Abstract
Researchers often identify robust design, based on the concept of building quality into products or processes, as one of the most important systems engineering design concepts for quality improvement and process optimization. Traditional robust design principles have often been applied to situations in which the quality characteristics of interest are typically time‐insensitive. In pharmaceutical manufacturing processes, time‐oriented quality characteristics, such as the degradation of a drug, are often of interest. As a result, current robust design models for quality improvement which have been studied in the literature may not be effective in finding robust design solutions. In this paper, we show how the robust design concepts can be applied to the pharmaceutical production research and development by proposing experimental and optimization models which should be able to handle the time‐oriented characteristics. This is perhaps the first attempt in the robust design field. An example is given, and comparative studies are discussed for model verification.
Highlights
Traditional robust design principles have often been applied to situations in which the quality characteristics of interest are typically time-insensitive
We show how the robust design concepts can be applied to the pharmaceutical production research and development by proposing experimental and optimization models which should be able to handle the time-oriented characteristics
Taguchi 1 introduced a systematic method for applying experimental design, which has become known as robust design which is often referred to as robust parameter design
Summary
Because product performance is directly related to product quality, Taguchi’s techniques 1, 2 of robust design RD have become increasingly popular in industry since the mid. R Lin and Tu 14 , pointing out that the robust design solutions obtained from the dual response model may not necessarily be optimal since this model forces the process mean to be located at the target value, proposed the mean-squared error model, relaxing the zerobias assumption. T than or at most equal to the variance obtained from the Vining and Myers model 9 ; the mean-squared error model may provide better or at least equal robust design solutions unless the zero-bias assumption must be met. As for an experimental strategy, Kovach and Cho 29–31 and Kovach et al 32 studied D-optimal robust design problems by minimizing the variance of the regression coefficients. D off studies on minimizing variance and achieving the predetermined target value
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