High variance observed in the measurement system can cause high process variation that can affect process capability badly. Therefore, measurement system analysis is closely related to process capability analysis. Generally, the evaluation for measurement system and process variance is performed separately in the industry. That is, the measurement system analysis is implemented before process monitoring, process capability and process performance analysis even though these analyses are closely related. This paper presents the effective concurrent evaluation procedure for measurement system analysis and process capability analysis using the table that contains Process Performance (Pp), Gage Repeatability & Reproducibility (%R&R) and Number of Distinct Categories (NDC). Furthermore, the long-term process capability index (Pp), which takes into account both gage variance and process variance, is used instead of the short-term process capability (Cp) considering only process variance. The long-term capability index can reflect well the relationship between the measurement system and process capability. The quality measurement and improvement guidelines by region scale are also described in detail. In conclusion, this research proposes the procedure that can execute the measurement system analysis and process capability analysis at the same time. The proposed procedure can contribute to reduction of the measurement staff’s effort and to improvement of accurate evaluation.

Recently, the manufacturing process system in the industrial field has become more and more complex and has been influenced by many and various factors. Moreover, these factors have the dependent correlation rather than independent of each other. Therefore, the statistical analysis has been extended from the univariate method to the multivariate method. The process capability indices have been widely used as statistical tools to assess the manufacturing process performance. Especially, the multivariate process indices need to be enhanced with more useful information and extensive application in the recent industrial fields. The various multivariate process capability indices have been studying by many researchers in recent years. Hence, the purpose of the study is to compare the useful and various multivariate process capability indices through the simulation. Among them, we compare the useful models of several multivariate process capability indices such as MCpm, MC+pm and MCpl. These multivariate process capability indices are incorporates both the process variation and the process deviation from target or consider the expected loss caused by the process deviation from target. Through the computational examples, we compare these process capability indices and discuss their usefulness and effectiveness.

Process capability indices (PCIs) have been widely used in manufacturing industries to provide a quantitative measure of process potential and performance to meet the specification limits on quality characteristics. The most of existing PCIs are concerned with a single variable. But, in many cases, people want to express a integrated PCI which includes a couple of sequential processes. In this paper, we analyzed the characteristics of system PCIs such as Cp(f), SCpk, SCpsk, Ctpsk(m) and SCpm(m).

An estimation of indices for potential process capability (Cpk) and/or overall process performance (Ppk) is considered in order to gain insight into the statistical process control. The similarity and the difference of the two indices are discussed in some detail to clarify the meaning and usage of the two indices. It is demonstrated that the short term variance can be estimated within the framework of analysis of variance (ANOVA). Theoretic background is examined and followed by a simple numerical example, with a view to the implementation of the concept in the industrial fields.

  Process Capability indices(PCIs) have been widely used in manufacturing industries to provide a quantitative measure of process performance. PCIs have been developed to represent process capability more exactly. The traditional process capability indice

In this paper, We consider some generalization of these five basic indices to cover non-normal distribution. The proposed generalizations are compared with the five basic indices. The results show that the proposed generalizations are more accurate than t