1st Edition
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.
Why?
The Ultimate Curse
A Metamorphosis is Possible
The Enormous Initial Mistake
The Origins of Six Sigma
Genesis
Understanding and Reducing Variation
Understanding the Sigma Level
Gaining Greatest Leverage
Some Structural Elements of Six Sigma
Conclusion
Evolution
In the Beginning…
The Advent of Mass Production
Illustrating Variation
Revolution
Is This Understanding Important?
Stabilize First
…Then Improve the Process
The First Principle
Deming Polishes the Diamond
Deming’s First Opportunity
Deming’s Second Opportunity
The Deming Approach
Limits to Knowledge
Paradox
How Do You Know?
Improving the Analysis
Detecting Instability Using Control Charts
Chemical Example from the Pharmaceutical Industry
Biological Example from the Pharmaceutical Industry
Compliance Example from the Pharmaceutical Industry
The Attributes or Binary Mindset
Action and Reaction
The Nelson Funnel (or Pen Dropping) Experiment
Results of the Exercise
Service Elements of the Pharmaceutical Industry
Close Enough; … Or On Target?
Make More…Faster!
The Dice Experiment
Little’s Law
Quality Control Considerations
Six Sigma and First Pass Yield
Pharmaceutical Case Study — Increasing Output
Case Studies
Biological Case Study — Fermentation
Parenterals Operation Case Study
Safety Case Study
Improved Control of Potency
Deviations in a Pharmaceutical Plant
The Camera Always Lies
In God We Trust…
How Exact is Exact?
Giving Data Meaning
Service Industries
Keeping It Simple
Time — The First Imperative
Pattern and Shape
The DTLF Approach
Why Use Control Charts?
Why Use Control Charts?
Types of Data
Control Charts Advantages
Developing Control Limits
Average and Range Control Charts
Constructing an Average and Range Control Chart
How the Formulae Work
Why the Chart Works
Sub-Group Integrity
Serial Sampling — Loss of Sub-Group Integrity and Over-Control
Origins and Theory
Developing Control Limits
Making the Control Chart
Control Limits Vary with Sub-Group Size
Specifications and Control Limits
Why Use Averages?
Interpreting the Charts
The Final Word
Appendix A Origins of the Formulae
Charts for Individuals
Constructing the Charts
Interpreting Individual Point and Moving Range Charts
Summary
Stratification
Pattern and Shape
Periodicity
Practical Considerations
What Do the Statistics Mean?
Rational Sub-Groups
The Blessing of Chaos
Stabilizing a Process
Causal Relationships
Process Control
Eliminate Waste
What to Measure and Plot
Appendix A Example Operational Directive
Improving Laboratories
Production Lines are the Laboratory’s Customers
Types of Methods
Variability Estimates
Understanding Capability
Accuracy vs. Precision
Use of Validation Data to Determine Laboratory Precision
Reducing Variability — More Is Not Always Better
Appendix A Implementing a Laboratory Variability Reduction Project
Appendix B Implementing a Blind Control Study
Beyond Compliance
We Have Met the Enemy, and He is Us
Appendix 1
Factors for Estimating s from R¯ and s¯
Appendix 2
Factors for x¯ and R Control Charts
Appendix 3
Factors for x¯ and s Control Charts
Biography
John S. McConnell, Brian K. Nunnally