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 18.2 lists the characteristics that are 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 Chap. 20 the authors recommend sample sizes of 100–200 for reasonable size confidence intervals.

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. This is also recommended by the FDA’s Process Validation Guidance (FDA 2011).

Although it is “a blinding flash of the obvious”, it 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 DeFeo 2010).

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 18.3 summarizes some examples of process problems and how new process understanding lead 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. The increased process understanding resulted in significant improvements.

Consistent with the FDA (2004) definition of process understanding noted previously in this chapter, we see in Fig. 18.3 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 Fig. 18.4 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 (80 % of the variation is due to 20 % of the causes) and experience of analyzing numerous processes in a variety of environments by many different investigators (Juran and DeFeo 2010).

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 (Fig. 18.5 and Table 18.4). We are fortunate that all the tools needed to develop 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. Development of process understanding is built into the method (Fig. 18.5).

Product and process understanding are fundamental to QbD and the development of the model
which is used to create the design space and create the process control methodology. The question is how to create the process model quickly without missing any important variables. Getting the right set of variables (i.e., critical process parameters, input variables such as raw-material characteristics and environmental variables) in the beginning is critical. Sources of variability and risk can be obtained in several ways. Interactions between raw-material characteristics and process variables are ever-present and more easily understood with the use of statistically designed experiments.

Figure 18.10 contains the critical elements of the approach. Identifying the critical variables often begins what is called “tribal knowledge,” meaning what the organization knows about the product and process under study. This information is combined with the knowledge gained in development and scale-up, a mechanistic understanding of the chemistry involved, literature searches, and historical experience. The search for critical variables is a continuing endeavor throughout the life of the product and process. Conditions change, and new knowledge is developed, thereby potentially creating a need to refine the process model and its associated design and control spaces.

The resulting set of variables are subsequently analyzed using a process map to round out the list of candidate variables, the cause-and-effect matrix to identify the high-priority variables, and the FMEA to identify how the process can fail. This work will identify those variables that require measurement system analysis and those variables that require further experimentation (Hulbert et al. 2008).

Identifying potential variables typically results in a long list of candidate variables, so a strategy for prioritizing the list is needed. In the author’s experience and that of others, the DOE-based strategy-of-experimentation approach (see Fig. 18.11), developed at DuPont Company in Wilmington, DE, is a very effective approach (Pfeiffer 1988). Developing an understanding of the experimental environment and matching the strategy to the environment is fundamental to this approach. A three-phase strategy (i.e., screening, characterization, and optimization) and two-phase strategy (i.e., screening followed by optimization and characterization followed by optimization) are the most effective. In almost all cases, an optimization experiment is run to develop the model for the system that will be used to define the design space and the control space.

The confirmation (i.e., validity check), through the experimentation model used to construct the design space and control space is fundamental to this approach. Confirmation experiments are conducted during the development phase. The model is confirmed periodically as the process operates over time. This ongoing confirmation is essential to ensure that the process has not changed and that the design and control spaces are still valid. The ongoing confirmation of the model happens during the second phase of the development process, as previously described.

The screening—characterization—optimization (SCO) strategy is illustrated by the work of Yan and Le-he (2007) who describe a fermentation optimization study that uses the screening followed by optimization strategy. In this investigation 12 process variables were optimized. The first experiment used a 16-run Plackett-Burman screening design (1946) to study the effects of the 12 variables. The four variables with the largest effects were studied subsequently in a 16-run optimization experiment. The optimized conditions produced an enzyme activity that was 54 % higher than the operations produced at the beginning of the experimentation work.