Although all the building blocks of QbD are important, the creation and use of the design space is arguably the most important
aspect of QbD. The design space is the multidimensional combination and interaction of input variables (e.g., material attributes)
and process parameters that have been demonstrated to ensure quality (2). Within the design space is the control space or
the combination of variables and limits used to keep the process on target. A process can have more than one control space.
By definition each control space is a subset of the design space.
A key question is how to create the design space, particularly when products are often locked into a design space before the
process is well understood. The following two-phase approach is recommended:
- Create the design space during the development phase by focusing on minimizing risk and paying close attention to collecting
the data that are most critically needed to speed up development and to understand the risk levels involved
- After the process has been moved into manufacturing, collect data during process operation to refine the process model, design
space, and control space as additional data become available over time.
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 variables and risk can be obtained in several ways.
Interactions between raw-material characteristics and process variables are ever-present and difficult to understand with
the use of statistically designed experiments.
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. Variables
that require further experimentation using DOE and measurement-system analysis will become evident (13).
Identifying potential variables typically results in a long list of candidates, 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 Table III),
developed at DuPont (Wilmington, DE), is a very effective approach (14). 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.
Table III: Comparison of experimental environments. (TABLE IS COURTESEY OF THE AUTHOR)
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.