Womack and Flowers (1999) present a case study involving the application of the Theory of Constraints (TOC) to improve throughput at the 366th Medical Group, an Air Force unit located in Idaho that provided inpatient and outpatient services.   The patients involved were guaranteed access within specified time limits: 24 hours for acute appointments, seven days for routine appointments, and four weeks for health maintenance exams, i.e., HME appointments.    The medical group was failing to meet the routine appointment time limit, and the ease of obtaining an appointment was the primary customer satisfaction complaint.   Wait times for routine appointments had been as high as 24 days.  Wait times for the acute and HME appointments were almost always less than the relevant standards. The medical group formed a team with the mission of increasing available appointments and providing care to more individuals without increasing costs. The team received instruction in the TOC.

The team flowcharted key processes and identified the constraint in each process.    The Scheduling Process and the Patient-Provider Encounter Process  had important constraints.  

Scheduling Process

Step 1.  Identify the System Constraint
The availability of routine appointments was the constraint.   The schedules were managed as if the proportions of appointments in each category (acute, routine and HME) were constant.   That is, the scheduler used a template where the availability in each time period for each appointment type was governed by the expected average number of appointments.  This affected routine appointments more than the other two categories.

Step 2. Improve or Exploit Its Capability
Patients perceived difficulty in getting routine appointments.   This perception resulted in a high no-show rate for routine appointments.   The team designed interventions to decrease the no-show rate.

Step 3. Subordinate Other Links to the Constraint
An Appointment Manager was designated and given the responsibility to adjust proportions of appointments dedicated to the three appointment categories on a daily basis.   Womack and Flowers (1999) stated that "Idle, nonconstraint time dedicated to acute and HME appointments was switched to routine appointment time."   This only took about 15 minutes per day.   Not only did this help to alleviate the routine appointment constraint but it prevented creating another constraint for the other appointment types.

Step 4. Strengthen the weak link or elevate it.
With the current level of available patients, steps 1, 2 and 3 prevented available routine appointments from being a constraint.

Step 5. Repeat the Improvement Process
The team had the objective of providing care to increased numbers of patients.   That objective motivated the team to examine the constraints in the Patient-Provider Encounter Process.   The next posting will address that process.

Potential Application of Statistical Thinking and Engineering

Womack and Flowers do not describe any statistical analysis of patient demand data.   The team may have done that to help the Appointment Manager and create an improved template and to assist appointment schedulers.  Clearly, the demand process for appointments could be analyzed.   What about categorizing routine and HME appointments into subcategories to more accurately predict time requirements?   Does the patient demand vary with time?

References
1. Womack, D. E. and S. Flowers (1999). "Improving System Performance: A Case Study in the Application of the Theory of Constraints." Journal of Healthcare Management 44(5): 397-407.

Dettmer (1997) views the Theory of Constraints (TOC) as a System Improvement Strategy.    This system viewpoint is the reason why Goldratt chose the term constraint rather than bottleneck.   Goldratt (1997) on page 139 defines a bottleneck as any resource whose capacity is equal to or less than the demand placed on it.   On page 301, Goldratt addresses the potential of a material release system and marketing as limiting system performance.  To do that he recommends the use of the more general term constraint rather than bottleneck.  In a manufacturing process, the machine station that is the most overloaded might be the weakest link, and places a constraint on the throughput of the entire process.  In a hospital, nurses of a particular specialty might be a weak link causing long waiting times and be a system constraint.   They are also bottlenecks.

Goldratt specifies five steps to improving system performance using the TOC.   They are:
1. Identify the weak link or constraint.  A machine with capacity less than the plant output rate objective.
2. Improve or exploit its capability.  Operate the machine during lunch breaks.    Eliminate defective machine output.
3. Subordinate other links to the constraint.  Synchronize the output rate of upstream machines with the output rate of the constraint    machine to avoid unnecessary work-in-process.
4. Strengthen the weak link or elevate it.  Install a faster machine or multiple machines.
5. Repeat the improvement process. Once the weak link is strengthened, another weak link likely becomes the new constraint.  That new   constraint may no longer be the production process.  It might be in marketing so the next step will be to strengthen marketing.
2. Goldratt, Eliyahu and Cox, Jeff (1992). The Goal: A Process of Ongoing Improvement, Second Revised Edition, Great Barrington, MA, North River Press, Inc.

The posting on June 27, 2011 introduces Statistical Engineering, and the article by Anderson-Cook et al (2012) discusses the definition of Statistical Engineering proposed by Hoerl and Snee (2010).  That is, the study of how to use known statistical principles and tools to solve high-impact problems for the benefit of mankind.   The posting on June 27 gives two examples of Statistical Engineering.  They are Lean Six Sigma (LSS) and the Hoerl-Snee process improvement strategy discussed at length in this blog starting with the posting of March 18, 2008.    The Theory of Constraints (TOC) is another example of Statistical Engineering, and this is mentioned by the author of this blog in the article by Anderson-Cook et al (2012). 

Dettmer (1997) distinguishes between Goldratt's Theory of Constraints (TOC) and a Process Improvement Strategy.   He states that the TOC is a System Improvement Philosophy rather than a Process Improvement strategy.  Goldratt's viewpoint is that organizations achieve their goals as systems not as processes.   The interaction among component processes determines how well the system performs.    Goldratt views the system as a chain or a network of chains.   The network of chains has a weakest link that limits system performance.    The weakest link is the system constraint.  One has to improve the weakest link or constraint in order to improve the system.  On page 8, Stein (1997) states the following important principle employed by the TOC.  "In any chain of events there can only be one weakest link, and if improvement is to occur only the weakest link needs to be strengthened."

Stein (1997) specifies that the first step in applying the Theory of Constraints is to select a method of measurement for system performance.  This measurement method must be agreeable to management and all involved parties.   A measurement commonly used in TOC publications is Throughput.   One example given by Stein for throughput is the rate at which the system generates money through sales.  Creasy (2009) calls this performance measurement the Capstone Metric.

Why connect TOC with Statistical Engineering?   Creasy (2009) and Nave (2002) recommend using the Theory of Constraints (TOC) with LSS to generate more effective system improvements.   Also, Stein states on page 9: "Not only the physical resources but also the individual functions of a corporation are subject to the laws governing probability and statistical fluctuation."

References
1. Anderson-Cook, C.M., Lu, L., Clark, G., DeHart, S.P., Hoerl, R., Jones, B., MacKay, R.J., Montgomery, D.C., Parker, P.A., Simpson, J., Snee, R., Steiner, S., Van Mullekom, J., Vining, G.G., Wilson, A.G.  (2012). “Statistical Engineering – Forming the Foundations”, Quality Engineering, 24(2), pages 110-132.
2. Creasy, T. (2009) "Pyramid Power", Quality Progress 42(6): 40-45.
3. Dettmer, H. William (1997). Goldratt's Theory of Constraints: A Systems Approach to Continuous Improvement, ASQC Quality Press, Milwaukee, Wisconsin.
4. Nave, D. (2002). "How to Compare Six Sigma, Lean and the Theory of Constraint." Quality Progress 35(3): 73-78.
5. Stein, R. E. (1997) The Theory of Constraints: Second Edition. New York, Marcel Dekker, Inc.

Hello,

My company performs cross-team internal audits, between our global mfg. sites.

I am seeking out scoring mediums used to produce an ISO 9001:2008 (QMS) Maturity report; such as by using a Radar chart.

Could you help to provide direction to what you have found to be the most effective scoring technique/check list/self-assessment?

Thank you!
Jesse Kryger

Gordon Clark,

A couple of thoughts:

Objectives are simply what the user of the technology wants to improve – coupled with the explicit parameters he/she does not want to sacrifice (constraints). 

I liked your continuing blog assuring that ‘statistical engineering’ is more than a study, but something to ‘use’.  Perhaps we can talk about ‘applied statistical engineering’ and some of its toolsets.

I would like to propose as one example ‘sequential empirical optimization (SEO)’.  SEO is used for maximizing performance of a system by making the best quantitative adjustment decisions when evidence of the outcomes of decisions is obtained sequentially – and which can be dynamic as important uncontrolled inputs (conditions) change.  Of course, this technology is more than statistics, and it fits your “with other relevant tools”.  BTW, I see this from the point of view of an industrial engineer. 

Please see www.ultramax.com, including examples, and technical details in http://www.ultramax.com/downloadlogin.asp > Blue Book.  

Kindly let me know whether this fits well with your intellectual position. 

Carlos
Carlos.moreno@ultramax.com

Using the definition of Statistical Engineering proposed in the previous posting, the following figure illustrates the use of statistical engineering in a project. That figure will appear in Anderson-Cook et al (2012).   Note the feedback loop between statistical engineering methods and operational methods, i.e., statistical and non-statistical methods and tools.  Results generated by the operational methods are evaluated by the statistical engineering methods.   The evaluation may determine that the project objectives have been achieved or generate new instructions for the operational methods.  



References
1. Anderson-Cook, C.M., Lu, L., Clark, G., DeHart, S.P., Hoerl, R., Jones, B., MacKay, R.J., Montgomery, D.C., Parker, P.A., Simpson, J., Snee, R., Steiner, S., Van Mullekom, J., Vining, G.G., Wilson, A.G.  (2012) “Statistical Engineering – Forming the Foundations” Quality Engineering (in press)

The posting on June 27 introduces the Statistical Engineering paradigm.   The posting quotes Hoerl and Snee (2010) defining Statistical Engineering as "as the study of how to best use statistical concepts, methods and tools, and integrate them with IT and other relevant sciences to generate improved results.”  The improved results are with respect to the concepts of Statistical Thinking.   That means understanding and reducing variation.   Hoerl and Snee (2010) do not refer to objectives other than reducing variation.    In the article by Anderson-Cook et al (2012), a panel addresses the definition of Statistical Engineering.

As a panelist,  I address the question: " What are improved results?  We need project objectives in order to evaluate results.  Successful implementation of statistical engineering will highlight the criteria for improved results.   Johnson (2009) reviewed the results of a survey polling nearly 200 Six Sigma practitioners to determine the primary reasons Six Sigma projects fail.  The two top reasons were the lack of management support and project goals were not linked to finances.   Having explicit project objectives and criteria for improved results will help gain management support.
  
Snee and Hoerl (2007) point out that improvement methods need an ultimate objective in order to succeed.  Consider Lean Six Sigma (LSS).  From a Lean viewpoint the ultimate objective would emphasize reducing waste and cycle time.  Activities that do not contribute to customer value are wasteful.  Reducing waste, e.g., reducing excessive work-in-process and customer wait time, can force quality improvement.  From the Six Sigma viewpoint the ultimate objective would emphasize reducing variation.  Excessive variation can degrade quality and increase cost.   So both the Lean and Six Sigma view points can lead to improved quality and reduced cost.   For a LSS project, Snee and Hoerl (2007) recommend a holistic approach where the ultimate objective includes both the Lean and Six Sigma viewpoints.   The statistical engineering methodology used to achieve the ultimate objective would be an integrated approach with respect to the Lean and Six Sigma viewpoints.

The first step in a LSS project is to define the project objectives in a more detailed manner than just reducing waste and variation.    Those objectives must be meaningful to management.   Consider two example LSS projects.  One is developing a new production line and the other is reducing the patient wait times in a hospital emergency department.   What are the specific conditions where the production and the emergency room will be operated?   What are the constraints under which these systems will be operated?   For example, the costs of adding new machines or doctors may limit other options.   Reducing waste and variation may be conflicting objectives where some alternatives reducing one may increase the other.    
 
In the article, I propose the following definition for statistical engineering.   Statistical engineering is the study of how to best use statistical concepts, methods and tools along with other relevant tools to generate improved results with respect to reducing variation and other system objectives.
       

References
1. Hoerl, R. W. and R. D. Snee (2010). "Statistics Roundtable: Closing the Gap." Quality Progress 43(5): 52-53.
2. Snee, R. D. and R. W. Hoerl (2007). "Integrating Lean and Six Sigma - A Holistic Approach." Six Sigma Forum Magazine 6(3): 15-21.
3. Anderson-Cook, C.M., Lu, L., Clark, G., DeHart, S.P., Hoerl, R., Jones, B., MacKay, R.J., Montgomery, D.C., Parker, P.A., Simpson, J., Snee, R., Steiner, S., Van Mullekom, J., Vining, G.G., Wilson, A.G.  (2012) “Statistical Engineering – Forming the Foundations” Quality Engineering (in press)

Hoerl and Snee (2010) propose a general paradigm for linking statistical thinking with statistical methods and tools to improve quality.   They call this paradigm statistical engineering.   They define statistical engineering “as the study of how to best use statistical concepts, methods and tools, and integrate them with IT and other relevant sciences to generate improved results.”   The following figure, taken from Hoerl and Snee (2010), depicts the relationship among statistical thinking, statistical engineering, and statistical methods and tools.  .   Statistical thinking is a philosophy of learning and action, and The History of Statistical-Thinking Definition posting specifies its fundamental principles.   They are:
1. All work occurs in a system of interconnected processes.
2. Variation exists in all processes.
3. Understanding and reducing variation are keys to success.

The Hoerl-Snee Process Improvement Strategy is an example of statistical engineering.  Postings in this blog describes it in detail.  The process improvement strategy tells us how to use, integrate and deploy methodologies and tools to perform statistical thinking and improve system performance.  These methodologies and tools include but are not limited to statistical methods and tools.  

Hoerl and Snee (2010) cite Lean Six Sigma (LSS) as an example of statistical engineering.   LSS using the DMAIC and lean process improvement strategies.   

Two features of the Hoerl-Snee Process Improvement Strategy that differ from LSS are:

• Improvement occurs in iterative sequential steps similar to Plan-Do-Check-Act (PDCA) approach.
• One of the first steps is to remove special-cause sources of variation.
Stauffer (2008) recommends that the DMAIC process improvement strategy be modified to remove special-causes in the Define phase.


References
1. Hoerl, R. W. and R. D. Snee (2010). "Statistics Roundtable: Closing the Gap." Quality Progress 43(5): 52-53.
2. Stauffer, R. (2008). "A DMAIC Makeover." Quality Progress 41(12): 54-59.

I will present a webinar this April 14 at 3 pm EDT on the topic of Continual Improvement Using Simulation and Lean Six Sigma. Register for the webinar by visiting https://www1.gotomeeting.com/register/851692968.

The blog posting on January 20 mentions the lack of emphasis on simulation in Six Sigma and by ASQ. Simulation, Six Sigma, Lean and Statistical Thinking all view the system as a process. When refer to simulation, we mean Discrete-Event Simulation (DES). By DES, we mean that the system changes state at discrete points in time. The simulation is a model of the system. We can analyze the model implementing designed experiments with much less cost than we can with the real system. DES models are used to improve performance in systems such as manufacturing, healthcare, computer-communications, transportation, and call centers. Why not use simulation in Six Sigma and Lean Six Sigma? Case Studies will illustrate the use and benefit of simulation in Lean Six Sigma. The DMAIC process aides the development of simulation models, and simulation improves the effectiveness of the DMAIC process.

Hello,

Can I know how to justify a reason whether to use the SPC Xbar-R chart subgroup size 5 instead of 3 statistically?

Thank you. 

Borawski, in his December 22 posting in A View from the Q, asked us to share our goals in quality for the coming year. Prior to joining ASQ, I had been very active in using simulation to improve system performance. In addition to my consulting and research, I am a past program chair for the Winter Simulation Conference, and I taught simulation for over 20 years at The Ohio State University. I am amazed at the lack of emphasis on simulation in ASQ’s Six Sigma Black Belt body of knowledge (BOK) and major Six Sigma text books. For example, if you search the ASQ Certified Six Sigma Black Belt (CSSBB) body of knowledge prior to 2007, the word simulation does not even appear. The current BOK mentions simulation once on page 9 stating simulation could be used to develop plans for implementing the improved process. Breyfogle (2003) does not mention simulation as a Six Sigma tool; however, he does cite simulation in a Six Sigma application, and he does mention its use as a Lean tool.

A 2011 goal for this blog is to provide guidance on the use and benefits of simulation in quality improvement. Many systems which are the focus of Six Sigma or Lean Six Sigma projects have a transactional nature that can be represented by discrete-event simulations. Simulation can be particularly useful in the design phase (Design for Six Sigma). Future postings of this blog will outline the integration and benefits of using simulation in the individual DMAIC phases. For example, in the Measure phase, simulation could be a superior tool for determining Critical to Quality (CTQ) characteristics and the capability of the existing system. In the Improvement phase, a valid simulation model will reduce the amount of physical experimentation required by experimental designs. However, the project team must verify the accuracy of the simulation model. Case studies will illustrate the use and benefits of simulation in Six Sigma and Lean Six Sigma. Lean Six Sigma Case studies that did not use simulation will illustrate the need for more emphasis on simulation in Lean Six Sigma.

References
1. Breyfogle, Forrest (2003). Implementing Six Sigma: Smarter Solutions Using Statistical Methods, 2nd Edition, Wiley, Hoboken, N.J.

Borawski in his December 1 posting of A View from the Q reacts to the National Commission on Fiscal Responsibility and Reform’s (NCFRR’s) recommendation to eliminate the Baldrige Performance Excellence Program. The commission’s reasoning is that the program costs the government $10 million dollars per year and that businesses already have sufficient incentives to maintain quality of products and services without awards from the Baldrige National Quality Program. Borawski states that the purpose of the Baldrige program goes beyond products and services. “ It serves to:
1. Identify and recognize role model organizations
2. Establish criteria for evaluating improvement efforts
3. Disseminate and share best practices”

The Quality Tool Box (Tague, 2005) describes the award criteria. They consist of seven major categories as shown in the figure.



















Clearly, these seven criteria represent  important factors for overall performance excellence. Award winners must share detailed information on how they achieved outstanding performance. That means other organizations benefit. One example showing the importance of this information sharing is Motorola. They won the award in 1988 drawing attention to Six Sigma as an approach to quality improvement. Now, Six Sigma has widespread use throughout our economy. This Motorola example illustrates the fact that the economic benefits are far greater than $10 million per year. General Electric’s 1997 annual report attributed $300 million to its bottom line in 1997 to Six Sigma. Also, the Baldrige award process has furthered the use of Statistical Thinking since Six Sigma applies Statistical Thinking.

Werner (2007) illustrates the ongoing benefits of the Baldrige criteria to the effectiveness of Six Sigma and Lean. Project selection is a very important factor for successful Six Sigma and Lean projects. The Baldrige criteria helps an organization select projects that align with their strategic needs. Werner illustrates the use of Baldrige scores in the seven categories shown above to select important Six Sigma and Lean projects.

ASQ Policy Recommendations

ASQ has appeared to relax its concern with the NCFRR recommendation to eliminate funding for the Baldrige Performance Excellence program. Since only eleven of the 14 commission members voted to approve the recommendations, Congress is not forced to consider them for legislation. However, we must continue to affirm the value of the Baldrige program since cost cutting is likely to be a continuing major political issue.

Having NIST, a government agency, help administer and support the award process is important. The Baldrige scoring system requires individuals skilled in organizational excellence principles, and these individuals should come from external organizations.

References
1. Tague, Nancy R. (2005). The Quality Tool Box, Second Edition, Quality Press, Milwaukee, WI.
2. Werner, John (2007). “Avoid Random Acts of Improvement with Baldrige”, Quality Progress, Vol. 40, No. 9, pp 33-41.

In his posting, Gil Smith asked three questions with respect to reasons for recalculating control limits on XmR charts. This posting addresses the third question.

Is it (the reason for recalculating control limits) when you've 'flat-lined' with respect to individual data point trends?

“Flat-lined” means the variable being monitored shows little variation. Flat-lined behavior can be a signal that the process has changed. A special or assignable cause may be the reason. XmR charts do not generate signals when the variation becomes very small or non-existent. That is because the lower limit on the moving range chart is zero. That is true for any moving range chart when the range is six observations of less (Rigdon, Cruthis, Champ, 1994).

Flat lined behavior is an example of non-random behavior. Montgomery (2005, page 166), Nelson (1984 and 1985) refer to tests for special causes based on non-random behavior. All three of these references refer to the Western Electric Handbook (1956). One test that might be very useful when we have flat-lined data is when eight consecutive points plot on one side of the center line on the X chart. Montgomery (2005, page 166) mentions this test. Nelson (1984 and 1985) suggest the same test, but recommend that the limit be increased to nine consecutive points to reduce the risk of a false alarm.

One cause of flat-lined behavior might be stratification or a change in structural variation. For example, a process might have four machines producing the same part. However, each machine has a different mean for the dimension monitored. The original control limits may have been calculated using data from all four machines. That gives a larger variation causing a larger difference between the control limits. Later, the X chart may only get inputs from one of the machines making the chart look flat-lined.

Flat-lined plots on a control chart may be a signal that the variation of the process characteristic is significantly lower than exhibited by the stable process previously monitored by the charts.


References
1. Montgomery, Douglas C. (2005). Introduction to Statistical Quality Control, Fifth Edition, John Wiley & Sons, Inc.
2. Nelson, Lloyd S. (1984), “The Shewhart Control Chart – Tests for Special Causes”, Journal of Quality Technology, Vol. 26, pp 237-239.
3. Nelson, Lloyd S. (1985). “Interpreting Shewhart Xbar Control Charts”, Journal of Quality Technology, Vol. 17, No. 2, pp 114-116.
4. Rigdon, Steven E., Emma N. Cruthis and Charles W. Champ (1994). “Design Strategies for Individuals and Moving Range Control Charts”, Journal of Quality Technology, Vol. 26, No. 4, October 1994, pp 274-287.
5. Western Electric Co. (1956). Statistical Quality Control Handbook, American Telephone and Telegraph Co., Chicago, Ill.

What Can ASQ Do?

Paul Borawski, ASQ’s Executive Director and Chief Strategic Officer, is the author of a new blog, A View from the Q. The topic of the first posting is Raising the Voice of Quality. Borawski states that ASQ viewed its role as bringing attention to the importance of quality. Now, ASQ wants to expand this role by creating opportunities for members to broadcast the importance of quality. The intent is to promote a greater awareness of the importance of quality and approaches to equip interested individuals and organizations with the knowledge to improve quality. Borawski emphasizes that the target of this message is a global audience.

Borawski’s posting has a broad view of the quality application areas: “Better quality in products and services, better healthcare, better education, better government, better nonprofit organizations, better communities – individually and collectively making the world a better place.” Borawski states that he has heard the “world’s best leaders extol the virtues of quality to improve bottom lines, top lines, and whole organizations.”

Borawski asks: “What would it take to have the world realize the full potential of quality?”. He is also asking what ASQ can do to broadcast the voices of individual members in promoting the importance of quality and showing the world how to improve quality.

What is Quality?

Borawski asks us to show the world the importance of quality. But, what is meant by the word quality? Juran (1989, page 15) points out that the word has many definitions. One of them is fitness for use. The product could be a physical good, software or a service. He mentions that some higher quality product features will increase cost, and higher quality production process enables companies to reduce costs such as rework, customer dissatisfaction, and shorten the time to put new products on the market.

Are these implications of the meaning of quality adequate to elevate the importance of quality throughout the world?

Improvement Strategies

The history of Six Sigma indicates that quality improvement can motivate wide spread attention to quality. Motorola initiated Six Sigma and it resulted in Motorola winning the Malcom Baldridge National Quality Award in 1988 (Breyfogle, 2003). In the mid 1990s, General Electric adopted Six Sigma throughout the company. Their 1997 annual report stated that Six Sigma contributed more than $300 million to its operating income. These and other success stories led to widespread adoption of Six Sigma. This means that we need to publicize our success stories.

However, the success of Six Sigma indicates problems with our definition of quality. Mikel Harry (2000) proposed the following definition:

Quality is a state in which value entitlement is realized for the customer and provider in every aspect of the business relationship.

Breyfogle (2003, page 3) refuses to use the word quality in the definition of his version of Six Sigma. He uses the term continuous improvement. He states that the term quality carries excess baggage since it associates Six Sigma with a program run by the quality department. He wants Six Sigma to be associated with all departments of an organization.

Snee and Hoerl (2007) propose a holistic approach. The motivation is that all organizations must improve continually due to international competition. An effective approach must improve quality, cost and delivery throughout the organization. For example, they propose an integrated Lean Six Sigma approach. Six Sigma methods will shift the process average, reduce variation, create robust processes, and reduce waste and cycle time. Lean methods will address waste, cycle time, process flow and nonvalue added work. The important point is that we must propose an integrated approach that improves quality, cost and delivery.

Instead of raising the voice of quality should we Raise the Voice of Quality and Improvement?

More Effective Use of the Internet

More emphasis on the use of the internet and ASQ web sites is a strategy ASQ could use in Raising the Voice of Quality. Nowadays, many of us turn to the internet (maybe a search engine) as the first step in acquiring new information. The use of the View From the Q blog to generate suggestions for increasing member involvement in spreading our quality message illustrates the internet approach. Web sites have a global audience. For the year ending May 1, 2010, the Statistics Division web sites had 30, 888 visits and 7, 345 of them were from locations other than United States and Canada. Then, 24% of the Statistics Division web site visits were from locations external to the United States and Canada. Also, the average number of monthly visits is about 2600.

Clearly, we need to improve the level of traffic visiting the Statistics and other division sites. Divisions have a key role in developing the society’s Body of Knowledge. The division sites need more content including case studies. Also, ASQ could do more to facilitate and motivate divisions to use their web sites. Some division site web masters are frustrated by their inability to input directly to the ASQ content management system. They have to submit content by a division upload form which is received and processed by Community Development. Why can’t division web masters personally use a content management system similar to the system ASQ uses to post blog entries? If they could, web masters could see their web pages immediately after inputting changes.

Case studies presenting actual examples of quality improvement show how to improve quality and the actual benefits to organizations pursuing quality improvement. The ASQ Knowledge Center on the ASQ web site lists more than 1500 case studies. One can search those case studies to find case studies relevant to a visitor’s interests. However, the Knowledge Center does not show descriptions of the case studies or any analysis of their benefits and approaches. The ASQ divisions should be publicizing and analyzing the case studies in their respective areas of interest and expertise. The results should appear on their web sites and in their blogs.

References
1. Breyfogle III, Forrest W. (2003). Implementing Six Sigma, Smarter Solutions Using Statistical Methods, Second Edition, John Wiley & Sons, Inc.
2. Harry, Mikel J. (2000). “A New Definition Aims to Connect Quality with Financial Performance”, Quality Progress, January 2000, Vol. 33, No. 1.
3. Juran, J. M. (1989). Juran on Leadership for Quality, An Executive Handbook, The Free Press, New York.
4. Snee, Ronald D., Roger W. Hoerl (2007). “Integrating Lean and Six Sigma – A Holistic Approach”, Quality Progress, May 2007,

Gil Smith asks in the previous posting three questions. The first two are addressed in this posting.

When is it legitimate to recalculate your UCL/LCL in an XmR chart?
An XmR chart consists of two component charts. That is, an X chart displaying individual observations (sample size of one) and a mR chart displaying the moving ranges. A moving range point is the difference between an observation and the previous observation.

Montgomery (2005, page 168) describes the usage of a control chart as consisting of two phases. In phase I, one gathers a set of process data and constructs trial control limits. A purpose of phase I is to determine whether the process is stable. Operating personnel react to signals generated by the phase I control chart to remove assignable causes. When the process changes, additional data are collected and control limits are recalculated. Phase II begins after data are collected, and the control chart indicates a stable process.

Wheeler (1995, page 84) states that one does not need to recalculate the control limits for a stable process unless a physical change is made to the process. We agree with Wheeler’s recommendation.

If you've arbitrarily picked an interval upon which to base your initial UCL/LCL, how would you know when it is appropriate to recalculate?

Recalculate control limits whenever one makes a physical change to the process. Removing assignable causes may result is physical process changes. If the arbitrary interval you have picked is not long enough to accurately estimate the control limits from a statistical variation viewpoint, add some data and recalculate.

References
1. Montgomery, Douglas C. (2005). Introduction to Statistical Quality Control, Fifth Edition, John Wiley & Sons, Inc.
2. Wheeler, Donald J. (1995). Advanced Topics in Statistical Process Control, The Power of Shewharts Charts, SPC Press.