Case Study: R&D Test Method Expedites Speed to Market

Alberto-Culver marketers had a vision for a product that provided a higher level of exfoliation and cleansing than any existing product. However, it had not been possible to increase the proportion of exfoliants beyond 10% because the viscosity of the product was too high.

“In the past, the personal care industry developed products using what can be called the spray and pray method,” said Christopher Felski, Formulator for Alberto-Culver. “You'd try a little more of this and a little more of that. The problem was that even when you ended up with a great product, you didn’t know how or why you got there. Every new product would start from scratch.”

In an effort to avoid the shortcomings of this method, Alberto-Culver developed a new line of scrubs using the design of experiments (DOE) method in which all formulation factors are simultaneously varied to explore the entire design space. Statistical analysis of the results revealed the expected performance of any formulation, not just the ones that were specifically evaluated in the experiments. “Using DOE we have pushed the performance of our product well past the limits of what we thought was possible,” Felski said. “The new scrubs have an exfoliant concentration that considerably exceeds the 10% level that was the upper limit in the past.”

According to the company, DOE drastically reduces the number of runs required to determine the optimal settings of each factor by varying the values of all factors in parallel. DOE determines not just the main effects of each factor but also the interactions between the factors, making it far superior to the traditional one-factor-at-a-time (OFAT) approach.

“We selected Design-Expert software from Stat-Ease, Inc. (Minneapolis, MN) because of its exceptional capabilities in the design and analysis of mixture design experiments,” said Felski. “Mixture design reduces the number of experiments required to develop formulations of personal care products. The end result is that we can get products to market faster and at a lower cost than with conventional experiment designs.”

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