Guest Column | August 23, 2016

A Working Introduction To Six Sigma For Pharmaceutical Manufacturers

By Steven Zebovitz, R.Ph., Ch.E.

Steven Zebovitz

Have you ever encountered one (or more) of the following scenarios in your pharmaceutical manufacturing operations?

  • A product suddenly shows a change in one or more of its critical quality attributes.
  • Some batches of a high-volume product compressed in a pool of tablet presses show variability in tablet weight.
  • The yield of a product gets worse over time.
  • Tech transfer from product development to commercial manufacturing is slow and clumsy.

If so, the toolkit known as Six Sigma can be used to address all of these challenges — and many others.  I had the privilege of mentoring under a Six Sigma Master Black Belt who steered me through these issues (and trained me for my own Black Belt). The Six Sigma techniques are highly applicable to the life sciences industries, and their use is even expected by many regulatory bodies.

This is the first in a series of articles on Six Sigma that will introduce you to this methodical, rigorous approach to process excellence, and share my experiences using the technique. My hope is that these articles will help more pharmaceutical organizations become better, faster, and less expensive to operate.

If, after reading any of the articles, you find yourself inclined to apply Six Sigma principles within your organization, you would be wise to consider obtaining credentials through a respected vendor such as the American Society of Quality (ASQ), and to mentor under an accomplished Six Sigma Master Black Belt. 

Defining Six Sigma

In a manufacturing context, the term “sigma” is a measure of the spread of data, or variation, within a given population of products.  If every member of a population is measured, then sigma is given the Greek symbol σ.  If a random sample of a population is measured, we use the letter English s.  Typically, samples of a population are used, since it is neither practical nor possible to measure a whole population. Imagine dissolution testing pharmaceutical oral tablets, wherein testing is destructive — testing an entire population (i.e., an entire batch) would leave the company without product.

The term “Six Sigma” refers to a quality level wherein no more than 3.4 defects occur per million opportunities (3.4 ppm) as a result of product variation.  We also refer to the sigma level of a population as its defect rate per million.  The chart below tabulates the sigma level with respect to product yield.

Relationship Between Sigma Levels and Yield

Sigma Level

Defects / million opportunities

% Defective

% Yield


























Manufacturing and business processes have inherent variability.  Common cause variation is the normal, expected variation of a process, often referred to as noise.  An example of common cause variation is tablet run weight, wherein individual weights should fall within ±5% and weights of 10 tablets should fall within ±2%.  Weights within these limits vary to a small degree so they're considered within common cause variation.

Special cause variation is abnormal to a process.  A group of batches suddenly and atypically falling below their lower yield specification is an example of special cause variation.  Special cause variation originates from one or more of the process inputs or environment and requires an investigation to determine its root cause. These inputs are termed “the 6Ms” and will be covered in detail in a subsequent article.

The Six Sigma Process

The roadmap to Six Sigma is through the process of DMAIC, which stands for define, measure, analyze, improve, and control.  The five steps in DMAIC will be the focus of ensuing articles in this series, but for the moment, here are brief definitions of each:

  • Define is quite likely the most difficult step of the improvement or investigative process.  This is the project goals and/or customer deliverables (both internal and external).  A proper define statement includes a target magnitude of improvement and timeframe in which that deliverable will be achieved.  It is also a statement requiring universal agreement from the Six Sigma team and management (see Six Sigma Belts and Roles below).
  • Measure is the process for acquiring future data on a prospective basis. It is also the process of prospectively accumulating future data or and tabulating archived (past, or retrospective) process or customer data. Data is not necessarily quantitative. An important repository of data is from individuals, such as equipment operators, manufacturing supervisors, product development scientists, maintenance mechanics, quality assurance technicians, and quality control technicians or chemists.
  • Analyze is the process of adding intelligence to raw data and of deriving correlations between input variables and output critical quality attributes (CQAs).  Note that correlations cannot be considered diagnostic or actionable until additional steps (e.g., a proper design of experiments) demonstrate that a correlation is causative.
  • Improve sets in place actions, specifications, or procedures that achieve the goals outlined in the define stage.  These improvements should be simple and easy to implement — easily incorporated into workflows with a minimum of upset to habits or culture.  Training from the Six Sigma team and associated professionals plays a key role at this phase.  Often, process validation phases are required.  When equipment modifications are needed, equipment validations often ensue.  Ideally, improvements should be vetted on a small scale, checking for effectiveness, workforce adoption, and achievement of goals listed in the define stage.  Afterward, they can be expanded in a stepwise fashion to entire lines, all the while observing and tweaking the improvements.
  • Control establishes additional procedures and steps to sustain the improvement.  Often these steps are codified in batch manufacturing records.  When possible, the changes are automated, thus removing any tendency to return to old habits.  As with any process automation project, process and project engineers determine measurable inputs, decisional algorithms, and outputs.  The system is validated and commissioned, and test batches are validated for their adherence to approved specifications and the goals of the define stage.

In the pharmaceutical industry, the term quality by design (QbD) is often used. QbD is similar to Six Sigma; however, for QbD the process/acronym changes to DMADV, or define, measure, analyze, design, and verify.  A key feature of QbD is perpetual feedback to ensure that customer needs are met. This is not unlike what is outlined in Stage 3 of the FDA’s Process Validation Guidance (2011), which provides a framework for continuous product/process verification as a feedback mechanism. In the data-intensive world of pharmaceutical manufacturing, process-related acronyms frequently become intertwined. 

Six Sigma Belts & Roles

Six Sigma uses martial arts terminology to classify the level of practitioner training and skills.

At the top are Six Sigma Master Black Belts, who has received training and mentorship to manage a Six Sigma program at an organization-wide level.  These professionals have completed many process-improvement projects, have mastery of project management and advanced statistics, and possess the ability to train and mentor others in the organization.

A Six Sigma Black Belt is a step below the Master Black Belt, who has completed some Six Sigma projects as well as received training at the Black Belt level.  Black Belts will typically handle up to three projects spanning departments outside of their own expertise, with opportunity costs ranging from $100,000 to $500,000.

Six Sigma Green Belts have received introductory courses in DMAIC, sufficient to be on a Six Sigma team assisting Black Belts and Master Black Belts.  Green Belts, once they are capable of working under mentorship, will handle single projects with opportunity costs of $50,000 to $100,000 within their own department.

Six Sigma Yellow Belt is a general overview course for those who have little need to apply its techniques but have a need to know.  Typically, this is typically an executive-level course that introduces the concepts as well as a plan to implement a program.

Six Sigma is also a management approach and a corporate culture. When implementing Six Sigma in an organization, a Six Sigma champion is assigned from management.  The champion is an active member of the Six Sigma team.  He sets measurable objectives, selects Six Sigma projects, and plays a role in choosing individuals for the Six Sigma team.  The champion is also tasked with securing funds (for training or testing), securing time for team members to dedicate to projects, and reporting the progress of the teams.  Through the champion’s efforts, Six Sigma is incorporated into business strategies. 

The team is most effective when broadly paired with individuals from across a manufacturing or business process.  Machine operators, scientists, quality assurance, quality control, finance, human resources, and others must have a vested interest in the success of the Six Sigma team — and their comments and suggestions must be valued by the team and management.

Conversations between those involved in a Six Sigma project must be valued, safe, and at times confidential.  Members of the Six Sigma team must therefore have integrity and have earned the respect of their colleagues throughout the organization.

 One Size Does Not Fit All

Six Sigma has broad application across the pharmaceutical industry, though each company and each issue will require a slightly different approach.  Measurable goals, objectives, personnel selection, training, and mentorship all must follow the needs of the specific organization and its customers, both internal and external.

Future articles in this series will take a deeper dive into DMAIC, the fundamentals of variability, and some of the statistical tools that come into play with Six Sigma.

About The Author

Steven Zebovitz, R.Ph., Ch.E., has three decades of pharmaceutical engineering and manufacturing experience, primarily in oral solid dose.  Much of his experience is in engineering and manufacturing support, including leadership positions in technology transfer, process scale-up, validation, and optimization.  He led a Six Sigma deployment that yielded 10 Black Belts and 1 Master Black Belt.  His interests include process excellence, lean manufacturing, and championing teams.  He may be reached at or at 215-704-7629.