tools of quality - augusta universityspots.gru.edu/jgrayson/mbaopsmgt... · · 2014-01-107 qc...
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Quality Lecture: Tools of Quality 1
Tools of quality
2
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Quality Lecture: Tools of Quality 2
7 QC Tools: The Lean Six Sigma Pocket Toolbook
DEVELOPED BY JIM GRAYSON, PH.D.
•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]
Pareto Diagram
DEVELOPED BY JIM GRAYSON, PH.D.
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Quality Lecture: Tools of Quality 3
Cause and Effect Diagram
DEVELOPED BY JIM GRAYSON, PH.D.
DEVELOPED BY JIM GRAYSON, PH.D.
“Failure to understand variation is the central problem of management.”
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Quality Lecture: Tools of Quality 4
DEVELOPED BY JIM GRAYSON, PH.D.
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.
source: Moen, Nolan and Provost, Improving Quality Through Planned Experimentation
DEVELOPED BY JIM GRAYSON, PH.D.
Red Bead experiment
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Quality Lecture: Tools of Quality 5
Red Bead Experiment
What are the lessons learned?
1.
2.
3.
4.
DEVELOPED BY JIM GRAYSON, PH.D.
DEVELOPED BY JIM GRAYSON, PH.D.
Statistical Process Control: Control Charts
Time
ProcessParameter
Upper Control Limit (UCL)
Lower Control Limit (LCL)
Center Line
• 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 variationis in the process while the processis “in control”?
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Quality Lecture: Tools of Quality 6
Choosing the Appropriate Control Chart
Attribute (counts) Variable (measurable)
Defect Defective
(MJ II, p. 37)
The Lean Six Sigma Pocket Toolbook, p. 123
Six Sigma Mem Jogger p. 76
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
Usource: Brian Joiner, Fourth Generation Management, p. 266‐267.
Six Sigma Memory Jogger, p. 78.
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Quality Lecture: Tools of Quality 7
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
npsource: Brian Joiner, Fourth Generation Management, p. 266‐267.
13
Six Sigma Memory Jogger, p. 78.
Different types of control charts
Variables (or measurement ) data
Situation Chart Control Limits
Variables data, sets of measurements
Xbar and R Charts
source: Brian Joiner, Fourth Generation Management, p. 266‐267.
RAX 2
RDLCL
RDUCL
3
4
X-”BAR” CHART
R CHARTSee MJ II p. 42 for constantsA2, D3 and D4.
14
Six Sigma Memory Jogger, p. 79.
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Quality Lecture: Tools of Quality 8
Different types of control charts
Variables (or measurement ) data
Situation Chart Control Limits
Variables data, sets of measurements
Xbar and R Charts
source: Brian Joiner, Fourth Generation Management, p. 266‐267.
RAX 2
RDLCL
RDUCL
3
4
X-”BAR” CHART
R CHARTSee MJ II p. 42 for constantsA2, D3 and D4.
Six Sigma Memory Jogger, p. 79.
Parameters for Creating X‐bar Charts
Number of Observations in Subgroup
(n)
Factor for X-bar Chart
(A2)
Factor for Lower
control Limit in R chart
(D3)
Factor for Upper
control limit in R chart
(D4)
Factor to estimate Standard
deviation, (d2)
2 1.88 0 3.27 1.128 3 1.02 0 2.57 1.693 4 0.73 0 2.28 2.059 5 0.58 0 2.11 2.326 6 0.48 0 2.00 2.534 7 0.42 0.08 1.92 2.704 8 0.37 0.14 1.86 2.847 9 0.34 0.18 1.82 2.970
10 0.31 0.22 1.78 3.078
Six Sigma Memory Jogger, p. 81MSWD p. 204.
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Quality Lecture: Tools of Quality 9
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 = 16.113 and R‐bar = 0.330.
What are the control limits for this process?
Source: Shirland, Statistical Quality Control, problem 5.2.
DEVELOPED BY JIM GRAYSON, PH.D.
15.70
15.80
15.90
16.00
16.10
16.20
16.30
16.40
1 3 5 7 9 11 13 15 17 19 21 23 25
Wei
gh
ts
Sub-groups
X-bar Chart
x-bar
LCL
CL
UCL
0.000.100.200.300.400.500.600.700.80
1 3 5 7 9 11 13 15 17 19 21 23 25
Wei
gh
ts
Sub-groups
R Chart
R
LCL
CL
UCL
Given these charts, how do we know if the process is “in control”?
DEVELOPED BY JIM GRAYSON, PH.D.
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Quality Lecture: Tools of Quality 10
Conceptualviewof SPC
DEVELOPED BY JIM GRAYSON, PH.D.
source: Donald Wheeler, Understanding Statistical Process Control
DEVELOPED BY JIM GRAYSON, PH.D.
Process Stability
vs.
Process Capability
Wheeler, Understanding Statistical Process Control
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Quality Lecture: Tools of Quality 11
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.
source: Juran and Gryna, Quality Planning and Analysis, p. 380‐381.
DEVELOPED BY JIM GRAYSON, PH.D.
Identifying Special Causes of Variation
source: Brian Joiner, Fourth Generation Management, pp. 260.
DEVELOPED BY JIM GRAYSON, PH.D.
Six Sigma Memory Jogger, p. 84‐85.
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Quality Lecture: Tools of Quality 12
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.
source: Brian Joiner, Fourth Generation Management, pp. 138‐139.
DEVELOPED BY JIM GRAYSON, PH.D.
“In a common cause situation, there is no such thing as THE cause.”
Brian Joiner
DEVELOPED BY JIM GRAYSON, PH.D.
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Quality Lecture: Tools of Quality 13
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.)
source: Brian Joiner, Fourth Generation Management, pp. 140‐146.
DEVELOPED BY JIM GRAYSON, PH.D.
“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.”
A Conversation with Joseph Juran
Source: A Conversation with Joseph Juran, Thomas Stewart, Fortune, January 11, 1999, p. 168-170.
DEVELOPED BY JIM GRAYSON, PH.D.
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Quality Lecture: Tools of Quality 14
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 = 16.113 and R‐bar = 0.330.
What are the control limits for this process?
Source: Shirland, Statistical Quality Control, problem 5.2.
DEVELOPED BY JIM GRAYSON, PH.D.
If the specification limits are USL = 16.539 and LSL = 15.829 is the process capable?
Process capability
28
sigma
LSLx
sigma
xUSLCor
sigma
LSLUSLC pkp *3
,*3
min*6
EXCEL: =NORMDIST(x, mean, std dev,1) to calculate percent non‐conforming material.