1. Tools of quality

2. Quality Lecture: Tools of Quality
2/4/2013
Process Analysis

3. 7 QC Tools: The Lean Six Sigma Pocket Toolbook
Quality Lecture: Tools of Quality
2/4/2013
7 QC Tools: The Lean Six Sigma Pocket Toolbook
Flowchart [p. 116]
Check Sheet [p. 95]
Histogram [p. 129]
Pareto [p. 178]
Cause-and-Effect [p. 49]
Scatter [p. 228]
Control Chart [p. 75]
Developed by Jim Grayson, Ph.D.
Process Variability Concepts

4. Pareto Diagram
Developed by Jim Grayson, Ph.D.

5. Quality Lecture: Tools of Quality
2/4/2013
Cause and Effect Diagram
Developed by Jim Grayson, Ph.D.
Process Variability Concepts

6. “Failure to understand variation is the central problem of management
Developed by Jim Grayson, Ph.D.

7. Stable vs. Unstable process
Stable process: a process in which variation in outcomes arises only from common causes.
Unstable process: a process in which variation is a result of both common and special causes.
Developed by Jim Grayson, Ph.D.
source: Moen, Nolan and Provost, Improving Quality Through Planned Experimentation

8. Red Bead experiment
Developed by Jim Grayson, Ph.D.

9. Red Bead Experiment What are the lessons learned? 1. 2. 3. 4.
Developed by Jim Grayson, Ph.D.

10. Statistical Process Control: Control Charts
Parameter
Track process parameter over time - mean - percentage defects
Distinguish between - common cause variation (within control limits) - assignable cause variation (outside control limits)
Measure process performance: how much common cause variation is in the process while the process is “in control”?
Upper Control Limit (UCL)
Center Line
Lower Control Limit (LCL)
Time
Developed by Jim Grayson, Ph.D.

11. Choosing the Appropriate Control Chart
(MJ II, p. 37)
Attribute (counts) Variable (measurable)
The Lean Six Sigma Pocket Toolbook, p. 123
Six Sigma Mem Jogger p. 76
Defect Defective

12. Quality Lecture: Tools of Quality
2/4/2013
Different types of control charts
Attribute (or count) data
Situation Chart Control Limits
Number of defects, accidents or flaws
# of accidents/week
# of breakdowns/week
# of flaws on a product C U
Six Sigma Memory Jogger, p. 78.
source: Brian Joiner, Fourth Generation Management, p
Tool: Control Chart

13. Quality Lecture: Tools of Quality
2/4/2013
Different types of control charts
Attribute (or classification) data
Situation Chart Control Limits
Fraction of defectives
fraction of orders not processed perfectly on first trial (first pass yield)
fraction of requests not processed within 15 minutes p np
Six Sigma Memory Jogger, p. 78.
source: Brian Joiner, Fourth Generation Management, p
Tool: Control Chart

14. Quality Lecture: Tools of Quality
2/4/2013
Different types of control charts
Variables (or measurement ) data
Situation Chart Control Limits
Variables data, sets of measurements
X-”BAR” CHART
Xbar and R Charts
R CHART
See MJ II p. 42 for constants
A2, D3 and D4.
Six Sigma Memory Jogger, p. 79.
source: Brian Joiner, Fourth Generation Management, p
Tool: Control Chart

15. Different types of control charts
Variables (or measurement ) data
Situation Chart Control Limits
Variables data, sets of measurements
X-”BAR” CHART
Xbar and R Charts
R CHART
See MJ II p. 42 for constants
A2, D3 and D4.
Six Sigma Memory Jogger, p. 79.
source: Brian Joiner, Fourth Generation Management, p

16. Parameters for Creating X-bar Charts
Six Sigma Memory Jogger, p. 81
MSWD p. 204.

17. What are the control limits for this process?
Exercise An automatic filling machine is used to fill 16 ounce cans of a certain product. Samples of size 5 are taken from the assembly line each hour and measured. The results of the first 25 subgroups are that X-double bar = and R-bar =
What are the control limits for this process?
Source: Shirland, Statistical Quality Control, problem 5.2.
Developed by Jim Grayson, Ph.D.

18. Given these charts, how do we know if the process is “in control”?
Developed by Jim Grayson, Ph.D.

19. Conceptual view of SPC
Developed by Jim Grayson, Ph.D.
source: Donald Wheeler, Understanding Statistical Process Control

20. Process Stability vs. Process Capability
Wheeler, Understanding Statistical Process Control
Developed by Jim Grayson, Ph.D.

21. Quality Lecture: Tools of Quality
2/4/2013
Advantages of Statistical Control
1. Can predict its behavior.
2. Process has an identity.
3. Operates with less variability.
4. A process having special causes is unstable.
5. Tells workers when adjustments should not be made.
6. Provides direction for reducing variation.
7. Plotting of data allows identifying trends over time.
8. Identifies process conditions that can result in an acceptable product.
Developed by Jim Grayson, Ph.D.
source: Juran and Gryna, Quality Planning and Analysis, p

22. Identifying Special Causes of Variation
source: Brian Joiner, Fourth Generation Management, pp. 260.
Six Sigma Memory Jogger, p
Developed by Jim Grayson, Ph.D.

23. Strategies for Reducing Special Causes of Variation
Get timely data so special causes are signaled quickly.
Put in place an immediate remedy to contain any damage.
Search for the cause -- see what was different.
Develop a longer term remedy.
Developed by Jim Grayson, Ph.D.
source: Brian Joiner, Fourth Generation Management, pp

24. “In a common cause situation, there is no such thing as THE cause.”
Brian Joiner
Developed by Jim Grayson, Ph.D.

25. Improving a Stable Process
Stratify -- sort into groups or categories; look for patterns. (e.g., type of job, day of week, time, weather, region, employee, product, etc.)
Experiment -- make planned changes and learn from the effects. (e.g., need to be able to assess and learn from the results -- use PDCA .)
Disaggregate -- divide the process into component pieces and manage the pieces. (e.g., making the elements of a process visible through measurements and data.)
Developed by Jim Grayson, Ph.D.
source: Brian Joiner, Fourth Generation Management, pp

26. Quality Lecture: Tools of Quality
2/4/2013
A Conversation with Joseph Juran
“Take this example: In finance we set a budget. The actual expenditure, month by month, varies - we bought enough stationery for three months, and that’s going to be a miniblip in the figures. Now, the statistician goes a step further and says, ‘How do you know whether it’s a miniblip or there’s a real change here?’ The statistician says, ‘I’ll draw you a pair of lines here. These lines are such that 95% of the time, you’re going to get variation between them.’
Now suppose something happens that’s clearly outside the lines. The odds are something’s amok. Ordinarily this is the result of something local, because the system is such that it operates in control. So supervision converges on the scene to restore the status quo.
Notice the distinction between what’s chronic [common cause] and what’s sporadic [special cause]. Sporadic events we handle by the control mechanism. Ordinarily sporadic problems are delegable because the origin and remedy are local. Changing something chronic requires creativity, because the purpose is to get rid of the status quo - to get rid of waste. Dealing with chronic requires structured change, which has to originate pretty much at the top.”
Source: A Conversation with Joseph Juran, Thomas Stewart, Fortune, January 11, 1999, p
Developed by Jim Grayson, Ph.D.
Process Variability Concepts

27. Statistical Process Control
Capability
Analysis
Conformance
Analysis
Eliminate
Assignable Cause
Investigate for
Assignable Cause
Capability analysis
What is the currently "inherent" capability of my process when it is "in control"?
Conformance analysis
SPC charts identify when control has likely been lost and assignable cause variation has occurred
Investigate for assignable cause
Find “Root Cause(s)” of Potential Loss of Statistical Control
Eliminate or replicate assignable cause
Need Corrective Action To Move Forward

28. What are the control limits for this process?
Exercise An automatic filling machine is used to fill 16 ounce cans of a certain product. Samples of size 5 are taken from the assembly line each hour and measured. The results of the first 25 subgroups are that X-double bar = and R-bar =
What are the control limits for this process?
Source: Shirland, Statistical Quality Control, problem 5.2.
If the specification limits are USL = and LSL = is the process capable?
Developed by Jim Grayson, Ph.D.

29. Quality Lecture: Tools of Quality
2/4/2013
Process capability
EXCEL: =NORMDIST(x, mean, std dev,1) to calculate percent non-conforming material.
Process Analysis

30. The Statistical Meaning of Six Sigma
Quality Lecture: Tools of Quality
The Statistical Meaning of Six Sigma
2/4/2013
Process capability measure
Lower
Specification
Limit (LSL)
Upper
Specification
Limit (USL)
Process A
(with st. dev sA)
x Cp P{defect} ppm
1 ,000
2 ,500
3 ,700 4
5 ,6
6 x ,00
X-3sA
X-2sA
X-1sA X
X+1sA
X+2s
X+3sA 3
Process B
(with st. dev sB)
X-6sB X
X+6sB
Estimate standard deviation:
Look at standard deviation relative to specification limits
Don’t confuse control limits with specification limits: a process can be out of control, yet be incapable s ˆ = R / d 2
Process Analysis