an introduction to statistical process control charts (spc)

An Introduction to Statistical Process Control Charts (SPC)

Steve HarrisonMonday 15th July 2013 12 – 1pmRoom 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)

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0

20

40

60

80

100

120

Consecutive trips

Min.

Special Cause - My trip to work

Mean

Upper process limit

Lower process limit

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

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

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.

Standard Deviation and a normal distribution

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

3s AND THE CONTROL CHART

6s

3s

3s

UCL

LCL

Mean

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

20

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60

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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

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

Point above Upper Control Limit (UCL)

SPECIAL CAUSES - RULE 1

MEAN

LCL

UCL

Or point below Lower Control Limit (LCL)

SPECIAL CAUSES - RULE 1

MEAN

LCL

UCL

MEAN

Eight points above centre line

SPECIAL CAUSES - RULE 2

LCL

UCL

A 1 in 256 chance or 0.3906%

MEAN

SPECIAL CAUSES - RULE 2

LCL

UCL Or eight points below centre line

A 1 in 256 chance or 0.3906%

MEAN

Six points in a downward direction

SPECIAL CAUSES - RULE 3

LCL

UCL

MEAN

SPECIAL CAUSES - RULE 3

LCL

Or six points in an upward direction

UCL

Considerably less than 2/3 of all the points fall in this zone

LCL

UCLSPECIAL CAUSES - RULE 4

MEAN

SPECIAL CAUSES - RULE 4

Or considerably more than 2/3 of all the points fall in this zone

MEAN

UCL

LCL

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%

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%

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%

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%

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…

I @:@ -1 : - f, I I .

'And this is the period when the cat was away. '

Example – Renal DT247J

PDSA 1 PDSA 2

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

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%

THANKS!

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2

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