Statistical Thinking to Improve Quality

This posting discusses the third step in the Hoerl-Snee Process Improvement Strategy.   Refer to the figure in the posting on April 4 for an overview of the process.    Use Britz et al (2000) and Hoerl and Snee (2002) as references.

Assess Stability

After collecting data on key measures, the next step is to analyze process stability based on that data.  First we define a stable process or one that is in-control.   Shewhart (1931, p. 6) states: “..a phenomenon will be said to be controlled when, through the use of past experience, we can predict, at least within limits, how the phenomenon may be expected to vary in the future.”   More recently, Montgomery (2005, p. 148) states: “In any production process, …., a certain amount of inherent or natural variability will always exist.  …. this natural variability is often called a ‘a stable system of chance causes.’  A process that is operating with only chance causes of variation present is said to be in statistical control.  … We refer to those sources of variability that are not part of the chance cause pattern as ‘assignable causes.’  A process that is operating in the presence of assignable causes is said to be out of control.”   Montgomery references Shewhart for the terminology chance and assignable causes.   He states that many now use the terminology common cause rather than chance cause and special cause rather than assignable cause.  

An important characteristic of a stable or in-control process is that it is predictable.   This comes from Shewhart’s definition.  That is, one can predict future behavior from past behavior.   Breyfogle (2003, p. 1109) and Wheeler (1993, p. 124 and 128) state that an in-control process is predicable whereas a process that is not in-control is unpredictable.   This means that statistical methods such as t tests and ANOVA are inappropriate for unstable processes.

The definitions stated above immediately suggest methods for identifying whether a process is in-control.   They include run charts and SPC control charts.   A run chart is a time plot of quality characteristic and a control chart is a run chart with control limits.   Using the points on these charts that signal lack of control, we can conduct investigations to determine what caused these points to be different.  Two previous postings that do that are:

• Rosin yield run chart on March 21.   The team found that their cause was a drop in air pressure.
• Customer complaint process posted on January 30.   The investigation identified the high defect rate in October, 91 was due to a supplier using the wrong material.

Two major reasons for assessing stability and removing assignable causes prior to addressing common-cause variation are:

• Detecting and removing assignable causes is easier than reducing the variation due to common causes.   Remove the low hanging fruit first.
• The analysis approach to removing assignable causes is different than reducing common-cause variation.   Common causes affect variation in all data points for a stable process.   When searching for the root cause of variation in specific observations due to an assignable cause, one can focus on the differences between these observations and the others.

Consider the possibility of wasting effort when a process is in-control (stable) but some results do not meet targets.   Managers could pressure staff to find the cause of specific results not meeting targets.    That is, managers could direct staff to find causes for specific undesirable outcomes when the variation is present in all outcomes.

References
1.     Breyfogle, Forrest W. (2003). Implementing Six Sigma: Smarter Solutions Using Statistical Methods, Second Edition, John Wiley & Sons, Inc.
2.     Britz, G. C., D. W. Emerling, et al. (2000). Improving Performance Through Statistical Thinking. Milwaukee, WI, ASQ Quality Press.
3.     Hoerl, R. and R. D. Snee (2002). Statistical Thinking - Improving Business Performance. Pacific Grove, CA, Duxbury.
4.     Montgomery, Douglas C. (2005). Introduction to Statistical Quality Control, John Wiley & Sons, Inc.
5.     Shewhart, Walter A. (1931). Economic Control of Quality of Manufactured Product, D. Van Nostrand Company, Inc.
6.     Wheeler, Donald J. (1993). Understanding Variation: The Key to Managing Chaos, SPC Press, Inc.

 

Posted by Gordon M. Clark at 02:52 PM | Permalink | Comments (0)

This posting discusses the second step in the Hoerl-Snee Process Improvement Strategy.   Refer to the figure in the previous posting for an overview of the process.    Use Britz et al (2000) and Hoerl and Snee (2002) as references.

Collect Data on Key Measures

After understanding and documenting the process, the next step is to collect data on key process and output measures.  These key measures can include the overall process performance measure(s) and measures derived from the inputs and outputs of each process step.

For example, the Ricoh team in the Resin Output Variation example, March 21 posting, was concerned with the product yields being greater than theoretical expectations so they collected yield-ratio data.   In the Pease Industry example, posted on March 4, the company team wanted to improve quality of their residential entry doors so they collected defect-rate data from their customers.  In the automotive door frame example, posted on February 21, the manufacturer wanted to improve the quality of critical dimensions on the welded door frame.   They collected data from incoming material and after each processing step, i.e., roll mill, bender and saw.  These data consisted mainly of dimensional measurements.  

The process may be a sequence of steps required to perform a task with a cycle-time principal performance measure.    For example, the process might be the activities required to fill a prescription in a hospital.    For each order submitted, the data might include the submittal time, the arrival time at each processing step, the actual step processing time, the completion time for each processing step, and the drug prescribed.   In addition, one would need the number of servers at each processing step.  

Breyfogle (2003) on page 10 introduces several terms that are useful in identifying important process variables.   A Key Process Output Variable (KPOV) in an important output for a process.   Another name for this variable is a Critical to Quality Characteristic (CTQ).   Key Process Input Variables (KPIVs) are process inputs that affect the KPOVs.  

One might ask: how do we select the data to collect?  We use a combination of the output of the previous step, Understand the Process, and existing process knowledge.   Also, a previous iteration of the Process Improvement Strategy (see the posting on April 4) may have identified some KPIVs.   The author has found in his consulting experience that manufacturers of process equipment may have important information regarding the sensitivity of their equipment to process variables.  Also, do not forget the internet.   A search may reveal research reports indicating the sensitivity of equipment to process variables.    

References
1.     Britz, G. C., D. W. Emerling, et al. (2000). Improving Performance Through Statistical Thinking. Milwaukee, WI, ASQ Quality Press.
2.     Hoerl, R. and R. D. Snee (2002). Statistical Thinking - Improving Business Performance. Pacific Grove, CA, Duxbury.
3.     Breyfogle, Forrest W. (2003). Implementing Six Sigma: Smarter Solutions Using Statistical Methods, Second Edition, John Wiley & Sons, Inc.

Posted by Gordon M. Clark at 09:49 PM | Permalink | Comments (0)

This posting discusses the first step in the Hoerl-Snee Process Improvement Strategy.   Refer to the figure in the previous posting for an overview of the process.    Use Britz et al (2000) and Hoerl and Snee (2002) as references.   

Understand the Process

The first step is to develop a common understanding of the process by recording and documenting it.   In the Monthly-Billing-Cycle Time example, posted on 1/21/2008, key participants did not have the same understanding of the principal process steps.  In the Ricoh Resin example, Hoerl-Snee Example posted on 3/21/2008, the team created a flowchart of the process which is the usual method for documenting the process.   We start by documenting the process as it is currently performed.   The flowchart serves as a reference and it facilitates communication.   Sometimes the flowchart is called a process map.

The flowchart is particularly important when on can not visually observe the process flow.   Many administrative or service processes have this property.   The Service-Time example, Service Time Flowchart posted on 2/18/2008, may have had this property.

The flowchart may immediately suggest areas for improvement.   Some steps in the flowchart may represent non-value added activity.    For example, customers waiting in  the Automatic Call Distribution (ACD) System Queue depicted in the service time example represent non-value added activity.

A flowchart or process map may have a number of variations.   The Ricoh resin example flowchart is a high-level flowchart.   Hoerl and Snee (2002) describe other more detailed flowcharts on pages 195-199.   Our flowchart of the automotive door frame example, Flowchart and Process Map posted on 2/21/2008, displayed key quality characteristics for the process steps.

References
1.     Britz, G. C., D. W. Emerling, et al. (2000). Improving Performance Through Statistical Thinking. Milwaukee, WI, ASQ Quality Press.
2.     Hoerl, R. and R. D. Snee (2002). Statistical Thinking - Improving Business Performance. Pacific Grove, CA, Duxbury.

 

Posted by Gordon M. Clark at 11:56 AM | Permalink | Comments (0)

This posting describes the Hoerl-Snee Process-Improvement Strategy.   This strategy was originally described in Hoerl-Snee (1995), and it also appears in Britz et (2000) and Hoerl-Snee (2002).   Prior to implementing the Process Improvement Strategy, one should define the scope and objectives for the improvement effort.  The following figure displays a flowchart of the improvement strategy steps and lists some example tools to perform the corresponding steps. 

 

The figure does not show the entire process improvement flow.    Eliminating special causes involves the Problem Solving Strategy.   Future postings will describe this strategy. 

Two primary features distinguish this strategy from the DMAIC strategy.   That is,
·        Improvement occurs in iterative sequential iterative steps.  One could call this strategy an enhanced PDCA approach to improvement.
·        One of the first steps is to remove special-cause sources of variation.   One reason for this is that the problem analysis for removing special causes often differs from the analysis to reduce common-cause variation.   Common causes are always present; however, special causes operate in isolated circumstances.

Note that the resin output variation case study clearly illustrated the above features of the process improvement strategy.  Improvement occurred in sequential cycles involving planning, implementing and collecting data.   Also, the first improvement action by the resin team was to determine whether special causes were present and then to correct them.   After that they moved on to reduce the variation contributed by common causes.

References
1.     Hoerl, R. W. and R. D. Snee (1995). Redesigning the Introductory Statistics Course. Madison, Wisconsin, University of Wisconsin, Center for Quality and Productivity Improvement.
2.     Britz, G. C., D. W. Emerling, et al. (2000). Improving Performance Through Statistical Thinking. Milwaukee, WI, ASQ Quality Press.
3.     Hoerl, R. and R. D. Snee (2002). Statistical Thinking - Improving Business Performance. Pacific Grove, CA, Duxbury.

Posted by Gordon M. Clark at 01:38 PM | Permalink | Comments (0)

This posting describes the final phase of the resin output variation example that illustrates the Hoerl-Snee process improvement strategy.   This example appears in Britz et al (2000) and in Hoerl and Snee (2002).   The Ricoh team is focusing on reducing the variability in resin output quantity.   The previous post stated that the next step for the team was to investigate the weighing processes.

The overall process had two weighing processes.  The first was an in-process manual method, and the second method was a final, automatic scale.   The manual method had individuals reading a line on a scale.  They observed that individuals of different heights read the line from different viewpoints.  Thus, they produced different readings.   The team changed the presentation of the line so people of different heights had the same view point.   This change reduced in-process measurement variation.

Next the team investigated the automatic scale and found significant measurement errors.   They reduced these errors by:

1.     Redesigning the scales protective cover.

2.     Establishing procedures for checking the alignment on a periodic basis.

The following figure presents a control chart showing the results for this project.  The difference between the final upper and lower control limits was less than the team objective of ± 5 kg.  However, the resulting average was 4292 kg which is less than the original target of 4300 kg.   Given the reduction in output variability, management regarded the results as more than adequate.   The improvement also resulted in reduction in the variation of resin viscosity.   This verified the team’s motivation to reduce variation of finished product quality by reducing the output quantity variation. To maintain the results, the team created procedure manuals and established a schedule adjusting the automatic weighing process.

The overall improvement process consisted of four Plan-Do-Check-Act (PDCA) cycles.   This posting describes the last one, the previous post describes two of them and the posting on March 21 (Hoerl-Snee Example) describes the first one.   That one focused on finding and correcting special causes.   This process is different than that suggested by a serial DMAIC process.   Our next posting will present the Hoerl-Snee process improvement strategy which has an overall PDCA approach.

References
1.     Britz, G. C., D. W. Emerling, et al. (2000). Improving Performance Through Statistical Thinking. Milwaukee, WI, ASQ Quality Press.
2.     Hoerl, R. and R. D. Snee (2002). Statistical Thinking - Improving Business Performance. Pacific Grove, CA, Duxbury.

 

Posted by Gordon M. Clark at 01:50 PM | Permalink | Comments (0)