Planning
With a general strategy to guide your experimentation, you are in a position to plan your experiments. Several issues need to be addressed during this step.
Defining the goals and objectives of your experiment is a very important activity because agreement among stakeholders regarding the objectives is critical to success and frequently not present until after a discussion. It is also important to circulate a proposed design for comment and revise it based on input, to get the best thinking of the organization incorporated in the experimentation, and gain alignment among the stakeholders on the proposed design.
Experimentation phase selection follows diagnosis of an experimental environment and determines the design(s) to be used. In one case I encountered in my work, a scientist was studying the effects of temperature and time on the performance of a formulation using three levels of each variable. Further discussion identified that the purpose of the study was to better understand the system by identifying effects and interactions of the variables. Optimization was not an objective, so only two levels of each variable needed to be studied. The result of recognizing “characterization” as the experimental environment lessened the amount of needed experiment by >50% — a significant reduction in time, personnel, and money that sped up study completion.
Repeatability and reproducibility of a measurement system for measuring process outputs (Y values) must be assessed. More replicate testing will be needed when measurement quality is poor. It should not be overlooked that DoE can be used to improve the repeatability, reproducibility, and robustness of analytical methods (10).
The amount and form of experimental replication must be addressed. When experimental reproducibility is good, single runs are sufficient. The law of diminishing returns is reached at about four runs per test condition.
In one recent product formulation case, a scientist was having trouble creating the formulation because he didn’t recognize that high experimental variation was making it difficult for him to see the variables’ effects. He suspected that something was wrong because sometimes he ran an experimental combination once; other times there would be two, three, and even four repeat runs for the same factor combination. After several rounds of experiments, the effects of the variables were still unidentified. In a single experiment — which took the high experimental variation into account and used “hidden replication” characteristic of DoE — factorial designs produced a design space and optimal formulation that had 50% better quality than previously found.
SCO strategy can also effectively address formulation optimization with an approach that is similar to that used for process variable optimization. The objective is to develop formulation understanding, identify ingredients that are most critical to formulation performance, and create formulation design space. One effective strategy is to use a screening experiment to identify the most critical ingredients and follow up with an optimization experiment to define formulation design space. That approach can effectively reduce the amount of experimentation and time needed to optimize a formulation by 30–50%.
Environmental variables such as different bioreactors and other equipment, raw material lots, ambient temperature and humidity levels, and operating teams can affect results. Even the best strategy can be defeated if the effects of environmental variables are not properly taken into account.
In one case, a laboratory was investigating the effects of upstream variables using two “identical” bioreactors. As an after-thought, both were involved in the same experiment. There was some concern that using both reactors would be a waste of time and resources because they were “identical.” However, data analysis showed a big difference between their results. Those differences were taken into account for future experiments.
Special experimental strategies are also needed to reduce the effects of extraneous variables that creep in w...
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