Editing Design for Six Sigma (section)

==DFSS as an approach to design==
DFSS seeks to avoid manufacturing/service process problems by using advanced techniques to avoid process problems at the outset (e.g., fire prevention).  When combined, these methods obtain the proper needs of the customer, and derive engineering system parameter requirements that increase product and service effectiveness in the eyes of the customer and all other people.  This yields products and services that provide great customer satisfaction and increased market share. These techniques also include tools and processes to predict, model and simulate the product delivery system (the processes/tools, personnel and organization, training, facilities, and logistics to produce the product/service).  In this way, DFSS is closely related to [[operations research]] (solving the [[knapsack problem]]), workflow balancing. DFSS is largely a design activity requiring tools including: [[quality function deployment]] (QFD), [[axiomatic design]], [[TRIZ]], [[Design for X]], [[design of experiments]] (DOE), [[Taguchi methods]], tolerance design, [[robustification]] and [[Response Surface Methodology]] for a single or multiple response optimization.  While these tools are sometimes used in the classic DMAIC Six Sigma process, they are uniquely used by DFSS to analyze new and unprecedented products and processes. It is a concurrent analyzes directed to manufacturing optimization related to the design.

===Critics===
Response surface methodology and other DFSS tools uses statistical (often empirical) models, and therefore practitioners need to be aware that even the best statistical model is an approximation to reality. In practice, both the models and the parameter values are unknown, and subject to uncertainty on top of ignorance. Of course, an estimated optimum point need not be optimum in reality, because of the errors of the estimates and of the inadequacies of the model. The uncertainties can be handled via a Bayesian predictive approach, which considers the uncertainties in the model parameters as part of the optimization. The optimization is not based on a fitted model for the mean response, E[Y], but rather, the posterior probability that the responses satisfies given specifications is maximized according to the available experimental data.<ref>{{Cite journal |last=Peterson |first=John J. |date=2004-04-01 |title=A Posterior Predictive Approach to Multiple Response Surface Optimization |url=https://doi.org/10.1080/00224065.2004.11980261 |journal=Journal of Quality Technology |volume=36 |issue=2 |pages=139–153 |doi=10.1080/00224065.2004.11980261 |s2cid=116581405 |issn=0022-4065|url-access=subscription }}</ref>
 
Nonetheless, response surface methodology has an effective track-record of helping researchers improve products and services: For example, [[George Box]]'s original response-surface modeling enabled chemical engineers to improve a process that had been stuck at a saddle-point for years.<ref>{{Cite web |title=Response Surfaces, Mixtures, and Ridge Analyses, 2nd Edition {{!}} Wiley |url=https://www.wiley.com/en-us/Response+Surfaces%2C+Mixtures%2C+and+Ridge+Analyses%2C+2nd+Edition-p-9780470053577 |access-date=2022-04-09 |website=Wiley.com |language=en-us}}</ref>