Six sigma in the pharmaceutical industry : understanding, reducing, and controlling variation in pharmaceuticals and biologics

The pharmaceutical industry is under increasing pressure to do more with less . Drug discovery, development, and clinical trial costs remain high and are subject to rampant inflation. Ever greater regulatory compliance forces manufacturing costs to rise despite social demands for more affordable health care. Traditional methodologies are failing and the industry needs to find new and innovative approaches for everything it does.

Six Sigma in the Pharmaceutical Industry: Understanding, Reducing, and Controlling Variation in Pharmaceuticals and Biologics is the first book to focus on the building blocks of understanding and reducing variation using the Six Sigma method as applied specifically to the pharmaceutical industry. It introduces the fundamentals of Six Sigma, examines control chart theory and practice, and explains the concept of variation management and reduction. Describing the approaches and techniques responsible for their own significant success, the authors provide more than just a set of tools, but the basis of a complete operating philosophy. Allowing other references to cover the structural elements of Six Sigma, this book focuses on core concepts and their implementation to improve the existing products and processes in the pharmaceutical industry. The first half of the book uses simple models and descriptions of practical experiments to lay out a conceptual framework for understanding variation, while the second half introduces control chart theory and practice. Using case studies and statistics, the book illustrates the concepts and explains their application to actual workplace improvements.

Designed primarily for the pharmaceutical industry, Six Sigma in the Pharmaceutical Industry: Understanding, Reducing, and Controlling Variation in Pharmaceuticals and Biologics provides the fundamentals of variation management and reduction in sufficient detail to assist in transforming established methodologies into new and efficient techniques.

Preface	p. xiii
The Authors	p. xv
Chapter 1 The Enormous Initial Mistake	p. 1
Why?	p. 3
The Ultimate Curse	p. 5
A Metamorphosis Is Possible	p. 6
The Enormous Initial Mistake	p. 6
One Point Learning	p. 7
References	p. 7
Chapter 2 The Origins of Six Sigma	p. 9
Genesis	p. 9
Understanding and Reducing Variation	p. 10
From Where Does the Term Six Sigma Spring?	p. 10
Early Six Sigma Companies	p. 11
Genesis - The Motorola Experience	p. 11
The Awakening at Motorola	p. 12
Stirrings at Ford	p. 14
Further Illustration - Vial Capping Issues	p. 16
Understanding the Sigma Level	p. 17
Gaining Greatest Leverage	p. 19
The Sniper Rifle Element	p. 19
Lessons from Little's Law	p. 19
Design	p. 20
Summary	p. 21
Some Structural Elements of Six Sigma	p. 21
A Business Strategy	p. 21
Conclusion	p. 24
One Point Learning	p. 24
References	p. 25
Chapter 3 Evolution	p. 27
In the Beginning	p. 27
The Advent of Mass Production	p. 27
Illustrating Variation	p. 30
The Frequency Distribution	p. 30
Case Study - Chromatography Yields	p. 32
Truncated Distributions	p. 34
The Normal Distribution	p. 34
Time Ordered Distributions	p. 36
One Point Learning	p. 38
References	p. 38
Chapter 4 Revolution	p. 39
Is This Understanding Important?	p. 41
Stabilize First!	p. 41
...Then Improve the Process	p. 42
The First Principle	p. 42
Deming Polishes the Diamond	p. 42
Deming's First Opportunity	p. 43
Deming's Second Opportunity	p. 43
The Deming Approach	p. 43
Limits to Knowledge	p. 45
One Point Learning	p. 45
References	p. 45
Chapter 5 Paradox	p. 47
How Do You Know?	p. 50
Improving the Analysis	p. 51
Detecting Instability Using Control Charts	p. 53
Chemical Example from the Pharmaceutical Industry	p. 54
Biological Example from the Pharmaceutical Industry	p. 55
Compliance Example from the Pharmaceutical Industry	p. 56
The Attributes of a Binary Mindset	p. 57
One Point Learning	p. 57
References	p. 57
Chapter 6 Action and Reaction	p. 59
The Nelson Funnel (or Pen Dropping) Experiment	p. 59
Rule 4	p. 59
A Pharmaceutical Example of Rule 4	p. 61
Rule 3	p. 61
A Pharmaceutical Example of Rule 3	p. 61
Rule 2	p. 63
A Pharmaceutical Example of Rule 2	p. 64
Rule 1	p. 65
Results of the Exercise	p. 66
Service Elements of the Pharmaceutical Industry	p. 67
One Point Learning	p. 68
References	p. 68
Chapter 7 Close Enough; ... Or On Target?	p. 69
One Point Learning	p. 73
References	p. 73
Chapter 8 Make More...Faster!	p. 75
The Dice Experiment	p. 75
Little's Law	p. 77
Quality Control Considerations	p. 80
Six Sigma and First Pass Yield	p. 80
Pharmaceutical Case Study - Increasing Output	p. 81
One Point Learning	p. 82
References	p. 82
Chapter 9 Case Studies	p. 83
Biological Case Study - Fermentation	p. 83
Introduction	p. 83
Approach	p. 83
Results	p. 85
Parenterals Operation Case Study	p. 85
Introduction	p. 85
Creasing of Metal Caps	p. 86
Close-Coupled Machines	p. 87
Safety Case Study	p. 88
Introduction	p. 88
Lessons Learned	p. 88
Improved Control of Potency	p. 89
Introduction	p. 89
Initial Analysis	p. 89
Addressing the Problems	p. 91
Phase 1 of Improvements	p. 91
Phase 2 of Improvements	p. 91
Deviations in a Pharmaceutical Plant	p. 92
Chapter 10 The Camera Always Lies	p. 93
In God We Trust	p. 94
How Exact Is Exact?	p. 95
Giving Data Meaning	p. 96
Service Industries	p. 97
One Point Learning	p. 98
References	p. 98
Chapter 11 Keeping It Simple	p. 99
Time - The First Imperative	p. 99
Pattern and Shape	p. 99
The DTLF (Darn That Looks Funny) Approach	p. 102
References	p. 104
Chapter 12 Why Use Control Charts?	p. 105
Why Use Control Charts?	p. 105
Types of Data	p. 105
Advantages of Control Charts	p. 106
Developing Control Limits	p. 107
One Point Learning	p. 109
References	p. 109
Chapter 13 Average and Range Control Charts	p. 111
Constructing an Average and Range Control Chart	p. 111
How the Formulae Work	p. 115
Why the Chart Works	p. 118
Sub-Group Integrity	p. 119
Special Causes	p. 119
Process Changes or Adjustments	p. 119
Duplicate and Triplicate Sampling	p. 121
Instantaneous Sampling	p. 121
Serial Sampling	p. 121
Serial Sampling - Loss of Sub-Group Integrity and Over-Control	p. 122
References	p. 123
Chapter 14 Origins and Theory	p. 125
Developing Control Limits	p. 127
Making the Control Chart	p. 127
Control Limits Vary with Sub-Group Size	p. 128
Specifications and Control Limits	p. 129
Why Use Averages?	p. 130
Normalization of Sample Averages	p. 130
Sensitivity to Drifts in the Process Mean	p. 130
Detection of Over-Control	p. 130
Interpreting the Charts	p. 131
Tests for Stability	p. 133
Guidelines for Investigation	p. 133
The Final Word	p. 134
References	p. 135
Appendix A Origins of the Formulae	p. 137
Chapter 15 Charts for Individuals	p. 141
Constructing the Charts	p. 141
Interpreting Individual Point and Moving Range Charts	p. 143
Summary	p. 146
Stratification	p. 146
Pattern and Shape	p. 147
Periodicity	p. 149
Reference	p. 149
Chapter 16 Practical Considerations	p. 151
What Do the Statistics Mean?	p. 151
Rational Sub-Groups	p. 152
The Blessing of Chaos	p. 153
Stabilizing a Process	p. 153
The Brute Force Approach	p. 153
Procedure - The Brute Force Approach	p. 154
Case Study	p. 155
Causal Relationships	p. 155
Process Control	p. 156
Eliminate Waste	p. 158
What to Measure and Plot	p. 160
References	p. 161
Appendix A Example Operational Directive	p. 163
Chapter 17 Improving Laboratories	p. 167
Production Lines are the Laboratory's Customers	p. 167
Types of Methods	p. 167
Variability Estimates	p. 168
Understanding Capability	p. 168
Accuracy vs. Precision	p. 169
Use of Validation Data to Determine Laboratory Precision	p. 170
Use of Stability Data	p. 171
Pharmaceutical Case Study - Laboratory Precision as Determined by Stability Data	p. 171
Use of Controls	p. 172
Pharmaceutical Case Study - Laboratory Precision as Determined by Control Data	p. 172
Implementing Controls	p. 173
Blind Controls	p. 174
Pharmaceutical Case Study - Blind Control Study	p. 174
Reducing Variability - More Is Not Always Better	p. 176
Pharmaceutical Examples	p. 176
Pharmaceutical Case Study - Reduction of Variability	p. 177
If Standards Are Met, Why Bother Reducing Variation?	p. 179
One Point Learning	p. 179
References	p. 179
Appendix A Implementing a Laboratory Variability Reduction Project	p. 181
Appendix B Implementing a Blind Control Study	p. 183
Chapter 18 Beyond Compliance	p. 185
We Have Met the Enemy, and He Is Us	p. 189
Appendix 1 Factors for Estimating [sigma] from R and [sigma]	p. 191
Appendix 2 Factors for x and R Control Charts	p. 193
Appendix 3 Factors for x and [sigma] Control Charts	p. 195
Index	p. 197

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