Six Tools for Common Cause
Variability Reduction for
Pharmaceutical QA/QC and
Manufacturing
Lynn Torbeck
Torbeck and Assoc.
Overview of Presentation
Linking science and measurement to
statistical thinking.
 Ten concepts of statistical thinking.
 Six tools for variability reduction.
 Suggestions for implementation.

Copyright c 2004 Torbeck & Assoc.
2
Measurement is the
Essence of Science

“When you can, count.”


Francis Galton (1822-1911) First cousin to Charles Darwin.
“When you can measure what you are speaking
about, and express it in numbers, you know
something about it; but when you cannot
measure it, when you cannot express it in
numbers, your knowledge is of a meager and
unsatisfactory kind …” Lord Kelvin, 1883.
3
Where there is science …

There is:
 Measurement
 Data
 Variation
 Statistics
 Statistical
Thinking
4
Statistical Thinking, The Book:
Improving Performance Through Statistical
Thinking
 ASQ
Statistics Division
 Britz, Emerling, Hare, Hoerl, Janis and Shade
 ASQ Quality Press, Milwaukee, WI
 2000
5
Statistical Thinking Principles
1.
2.
3.
All work occurs in a system of
interconnected processes.
Variation exists in all processes.
Understanding and reducing variation is
key to success.


R. D. Snee, “Statistical Thinking and Its Contribution to
Total Quality.”
ASQ Statistics Division Newsletter, Winter 1991.
6
Expanded Concepts: #1
Take a more global view of problems.
 Work occurs in systems of processes and
sub-processes of interconnected and
interrelated steps.
 Processes can be modeled with the
S.I.P.O.C. view point.

7
Culture
Management
Supplier
Input
Supplier
Input
Supplier
Input
SPO's
Facilities
People
Process
Equipment
Systems
Regulations
Output
Customer
Output
Customer
Output
Customer
Measurement
Environment
8
Expanded Concepts: #2
Processes can be mapped, flowcharted,
studied systematically, understood and
improved. Create a 3-ring binder.
 Optimizing each step may cause the
whole process to be sub-optimum.

The Goal, 1984, 1986. Eliyahu M. Goldratt
 North River Press

9
Flowcharts
3 Coats
Start
Separate
Mix
Mix
5 Coats
Package
Wax
10
Process Mapping

Process mapping expands flowcharting:
 Critical
factors
 Critical responses
 Data collection points identified
 Statistical analysis and summary of data.

HAACP, FMEA
11
Process Mapping
D
3 Coats
Start
Separate
Mix
Mix
5 Coats
Package
Wax
D
D
12
Expanded Concept: #3
Work is performed by teams of people with
differing backgrounds, education,
expertise, skills, needs and expectations.
 Management vs. administration:

The Team Handbook, 1988
 Peter Scholtes, et all.
 Joiner and Assoc, Madison, WI

13
Expanded Concepts: #4
Process outputs vary as a result of both
special or systematic causes and common
causes or random causes.
 Special causes are the result of one or two
factors changing.
 Common causes are the result of many
factors changing more or less at random.

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Indication of Control or Lack of Control
1
0.9
0.85
0.8
0.75
0.7
0.65
0.6
0.55
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
28
29
30
Process At tribute
0.95
Sample Number
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Common Cause Variation
m - 3s
m - 2s
m - 1s
m
m + 1s
m + 2s
m + 3s
68.26%
95.46%
99.73%
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Expanded Concepts: #5
Cause and effect relationships are the
foundations of science.
 These relationships can be found, studied,
quantitated and understood.
 Design of Experiments has the goal of
describing cause and effects in exact
mathematical terms.

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1.
3.
5.
R=
2.
4.
6.
18
Expanded Concepts: #6

Variability is the ENEMY of:
 Quality
/ CGMPs
 Efficiency
 Productivity
 Cycle time
 Validation
 Profits
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Expanded Concepts: #7

Variability can be measured:
 Range
and Inter-quartile range or IQR
 Standard Deviation
 % RSD = 100*SD / Xbar
Sources of variability can be found and
understood.
 Variability can be separated out, ANOVA.

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Visualizing Variability Graphically

Graphical Tools to Use:
 Time
Plot or Run Chart
 Control Charts
 Histograms
 Scatter Plots
 Pareto Charts
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Time Plot or Run Chart
Time Series Plot of C1
103
102
C1
101
100
99
98
97
1
10
20
30
40
50
Index
60
70
80
90
100
22
Control Charts
Xbar-R Chart of C1, ..., C5
Sample Mean
101.4
U C L=101.417
100.8
_
_
X=100.091
100.2
99.6
99.0
LC L=98.765
1
10
20
30
40
50
Sample
60
70
80
100
1 1
4.8
Sample Range
90
U C L=4.859
3.6
_
R=2.298
2.4
1.2
0.0
LC L=0
1
10
20
30
40
50
Sample
60
70
80
90
100
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Histograms
Histogram of C1
Normal
Mean
StDev
N
20
100.1
1.054
100
Frequency
15
10
5
0
97
98
99
100
C1
101
102
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Scatter Plots
Scatterplot of C1 vs C2
103
102
C1
101
100
99
98
97
97
98
99
100
C2
101
102
103
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Pareto Plots
Pareto Chart of Damage
9
8
100
7
Count
5
60
4
Percent
80
6
40
3
2
20
1
0
Damage
Count
Percent
Cum %
Scratch
4
50.0
50.0
Chip
2
25.0
75.0
Bend
1
12.5
87.5
Dent
1
12.5
100.0
0
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Expanded Concepts: # 8
Statistics is the science of variation.
 “Good statistics is not equated with
mathematical rigor or purity, but is more
closely associated with careful thinking.”
 Professor Robert Hogg:

The American Statistician
 November 1991

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Expanded Concepts: #9
Good News! Variability can be reduced.
 Variability is not “inherent” or “given.”
 Variability is subject to cause and effect
relationships.
 There are tools and techniques for
reducing variation.

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6 Tools for Variability Reduction
1.
2.
3.
4.
5.
6.
Work to target.
Flexible Consistency.
Operational Definitions.
Control what can be controlled.
Average out the variation.
New technology, P.A.T.
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1. Work to Target
The goal is to hit the target every time if at
all possible.
 T(L, H), 20(15, 30)
 The specification limits are not our
playground. If for some reason we can not
hit the target, the limits allow us to not
reject. Individual consistency.

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1. Work to Target
A company close to one of the hotbeds of biotechnology wanted to do a designed experiment on one of their
process/products to improve it. They had a test method that was to be used to test the product. During the
planning sessions, it came out that the method was a bioassay and had a %RSD of about 25%.
The consultant noted that that was large given the objective of the experiment and that to compensate more
runs and samples would have to be done. He suggested that rather than spending the money to do many more
runs and samples for the one experiment that some of that money and effort be put into reducing the variation
of the method.
During long pause after that statement, one attendee left the room. The lab manager pointed out that the
method had been validated and found to be acceptable. The consultant pressed the issue suggesting that
flowcharting the method may highlight points where variability could be reduced. The attendee returned to
announce that the developer of the method had been called at the local university. When told the method had a
%RSD of 25%, the developer declared “take everybody out for pizza, thatsagood method.”
As part of the visit, the consultant was taken on a tour of the facility including the laboratory. As the tour
entered the lab, it was noted that the method was being done at that time. The consultant engaged the analyst
in conversation. “Where are you in the method?’ The analyst replied, “Up to the point where we hold it
overnight.” “What does that mean the consultant replied. “Well, hold overnight,” was the answer. Doggedly the
consultant drove forward. “Is that 8 hours, 10 hours or 12 hours?” “Yes” was the response. “Do you think the
result is effected by how long you hold this step?” said the consultant fearing the answer. It came, “yes” was
the reply after a thoughtful silence.
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2. Flexible Consistency
Sounds contradictory.
 In many situations it may not matter which
way we do an action even if there is a
difference.
 The goal is for everyone to do exactly the
same thing the same way every time if at
all possible. Group consistency.

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2. Flexible Consistency
A one product company had a test method that determined the content of the batch. The value of the batch was in
direct relationship to the result of the test method. Low result, less money. Higher result, more money. Of course
batches varied, but they varied more than would be expected based on an understanding of the process. On
average, the results were about where they were expected to be. But it was uncomfortable to charge more one
time than the other when there was reason to believe they were almost all the same.
A consultant was brought in to look at the variability of the data and the historical records. The consultant
discussed the issue with the lab manager. The manager indicated that the analysts were following the test
procedure and each had been trained.
As part of the visit the consultant asked to see the method being done. During a tour in the morning the consultant
watched an analyst perform the method. At one point in the method, the analyst was instructed to “cool off the
metal box.” Part of the equipment was home-built including the “box.” The analyst obtained a chunk of dry ice and
a towel and placed it on the box and continued with the procedure.
In the afternoon, the consultant and manager were passing through the lab and the consultant stopped to watch a
different analyst perform the method. As it happened, the analyst was up to the point in the method to cool off the
box. The analyst took an can of CO2 and gave the box a good long blast and then finished the method.
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3. Operational Definitions
“Sample the tank.”
 We need a full and exact description of

 Who
 What
 Where
 When
and
 How.
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3. Operational Definitions
A Midwest pharmaceutical company made a bulk product in England and shipped it to Puerto Rico for packaging
and sale in the United States.
The bulk product was tested by QC before it was shipped. Upon receipt, the PR QC group sampled and tested it
as well. As might be expected, sometimes a batch that passed in England failed in PR. Unfortunately the practice
was to ship the material back to England even though it had passed the English QC test. Of course, the English
had no recourse but to test it again and ship it again. Again upon receipt, PR would faithfully test it and it would
inevitably pass. If this only happened once in a great while, it wouldn’t have been a problem, but it was happening
all too frequently. Clearly the company didn’t need to spend its money shipping its own product back and forth
across the Atlantic.
A review of the historical data indicated that there was a consistent 5% bias between the two laboratories. Phone
interviews with both labs, managers and analysts, yielded little information other than both groups adamantly
swore that they followed the test method faithfully without deviations.
An experienced chemist was dispatched to England with the instructions, “I want you to watch the method being
performed until you have memorized everything that is not written in the test procedure. Then I want you to go to
PR and do the same.”
Upon his return, he reported, “I found it. In England they stir before they scoop to weigh a sample and in PR they
just scoop without stirring. The material is segregating in PR resulting in the average 5% lower values.”
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4. Control the Controllable
Build an environment of consistency.
 Within economic reason, control
everything that can be controlled. Even if
not considered critical.
 Continuous improvement in control and
consistency is a mindset and a company
culture.

36
5. Average Out the Variability
Define or redefine the reportable value to
be the average of multiple determinations.
 Variability decreases by the square root of
the sample size.
 Averages tend to be Normally distributed.
 Spend the money in the laboratory, not in
rejected lots of product.

37
6. New Technology
As a last resort, purchase new equipment
and new techniques.
 The goal behind Process Analytical
Technologies, P.A.T.
 NIR control of mixing granulations is an
example.

38
Final Concept: #10

Organizational and financial success is
enhanced by using teams for continuous
improvement through variation reduction
and bringing processes into a state of
stability. The tools are simple, the
implementation difficult.
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Implementing Statistical
Thinking (Source: Edward Tufte)
Don’t be a prisoner of the graphs, charts
and tables presented to you. It is only a
selection of what is available.
 Step back mentally and ask: “What would I
really like to see to answer this question or
solve this problem.” Then ask for it.

40
Implementing Statistical
Thinking (Source: Edward Tufte)
Always ask to see the data.
 Don’t accept folklore and war stories.
 Always ask for the data to be plotted.
 Put cause and effect on the same page.
 Maximize data density per page.

41
Summary
Special cause variation is solved using
root-cause analysis.
 Common cause variation can be reduced
using the six tools presented here.
 The tools are simple but effective.
 Implementation requires persistence.

42
Take Back Nugget

We all must ask every day:
“Where is the variability coming from
and what have you and I personally
done today to eliminate it or reduce it?”
43
References
Improving Performance Through Statistical Thinking, ASQ Statistics
Division, Britz, Emerling, Hare, Hoerl, Janis and Shade, ASQ Quality
Press, Milwaukee, WI, 2000
Mapping Work Processes, Dianne Galloway, ASQ Quality Press,
Milwaukee, WI, 1994.
The Team Handbook, Peter Scholtes, Joiner and Assoc., Madison,
WI, 1988
The Goal, Eli Goldratt and Jeff Cox, North River Press, Great
Barrington, MA, 1984
44