This research discusses the characteristics and the implementation strategies for two types of quality metricsto analyze innovation effects in six sigma projects: fixed specification type and moving specification type. Z_st,P_pk are quality metrics of fixed specification type that are influenced by predetermined specification. Incontrast, the quality metrics of moving specification type such as Strictly Standardized Mean Difference(SSMD),Z-Score, F-Statistic and t-Statistic are independent from predetermined specification.Z_stsigma level obtains defective rates of Parts Per Million(PPM) and Defects Per MillionOpportunities(DPMO). However, the defective rates between different industrial sectors are incomparable due totheir own technological inherence. In order to explore relative method to compare defective rates betweendifferent industrial sectors, the ratio of specification and natural tolerance called, P_pk , is used. The drawback ofthis P_pk metric is that it is highly dependent on the specification.The metrics of F-Statistic and t-Statistic identify innovation effect by comparing before-and-after ofaccuracy and precision. These statistics are not affected by specification, but affected by type of statisticaldistribution models and sample size. Hence, statistical significance determined by above two statistics cannotgive a same conclusion as practical significance.In conclusion, SSMD and Z-Score are the best quality metrics that are uninfluenced by fixed specification,theoretical distribution model and arbitrary sample size. Those metrics also identify the innovation effects forbefore-and-after of accuracy and precision. It is beneficial to use SSMD and Z-Score methods along withpopular methods of Z_st sigma level and P_pk that are commonly employed in six sigma projects.The case studies from national six sigma contest from 2011 to 2012 are proposed and analyzed to provide theguidelines for the usage of quality metrics for quality practitioners.