Design for six sigma (DFSS) is a powerful approach of designing products,processes,and services with the objective of meeting the needs of customers in a cost-effective manner.DFSS activities are classified into four major phases viz.identify,design,optimize,and validate (IDOV).And an adaptive design for six sigma (ADFSS) incorporating the traits of artificial intelligence and statistical techniques is presented. In the identify phase of the ADFSS,fuzzy relation measures between customer attributes (CAs)and engineering characteristics (ECs) as well as fuzzy correlation measures among ECs are determined with the aid of two fuzzy logic controllers (FLCs).These two measures are then used to establish the cumulative impact factor for ECs.In the next phase ( i.e.design phase),a transfer function is developed with the aid of robust multiple nonlinear regression analysis.Furthermore,this transfer function is optimized with the simulated annealing (SA) algorithm in the optimize phase.In the validate phase,t-test is conducted for the validation of the design resulted in earlier phase.Finally,a case study of a hypothetical writing instrument is simulated to test the efficacy of the proposed ADFSS.