Six Sigma in the Pharmaceutical Industry: Understanding, Reducing, and Controlling Variation in Pharmaceuticals and Biologics Book

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