# An Introduction to Statistical Process Control Charts (SPC) Steve Harrison Monday 15 th July 2013 12 – 1pm Room 6 R Floor RHH.

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An Introduction to Statistical Process Control Charts (SPC) Steve Harrison Monday 15 th 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

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 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 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 MeanMean + 3sMean - 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 UCL LCL Mean

Run Charts vs. SPC Charts Run 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 17

Special cause variation ND

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 UCL SPECIAL 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

2. How many special cause signals are present on this chart? A. 0 B. 1 C. 2 D. 3 E. 16

3. How many special cause signals are present on this chart? A. 0 B. 1 C. 2 D. 3 E. 16

4. How many special cause signals are present on this chart? A. 0 B. 1 C. 2 D. 3 E. 16

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…

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 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 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! 41

THANKS!

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