Transcript Service Improvement Programme

An Introduction to Statistical Process Control
Charts (SPC)
Steve Harrison
Monday 15th July 2013 12 – 1pm
Room 6 R Floor RHH
Topics
• Variation – A Quick Recap
• An introduction to SPC Charts
• Interpretation
• Quiz
• Application in Improvement work
Variation
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
5
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)
6
Special Cause - My trip to work
120
Upper process limit
100
Mean
80
Lower process limit
Min. 60
40
20
0
Consecutive trips
Two Types of Variation
Common Cause:
• chance cause
• noise
Special Cause:
•assignable cause
•signal
Statistically significant (not good or bad)
8
SPC Charts
9
SPC, Statistical Process Control
or The Control Chart
Elements
1. Chart/graph showing data, running record, time
order sequence
2. A line showing the mean
3. 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
The Anatomy of an SPC or Control Chart
80
Upper
process
control limit
70
60
Mean
50
40
Lower
process
control limit
30
20
10
0
F M A M J J A S O N D J F M A M J J A S O N D
Measures of Central Tendency
•Mean = Average – SPC Chart
•Median = Central or Middle Value – Run Chart
•Mode = Most frequently occurring value
12
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.
Standard Deviation and a normal distribution
PRACTICAL INTERPRETATION OF THE STANDARD
DEVIATION
99.6% will be within 3 s
Mean - 3s
Mean
0.4% will be outside 6s in
a normal distribution
Mean + 3s
3s AND THE CONTROL CHART
UCL
3s
3s
6s
Mean
LCL
Run Charts vs. SPC Charts
Run Chart
• Simple
SPC
• More Powerful
• Easy to create in Excel
• Control lines show the
degree of variation
• Less Sensitive
• Need Special Software
• Only need 10 data points
• Need 25+ data points
Ward x – % of total TTOs completed by 12 noon
April 4 - May 15, 2012
70
60
50
40
30
20
10
17
15-May
13-May
11-May
9-May
5-May
3-May
22-Apr
20-Apr
18-Apr
14-Apr
12-Apr
8-Apr
6-Apr
0
4-Apr
% Daily TTOs Completed by Noon
80
Special cause variation
90
80
70
60
50
40
30
20
10
0
F M A M J J A S O N D J F M A M J J A S O N D
SPECIAL CAUSES - RULE 1
UCL
Point above Upper Control Limit
(UCL)
MEAN
LCL
SPECIAL CAUSES - RULE 1
UCL
MEAN
LCL
Or point below Lower Control
Limit (LCL)
SPECIAL CAUSES - RULE 2
UCL
MEAN
LCL
Eight points above centre line
A 1 in 256 chance or 0.3906%
SPECIAL CAUSES - RULE 2
UCL
Or eight
points
below
centre line
MEAN
LCL
A 1 in 256 chance or 0.3906%
SPECIAL CAUSES - RULE 3
UCL
Six points in a downward
direction
MEAN
LCL
SPECIAL CAUSES - RULE 3
UCL
Or six points in an
upward direction
MEAN
LCL
SPECIAL CAUSES - RULE 4
UCL
Considerably less than 2/3 of all
the points fall in this zone
MEAN
LCL
SPECIAL CAUSES - RULE 4
UCL
Or considerably more than 2/3 of
all the points fall in this zone
MEAN
LCL
Quiz – 1. Does the chart show
A. Special Cause
Variation?
B. Common Cause
Variation?
C. Both of the above
D. No Variation
100%
nt
io
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ria
Va
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t
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t io
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0%
ab
ov
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tio
ia
na
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ria
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se
m
on
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au
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m
cia
Sp
e
0%
t io
n?
n?
0%
2. How many special cause signals are
present on this chart?
A. 0
B. 1
C. 2
D. 3
E. 16
90%
0%
0%
16
2
1
0
0%
3
10%
3. How many special cause signals are
present on this chart?
A. 0
B. 1
C. 2
D. 3
E. 16
90%
0%
0%
16
2
1
0
0%
3
10%
4. How many special cause signals are
present on this chart?
A. 0
B. 1
C. 2
D. 3
E. 16
70%
20%
10%
16
3
2
0%
1
0
0%
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
Example
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
Why We Want to Annotate Our Charts…
'And this is the period when the cat was away. '
Example – Renal DT247J
PDSA 1
PDSA 2
Application – Responding to Variation
36
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
37
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
38
Process with
special cause
variation
Process with
common cause
variation
Identify the cause:
if positive then can it be replicated or
standardized. If negative then cause
needs to be eliminated
Reduce variation:
make the process even more reliable
Not satisfied with result:
redesign process to get a better
result
39
DISCUSSION
Evaluation
1. Absolute Rubbish
2. Terrible
3. Fairly Bad
4. Not that Great
5. Alright
6. Quite Good
7. Really Quite Good
8. Very Good
9. Excellent
10. Amazing!
50%
40%
10%
41
0%
0%
0%
0%
0%
0%
Te
rr
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ry d
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Ab
so
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THANKS!
2
1
4
3
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