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 when an experimental program is conducted over a long time period. In one case, an experiment was designed using DoE procedures to study the effects of five upstream process variables. Data analysis produced confusing results and a poor fit of the process model to the data (low adjusted R2 values). Model residuals analysis showed that one or more variables not controlled during the experiment had changed during the study, thereby leading to poor model fit and confusing results. During an investigation, analysts realized that the experiment had been conducted over eight months. It is very difficult to hold an experimental environment constant over such a long time period.
In each of those cases, it is appropriate to use “blocking” techniques when conducting experiments (11–12). Blocking accounts for the effects of extraneous factors such as raw material lots, bioreactors, and time trends. Experimentation is divided into blocks of runs in which experimental variation within a block is minimized. In the first case above, the blocking factor is the bioreactor; in the second case, the blocking factor is a time unit (e.g., months). The effects of those blocks are taken into account during data analysis, and effects of the variables being studied are not biased.

Desesign Creation

Now that you know the factors and levels to be studied and have selected the amount of replicate runs and repeat tests at each experimental combination to be made, blocking to be used (if needed), and randomization to be used, you are ready to construct the design. You can select one from a catalog of designs contained in electronic or hard-copy files or use software that selects a design based on optimization criteria.
Computer optimization (e.g., JMP, www.jmp.com; Minitab, www.minitab.com; and Stat-Ease, www.statease.com statistical software) is a useful tool for helping select a design. Be careful, however, in its use. Using software is not a substitute for understanding and using good DoE strategy. Computer selection will always give you a design. The important question is whether the design obtained is best for your situation.
You should always beware of automatic procedures. If a process model you select for a situation is correct, then the design selected by computer optimization is likely to work. If the model is wrong, then the computer-generated design will have issues and be less than useful. The problem is that you never know whether the model you select to create a design is appropriate until you have completed the experiment and analyzed the resulting data (9). Further discussion of computer-aided design of experiments is contained in a separate article (11).

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. He provides guidance to senior executives in their pursuit of improved business performance
using QbD, Lean Six Sigma, and other improvement approaches. Ron received his BA from Washington and Jefferson College and MS
and PhD degrees from Rutgers University. He is a frequent speaker and has published four books and more than 200 papers.

Next Update: Mar.27th