The Taguchi method is a useful technique to improve the performance of products or Processes at a lower cost and in lesser time. This procedure can be categorized as the static and dynamic quality characteristics. The optimization of multiple responses has received increasing attention over the last few years in many manufacturing organizations.Several approaches dealing with multiple static quality characteristic problems have been reported in the literature. However, little attention has been made on optimizing the multiple dynamic quality characteristics.In this paper, we investigate multivariate signal response systems (Dynamic Taguchi) and propose a method based on multivariate Process capability. Simulated data shows that the proposed method can increase robustness of dynamic Taguchi method. Furthermore, the proposed method is capable to find the optimal value of controllable factors in a continuous space.

Acceptance sampling models have been widely applied in companies for evaluation the raw material as well as the final products. Meanwhile, Process capability indices (PCIs) have been used in various industrial environments as capability measures that are obtained based on Process departure from a target, Process yield, Process consistency and Process loss. In this research, first a repetitive group sampling (RGS) plan based on Process capability index is developed for variables inspection. Then the optimal parameters of proposed RGS plan are determined and also a new multiple dependent state (MDS) sampling plan, a double sampling plan (DSP) and a sampling plan for resubmitted lots are developed and finally, a comparison study is carried out between the proposed sampling plans and the results are elaborated.

Statistical Process control is an excellent quality assurance tool to improve the quality of manufacture and ultimately scores on end-customer satisfaction. SPC uses Process monitoring charts to record the key quality characteristics (KQCs) of the component in manufacture. This paper elaborates on one such KQC of the manufacturing of a connecting rod of an internal combustion engine. Here the journey to attain the Process potential capability index (Cp) and the Process performance capability index (Cpk) values greater than 1.33 is elaborated by identifying the root cause through quality control tools like the cause-and-effect diagram and examining each cause one after another. In this paper, the define-measure-analyze-improve-control (DMAIC) approach is employed. The definition phase starts with Process mapping and identifying the KQC. The next phase is the measurement phase comprising the cause-and-effect diagram and data collection of KQC measurements. Then follows the analysis phase where the Process potential and performance capability indices are calculated, followed by the analysis of variance (ANOVA) of the mean values. Finally, the Process monitoring charts are used to control the Process and prevent any deviations. By using this DMAIC approach, standard deviation is reduced from 0.48 to 0.048, the Cp values from 0.12 to 1.72, and the Cpk values from 0.12 to 1.37, respectively.