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.

In every summer, cicadas emerge and become numerically and ecologically dominant in Korean penninsula. Especially, cicada emergence is affected by the environmental factors. In order to evaluate the effect of environmental factors in cicada species, we analyzed the temporal changes in cicada exuviae based on meteorological and non-meteorological factors such as artificial light intensity and habitat characters on urban park area surrounded by residential houses. Combined multivariate analyses with a cluster analysis and a principal component analysis (PCA) were conducted. Samples were classified into 3 different cluster based on differences of meteorological factors such as temperature and humidity. Moreover, Random Forest model (RF) showed a high predictability of daily mean temperature on species peak abundance. These results provide a strong evidence that meteorological factors have significant effects on cicada emergences. Regarding the non-meteorological factors, we found no relationship in cicada emergence.