Module 7: SPC, p Charts, and XmR Charts

QI Measurement Module 7: SPC, p Charts, and XmR Charts Brant Oliver, PhD, NP, MS, MPH Adjunct Assistant Professor 1 Mini-Modules •  Module 1: Micr...
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QI Measurement

Module 7: SPC, p Charts, and XmR Charts

Brant Oliver, PhD, NP, MS, MPH Adjunct Assistant Professor 1

Mini-Modules •  Module 1: Microsoft Excel Basic Training •  Module 2: Defining Measures •  Module 3: Data Collection Plan •  Module 4: Data Display •  Module 5: Balanced Measurement •  Module 6: Variation and Run Charts •  Module 7: SPC, p Charts, XmR Charts

SPC Mini-Module Competencies  

Videos  and  Quick-­‐ Reference  Materials  

Textbook  Readings  

Templates  &   Worksheets  

Applied  Learning   AcAviAes  

1.  XmR  Charts     2.  p  Charts     3.  Data  types     4.  Appropriate   SPC  chart   selec;on     5.  Sta;s;cal   control  and   when  to   transi;on  to   audi;ng.      

1 .  How  to  use  the  XmR   Template  (video)     2.   How  to  use  the  p  Chart   Template  (video)     3.  Chart  selec;on   (algorithm)     4.  SPC  1  page  book   (Harrison)  

Team  Handbook:   Control  Charts  and   Types  of  Varia;on,  p.   4-­‐24.       Quality  by  Design:   Control  Charts,  pp.   350-­‐360.    

1.  XmR  Template     2.  XmR  Example     3.  p  Chart  Template     4.  p  Chart  example      

SPC  exercises   with  data  set  

Types of Variation Common Cause

Special Cause

Variation caused by chance causes, by random variation in the system, resulting from many small factors.

Variation caused by special 
 circumstances or assignable cause not inherent to the system.

Example: Variation in work 
 commute due to traffic lights, 
 pedestrian traffic, parking issues.

Example: Variation in work commute impacted by flat tyre, road closure, heavy frost/ice.

Statistically significant 4

Addressing Variation

Special Cause Variation (Unpredictable)

Common Cause Variation (Predictable)

Identify the Cause: If Positive: “Maximize, optimize, replicate, or standardize.” If Negative: “Minimize or eliminate”

Reduce Variation (Increase Precision): Make the process even more reliable. Sub-Optimal Average Performance: Redesign process to get a better result. 5

SPC Control Limits

Mean (Avg.)

Measured Value (“x”)

Time-ordered Observations (1 n) Anatomy of a SPC Chart…

SPC UCL X XX XXX XXXX XXXXX XXXXX XXXX XXX XX X

_ X

LCL Example of a relationship between a normal distribution and a XmR chart. N.B. – XmR charts do not require a normal distribution for validity.

The Signals

17

8

SPC XmR vs. p Charts: What type of data do I have?

Attribute or Nominal

Measurement or Variable

P chart

XmR chart

XmR Chart Upper Control Limit of X: X bar + (2.66 * R bar)

160

120

X bar

100 80 60

Lower Control Limit of X: X bar - (2.66 * R bar)

40 20 0 1

Moving Range (mR)

Fast Blood Glucose

140

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Control Limit of R: (3.27 * R bar)

60 40 20

R bar

0 1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 10 28 29 30 31

10

p Chart

Interpretation

“In Statistical Control” = No Special Cause (Common Cause Variation) §  8 points on same side of central line (SHIFT).* §  6 consecutive increases or decreases (7 points) without a change in direction (TREND).*

§  Any point outside of the control limits.

* Same as for Run Charts

N.B. – Do not count runs for SPC charts

Exercise

PortCF Data -- Example #1 Average Maximum BMI

13

Apply the Algorithm What type of data do I have?

Attribute or Nominal

P chart

14

Quarter   2007-1   2007-2   2007-3   2007-4   2008-1   2008-2   2008-3   2008-4   2009-1   2009-2   2009-3   2009-4   2010-1   2010-2   2010-3   2010-4   2011-1   2011-2   2011-3   2011-4  

Avg Of Max BMI   23.81   24.57   23.98   24.41   24.13   24.08   24.08   24.16   24.72   24.22   24.83   24.67   25.67   26.14   26.11   24.56   25.75   26.10   24.93   29.40  

Measurement or Variable

XmR chart

15"

2007-1"

15 2011-4"

2011-3"

2011-2"

2011-1"

2010-4"

2010-3"

2010-2"

2010-1"

2009-4"

2009-3"

2009-2"

2009-1"

2008-4"

2008-3"

2008-2"

2008-1"

2007-4"

2007-3"

2007-2"

Avg of Maximum BMI"

Question

How many special cause signals are there in this XmR chart? CF Center 1!

31"

29"

1.  2.  3.  4. 

27"

25"

23"

21"

19"

17"

One Two Three Four

15"

16

Analysis Trends = 0 Shifts = 1 Points outside limits = 1 2011-4"

2011-3"

2011-2"

2011-1"

2010-4"

2010-3"

2010-2"

2010-1"

2009-4"

2009-3"

2009-2"

2009-1"

2008-4"

2008-3"

2008-2"

2008-1"

2007-4"

2007-3"

2007-2"

2007-1"

Avg of Maximum BMI"

Answer

There are TWO special cause signals. CF Center 1!

31"

29"

27"

25"

23"

21"

19"

17"

Exercise

PortCF Data -- Example #2 Exercise as Part of ACT

17

SPC Chart Selection Decision What type of data do I have?

Attribute or Nominal

P chart

18

Quarter   2007-1   2007-2   2007-3   2007-4   2008-1   2008-2   2008-3   2008-4   2009-1   2009-2   2009-3   2009-4   2010-1   2010-2   2010-3   2010-4   2011-1   2011-2   2011-3  

Opportunities   79   95   76   100   111   103   101   49   119   88   80   86   145   108   129   138   138   132   92  

Events   18   27   23   32   31   38   29   14   37   30   19   17   24   13   19   28   49   54   42  

Measurement or Variable

XmR chart

Percent Incorporating Exercise 0%

19 2007-1 2007-2 2007-3 2007-4 2008-1 2008-2 2008-3 2008-4 2009-1 2009-2 2009-3 2009-4 2010-1 2010-2 2010-3 2010-4 2011-1 2011-2 2011-3

Question

How many special cause signals are there? CF Center 3

50%

45%

40%

35%

30%

25%

1.  2.  3.  4. 

20%

15%

10%

5%

Quarter

One Three Five Seven

Answer There are FIVE special cause signals. CF Center 3 50%

40% Percent Analysis

35% 30%

Average LCLp

25%

UCLp 1. Shifts =1 2. Trends = 0 3. Points Outside Limits = 4

20% 15% 10%

2011-3

2011-2

2011-1

2010-4

2010-3

2010-2

2010-1

2009-4

2009-3

2009-2

2009-1

2008-4

2008-3

2008-2

2008-1

2007-4

2007-3

0%

2007-2

5% 2007-1

Percent Incorporating Exercise

45%

Quarter

20

20

Review: Variation

Special Cause Variation (Unpredictable)

Common Cause Variation (Predictable)

Identify the Cause: If Positive: “Maximize, optimize, replicate, or standardize.” If Negative: “Minimize or eliminate”

Reduce Variation (Increase Precision): Make the process even more reliable. Sub-Optimal Average Performance: Redesign process to get a better result. 21

SPC Mini-Module

Applied Exercises Create  and  interpret  p  Charts  and  XmR  Charts   using  data  from  your  seQng  or  the  data  set   provided.

QI Measurement

Done! You have completed the QI Measurement Mini-Modules!

Brant Oliver, PhD, NP, MS, MPH Adjunct Assistant Professor 23