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.

The process control methods based on the statistical analysis apply the analysis method or mathematical model under the assumption that the process characteristic is normally distributed. However, the distribution of data collected by the automatic measurement system in real time is often not followed by normal distribution. As the statistical analysis tools, the process capability index (PCI) has been used a lot as a measure of process capability analysis in the production site. However, PCI has been usually used without checking the normality test for the process data. Even though the normality assumption is violated, if the analysis method under the assumption of the normal distribution is performed, this will be an incorrect result and take a wrong action. When the normality assumption is violated, we can transform the non-normal data into the normal data by using an appropriate normal transformation method. There are various methods of the normal transformation. In this paper, we consider the Box-Cox transformation among them. Hence, the purpose of the study is to expand the analysis method for the multivariate process capability index using Box-Cox transformation. This study proposes the multivariate process capability index to be able to use according to both methodologies whether data is normally distributed or not. Through the computational examples, we compare and discuss the multivariate process capability index between before and after Box-Cox transformation when the process data is not normally distributed.

This study is about the process capability index (PCI). In this study, we introduce several indices including the index CPR and present the characteristics of the CPR as well as its validity. The difference between the other indices and the CPR is the way we use to estimate the standard deviation. Calculating the index, most indices use sample standard deviation while the index CPR uses range R. The sample standard deviation is generally a better estimator than the range R . But in the case of the panel process, the CPR has more consistency than the other indices at the point of non-conforming ratio which is an important term in quality control. The reason why the CPR using the range has better consistency is explained by introducing the concept of ‘flatness ratio’. At least one million cells are present in one panel, so we can’t inspect all of them. In estimating the PCI, it is necessary to consider the inspection cost together with the consistency. Even though we want smaller sample size at the point of inspection cost, the small sample size makes the PCI unreliable. There is ‘trade off’ between the inspection cost and the accuracy of the PCI. Therefore, we should obtain as large a sample size as possible under the allowed inspection cost. In order for CPR to be used throughout the industry, it is necessary to analyze the characteristics of the CPR . Because the CPR is a kind of index including subgroup concept, the analysis should be done at the point of sample size of the subgroup. We present numerical analysis results of CPR by the data from the random number generating method. In this study, we also show the difference between the CPR using the range and the CP which is a representative index using the sample standard deviation. Regression analysis was used for the numerical analysis of the sample data. In addition, residual analysis and equal variance analysis was also conducted.

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.

In the industrial fields, the process capability index has been using to evaluate the variation of quality in the process. The traditional process capability indices such as Cp, Cpk, Cpm, and C┼pm have been applied in the industrial fields. These traditional process capability indices are mainly applied in the univariate analysis. However, the main streams in the recent industry are the multivariate manufacturing process and the multiple quality characteristics are corrected each other. Therefore, the multivariate statistical method should be used in the process capability analysis. The multivariate process indices need to be enhanced with more useful information and extensive application in the recent industrial fields. Hence, the purpose of the study is to develop a more effective multivariate process index (MCpI ) using the multivariate inverted normal loss function. The multivariate inverted normal loss function has the flexibility for the any type of the symmetrical and asymmetrical loss functions as well as the economic information. Especially, the proposed modeling method for the multivariate inverted normal loss function (MINLF) and the expected loss from MINLF in this paper can be applied to the any type of the symmetrical and asymmetrical loss functions. And this modeling method can be easily expanded from a bivariate case to a multivariate case.

Process capability is well known in quality control literatures. Process capability refers to the uniformity of the process. Obviously, the variability in the process is a measure of the uniformity of output. It is customary to take the 6-sigma spread in the distribution of the product quality characteristic as a measure of process capability. However there is no reference of process capability when maximum material condition is applied to datum and position tolerance in GD&T (Geometric Dimensioning and Tolerancing). If there is no material condition in datum and position tolerance, process capability can be calculated as usual. If there is a material condition in a feature control frame, bonus tolerance is permissible. Bonus tolerance is an additional tolerance for a geometric control. Whenever a geometric tolerance is applied to a feature of size, and it contains an maximum material condition (or least material condition) modifier in the tolerance portion of the feature control frame, a bonus tolerance is permissible. When the maximum material condition modifier is used in the tolerance portion of the feature control frame, it means that the stated tolerance applies when the feature of size is at its maximum material condition. When actual mating size of the feature of size departs from maximum material condition (towards least material condition), an increase in the stated tolerance-equal to the amount of the departure-is permitted. This increase, or extra tolerance, is called the bonus tolerance. Another type of bonus tolerance is datum shift. Datum shift is similar to bonus tolerance. Like bonus tolerance, datum shift is an additional tolerance that is available under certain conditions. Therefore we try to propose how to calculate process capability index of position tolerance when maximum material condition is applied to datum and position tolerance.

This study is concerned about the process capability index in single process. Previous process capability indices have been developed for the consistency with the nonconforming rate due to the process target value and skewness. These indices calculate the process capability by measuring one spot in an item. But the only one datum in an item reduces the representativeness of the item. In addition to the lack of representativeness, there are many cases that the uniformity of the item such as flatness of panel is absolutely important. In these cases, we have to measure several spots in an item. Also the nonconforming judgment to an item is mainly due to the range not due to the standard variation or the shift from the specifications. To imply the uniformity concept to the process capability index, we should consider only the variation in an item. It is the within subgroup variation. When the universe is composed of several subgroups, the sample standard deviation is the sum of the within subgroup variation and the between subgroup variation. So the range R which represents only the within subgroup variation is the much better measure than that of the sample standard deviation. In general, a subgroup contains a couple of individual items. But in our cases, a subgroup is an item and R is the difference between the maximum and the minimum among the measured data in an item. Even though our object is a single process index, causing by the subgroups, its analytic structure looks like a system process capability index. In this paper we propose a new process capability index considering the representativeness and uniformity.

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).

신제품 또는 신기술의 개발은 기업의 생존과 직결한 문제로써 급변하는 경영환 경과 심화하는 경쟁 속에서 성과 창출과 향상에 대한 관심이 증대하고 있다. 그러나 중소기 업은 국가 경제에서 차지하는 위상에 비해 규모와 보유자원 등의 한계를 가지므로 국가에서 는 다양한 정책적 지원을 제공한다. 조직수명주기에 관한 연구에 따르면 성장단계별로 기업 의 한계와 애로사항이 다르므로 차별적 지원이 이루어져야 할 필요가 있음을 알 수 있다. ‘2011년 중소기업기술통계’에 응답한 2,575개 기업 자료를 대상으로 기술개발공정의 기술 능력 수준과 편차가 R&D 매출성과에 어떤 영향을 끼치는지 검증하였다. 분석 결과, 기술능 력 수준은 R&D 매출성과에 정(+)의 영향을 끼쳤다. 또한, 기술능력의 편차와 R&D 매출성과 간에 관계를 정부지원이 조절하는 것을 알 수 있었다. 하지만, 정부지원이 기술수준이 R&D 매출성과에 미치는 영향을 성장단계별로 다르게 조절할 것이라는 가설에 대한 실증 분석 결 과 성장단계별로 조절효과가 유의적으로 각각 다르게 나타났다. 연구의 이론적인 시사점은 성장단계를 고려하지 않은 단순한 역학관계를 떠나 좀 더 실제 로 복잡한 중소기업 환경을 직시할 수 있는 현실적인 인과 모형을 제시하였고, 따라서 다양 성을 다각적인 방법으로 측정하여 연구변수로 활용해야하는 당위성을 마련하였다. 또한 실 무적 시사점은 성장단계별 정부지원에 대한 정책을 차별적으로 수립할 수 있고, 각자 다른 성장단계에 있는 중소기업으로 하여금 맞춤식의 정책을 순응하도록 하여 정부지원금의 효과 적으로 사용할 수 있는 가이드라인을 제시하였다는 것이다.

This study is concerned with process capability index in single process. We enumerated issues on the calculation of process capability index and described the effects of these issues. We explained the development process and the reason of the representative existing process capability indices. We investigated whether the indices agree with the concept of process capability and drew the problems from those results. In addition, we proposed alternative and direction to seize the process capability necessary to the field.

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.

Recently, as organizational systems have become larger and more complicated, the evaluation for their efficiency and effectiveness has become more difficult but important. It is essential to understand the current strength and weakness of the organization

Process capability indices (PCIs) have been widely used in manufacturing industries to provide a quantitative measure of process potential and performance. The previous studies have measured only one location on each part in the case of single variate. To

This research presents an implementation strategy of Process Capability Index ( PCI) according to the types of process characteristics. The types of process feature are classified as four perspectives of variation range, time period, error position, and process stage. The paper examines short-term or long-term PCI, within or between variation, position of precision or accuracy, and inclusion of measurement or calibration stage. Moreover, the study proposes normality test of unilateral PCI.

We develop methods for propagating and analyzing EPCI(Extended Process Capability Index) by using the error type that classifies into accuracy and precision. EPCI developed in this study can be applied to the three combined processes that consist of production, measurement and calibration. Little calibration work discusses while a great deal has been studied about SPC(Statistical Process Contol) and MSA(Measurement System Analysis). EPCI can be decomposed into three indexes such as PPCI(Production Process Capability Index), PPPI(Production Process Performance Index), MPCI(Measurement PCD, and CPCI(Calibration PCI). These indexs based on the type of error classification can be used with various statistical techniques and principles such as SPC control charts, ANOVA(Analysis of Variance), MSA Gage R&R, Additivity-of-Variance, and RSSM(Root Sum of Square Method). As the method proposed is simple, any engineer in charge of SPC. MSA and calibration can use efficientily in industries. Numerical examples are presentsed. We recommed that the indexes can be used in conjunction with evaluation criteria.

Designers have to consider voice of customer, process capability, manufacturing standards & condition, manufacturing method and characteristics of products to decide tolerances. Especially, in case of position of hole and pin, designers have to consider process capability to decide tolerances. The traditional position tolerances used in a drawing are theoretical values which are allocated to position under the worst case assembling condition that both hole and pin are the maximum material condition(MMC). However, when the process capability is high, more exact product size can be produced under stable manufacturing condition. Larger clearance of hole and pin can be allocated. In this point of view, manufacturer could increase the yield by allocating larger position tolerance than theoretical position tolerance of hole and pin considering process capability.

Process capability indices 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. In the previous studies, only one designated loca

  Because of different hardware specifications, there are no unified protocol commands to use with various kinds of RFID readers. The current commercial RFID middlewares do not satisfy the various requirements from users to support business process logic

The purpose of this study is to develop a guideline of process capability evaluation and to apply this guideline improving the quality of products, especially in the small and medium enterprises. In this study we deal in the concept of process capability evaluation, the calculation of process capability index, and the application of a case study. Man must compare the state of process with the standards in evaluating of the process capability. Control chart can be used as a yardstick for judgement for the long term period and the distribution shape of histogram for the short term period. Man should regard to the significant figure by the calculation of process capability index.