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Process understanding: critical to successful process development, operation and improvement.

A new solid-close, 24-hour controlled-release product for pain management had been approved but not yet validated because it had encountered wide variations in its dissolution rate. The manufacturer did not know whether the dissolution problems were related to the API, the excipients, or to variables in the manufacturing process-or to some combination of these factors.

Frustrated with the lack of process understanding, the manufacturer narrowed the range of possible causes of the unacceptable dissolution rate to nine potential variables-four properties of the raw material and five process variables. The team used a designed experiment (DOE) to screen out irrelevant variables and to find the best operating values for the critical variables (Snee, et al. 2008).


The analysis showed that one process variable exerted the greatest influence on dissolution and that other process and raw material variables and their interactions also played a key role. The importance of the process variable with the largest effect had been unknown prior to this experiment even after more than eight years of development work. This enhanced process understanding enabled the company to define the design space and the product was validated and launched.

This case illustrates the criticality of process understanding. The FDA noted the importance of process understanding when they released "Guidance for Industry: PAT-A Framework for Pharmaceutical Development, Manufacturing and Quality Assurance", (FDA 2004). The FDA was responding to the realities of the pharmaceutical and biotech industries; namely that pharma/biotech needs to improve operations and speed up product development. Compliance continues to be an issue and risks must be identified, quantified and reduced. The root causes of many compliance issues relate to processes that are neither well understood nor controlled.

The FDA is promoting QbD and Design Space as control strategies to deal with these issues. Fundamental to this strategy is the development of process understanding. The bottom line is that you can't effectively and efficiently control, improve or transfer a process that you don't understand. Lack of process understanding leads to delayed schedules, extensive rework and increased cost.


The FDA (2004) defined process understanding as "A process is generally considered to be well understood when (1) all critical sources of variability are identified and explained, (2) variability is managed by the process, and (3) product quality attributes can be accurately and reliably predicted over the design space established for the materials used, process parameters, manufacturing, environmental and other conditions".

Item (3) in this definition relates to prediction of process performance, which requires some form of a model, Y=f(X). In this conceptual model, Y is the process outputs such as Critical Quality Attributes (CQAs) of the product and X denotes the various process and environmental variables that have an effect of the process outputs, often referred to as Critical Process Parameters (CPPs). Models may be empirical, developed from data, or mechanistic, based on first principles.

Models come in two forms: qualitative and quantitative. In qualitative models we typically know the important variables and whether the effects of the variables are positive or negative. In quantitative models we know the important variables and have a mathematical equation that describes the effects of the variables. The quantitative model enables us to estimate the effects of the variables and rank the variables from most important to least important.

For example, McCurdy, et al (2010) developed models for a roller compaction process. Among the models reported was a model in which tablet potency relative standard deviation (RSD) was increased by increasing mill screen size (SS) and decreased with increasing roller force (RF) and gap width (GW). They reported a quantitative model for the relationship: Log (Tablet Potency RSD) = -0.15 -0.08 (RF) -0.06 (GW) + 0.06 (SS)

Process understanding summarized and codified in the form of the process model, conceptually represented as Y=f(X), can contain any number of variables (Xs). These models typically include linear, interaction and curvature terms as well as other types of mathematical functions.

At a strategic level, a way to assess process understanding is to observe how the process is operating. When process understanding is adequate the following will be observed:

* Stable processes (in statistical control) are capable of producing product that meets specifications

* Little fire fighting and heroic efforts required to keep the process on target

* Processes are running at the designed speed with little waste

* Processes are operating with the expected efficiency and cost structure

* Employee job satisfaction and customer satisfaction is high

* Process performance is predictable

To assess the state of process understanding at an operational level we need a list of desired characteristics.


The FDA definition of process understanding is useful at a high level but a more descriptive definition is needed; a definition that can be used to determine if a process is understood at an operational level.

Table 1 lists the characteristics I have found useful in determining when process understanding exists for a given process. First it is important that the critical variables (Xs) that drive the process are known. Such variables are typically called critical process parameters (CPP). It is helpful to broaden this definition to include both input and environmental variables as well as process variables; sometimes referred as the "knobs" on the process.

It is important to know the critical environmental variables (uncontrolled noise variables) such as ambient conditions and raw material lot variation can have a major effect on the process output (Ys). Designing the process to be insensitive to these uncontrolled variations results in a "robust" process.

Measurement systems are in place and the amount of measurement repeatability and reproducibility is known for both output (Y) and input (X) parameters. The measurement systems need to be robust to minor and inevitable variations in how the procedures are used to implement the methods on a routine basis. This critical aspect or process understanding is often overlooked in the development process. Gage Repeatability and Reproducibility studies and method robustness investigations are essential to proper understanding of the measurement systems.

Process capability studies involving the estimation of process capability and process performance indices (Cp, Cpk, Pp and Ppk) are useful in establishing process capability. Sample size is a critical issue here. From a statistical perspective 30 samples is the minimum for assessing process capability; much more useful indices are developed from samples on 60-90 observations.

In assessing the various sources of risk in the process, it is essential that the potential process failure modes be known. This is greatly aided by performing a failure modes and effects analysis at the beginning of the development process and as part of the validation of the product formulation and process selected for commercialization.

Process control procedures and plans should be in place. This will help assure that the process remains on target at the desired process settings. This control procedure should also include a periodic verification of the process model, Y=f(X), used to develop the design space, as recommended in the Phase 3 of the FDA's Process Validation Guidance (FDA 2011).

Table 1. Characteristics of Process Understanding

* Critical Process Parameters (Xs) that drive the process are known and used to construct the process design space and process control approach.

* Critical environmental and uncontrolled (Noise) variables that affect the Critical Quality Attributes (Ys) are known and used to design the process to be insensitive to these uncontrolled variations (robustness)

* Robust measurement systems are in place and the measurement repeatability and reproducibility is known for all Critical Quality Attributes (Ys) and Critical Process Parameters (Xs)

* Process capability is known

* Process failure modes are known and removed or mitigated

* Process control procedures and plans are in place


Although it is "a blinding flash of the obvious", it is often overlooked that when you have a process problem it is due to a lack of process understanding. When a process problem occurs you often hear "Who did it; who do we blame"? or "How do we get it fixed as soon as possible?" Juran emphasized that 85% of the problems are due to the process and the remaining 15% are due to the people who operate the process (Juran and Godfrey 1999).

While a sense of urgency in fixing process problems is appropriate some better questions to ask are "How did the process fail?" and "What do we know about this process; do we have adequate understanding of how this process works"?

Table 2 summarizes some examples of process problems and how new process understanding leads to significant improvements; sometimes in unexpected areas. Note that these examples cover a wide range of manufacturing and non-manufacturing issues including capacity shortfalls, defective batches, process interruptions, batch release time and report error rates. All were significant problems in terms of both financial and process performance. Increased understanding resulted in significant improvements.
Table 2. Process Understanding Leads to Improved Process Performance:
Some Examples

Problem New Process Understanding Result of Improvements
 Based on New Process

Batch release Batch record review system Batch release time reduced
takes too long flow improved. Source of 35-55% resulting in
 review bottleneck inventory savings of $5MM
 identified. and $200k/yr cost

Low capacity not Yield greatly affected by Yield Increased 25%
able to meet media lot variation. New
market demand raw material
 specifications needed.

Batch defect Better mixing operation Defect rate significantly
rate too high needed including: methods reduced saving $750k/yr
 and rate of ingredient
 addition, revised location
 of mixing impeller,
 tighter specs for mixing
 speeds and times and
 greater consistency is
 blender set-up.

Process Root cause was Inadequate Process interruptions
interruptions supporting systems reduced 67% saving
too frequent including, lack of spare $1.7MM/yr
 parts, missing batch
 record forms and lack of
 standard operating

Report error Report developer not Error rate reduced 70%
rate too high checking spelling, fact
 accuracy and grammar.


Consistent with the FDA (2004) definition of process understanding noted previously in this article, we see in Figure 1 that a critical first step in developing process understanding is to recognize that process understanding is related to process variation. As you analyze process variation and identify root causes of the variation, you increase your understanding of the process. Process risk is an increasing function of process variation and a decreasing function of process understanding. Increasing process understanding reduces process risk and increases compliance.


In Figure 2 we see that analyzing the process by combining process theory and data (measurements and observations, experiments and tribal knowledge in the form of what the organization knows about the process). Science and engineering theory when interpreted in the light of data enhances process understanding and results in more science and engineering being used in understanding, improving and operating the process.

The integration of theory and data produces a process model, Y=f(X), and identifies the critical variables that have a major effect on process performance. Fortunately there are typically only 3-6 critical variables. This finding is based on the Pareto principle (85% of the variation is due to 15% of the causes) and experience of analyzing numerous processes in a variety of environments by many different investigators (Juran and Godfrey 1999).


As we see in Figure 2 process analysis is strongly data based, creating the need for data-based tools for the collection and analysis of data and knowledge-based tools that help us collect information on process knowledge. We are fortunate that all the tools needed to develop the needed process understanding described above are provided by QbD and Process Analytical Technology (FDA 2004) and Lean Six Sigma methodologies (Snee and Hoerl 2003, Snee 2007).

It all starts with a team which includes a variety of skills including formulation science, process engineering, data management and statistics. In my experience Improvement teams often have limited formulation science and data management skills. Process knowledge tools include the process flow chart, value stream map, cause and effect matrix and failure modes and effects analysis (FMEA).

The data-based tools include design of experiments, regression analysis, analysis of variance, measurement system analysis and statistical process control. The DMAIC (Define, Measure, Analyze, Improve and Control) process improvement framework and its tools are particularly useful for solving process problems. A natural by-product of using DMAIC is the development of process knowledge and understanding, which flow from the linking and sequencing of the DMAIC tools.


We cannot end this discussion of process understanding without addressing how to help the organization make development of process understanding a focus for the organization. This requires a planned initiative which includes a management system to sustain the effort. We are reminded if you want something to happen on a regular and sustained basis you have to put a management system in place to guide and sustain the effort.

Following Kotter's model for creating lasting change (Kotter 1996); we begin with a sense of urgency and a vision statement which might look something like: "We know the elements of process understanding and work to create and use those elements in a way that creates competitive advantage for our organization".

After a gap analysis has been made and the organization knows its current state regarding process understanding, a deployment plan is constructed which includes the strategy to guide the effort, goals for the initiative, demonstration projects, needed personnel training and schedule for management reviews.

A significant goal for the initiative should be to create a "process understanding mindset" for the organization as well as the individuals working in the organization. They should be thinking process understanding at all times. Some critical elements of such a mindset are summarized in Table 3. At a bare minimum a knowledge base should be available to support each process containing the following information: the critical variables that have a major impact on the performance of the process, the nature and magnitude of the effects of each major variable including interactions with other variables and what studies and data were used to develop this knowledge and understanding.

Two elements of this approach require particular emphasis. First it is critical to success that the initiative includes demonstration projects that will create a positive contribution from developing process understanding in 3-6 months. In Kotter's terminology "short term wins" must be created. These successes will show the organization what increased process understanding looks and feels like, its value and provides clear evidence that the organization can do it.

Essential to success is regular management review. Just as an organization has a variety of financial reviews, an organization's approach for developing process must be reviewed on a regular basis. Such a review process should include monthly reviews of specific projects designed to increase process understanding and quarterly and annual reviews by senior management regarding process understanding status in the organization and needed changes. If we have learned anything over the years of corporate change it is that periodic management review is essential to the success of any effort to change how an organization works and to maintain the change.


Process understanding is not an abstract concept. It is fundamental to all aspects of the business and critical to success. The needed concepts, methods and tools are available and well tested. User-friendly process understanding enabling software is much more available and powerful than ever before and will continue to develop in the future. The trick is to continually iterate between data and theory building process understanding over time. It is critical to use change management techniques to embed the gains in the organization and its processes so the new and better ways of operating becomes an integral part of how processes are operated and product manufactured.

Table 3. Process Understanding Mindset

Deep passion for end-to-end understanding all major processes including:

* The key process variables (Xs) that enable prediction of process performance (Y) and on-target adjustment of process

* Customer needs

* Capability of the process to meet specifications

* Sources of costs

* Material and information flow - routes and timing

* Critical operator skills

* Sources of process failures

* Measurement capability


FDA (2004). "Guidance for Industry: PAT-A Framework for Pharmaceutical Development, Manufacturing and Quality Assurance", US Food and Drug Administration, Rockville, MD, September 2004.

FDA (2011). "Guidance for Industry: Process Validation: General Principles and Practices", US Food and Drug Administration, Rockville, MD, January 2011.

Juran, J. M. and A. B. Godfrey (1999). Juran's Quality Handbook, 5th Edition, McGraw-Hill, New York, NY

McCurdy, V., M. T am Ende, F. R. Busch, J. Mustakis, R. Rose, and M. R. Berry (2010). "Quality by Design Using Integrated Active Pharmaceutical Ingredient-Drug Product Approach to Development", Pharmaceutical Engineering, July/August 2010, 28-29.

Snee, R. D. (2007) "Use DMAIC to Make Improvement Part of How We Work", Quality Progress, September 2007, 52-54.

Snee, R. D., P. Cini, J. J. Kamm and C. Meyers (2008). "Quality by Design-Shortening the Path to Acceptance", Pharmaceutical Processing, February 2008, 20-24.

Kotter, J. P. (1996). Leading Change, Harvard Business School Press, Boston, MA.

Snee, R. D. and R. W. Hoerl (2003). Leading Six Sigma-A Step-by-Step Guide Based on Experience with GE and Other Six Sigma Companies, Financial Times Prentice-Hall, Upper Saddle River, NJ.

ABOUT THE AUTHOR: Ronald D. Snee, PhD is founder and president of Snee Associates, a firm dedicated to the successful implementation of process and organizational improvement initiatives.

By Ronald D, Snee, PhD; Snee Associates, LLC, Newark, Delaware
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Title Annotation:PROCESSING
Author:Snee, Ronald D.
Publication:Pharmaceutical Processing
Date:May 1, 2012
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