An Introduction to Statistical Process Control

Charts (SPC)

Steve Harrison

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

• Multiple factors

5

Special Cause Variation

• Are not part of the normal process. Arises from

special circumstances

• Example: Variation in work commute impacted

by flat tyre, road closure, ice and snow.

• Usually best uncovered when monitoring data in

real time (or close to that)

• Assignable cause

6

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)

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

Its best if you have 25 data points to set up a control chart, but

50 are better if available. Be careful of too many points…

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

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

Mean Mean + 3s Mean - 3s

99.7% will be within 3 s

3s AND THE CONTROL CHART

6s

3s

3s

UCL

LCL

Mean

Run Charts vs. SPC Charts

Run Chart

• Simple

• Easy to create in Excel or

on paper

• Less Sensitive

• Only need 12-15 data

points

SPC

• More Powerful

• Control lines show the

degree of variation

• Need software

• Better with 25+ data

points

17

0

10

20

30

40

50

60

70

80

4-A

pr

6-A

pr

8-A

pr

12

-Ap

r

14

-Ap

r

18

-Ap

r

20

-Ap

r

22

-Ap

r

3-M

ay

5-M

ay

9-M

ay

11

-May

13

-May

15

-May

% D

aily

TT

Os C

om

ple

ted

by N

oo

n

Ward x – % of total TTOs completed by 12 noon April 4 - May 15, 2012

Special cause variation

0

10

20

30

40

50

60

70

80

90

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

MEAN

SPECIAL CAUSES - RULE 2

LCL

UCL Or eight points below centre line

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

Quiz – Does the chart show

A. Special Cause

Variation?

B. Common Cause

Variation?

C. Both of the above

D. No Variation

Special C

ause V

ariatio

n?

Comm

on Cause

Varia

tion?

Both o

f the above

No Varia

tion

0% 0%

33%

67%

How many special cause signals are present

on this chart?

A. 0

B. 1

C. 2

D. 3

E. 16

0 1 2 3 16

33%

67%

0%0%0%

How many special cause signals are present

on this chart?

A. 0

B. 1

C. 2

D. 3

E. 16

0 1 2 3 16

0% 0% 0%0%

100%

How many special cause signals are present

on this chart?

A. 0

B. 1

C. 2

D. 3

E. 16

0 1 2 3 16

0% 0% 0%

100%

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

Annotated SPC Charts

Application – Responding to Variation

33

Process with

common cause

variation

Reduce variation:

make the process even more reliable

Not 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

34

PRACTICAL

Length Of Stay for Bowel Surgery Patients

Quality Composite Ratio

Outpatient attendances (Part 1)

Outpatient attendances (Part 2)

% discharged by noon

THANKS!