1. The Quality Improvement Model
Define Process
Select Measures
Collect & Interpret Data
Is Process Stable?
Is Process Stable ?
This module explains this model step
Good point to use the quincunx to demonstrate variability
Talk about “expected variability” of the quincunx
Get class to understand that individual bead drops are unpredictable in an exact sense, but predictable within a range No
Investigate & Fix Special Causes
Purpose: Determine the stability of key measures of the product.
Yes
Is Process Capable ? No
Improve Process Capability
Yes
Use SPC to Maintain Current Process

2. Types of Variation Common Causes
Run Chart
Causes that are inherent in the
process over time, and affect all
outcomes of the process.
Ever-present
Create small, random fluctuations in the process
Lots of them
The sum of their effects creates the expected variability
Predictable
Quality Characteristic
Common causes create the “expected variability”
Q: “What are the common causes in the quincunx?” Pins
Time

3. Types of Variation Special Causes
Causes that are not present in the
process all the time, but arise
because of specific circumstances.
Not always present in the process
Can create large process disturbances, or sustained shifts
Relatively few in number
Pull the process beyond the expected level of variability
Unpredictable
Run Chart
Quality Characteristic
Special causes create additional variability in the process
Will try to prevent them from happening again in the future
What could be special causes in the quincunx
May go back to the cause-effect diagram of the “Going to Work” process and diagnose the various causes as common or special - Some may be classified as both; don’t lead them to think that all special causes are just common causes gone really bad
Time
Control charts help identify the presence of special causes.

4. Control Chart Components
Run chart of the data
Center Line (CL)
A line at the average of the data or target of the process
Upper Control Limit (UCL)
A line at the upper limit of expected variability
Lower Control Limit (LCL)
A line at the lower limit of expected variability 8 22 24 2 4 6 10 12 14 16 18 20 26 28 30 32
Run Order
UCL CL
LCL
Control Chart
Bracket common causes between the limits and special causes outside
Points outside the limits indicate the process has changed
Limits based on DATA
Limits of “expected variability” on quincunx were derived by dropping beads - collected DATA
Will not concern ourselves
The control limits are based on data collected from the process.

5. Rules for Separating Common & Special Causes
Two commonly used signals of special causes are:
Rule 1: Any point above the Upper Control Limit (UCL) or below the Lower Control Limit (LCL)
Rule 2: 8 points in a row on the same side of the center line (CL)
A common practice is to circle the special cause on the control chart so others will know it has been recognized
Additional rules are “beyond the scope of this course”
Note: Additional rules do exist.

6. Process Stability Stable Process Unstable Process
A process in which the key measures of the output from the process show no signs of special causes. Variation is a result of common causes only.
Unstable Process
A process in which the key measures of the output from the process show signs of special causes in addition to common causes. Variation is a result of both common and special causes.
Special causes are the difference between stable and unstable processes

7. Process Stability

8. A Stable Process • o m n a u s e A l r t W k : B h v i R d M N y c T Sf D P

9. Un-Stable Process L o k F r : • P i n t s O u d e h C l m S f y c R Tg a b ( ) I V

10. Advantages of Stable Processes Are:
Process output is predictable (Know the limits of process variation)
Customers see consistent product
Less time and effort spent firefighting
Easier to make (and see) changes to the process
Reduces complexity

11. Polymer Manufacturing Data1 2 3 4 5 20 40 60 80
100
120
140
Avg=1.4
LCL=0.5
UCL=2.2 b*
Sample
Control Chart 1 2 3 4 5 b*
Histogram LS US
Circle the special causes on the control chart
Histogram unable to reflect time order needed to assess stability
Histogram may look typical, but process be highly unstable
Note: b* is a measure of yellowness
Histogram does not show whether the process is stable!

12. Histograms & Control Charts
Plot past data
Cannot tell if process is stable
Only useful for prediction if the process is stable
Control Charts
Real-time evaluation
Help identify presence of special causes
Assess past and present stability of process
Discuss the need for stability if histogram is to be used for prediction
Both tools used in tandem tell you a great deal about what your process is doing and can do

13. Pump Maintenance Data Circle the special causes Target is 0
Is Process Stable ?
Pump Maintenance Data 2 4 6 8 10 12 14 16 18 20
Number of
Failures
UCL=11.4
Circle the special causes
Target is 0
NOTE: No Lower Control Limit Common when you are up against a boundary which is also your target
Avg=4.8
LCL=None 2 4 6 8 10 12 14 16 18 20 22 24
Week
Are there any signals of special causes? Circle them.