Using Quality by Design to Improve Manufacturing

Process control and process improvement 
are important parts of life cycle management.

Much of the literature on quality by design (QbD) has focused on the creation of the “Design Space” (ICH 2009) which is the combination of levels of the critical process parameters that produces in-specification product. Design space creation is critical but a process control strategy is also essential and is an integral part of QbD. Stage 3 of  the FDA Process Validation Guidance calls for “Continued Process Verification” (CPV),  or continually verifying the process is doing what it is supposed to do throughout the life of the product (FDA 2011, Snee 2015). Also if we are to be effective in using life cycle management to manage the process over time. we must have a process management system in place that integrates both process control and process improvement (Snee and Gardner 2008, Snee 2010b).

The focus on QbD places an even greater emphasis on the quality of pharmaceutical products and manufacturing process performance. Indeed process and product control is a major building block of QbD (Snee 2009). A systematic approach to achieve this objective is described below.  Improved manufacturing performance and reduced costs are a natural byproduct of the system.

Creating Stable and Capable Processes

Central to any process control system is the assessment of process stability and capability. Manufacturing processes that are “stable and capable” over time can be expected to consistently produce product that is within specifications and harmless to patients due to nonconforming product. Stability and capability are described by Montgomery (2013):
Stable manufacturing processes are in a state of statistical control as each batch of tablets is being produced and as batches of tablets are produced over time. A process in a state of statistical control consistently produces product that varies within the process control limits, typically set at the process average plus and minus three standard deviations of the process variation. Separate control limits are set for each parameter e.g.; tablet thickness and hardness. Any sample value that is outside of these limits, or significant trends and shifts within the limits. is an indication that the process may not be in a state of statistical control. 
Capable process consistently produces tablets that are within specifications for all tablet parameters. A process capability analysis compares the process variation to the lower and upper specification limits for the product. Broadly used measures of process capability are the Cp, Cpk, Pp and Ppk indices.
These descriptions show that process control limits and product specification limits are different and are used for different purposes. 

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Process stability and capability are evaluated at two levels:
1) During the production of each individual batch to ensure that the process remains in control and identify when process adjustments are needed. Some critical questions that need to be addressed are:
  • Is the batch production process stable over the production of the batch with no trends or shifts?
  • Is the process capable of meeting specifications – are process capability indices acceptable?
  • Within batch sampling, is the variation small, which indicates that the production process is stable during the production of the batch?
2) Batch-to-batch control during a given year and over time from year to year. Some important questions to be addressed include: 
  • Is the batch-to-batch variation stable from year to year and within years with no shifts, trends or cycles present?
  • Is the batch-to-batch variation small?
These analyses will also assess the robustness of the process, determining the capability of the process to produce product that meets specifications when it drifts off target.

Systems Approach to CPV

It is generally agreed that any activity works best and is more sustainable over time when a systems approach is used to guide the associated activities and work. A schematic of such system is shown in Figure 1 for monitoring batch-to-batch variation within the year and from year-to-year (Snee and Hoerl 2003, 2005; Snee and Gardner 2008, Snee 2015).

Figure 1. Batch Production Monitoring System (Snee 2015)

The underlying system has the following characteristics:
  • Data are periodically collected from the process. Processes are often monitored using samples drawn ever 30 – 60 minutes.
  • Data are used to monitor processes for stability and capability using control charts, process capability indices, analysis of variance, time plots, box plots and histograms. 
  • Analysis identifies when process adjustments are needed to get the process back on target
  • Records are kept on the types of problems that are identified. As significant problems are identified or problems begin to appear on a regular basis, the resulting issues and data are incorporated in process improvement activities to develop permanent fixes. Ranking processes with regard to process stability is another useful way to identify where improvement resources should be spent (Snee and Hoerl 2012). 
The process improvement work can be effectively completed using the DMAIC (define, measure, analyze, improve, control) problem solving and process improvement framework (Snee and Hoerl 2003, 2005). Process performance deteriorates over time due to a variety of factors. Process performance gets worse if left alone; it does not get better. In addition to problem solving, there are almost always opportunities for cost reduction studies. Process robustness studies are another big opportunity, as over time, process performance becomes more sensitive. causing changes in one of more variables. Statistical design of experiments, used in process development, is also very useful in manufacturing for conducting such studies (Snee 2010a).
Another aspect of the QbD system is the measurement system. Process problems can often be traced to measurement system issues. Measurement is a process just like manufacturing. Over time. a measurement system can drift off target, or become highly variable, or both if not properly maintained. Gage repeatability and reproduceability (R&R) and robustness studies are useful tools for evaluating the effectiveness of measurement systems (Borman 2007, Montgomery 2013). 

A Team Sport – Management Reviews

The ingredient that keeps any system operating effectively is management review. In CPV, management reviews take place at several levels in the organization, as shown below.  

The data reviewed and actions taken at each management level in the organization differ. In general, as the level of management increases in the organization, the aggregation of the data increases as does the time between reviews. Management review is a “team sport.” Teams conduct the reviews at each organizational level. Each team plays a role in the overall review process.

The reviews should be an integral part of the regular management review process, not a special event. A critical aspect of the reviews is the identification of needed improvements and assessment of improvements recently put in place. I recall a CEO admonishing his management staff to “schedule the reviews and show up. Good things will happen. In many cases you don’t have to say a word.” It is, of course, appropriate to ask questions and engage in discussion when needed.

Creating Win-Win Solutions for CMOs and Sponsors

As pharmaceutical companies and CMOs develop relationships, it is clear that success depends on three elements: use of a common language, implementation of methods of effective communication and cooperation and effective operation of manufacturing processes. QbD provides a system to satisfy this goal. QbD is becoming more and more accepted and understood around the world, enabling effective communication between CMOs and sponsoring organizations.

Three critical aspects of this system are development of process understanding, a strategy of experimentation that collects the right data in the right amount at the right time, and a process management system that integrates process control and process improvement (Snee 2010b). Using QbD as a vehicle to construct, implement and communicate these systems will go a long way to operating processes and delivering products that satisfy the needs of all parties involved: patients, pharmaceutical companies, CMOs and the FDA (Snee 2011).

© Ronald D. Snee 2015


Borman, P., M., M. Chatfield, P. Nethercote, D. Thompson, and K. Truman, (2007) “Application of Quality by Design to Analytical Methods”, Pharmaceutical Technology, October 2007, 142-152.

FDA (2011) “Guidance for Industry - Process Validation: General Principles and Practices”, US Food and Drug Administration, January 11, 2011. 

ICH (2009) “ICH Harmonized Tripartite Guideline: Pharmaceutical Development, Q8 (R2),” Current Step 4 Version, August 2009.

Montgomery, D. C. (2013) Introduction to Statistical Quality Control, 7th Edition, John Wiley and Sons, New York, NY

Snee, R. D. (2009) “Building a Framework for Quality by Design”, Pharmaceutical Technology Online, October 2009.

Snee, R. D. (2010a) “Experimental Strategies for Implementing Quality by Design”, Pharmaceutical Processing, April 2010, 12-16.

Snee, R. D.  (2010b) “Critical Considerations in Monitoring Process Performance and Product Quality”, Pharmaceutical Technology – Analytical Technology and Instrumentation, October 2010, 38-42.

Snee, R. D. (2011) “Using Quality by Design to Enable CMO Manufacturing Process Development, Control and Improvement”, Pharmaceutical Outsourcing, January/February 2011, 10-18.

Snee, R. D. (2015) “Management Holds the Key to Continued Process Verification”, Pharmaceutical Manufacturing, January/February 2015, 33-35

Snee, R. D. and E. C. Gardner (2008) “Putting It All Together – Continuous Improvement is Better than Postponed Perfection”, Quality Progress, October 2008, 56-59.

Snee, R. D. and R. W. Hoerl (2003) Leading Six Sigma – A Step by Step Guide Based on the Experience With General Electric and Other Six Sigma Companies, FT Prentice Hall, New York, NY.

Snee, R. D. and R. W. Hoerl (2005) Six Sigma Beyond the Factory Floor: Deployment Strategies for Financial Services, Health Care and the Rest of the Real Economy, FT Pearson Prentice Hall, New York, NY.

Snee, R. D. and R. W. Hoerl (2012) “Going on Feel- Monitor and Improve Process Stability to Make Customers Happy”, Quality Progress, May 2012, 39-41.

Ronald D. Snee is president of Snee Associates, LLC, a firm dedicated to successful improvement initiatives using quality by design, Lean Six Sigma and other approaches.  He is also adjunct professor at Rutgers University Pharmaceutical Engineering and Temple University School of Pharmacy. Prior to entering the consulting field. he worked at DuPont for 24 years in a variety of assignments, including pharmaceuticals, statistical studies, manager of statistical, software and engineering consultants and process improvement. Ron received his PhD from Rutgers University, is a Fellow of American Society of Quality, American Statistical Association, and American Association for the Advancement of Science and has received numerous awards and honors. He has co-authored five books and more than 270 publications. He may be contacted at