions to society. At the present time, Dr. Harry is President and COB of the Six Sigma Management Institute.

As founder of the Six Sigma Academy in 1994, he served as a board member and chief executive officer. In addition, he has served on the Board-of-Directors for the International Statistics Application Institute and the International Design Institute, Singapore. Dr. Harry was employed by Asea Brown Boveri and served as Corporate Vice President, Quality Systems Deployment. As such, he was directly responsible for the global leadership, implementation, and deployment of Six Sigma at ABB. His activity was focused on the creation of world-class levels of improvement in product quality, performance, producibility, and cost.

Before this, Dr. Harry founded Motorola's Six Sigma Research Institute and served as Corporate Director and Senior Member of Technical Staff. In this position, he was responsible for the development of Six Sigma implementation strategy, deployment guidelines, and advanced application tools. For his technical leadership, Dr. Harry was appointed as an associate member of Motorola's prestigious Science Advisory Board (SABA). In recognition of his technical contributions, he was inducted into the Scientific and Technical Society at Motorola's Government Electronics Group.

As one of the original architects and pioneers of Six Sigma at Motorola, he was responsible for the research and development of advanced Six Sigma engineering models and methods. While serving the Motorola Government Electronics Group, Dr. Harry also held the positions of Member of Technical Staff, Group Operations. Before this, he held the position of Manager and Principal Staff Engineer, Advanced Quantitative Research Laboratory. He also served as a Quality and Reliability Engineer when he first joined Motorola.

Before joining Motorola, Dr. Harry was a consultant to several Fortune 500 corporations in the areas of statistical engineering, experiment design, statistical process control, and quality management. His industrial experiences also include manufacturing management at General Motors Corporation and industrial engineering with Dayton Walther Corporation. Dr. Harry has also served as an instructor within the College of Engineering and Applied Sciences, Division of Technology, Arizona State University. In addition, he was a member of the ASU/IBM Joint Engineering Study.

Prior to beginning his professional career, he was commissioned into the U.S. Marine Corps as a Second Lieutenant. His tour of duty included serving as an infantry platoon leader and later as an executive officer and company commander. He was also certified as a nuclear-biological-chemical warfare officer and was honorably discharged with the rank of Captain.

Significant professional contributions include creation of the Six Sigma Breakthrough Strategy and the Six Sigma Black Belt concept. In addition, Dr. Harry authored the first substantive publication on Six Sigma. This book was designed to articulate the philosophy, theory, and application of Motorola's Six Sigma Program and was published under the title "The Nature of Six Sigma Quality." In addition, Dr. Harry was responsible for the research and development of an advanced mechanical design engineering tolerancing system, for which he received a major engineering award from Motorola. The design algorithms have since been translated to functional engineering software. This work was published by Motorola, Inc. under the title "Six Sigma Mechanical Design Tolerancing.".

Another major contribution was the research and development of an RF/Microwave design analysis and optimization procedure. This work was published by Motorola University Press and used by Motorola, Texas Instruments and other noted corporations. Dr. Harry was directly responsible for the research and development of a process characterization methodology, as well as the supporting mathematical statistics. This work was published by Addison-Wesley under the title "Six Sigma Producibility Analysis and Process Characterization." .

Dr. Harry has over 50 major publications to his credit. His work has appeared in such journals as Quality Progress, IEEE Micro and Circuit World, Journal of Circuit Technology. In addition, he has authored a substantial reference book on the application of experiment design, inferential statistics, and statistical process control. The book is entitled "Achieving Quality Excellence: The Strategy, Tactics and Tools". Most recently, Dr. Harry has published an eight volume set of books entitled The Six Sigma Series. This extensive work presents the implementation guidelines, deployment strategy and application tools related to Six Sigma. Supporting this series and furthering the power of quality, he published a unified set of articles in Quality Progress entitled New Frontiers.

His work is actively used and promoted by such noted institutions as General Electric, Ford Motor Company, Sony, Allied Signal, Stanford University, Motorola, Texas Instruments, Unisys, IBM, Rockwell, Kodak, and the Department of Defense, as well as many others. He is a contributing author to a textbook on the application of SPC methods and experiment design in automated manufacturing, Marcel Decker. In addition, he is a contributing author to a textbook used by the Mathematics Department, U.S. Air Force Academy. He has served as chairman of the Product Design Sub-committee for Producibility Metrics, United States Navy. In addition, he was technical co-chairman of the SPC standards committee for the Interconnecting and Packaging Electronics Circuits Institute (IPC). He received the President's Award from IPC at the 1990 Annual Conference for outstanding technical contribution to the industry.

Dr. Harry has personally trained and worked with such Chief Executive Officers as Jack Welch (General Electric), Jac Nassar (Ford Motor Company) and Larry Bossidy (Allied Signal), as well as their senior executive teams and technical/scientific communities. In addition, Dr. Harry has worked with several distinguished professional societies around the world. He has personally trained thousands of leaders and practitioners around the globe. He is frequently retained as a keynote speaker and presenter for industry symposiums and prestigious functions such as the Young President's Organization (YPO). Dr. Harry has also been featured in several documentaries and was the subject of a feature article in "Personal Success" magazine, Quality Progress magazine and the international magazine: The Globe and Mail Report on Business.

Trend Chart - Run Chart

Trend charts are also known as Run charts, and are used to show trends in data over time. All processes vary, so single point measurements can be misleading. Displaying data over time increases understanding of the real performance of a process, particularly with regard to an established target or goal. Following is an example of a trend chart of order fill rate performance:

Major Elements:

A good trend chart has the following characteristics:

A clear Title to describe the subject of the chart. Labels on the vertical Y-axis and horizontal X-axis to describe the measurement and the time period. A Legend to differentiate the plotted lines - in this case, the actual vs. the goal. Appropriate Scales that are narrow enough to show variation. Limited Characteristics on each chart to avoid confusion from too many lines. An appropriate Time Frame. Notations on any major spikes. Targets or Goals should be noted on the chart for reference. Note Who Prepared the chart in case there are questions about the chart or the data.

Two common errors in chart construction are shown below:

The first chart has a scale that is so wide that little variation can be seen. The data are correct (and are the same as in Figure 1 shown above), but the chart is not very useful because the scale is so wide (0-100%).

The second chart has a scale that includes impossible numbers based on the definition of the metric being charted. In this case, fill rate can not be higher than 100%, so a scale that goes to 120% is misleading. Again, this chart uses the same data as Figure 1 and 2, but conveys a different message.

A third problem arises from using long time scales and inappropriate trend line plots. The MoreSteam editors know a Quality Manager at a major automobile manufacturer who was renowned for choosing time scales long enough to pick up an unfavorable baseline, and therefore indicate improvement in subsequent periods. Sometimes this practice is helpful for a long term perspective, but it can be confusing if it diverts attention from more recent events - especially when a trend line is plotted through the data. Consider the following chart of quality complaints, or "Things Gone Wrong" (TGW's):

It is a fact that the TGW level in 1999 (1,320) is 44.5% lower than it was in 1989 (2,378). The trend line appears to indicate continuous improvement over time. However, the process is relatively stable since 1990, with little sustained improvement since that time, and an increase in TGW's over the last two years. The presentation of the chart can tell two different stories, and the trend line is not appropriate in this instance. See the discussion below and Figure 6 on the use of reference bars. Many times a chart will exhibit an apparently abnormal fluctuation, or "spike", as seen in June of the chart below. Since such spikes always raise questions, a good rule of thumb is to pro-actively answer the question by putting a note on the chart as shown below. This practice also provides documentation of the history of a process and helps to connect cause with effect.

A further improvement to aid understanding of a trend chart is to add Reference Bars. The chart below is the same as that represented by Figure 5, but has reference bars added to show the performance in prior years. The addition broadens the reader's perspective by showing the extent of improvement over a longer time horizon.

Failure Mode and Effect Analysis - FMEA

Failure Mode and Effects Analysis (FMEA) is a model used to prioritize potential defects based on their severity, expected frequency, and likelihood of detection. An FMEA can be performed on a design or a process, and is used to prompt actions to improve design or process robustness. The FMEA highlights weaknesses in the current design or process in terms of the customer, and is an excellent vehicle to prioritize and organize continuous improvement efforts on areas which offer the greatest return.

The process is very straightforward, and begins by identifying all of the probable failure modes. This analysis is based on experience, review, and brainstorming, and should use actual data if possible. New designs or processes may not have actual historical data to draw from, but "proxy" data may be available from similar designs or processes.

The next step is to assign a value on a 1-10 scale for the severity, probability of occurrence, and probability of detection for each of the potential failure modes. After assigning a value, the three numbers for each failure mode are multiplied together to yield a Risk Priority Number (RPN).

The RPN becomes a priority value to rank the failure modes, with the highest number demanding the most urgent improvement activity. Error-proofing, or poka-yoke actions are often an effective response to high RPN's.

Following is an example of a simplified FMEA for a seat belt installation process at an automobile assembly plant.

As you can see, three potential failure modes have been identified. Failure mode number two has an RPN of 144, and is therefore the highest priority for process improvement.

FMEA's are often completed as part of a new product launch process. RPN minimum targets may be established to ensure a given level of process capability before shipping product to customers. In that event, it is wise to establish guidelines for assessing the values for Severity, Occurrence, and Detection to make the RPN as objective as possible.

Six Sigma in Government--Indianapolis Star Article

The state's human services agency, known for its fiscal foul-ups, says it will save taxpayers at least $1.5 million over one year using a quality control program known as Six Sigma.

Fortune 500 companies swear by this cost-cutting tool. But critics question the time and money the Indiana Family and Social Services Administration has devoted to training 30 workers to use it.

The agency's leader, John Hamilton, says savings should more than cover the $250,000 spent on Six Sigma, even as his agency has made deep cuts in social programs, including $500 million in Medicaid. The initial cost-saving projects included finding faster ways to process child support and eliminating overpayments to companies that provide in-home care for people with mental disabilities.

Rhonda Hall, a 27-year-old social services coordinator for a Franklin nursing home, has never heard of Six Sigma. But the Greenwood woman favors trying anything that would help her get the $74-a-week support payment ordered for her son, Kyle, 4.

"They took his father's tax refund six months ago, and I just got it," said Hall, who received about $3,000 from her son's father, who now lives in Tennessee.

It's too early to say what potential cost savings from Six Sigma will be. But to address skeptics, the agency has asked managers with four companies in Indiana that use Six Sigma -- Cummins Inc., International Truck and Engine Corp., ITT Aerospace and Roche -- to validate the savings.

"We wanted from the beginning to have some outside voices see what we're doing," said Hamilton, who spearheaded the controversial training effort.

Six Sigma requires workers to go beyond using gut instinct and experience to solve problems. It involves using advanced statistical techniques to identify what really causes errors so they can be fixed. The aim is to achieve near perfection on the assembly line or with administrative tasks.

"Everyone thinks it's a program just for manufacturers," said Peter Maniago, a former state health official who works as a government process manager for Roche, a maker of drug and medical diagnostic products.

"It's not just about widgets," he said. "And it's perfectly applicable to state government."

The outside advisory group met this week for the first time and will continue to meet quarterly. Its members want more data before they sign off on the agency's savings, but they offered praise -- and some criticism -- as state managers went over the first three projects.

"It's neat to see a government agency embracing continuous improvement and a technique that will get you there," said Paul Gambino, an Indianapolis-based manager of quality systems for International Truck and Engine Corp. "These are our tax dollars."

But the outside advisers also urged the agency to develop ways to monitor whether its cost-cutting efforts are hampering the delivery of services people desperately need.

Gov. Frank O'Bannon has given Hamilton the freedom to pursue Six Sigma, but he wants to see results, said Andrew Stoner, the governor's executive assistant for human services and an administration spokesman.

"If we're going to spend that kind of money," Stoner said, "we need to show it was worth it."

Six Sigma was pioneered by Allied Signal, General Electric Co. and Motorola Inc. in the mid-1980s. Indiana may be the only state using it and the first to apply it to human services, according to the Denver-based National Conference of State Legislatures.

In July, Hamilton asked state workers to pitch in and help managers who have been through the two- or four-month training sessions with their cost-cutting projects.

The program designates the level of training using martial arts terms. Black belts get four months of advanced statistical training to oversee big projects, and green belts get half as much to assist.

Matt Raibley, a black belt, manages Indiana's welfare, food stamp and welfare-to-work programs. He oversaw a team that reduced the time workers spent manually researching noncustodial parents' payment histories for 92 county prosecutors. There was a 39-day backlog of research requests, with each case taking an average of three hours.

Before any money can be disbursed, researchers must pore over 120 million documents on microfiche and electronic records kept since 1996 to ensure state and federal governments are first repaid for such expenses as prior welfare benefits and overdue taxes.

Raibley's team saved $153,940 by limiting overtime work to the most experienced researchers for several months. But a dispute with the Unity Team, the union that represents human services workers, stalled the project last month, with the backlog cut to fewer than 21 days.

State officials hope to reach a compromise that would allow the overtime savings to continue.

The same team also:

?Found a $350,000 federal grant that could help automate the research process.

?Saved $57,024 by encouraging employers to deposit child support payments withheld from workers' pay electronically, using the state's Internet site.

?Found a $350,000 grant to experiment with making support payments using debit cards.

Raibley is relieved the project is almost behind him -- and he can't wait to start another.

"It's been very beneficial to my own personal development," said Raibley, who's worked for the state for nine years. "There's a lot of pressure, but it's somewhat of a relief to know that these tools do work."

Six Sigma program takes aim at mistakes

Six Sigma is a method of improving the way things are done by eliminating mistakes. The idea is that if you can measure defects, you can eliminate them, saving money and improving service.

The word "sigma" is a statistical term that measures how far a given way of doing things falls short of perfection. The number preceding the term indicates the closeness to perfection. For instance, a process that is 2 sigma is perfect 69.1 percent of the time, one that is 3 sigma is perfect 93.32 percent of the time, and one that is 6 sigma is perfect 99.99966 percent of the time.

It's more than an abstract statistical concept. A municipal water utility operating at 3.8 sigma would produce unsafe drinking water 15 minutes a day. At 6 sigma, the incidence of unsafe water would drop to one minute every seven months.

Source: U.S. Mayor magazine

Six Sigma project savings estimates

The Indiana FAmily and Social Services Administration has wrapped up three Six Sigma projects. The agency has asked otuside experts to validate its savings estimates, which are:

?$938,250 from eliminating overpayments to companies and nonprofits caring for people with mental disabilities in their homes.
?$315,900 from reducing overtime paid to caregivers at the Fort Wayne State Developmental Center, which serves about 300 severely mentally disabled people.
?$210,964 by reducing overtime paid to process child-support payments and eliminating the jobs of two clerks who process child support that is now submitted via a state Internet site.

Flow Chart

The Process Flow chart provides a visual representation of the steps in a process. Flow charts are also referred to as Process Mapping or Flow Diagrams. Constructing a flow chart is often one of the first activities of a process improvement effort, because of the following benefits:

Gives everyone a clear understanding of the process Helps to identify non-value-added operations Facilitates teamwork and communication Keeps everyone on the same page

There are many symbols used to construct a flow chart; the more common symbols are shown below:

If you have Microsoft Word or Excel, you can access a gallery or symbols in the Autoshapes function, together with a description of their use. The next step is to identify the process steps and link them together with direction arrows.

Following is an example of a very simple flow chart for the process of getting out of bed in the morning:

You can make a flowchart more useful by adding information beside the boxes. This flowchart gives a better description of the process when you know that the snooze bar gets hit three times, postponing the inevitable by five minutes each time.

Pareto Chart

The theory behind the Pareto Chart originated in 1897 when an Italian economist named Vilfredo Pareto created a formula representing the uneven distribution of wealth - what later came to be known as the 80-20 rule. You have probably heard a version of it like: "20% of the people cause 80% of the problems", or a derivative. Dr. J. M. Juran started applying this principal to defect analysis - separating the "vital few" from the "trivial many", and called it the "Pareto Chart". In fact, many (most) defect distributions follow a similar pattern, with a relatively small number of issues accounting for an overwhelming share of the defects. The Pareto Chart shows the relative frequency of defects in rank-order, and thus provides a prioritization tool so that process improvement activities can be organized to "get the most bang for the buck", or "pick the low-hanging fruit". Following is an example of paint defects from an automotive assembly plant:

After reviewing the chart above, there is no question which defect to work on first. However, this pareto chart is constructed from one dimension only - defect frequency. If you learned that it costs $10 to fix a Dirt defect, while Sag defects cost $100 to correct, Sags would probably be the highest priority. Likewise, if one category represents a constraint on the whole process, its priority would be elevated. You may wish to consult the Project Priority Calculator for a template to prioritize along multiple dimensions. You can generate a pareto chart using virtually any spreadsheet or charting software. These charts were created using Microsoft Excel, Minitab. Pareto charts are often constructed with horizontal bars, and without the cumulative percentage line, as shown below:

C-chart - Application of Control Charts

Attribute Charts in General, c Charts in Particular

Attribute control charts arise when items are compared with some standard and then are classified as to whether they meet the standard or not. The control chart is used to determine if the rate of nonconforming product is stable and detect when a deviation from stability has occurred. The argument can be made that a LCL should not exist, since rates of nonconforming product outside the LCL is in fact a good thing; we WANT low rates of nonconforming product. However, if we treat these LCL violations as simply another search for an assignable cause, we may learn for the drop in nonconformities rate and be able to permanently improve the process.

The c Chart measures the number of nonconformities per "unit" and is denoted by c. This "unit" is commonly referred to as an inspection unit and may be "per day" or "per square foot" of some other predetermined sensible rate.

Steps in Constructing a c Chart

1. Determine cbar.

There are k inspection units and c(i) is the number of nonconformities in the ith sample.

1. Since the mean and variance of the underlying Poisson distribution are equal,

   

   Thus,

   

   and the UCL and LCL are:

   

2. Plot the centerline cbar, the LCL and UCL, and the process measurements c(i).
3. Interpret the control chart.

Example:

Farnum Example:    
data is from Farnum (1994):     
Modern Statistical Quality Control and Improvement, p. 248    

 Non-conforming   
Day Errors/1000 lines   
1 6   
2 7   
3 7   
4 6   
5 8   
6 6   
7 5   
8 8   
9 1   
10 6   
11 2   
12 5   
13 5   
14 4   
15 3   
16 3   
17 2   
18 0   
19 0   
20 1   
21 2   
22 5   
23 1   
24 7   
25 7   
26 1   
27 5   
28 5   
29 8   
30 8   
    
    
Calculations:    
    
CBAR = 4.4667   
    
UCL = cbar + 3*sqrt(cbar) = 10.80701366 
LCL = cbar - 3*sqrt(cbar) = -1.873680327 = 0
 (when LCL < 0, set LCL = 0) 
  
Day CL UCL  LCL NonConforming
1 4.4667 10.80701366 0 6
2 4.4667 10.80701366 0 7
3 4.4667 10.80701366 0 7
4 4.4667 10.80701366 0 6
5 4.4667 10.80701366 0 8
6 4.4667 10.80701366 0 6
7 4.4667 10.80701366 0 5
8 4.4667 10.80701366 0 8
9 4.4667 10.80701366 0 1
10 4.4667 10.80701366 0 6
11 4.4667 10.80701366 0 2
12 4.4667 10.80701366 0 5
13 4.4667 10.80701366 0 5
14 4.4667 10.80701366 0 4
15 4.4667 10.80701366 0 3
16 4.4667 10.80701366 0 3
17 4.4667 10.80701366 0 2
18 4.4667 10.80701366 0 0
19 4.4667 10.80701366 0 0
20 4.4667 10.80701366 0 1
21 4.4667 10.80701366 0 2
22 4.4667 10.80701366 0 5
23 4.4667 10.80701366 0 1
24 4.4667 10.80701366 0 7
25 4.4667 10.80701366 0 7
26 4.4667 10.80701366 0 1
27 4.4667 10.80701366 0 5
28 4.4667 10.80701366 0 5
29 4.4667 10.80701366 0 8
30 4.4667 10.80701366 0 8

c - Chart:

Six Sigma Answer to Material Shortages

One of Six Sigma¡¯s strengths is its facility for revealing causes and solutions that run contrary to our initial assumptions. When a persistent condition resists all attempts at improvement, or when an obvious fix to a newly discovered problem turns out to be lacking, a methodical approach like Six Sigma¡¯s can uncover even the most unlikely of causes and deliver results.

In the following case study the continuous improvement team was in for just such a surprise. Conventional wisdom was wrong, and the path the team started down hid unexpected complexities.

Definition

The XYZ Pump Garage program overall performance was poor. Future customer orders would not have been forthcoming without substantial improvements in quality and delivery.

• On-time delivery was 80% vs. >99% goal.
• Direct labor overtime was running 15% vs. a goal of zero.
• Field reported defects were found in 50% of system shipments vs. 0.5% goal.
• Project margin was approximately 22% vs. a 33% goal.

A process improvement team was formed with members from Customer Service, Manufacturing, Production Control, Engineering, Operations, and Purchasing.

Measurement

' Initial ' majority team consensus was that the program¡¯s poor on-time delivery was the result of material shortages due to understaffing in Purchasing. More buyers seemed to be the probable solution. The team suspected that the field defects were principally a result of poorly trained assembly staff.

The team began daily monitoring of data for number of daily kit shortages, overdue suppliers, and daily purchasing workload based on Material Requirements Planning (MRP) demands. Field personnel were interviewed for detailed descriptions of field defect rework.

Briefly summarized, the data showed:

• Typical labor overtime occurred near the end of the manufacturing process.
• 100% of all kits were issued with shortages.
• The key suppliers were >3 days late 50% of the time.
• The MRP system was posting material demands inside the material lead times!
• The requested delivery dates for material in the MRP system did not match well with project ship dates!
• A majority of customer-reported defects appeared to be the result of incomplete or incorrect manufacturing documentation.

1. Overtime was being worked to make up lost time due to late material deliveries.
   
2. Understaffing in Purchasing was not the problem! An army of buyers would not result in on-time material when the MRP ¡®buy¡¯ signal came too late or not at all. The team¡¯s true analysis problem was to understand why the MRP System was giving wrong signals. The team decided to focus on one specific sales order line item that exemplified the problem set for a typical system.
   
   What they found:

1. The sales order was coded incorrectly in a fashion that would generate several MRP problems.
2. Item master attributes were not properly populated for many of the material items that had MRP problems.
3. Customer engineering change orders (ECO) had been accepted without renegotiating product delivery dates with the customer to allow time for ECO implementation, including new material delivery.
4. A check of other customer order line items showed similar problems.
   

1. Customer ECO information was not being properly transmitted and propagated throughout the organization, resulting in out-of-date manufacturing instructions and field defects.
   
2. Problems would not have occurred if program participants had properly followed the procedures and work instructions documented in the Quality Management System.

Improvement

The improvements we implemented can be summed up in one word: training. The company had grown significantly during the past year and while all employees had received training, it had sometimes been rushed or had not been completely absorbed by the new personnel. Mandatory training was scheduled immediately for all Customer Service, Engineering, and Operations personnel on the documented procedures for sales order entry, customer engineering change orders, creating item masters, and creating engineering masters. Retraining took 7 working days with approximately 30 personnel participating.

ERP data for all active purchase orders was audited for the most common errors the team had recently discovered. This process required 5 working days.

New delivery dates were negotiated with the customer¡¯s buyer based on the new solid data foundation. This was difficult, but fortunately the customer¡¯s buyer is a mature personality with a long-term partnership attitude.

Results:

• Within 4 weeks material shortages had improved considerably.
• On-time delivery reached 100%.
• Overtime labor became negligible.
• After 8 weeks there had been no field defects found in the 6 systems shipped in the prior 5 weeks.
• Margin has improved to 28%, but this needs further investigation.
• Teamwork between organizations improved as a result of greater appreciation for the needs and complexities of their respective jobs.

Control

On-time delivery, customer field defects, and margin remain the bottom-line metrics for process control on the XYZ program. However, most importantly, as a result of the XYZ team findings, a new continuous improvement team was formed: the Enterprise Resource Planning Data Integrity Team (EDIT). EDIT is tasked with developing a set of strategies and process control tools to insure there are no repeats of the XYZ difficulties on other programs.

Implications

This single Six Sigma project thus had far-reaching implications for the XYZ Pump Garage program. First, in fulfilling the immediate purpose of improving our performance, we achieved customer retention for the near future. On a broader level, we also seized an opportunity to enhance our overall long-term approach to improvement. The value of reaching beyond obvious solutions having been so dramatically reinforced, we created a new continuous improvement team charged with making the pursuit of quality a more proactive endeavor.

Choosing the Best Method for Listening to the Customer

Once you decide what you want to know, from whom you want to know it, and what you will do with the data, you must carefully consider what method is best suited for gathering customer information for your Six Sigma project. Among the factors to consider when deciding on a research method are the length of time it will take to collect data, the type of data that will be collected, the cost of the collection method, and the advantages and disadvantages of each method.

Here is a table which summarizes these aspects of eight types of data collection. It is followed by a short discussion of each method.

Telephone Surveys

The results of a telephone survey of a randomly selected sample can usually be generalized to an entire population. For example, if a person wants to learn how satisfied customers are with a type of software, a survey of randomly selected individuals who have purchased that software will provide the information. A standard questionnaire must be developed, a sample selected, and interviewers must make the phone calls and record the data.

This type of data can produce valuable information but it is expensive. A 10-minute telephone interview with a random sample could cost in the neighborhood of $20,000 or more; the actual cost will, of course, depend on a number of factors such as how many people will be interviewed and how readily available an accurate sample of the target group is. Actual data collection usually takes a couple weeks although the preparation time needed to write the questionnaire can be several weeks.

The primary disadvantage of this method is that standardized, quantifiable telephone surveys are somewhat rigid. For example, all questions must be asked to all respondents in the same manner. Question wording should not vary. With some exceptions, all response categories must be the same and all inclusive. Also, no visual aids can be used.

Mail Surveys

A mail survey also can yield quantifiable data that can be generalized to an entire population and typically at a much lower cost than a telephone survey. However, the survey can take several months to complete and response rates are low. As a rule of thumb, the lower the response rate, the less reliable the data. For example, if a questionnaire is sent out to learn whether customers are satisfied with that same software, a small percentage ?maybe 5 to 20 percent ?will usually return the survey. Follow-up letters and questionnaires must be sent which adds to the cost and, more importantly, the time it takes to complete the study. After all of those efforts, it is not unusual to end up with a completion rate of 40 or 50 percent. Fifty percent is generally considered the lowest acceptable response rate.

Focus Groups: In-Person

The trade off in an in-person focus group ?a discussion of 5 to15 people, usually for two hours, guided by a moderator ?is that detailed information can be obtained but the information cannot be generalized to a population larger than the group itself. Using the same software example as above, a questionnaire may be able to determine how satisfied the customer is with the product and perhaps even areas of dissatisfaction. In a focus group, the moderator can probe about the reasons behind the dissatisfaction and perhaps discuss some possible solutions to the problems. The discussion can be quite in-depth. Visual aids can be provided. Much flexibility exists. To help mediate the fact that the results cannot be generalized to a larger population, focus groups and surveys are often conducted in partnership which provides both qualitative and quantitative data.

The cost of a focus group varies somewhat depending on location and availability of participants. However, a focus group typically costs about $5,000. Travel also can be expensive. Groups should be conducted in a variety of locations throughout the area in which the product or service is used. That often can require clients, moderators and viewers to travel to the different locations. Although a focus group takes only a couple of hours to complete, recruiting participants and preparation of guidelines usually takes at least a couple of weeks.

Another issue to consider is that many focus group facilities have a list of people they tap into to participate in focus groups. They can and often do use the same people over and over. Although research has yet to be done on the effects of using the same individuals frequently as focus group participants, one might conclude that these people could lack the spontaneity or freshness that is sought from focus group participants.

Focus Groups: Online

While in-person focus groups have been used by researchers since the 1940s, online focus groups are a recent development. Online groups consist of a dozen or so people who log onto an Internet chat site at the same time. A moderator leads the group. The moderator types in questions and participants respond and a dialogue among participants ensues.

The biggest disadvantage is that this type of research is suited only for younger age groups. Individuals age 40 and over generally are not as comfortable or accustomed to online chatting. Their input can be stifled by the technology. In contrast, younger participants are comfortable and this method can yield a lot of information.

The cost of this method can be as much as an in-person focus group. The primary savings is in the elimination of travel expenses. Viewers, clients and the moderator can all log on from different locations.

One-on-One Interviews

One-on-one interviews typically provide qualitative information that cannot be generalized to a larger population. As with focus groups, however, the interview allows for detailed information that cannot be obtained from a survey. One disadvantage is that the interaction or discussion that results from a group is eliminated. There is only a dialogue between the interviewer and interviewee.

One-on-one interviews can be particularly useful for people with limited availability such as doctors, CEOs and celebrities. Getting a group of these people together in a room at the same time for a two-hour focus group discussion can be difficult. One-on-one interviews can be planned more easily around a busy individual's schedule. Regardless, interviews with such people can still be difficult to obtain because of the demands on their time. And often high-profile individuals expect some type of financial incentive, like compensation for their time or a donation to a favorite charity in their name.

An advantage of one-on-one interviews is the cost usually is low. Of course, possible compensation for interviewees and the cost of the interviewer must be considered. If the same interviewer is used ?which is often a good approach ?travel expenses may become a factor as the interviewer travels to various locations to meet with the individuals.

Intercepts

Intercepts consist of approaching an individual in a public location. For example, if information is needed from mothers of infants, the interviewer may go to a shopping mall and approach people who fit that description and ask for their input. Often these individuals are provided an incentive, perhaps $10 or $20.

This information is qualitative in most respects. Intercepts also can yield limited quantitative information if enough interviews are conducted. However, the population to which the data is being generalized must be clearly noted. Using the above example, if data is gathered from 200 mothers with a standardized questionnaire who were randomly selected (such as every fourth woman with an infant), the data can be generalized only to women at the mall on that specific day and time frame who passed the location of the interviewer and who had brought an infant.

Another example of intercept data is to approach customers who are leaving a movie theater to ask them about their experience at the theater or their reaction to the movie, advertisements or whatever else was shown. Again, the researcher must be careful in generalizing the data. Interviewers also must be trained because they tend to want to approach only people who look friendly even though data is needed from all types of people.

User Testing

This methodology requires asking individuals to use a product, often while they are being observed. Or, instead of being observed, the individuals could be asked to keep a diary. User testing can be extremely valuable in understanding how to make a product easier to use. Web sites often undertake user testing to see if their site is easy to navigate. It can be determined at what point and why, for example, people who have initiated a purchase on a web site suddenly exit and the sale is lost. This data is primarily qualitative.

The time frame for user testing varies. If a group of people can be observed while navigating a web site, the data can be collected in one evening. However, research may need to done over the course of several weeks. The cost depends on specific situations but it generally is comparable to a focus group.

Customer Complaints

Obtaining input from customers who complain can provide insight into problem areas but it also is qualitative data that cannot be generalized. For example, if customers write letters or phone with complaints, there has been no systematic way of collecting the data. And it is often the case that people who complain are simply those who are habitual complainers, who have had a particularly bad experience, or who have the time to register a complaint. These individuals do not provide an accurate reading of the experiences of all customers. Customer complaints can provide a sense of where problems exist, but the data cannot be generalized.

The cost of collecting customer complaints usually is low. The only effort required is for someone to monitor and tabulate the complaints as they come in. It usually is necessary to collect complaints for several months to provide insights into any but the most obvious problems.

The Cause and Effect Diagram (a.k.a. Fishbone)

By Kerri Simon

When utilizing a team approach to problem solving, there are often many opinions as to the problem's root cause. One way to capture these different ideas and stimulate the team's brainstorming on root causes is the cause and effect diagram, commonly called a fishbone. The fishbone will help to visually display the many potential causes for a specific problem or effect. It is particularly useful in a group setting and for situations in which little quantitative data is available for analysis.

The fishbone has an ancillary benefit as well. Because people by nature often like to get right to determining what to do about a problem, this can help bring out a more thorough exploration of the issues behind the problem - which will lead to a more robust solution.

To construct a fishbone, start with stating the problem in the form of a question, such as 'Why is the help desk's abandon rate so high?' Framing it as a 'why' question will help in brainstorming, as each root cause idea should answer the question. The team should agree on the statement of the problem and then place this question in a box at the 'head' of the fishbone.

The rest of the fishbone then consists of one line drawn across the page, attached to the problem statement, and several lines, or 'bones,' coming out vertically from the main line. These branches are labeled with different categories. The categories you use are up to you to decide. There are a few standard choices:

Table 1: Fishbone Suggested Categories Service Industries (The 4 Ps) Manufacturing Industries (The 6 Ms) Process Steps (for example) Policies Procedures People Plant/Technology Machines Methods Materials Measurements Mother Nature (Environment) Manpower (People) Determine Customers Advertise Product Incent Purchase Sell Product Ship Product Provide Upgrade

You should feel free to modify the categories for your project and subject matter.

Once you have the branches labeled, begin brainstorming possible causes and attach them to the appropriate branches. For each cause identified, continue to ask 'why does that happen?' and attach that information as another bone of the category branch. This will help get you to the true drivers of a problem.

Once you have the fishbone completed, you are well on your way to understanding the root causes of your problem. It would be advisable to have your team prioritize in some manner the key causes identified on the fishbone. If necessary, you may also want to validate these prioritized few causes with a larger audience.

Blackbelts - Who and How ?

Black Belt Selection & Training :
An important, but not comprehensive, role of a Six Sigma Black Belt is that of technical expert in the area of Six Sigma methods. This expertise allows the Black Belt to understand the link between complex customer needs and the critical internal process elements designed to achieve them.

In the fall of 2000, I participated as a subject matter expert on a panel to develop an industry-wide Body of Knowledge for Six Sigma Black Belts. The panel, commissioned by the American Society for Quality (ASQ), drew upon the collective experience and expertise of leading Six Sigma consultants and trainers.

It is interesting to note the general similarities between the participating organizations' training topics. There were, however, two sources of disparity with regard to training:

• Some topics were not covered for selected Black Belt programs. For example, a handful of training firms provided only a cursory overview of Designed Experiments and Multivariate Analysis for Black Belts in the services industries, on the belief that those tools were less needed in service industries. These same training organizations tended to ignore Lean Thinking as a viable topic for these clients.
• There was disparity on the level of comprehension (i.e. the cognitive level) for some topics.

While there is a credible argument that many Six Sigma projects will require use of only a handful of tools, and that a portion of these will require only rudimentary statistical knowledge, Black Belts nonetheless need to learn these skills. Black Belts should be taught to think critically and challenge conventional thought. Six Sigma levels of improvement require what Juran termed "breakthrough thinking." Successful breakthrough requires rigorous analysis. Black Belts must be taught to accept ideas and opinions as just that, noting their limitations. They need to learn to use analytical tools to examine these ideas and to find sustainable solutions to the problems plaguing the company. This applies equally to manufacturing and service applications. Statistical tools allow Black Belts to prove concepts with minimal data and process manipulation, so that great advances can be made in a short amount of time. Problems that have gone unsolved for years can be attacked and conquered.

While Six Sigma Black Belts are generally given credit for their expertise in analytical, statistical and problem solving techniques, successful Black Belts must be much more than technical experts. The advancement of an organization from a nominal 3.5 Sigma to Six Sigma represents a vast organizational (and cultural) change. As such, Black Belts are primarily Change Agents.

Effective Change Agents are:

• Positive Thinkers: Black Belts need to have faith in management and in the direction of the business and its Six Sigma program. They must be upbeat and optimistic about the program success, or they risk undermining management or the Six Sigma initiative. They need to exude self-confidence, without the pitfalls of being overbearing, defensive or self-righteous. Proper Management support and vision allow Black Belts to both believe in and experience their potential as Change Agents.

• Risk Takers: Black Belts need to be comfortable as Change Agents. While ineffective Change Agents agonizes over implementing change, effective Change Agents relish it. They enjoy the excitement and the challenge of "making things happen" and "grabbing the bull by the horns". They know that change is necessary for the company and the customers' sake; and that change is inevitable, given the competitive market. Only by leading the change can we hope to steer its outcome. The effective Change Agent wants to lead the charge.

• Good Communicators: An effective Black Belt needs to be capable of distilling a vast amount of technical material in an easily comprehensible fashion to team members, Sponsors, Champions and management. Many of these personnel will have only minimal training (Green Belt or Champion level) in statistical techniques, if any at all. The Black Belt that can clearly and succinctly describe to the team why, for example, a designed experiment is better than one-factor-at-a-time experimentation will strengthen the team and shorten its project completion time.

  Of course, being a good communicator is much more than just being capable of distilling technical material. An effective Change Agent must also comprehend and appreciate others' concerns. These concerns must be responded to in a thorough, respectful and thoughtful manner. Through the use of the Six Sigma statistical techniques, data can be used to predict the merits of various improvement strategies, and address these concerns. The effective Change Agent will enlist those with concerns to participate in these efforts, either as team members or Project Sponsors. Through participation, these employees learn to understand the nature of the problem and the most viable solution. 'Buy-in', a necessary part of sustainability, is greatly enhanced through this participation.

• Respected by Peers: It is often said that an individual's position in an organization can be either earned or granted, but that true power must be earned. Effective Change Agents have earned the respect of others in the organization by their hard work and effective communication. Those new to an organization, or who have not gained respect from others, will find it harder to implement changes.

• Leaders: Black Belts will often serve as Team Leaders; other times they need to show respect to others (and true leadership) by allowing them to assume leadership roles. First wave Black Belts will also serve as role models and mentors for Green Belts and subsequent waves of Black Belts.

Many of these Change Agent skills are facets of one's personalities, but they can be supported through awareness training, management policy, and coaching and mentoring by Master Black Belts and Champions. The best Black Belts are those individuals who show a balance between these softer attributes and the technical skills described in the Body of Knowledge. Many firms demand demonstration of these Change Agent attributes, through work history and personal recommendations, as a pre-requisite for consideration of Black Belt candidates. Depending on the business and functional area, a technical college degree may also be required. For example, a BS in Engineering may be required for manufacturing areas, whereas a Business Degree may be required for sales or business development areas.