An Introduction to Statistical Process Control Charts (SPC)
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An Introduction to Statistical Process Control Charts (SPC)
Steve HarrisonMonday 15th July 2013 12 – 1pmRoom 6 R Floor RHH
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Topics• Variation – A Quick Recap• An introduction to SPC Charts• Interpretation• Quiz• Application in Improvement work
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Variation
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Common Cause Variation• Typically due to a large number of small
sources of variation • Example: Variation in work commute due to traffic lights, pedestrian traffic, parking issues• Usually requires a deep understanding of the
process to minimise the variation
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Special Cause Variation
• Are not part of the normal process. Arises from special circumstances
• Example: Variation in work commute impacted by flat tire, road closure, ice-storm.• Usually best uncovered when monitoring
data in real time (or close to that)
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20
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Consecutive trips
Min.
Special Cause - My trip to work
Mean
Upper process limit
Lower process limit
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Two Types of Variation
Special Cause: • assignable cause• signal
Common Cause: • chance cause• noise
Statistically significant (not good or bad)
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SPC Charts
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SPC, Statistical Process Control or The Control Chart
Elements
1. Chart/graph showing data, running record, time order sequence2. A line showing the mean3. 2 lines showing the upper and lower process ‘control’ limits
• You only need 25 data points to set up a control chart, but 50 are better if available
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The Anatomy of an SPC or Control Chart
0
10
20
30
40
50
60
70
80
F M A M J J A S O N D J F M A M J J A S O N D
Upper process control limit
Mean
Lower process control limit
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Measures of Central Tendency• Mean = Average – SPC Chart• Median = Central or Middle Value – Run
Chart• Mode = Most frequently occurring value
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Standard Deviation or σ
In statistics, standard deviation shows how much variation exists from the mean.A low standard deviation indicates that the data points tend to be very close to the mean; high standard deviation indicates that the data points are spread out over a large range of values.
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Standard Deviation and a normal distribution
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PRACTICAL INTERPRETATION OF THE STANDARD DEVIATION
Mean Mean + 3s
Mean - 3s
99.6% will be within 3 s
0.4% will be outside 6s in a normal distribution
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3s AND THE CONTROL CHART
6s
3s
3s
UCL
LCL
Mean
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Run Charts vs. SPC ChartsRun Chart• Simple• Easy to create in Excel• Less Sensitive• Only need 10 data
points
SPC• More Powerful• Control lines show the
degree of variation• Need Special Software• Need 25+ data points
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0
10
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4-Ap
r
6-Ap
r
8-Ap
r
12-A
pr
14-A
pr
18-A
pr
20-A
pr
22-A
pr
3-M
ay
5-M
ay
9-M
ay
11-M
ay
13-M
ay
15-M
ay
% D
aily
TTO
s C
ompl
eted
by
Noo
n
Ward x– % of total TTOs completed by 12 noon April 4 - May 15, 2012
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Special cause variation
0102030405060708090
F M A M J J A S O N D J F M A M J J A S O N D
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Point above Upper Control Limit (UCL)
SPECIAL CAUSES - RULE 1
MEAN
LCL
UCL
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Or point below Lower Control Limit (LCL)
SPECIAL CAUSES - RULE 1
MEAN
LCL
UCL
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MEAN
Eight points above centre line
SPECIAL CAUSES - RULE 2
LCL
UCL
A 1 in 256 chance or 0.3906%
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MEAN
SPECIAL CAUSES - RULE 2
LCL
UCL Or eight points below centre line
A 1 in 256 chance or 0.3906%
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MEAN
Six points in a downward direction
SPECIAL CAUSES - RULE 3
LCL
UCL
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MEAN
SPECIAL CAUSES - RULE 3
LCL
Or six points in an upward direction
UCL
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Considerably less than 2/3 of all the points fall in this zone
LCL
UCLSPECIAL CAUSES - RULE 4
MEAN
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SPECIAL CAUSES - RULE 4
Or considerably more than 2/3 of all the points fall in this zone
MEAN
UCL
LCL
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Quiz – 1. Does the chart show
A. Special Cause Variation?
B.Common Cause Variation?
C.Both of the above
D.No Variation
Specia
l Cause Varia
tion?
Common Cause Varia
tion?
Intentional V
ariation
All of t
he above
No Variation
0%
100%
0%0%0%
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2. How many special cause signals are present on this chart?A. 0B.1C.2D.3E. 16
0 1 2 3 16
10%
90%
0%0%0%
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3. How many special cause signals are present on this chart?A. 0B.1C.2D.3E. 16
0 1 2 3 16
0%
10%
0%0%
90%
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4. How many special cause signals are present on this chart?A. 0B.1C.2D.3E. 16
0 1 2 3 16
0%
20%
0%
70%
10%
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What use is this?
• Evaluate and improve underlying process• Is the process stable? • Use data to make predictions and help
planning• Recognise variation• Prove/disprove assumptions and
(mis)conceptions• Help drive improvement – identify statistically
significant change
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Example
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Annotated SPC Charts• One of the most powerful tools for
improvement• Describe a process captured over time (as
opposed to being a single sample)• Reveal any trends a process might be
experiencing• When combined with careful annotation they
track the impact of change
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Why We Want to Annotate Our Charts…
I @:@ -1 : - f, I I .
'And this is the period when the cat was away. '
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Example – Renal DT247J
PDSA 1 PDSA 2
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Application – Responding to Variation
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Responding to Special Cause Variation
• Identify the cause: • If positive then can it be replicated or standardised. • If negative then cause needs to be eliminated
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Responding to Common Cause Variation
1. Reduce variation: make the process even more predictable or reliable (and/or)
2. Not satisfied with result: redesign process to get a better result
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Process with common cause
variation
Reduce variation: make the process even more reliableNot satisfied with result: redesign process to get a better result
Process with special cause
variation
Identify the cause:if positive then can it be replicated or standardized. If negative then cause needs to be eliminated
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DISCUSSION
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Evaluation1. Absolute Rubbish2. Terrible3. Fairly Bad4. Not that Great5. Alright6. Quite Good7. Really Quite Good8. Very Good9. Excellent10. Amazing!
41Abso
lute Rubbish
Terrible
Fairly
Bad
Not that
Great
Alright
Quite Good
Really Q
uite Good
Very Good
Excelle
nt
Amazing!
0% 0% 0% 0% 0%
40%
50%
10%
0%0%
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THANKS!
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