non linear modeling, axiomatic design, simulation have their origin in inferential statistics, statistical modeling may overlap with data analytics and mining, However, despite that DFSS as a methodology has been successfully used as an end-to-end [technical project frameworks ] for analytic and mining projects, this has been observed by domain experts to be somewhat similar to the lines of CRISP-DM DFSS is claimed to be better suited for encapsulating and effectively handling higher number of uncertainties including missing and uncertain data, both in terms of acuteness of definition and their absolute total numbers with respect to analytic s and data-mining tasks, six sigma approaches to data-mining are popularly known as DFSS over CRISP [ CRISP- DM referring to data-mining application framework methodology of SPSS ] With DFSS data mining projects have been observed to have considerably shortened development life cycle . This is typically achieved by conducting data analysis to pre-designed template match tests via a techno-functional approach using multilevel quality function deployment on the data-set. Practitioners claim that progressively complex KDD templates are created by multiple DOE runs on simulated complex multivariate data, then the templates along with logs are extensively documented via a decision tree based algorithm DFSS uses Quality Function Deployment and SIPOC for feature engineering of known independent variables, thereby aiding in techno-functional computation of derived attributes Once the predictive model has been computed, DFSS studies can also be used to provide stronger probabilistic estimations of predictive model rank in a real world scenario DFSS framework has been successfully applied for predictive analytics pertaining to the HR analytics field, This application field has been considered to be traditionally very challenging due to the peculiar complexities of predicting human behavior. References Further reading Del Castillo, E. (2007). Process Optimization, a Statistical Approach. New York: Springer. https://link.springer.com/book/10.1007/978-0-387-71435-6 Product design Six Sigma Product development