[PPT] - Understanding Quality Control: A Process Improvement Perspective PowerPoint Presentation

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Understanding Quality Control:

A Process Improvement Perspective

Robert L. Schmidt MD, PhD, MBA Lauren N Pearson DO, MPH

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DISCLOSURE: Robert Schmidt In the past 12 months, I have not had any significant financial interest or other relationship with the manufacturers of the products or providers of the services that will be discussed in my presentation.

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DISCLOSURE: Lauren Pearson In the past 12 months, I have not had any significant financial interest or other relationship with the manufacturers of the products or providers of the services that will be discussed in my presentation.

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Understanding Quality Control A process improvement perspective

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Inspecting poor quality out Building quality in

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Inspecting poor quality out Building quality in Compliance: Improvement:

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Inspecting poor quality out Building quality in Compliance: Improvement: Immediate perspective Long-term perspective

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What you will learn:

Knowledge Skills Key Concepts Underlying QC Stability Capability Common Cause Variation Assignable Cause Variation Long vs Short Term Variation Controllability How to calculate control limits correctly The improvement cycle How to assess patterns in a control chart Compliance vs Improvement How to tell whether your control plan can detect important errors

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Background and Motivation

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Im Impact of f QC Im Improvement TTE Lab

74.5% Reduction in Troubleshooting Time
43% Reduction Labor Cost
50% Improvement in Turnaround Time

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How did they do it?

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QC- Opportunity for Process Improvement?

Compliance versus process

improvement

Remove bad quality
Reduce variation
Reduce costs
Toxicology and Trace Elements (TTE)

provides an example at ARUP

Reduced costs, increased capacity

Monitor Analytical Performance Drive System-Wide Quality Improvement Prevent Failures

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Three key questions:

1. Stable?
2. Capable?
3. Controllable?

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6 8 10 12 14 20 40 60 80 100 time, t

mean==10, sd==1

Process Behavior Chart

Process behavior chart answers this question: Is this process stable?

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6 8 10 12 14 20 40 60 80 100 time, t

mean==10, sd==1

Process Behavior Chart

Measurement System Result, Y

Why do measurements vary?

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6 8 10 12 14 20 40 60 80 100 time, t

mean==10, sd==1

Process Behavior Chart

Measurement System Result, Y Output Inputs X1 X2 X137

SLIDE 17 6 8 10 12 14 20 40 60 80 100 time, t

mean==10, sd==1

Process Behavior Chart

Measurement System Result, Y Output Inputs X1 X2 X137

X1 20 40 60 80 100 t X2 20 40 60 80 100 t X137 20 40 60 80 100 t

Input variation Output variation

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Multiple inputs combine to produce the final result
Temperatures, concentrations, etc.
Most are unobserved but usually cause small variation
This variation is intrinsic to the process and causes the natural

variation in QC results

Best achievable assay performance
Exhibits no patterns e.g. shifts or trends
Output is random but predictable

Common Cause Variation

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Measurement System Result, Y Output Input

Input variation Output variation

1 1.5 2 2.5 3 3.5 x3 20 40 60 80 100 t 12 14 16 18 20 22 y2 20 40 60 80 100 t

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Measurement System Result, Y Output Input

Assignable Cause Variation

1 1.5 2 2.5 3 3.5 x3 20 40 60 80 100 t 12 14 16 18 20 22 y2 20 40 60 80 100 t

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Assignable Cause Variation

A process becomes unstable and produces results that are unusual or

contain a pattern

The output no longer represents common cause variation
Input is extrinsic to the process and reflects a change that is outside the

normal operation of the process

Change in output can be linked (in theory) to a particular input, or

assignable cause

Challenge is to identify that input or cause!
When present, the process is not operating as designed and is

unstable

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How do you achieve process control?

1. Identify causes of assignable cause variation
2. Eliminate variation in key inputs (control)

Requires process knowledge Ability to relate output signals to inputs Process Behavior Chart is the key

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Measurement System Result, Y Output Input

1 1.5 2 2.5 3 3.5 x3 20 40 60 80 100 t 12 14 16 18 20 22 y2 20 40 60 80 100 t

Process Monitoring Signal Process Knowledge

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6 8 10 12 14 20 40 60 80 100 time, t

mean==10, sd==1

Process Behavior Chart

Common Cause Variation

Process is stable
No assignable causes
No pattern in the data
Process is in “statistical control”
Basis for control chart

What does a controlled process look like?

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Stability Assessment

Short-term vs Long term Variation

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Control Chart: Basic Tool for Stability Assessment

3
2
1

1 2 x

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Two parameters of interest:

1. Location
2. Dispersion
4
2

2 4

3
2
1

1 2 x

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A B C

Stable process Unstable process: shifts Unstable process: drift

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D E F

Stable mean Unstable variance Unstable mean Unstable variance Unstable mean Unstable variance

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A B C

Variance estimate short long

1 1 1 2.4 1 3.1

Short-term vs long-term variation

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Measuring Short Term Variability

B

Form “rational subgroups”

1. Measure sd for each group
2. Measure range for each group

𝑡𝑒 =

ത 𝑆 𝑒2

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Measuring Short Term Variability

B

Form groups using successive values (moving range)

𝑆𝑗 = |𝑌𝑗 - 𝑌𝑗−1| 𝑡𝑒 =

ത 𝑆 𝑒2

Actual short-term sd = 1.0 Estimated short-term sd 1.06 Long-term sd= 2.4

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SR Ratio=

𝑚𝑝𝑜𝑕 𝑢𝑓𝑠𝑛 𝑤𝑏𝑠𝑗𝑏𝑢𝑗𝑝𝑜 𝑡ℎ𝑝𝑠𝑢 𝑢𝑓𝑠𝑛 𝑤𝑏𝑠𝑗𝑏𝑢𝑗𝑝𝑜

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20 40 60 80 100 120 20 40 60 80 100 120

U/mL

0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 20 40 60 80 100 120

Assay X – in control Assay Y – out of control

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Statistic Assay X Assay Y Number of observations 123 123 mean 69.6 0.072 Average moving range ( ത 𝑆) 16.8 0.021 Short-term (ST) standard deviation (df) 14.9 (76) 0.015 (76) Long-term (LT) standard deviation (df) 15.9 (122) 0.020 (122) Ratio LT/ST 1.07 1.30 F statistic (SR statistic) 1.13 1.69 P value 0.27 0.007

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Which assay can we improve?

NO!

𝑚𝑝𝑜𝑕−𝑢𝑓𝑠𝑛 𝑡ℎ𝑝𝑠𝑢−𝑢𝑓𝑠𝑛 ~ 1.0

YES!

𝑚𝑝𝑜𝑕−𝑢𝑓𝑠𝑛 𝑡ℎ𝑝𝑠𝑢−𝑢𝑓𝑠𝑛 ~ 1.3

Assay X

.2 .4 .6

4
2

2 4

Assay Y

.2 .4 .6

4
2

2 4

Assay X Assay Y

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5 10 15 1.00 2.00 3.00 4.00 5.00 Ratio of variation, Long-term estimate/Short-term estimate

𝑚𝑝𝑜𝑕−𝑢𝑓𝑠𝑛 𝑡ℎ𝑝𝑠𝑢−𝑢𝑓𝑠𝑛 for 95 assays ......

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Control charts should be based on short-term variation!!

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Stability - Key Ideas

Variation
Assignable Cause
Common Cause
How to assess stability
How to assess potential for improvement
How to construct control charts
Short term or common cause variation

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Three key questions:

1. Stable?
2. Capable?
3. Controllable?

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7 8 9 10 11 12

Process Capability =

𝑏𝑚𝑚𝑝𝑥𝑏𝑐𝑚𝑓 𝑤𝑏𝑠𝑗𝑏𝑢𝑗𝑝𝑜 𝑏𝑑𝑢𝑣𝑏𝑚 𝑤𝑏𝑠𝑗𝑏𝑢𝑗𝑝𝑜

=

𝑉𝑇𝑀−𝑀𝑇𝑀 σ

Lower Specification Limit Upper Specification Limit Target LSL USL

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5 10 15 20

Process Capability

Target

7 8 9 10 11 12

Not Capable (imprecision) Capable

10 11 12 13 14 15

Not Capable (bias)

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bias TEa

s

Units Unacceptable results

𝐷𝑏𝑞𝑏𝑐𝑗𝑚𝑗𝑢𝑧 = 𝑡𝑗𝑕𝑛𝑏 = 𝑈𝐹𝑏 − 𝑐𝑗𝑏𝑡 𝑡 = 𝑏𝑚𝑚𝑝𝑥𝑏𝑐𝑚𝑓 𝑤𝑏𝑠𝑗𝑏𝑢𝑗𝑝𝑜 𝑏𝑑𝑢𝑣𝑏𝑚 𝑤𝑏𝑠𝑗𝑏𝑗𝑝𝑜

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Capability tells us whether a stable process can perform according to requirements

Capability (sigma) errors > TEa 1 35% 2 16% 3 3.3% 4 0.3% 5 0.01% 6 0.00015%

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Prioritizing Im Improvement Projects: The Capability-Stability Matrix

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The Stability-Capability Matrix The dotted lines represent acceptable levels of stability and capability. Each circle represents an assay (numbered 1 to 6). The size of the circle corresponds to the annual volume of the assay. A B C D

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Stability-Capability Matrix

A B C D