Robust parameter design (RPD) involves the effects size, component restrictions, and fixed effects of mixture-process-noise variables (MPNV) in product and process design. The traditional methodology does not necessarily ensure a minimum variance and a reasonable model adjustment. Contrary to process and noise variables, mixture factor levels are not independent, negative proportions are not allowed, and their summary must be equal to unity. A transformation function enables the incorporation of noise factors into the response surface model, thus estimating the mixture-process optimum variables that minimize the variance expression without a cross array. In addition, it allows the determination of a confidence region over a point to achieve another component combination with similar or higher quality products. The third-generation process capability index, Cpkm as an alternative signal to noise ratio (SNR) estimator, improve process capability and product quality for fixed factor problems for full factorial and fractional experiment designs with or without repeated measures. Keywords: Robust Parameter Design, Dual Response, Process Capability, Mixture Experiments, Signal-To-Noise Ratio, Response Surface Methodology.