Dr.Ron's Insight: Robust Experimental Strategies for Improving Upstream Productivity (2)

STREAMLINING EXPERIMENTATION

Critical to success is the development of a strategy of experimentation, which streamlines the experimental process. Such a strategy, summarized in Table I, identifies three experimental environments: screening, characterization, and optimization.

The objectives of each of the three phases are summarized in Table 1. The strategy sequences and links together a variety of
experimental designs, which enables scientists to achieve greater results than they could achieve previously using the same DOE
techniques in isolation.

The strategy used depends on the experimental environment. These characteristics involve program objectives, the nature of the factors and responses, resources available, quality of the information to be developed, and the theory available to guide the experiment design and analysis.

A careful diagnosis of the experimental environment along these lines can have a major effect on the success of the experimental
program."

The screening-characterization-optimization (SCO) strategy is illustrated by the work of Yan and Le-he, who describe a fermentation optimization study that uses screening followed by an optimization strategy.e In this investigation, 12 process variables were optimized. The first experiment used a 16-run Plackett-Burman screening design 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 increased fermentation yield by 54%.

The Screening Phase. The screening phase explores the effects of a large number of variables with the objective of identifying a smaller number of variables to study further in characterization or optimization experiments. Screening studies typically use fractional-factorial and Plackett-Burman designs to collect data.

More screening experiments involving additional factors may be needed when the results of the initial screening experiments are not promising. The screening experiment often solves the problem.

The Characterization Phase.The characterization phase helps us better understand the system by estimating interactions as well as linear (main) effects. The process model is thus expanded to quantify how the variables interact with each other as well as measure the effects of the variables individually. Full-factorial and fractionalfactorial designs are used here.

The Optimization Phase. The optimizationphase develops a predictive model for the system that can be used to find useful operating
conditions (design space) using response surface contour plots and mathematical optimization.

In these studies, response surface designs are used to collect data.

The seo strategy in fact embodies several strategies which are a subset of the overall seo strategy, including: -.
• screening-characterization-optimization
• screening-optimization
• characterization-optimization
• screening-characterization
• screening
• characterization
• optimization.

The end result of each of these sequences is a completed project. There is no guarantee of success in a given instance, only that the seo
strategy will "raise your batting average."?

REDUCING THE EFFECTS OF ENVIRONMENTAL FACTORS

It is important to discuss how the effects of environmental factors are included in the experimental strategy. Part of diagnosing the. experimental environment is identifying environmental variables, which often are overlooked. Even the best strategy can be defeated if the effects of environmental
variables are not properly taken into account. Environmental variables include factors such as bioreactors, raw material lots, operating teams, ambient
temperature, and humidity. Recognizing the effects of variation from environmental factors, can go a long way in ensuring that the' resulting data are not biased.

Special experimental strategies also are needed to reduce the effects of extraneous variables that creep in when the experimental program is conducted over a long time period.

In one case, a laboratory was investigating the effects of upstream variables using two identical bioreactors. As an after-thought, the scientists decided to use both bioreactors in the same experiment. There was some concern that using both reactors would be a waste of time and resources because they
were identical. Data analysis showed, however, that there was a big difference between the results from the two bioreactors. These differences were taken into account in future experiments.

In another situation, an experiment was designed using DOE procedures to study the effects of five upstream process variables.

The data analysis produced some confusing results and a poor fit of the process model to the data (R2 values were low). An analysis of the model residuals showed that one or more variables, not controlled during the experiment, had changed during the study, leading to the poor model fit and confusing results.

An investigation showed that the experiment had been conducted over an eightmonth period. It is very difficult to hold the experimental environment constant for such a long time.

Heilman and Kamm report a study in which the biggest effect was raw materiallot-variation introduced by different lots of medias. This effect was present during the production of more than 50 batches before it was discovered.

In each of these cases, it is appropriate to use "blocking" techniques when conducting upstream experiments.? Blocking accounts for the effects of extraneous factors, such as raw material lots, bioreactors, and time trends.

The experimentation is divided into blocks of runs in which the experimental variation within the block is minimized. In the first case above, the blocking factor was the bioreactor.

In the second case, the blocking factor was a time unit (e.g., months). The effects of the blocking factors are considered in the data analysis, and the effects of the variables being studied are not biased.

Click here to read: Dr.Ron's Insight: Robust Experimental Strategies for Improving Upstream Productivity (1)

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: Aug.28th