The objective of this study was to evaluate the perfomance of the process of lamination of the endosperm of corn in an agricultural company by means of the capability indices of processes, (PCI), low two focuses: traditional and bayesian. Both PCI was evaluated with simulated data under different suppositions. To the real case the results obtained by the bayesian PCI were Ĉb (D) = 0,81, while the traditional was Ĉp = 0,46 and Ĉpk = 0,81. It is concluded that the process of lamination is not capable. With regard to the simulated data, the results correspond to 100 samples of size 50, under three conditions: 1) the process is capable, centered and normal distributed. 2) It not process capable, not centered and normal. 3) The process is lightly capable, not centered and normal. Being concluded that so much in the condition 1 as in the 2, it is obtained that the index bayesian is smaller ...

This paper evaluates the performance of process capability indices, PCI in non-normal situations percentiles using the methods of Clements (CCP CCpk) and Burr (BCP BCpk). Although the PCI is used in industry, there is insuffi cient literature to determine their accuracy by taking into account moderate and severe deviations of the normality. To study these deviations, it performs a comparison of both methods considering simulated data distributions with the Weibull, Lognormal, Beta and Generalized Pareto, GP. In calculating the PCI the CCpk generates smaller deviations with the Weibull distribution, whereas that the BCpk, deviations are higher. On the other side with each distribution the BCP generates lower average deviation from the CCP. Finally a real case considering the distribution GP.

The objective of this study was to evaluate the perfomance of the process of lamination of the endosperm of corn in an agricultural company by means of the capability indices of processes, (PCI), low two focuses: traditional and bayesian. Both PCI was evaluated with simulated data under different suppositions. To the real case the results obtained by the bayesian PCI were Ĉb (D) = 0,81, while the traditional was Ĉp = 0,46 and Ĉpk = 0,81. It is concluded that the process of lamination is not capable. With regard to the simulated data, the results correspond to 100 samples of size 50, under three conditions: 1) the process is capable, centered and normal distributed. 2) It not process capable, not centered and normal. 3) The process is lightly capable, not centered and normal. Being concluded that so much in the condition 1 as in the 2, it is obtained that the index bayesian is smaller ...

This paper evaluates the performance of process capability indices, PCI in non-normal situations percentiles using the methods of Clements (CCP CCpk) and Burr (BCP BCpk). Although the PCI is used in industry, there is insuffi cient literature to determine their accuracy by taking into account moderate and severe deviations of the normality. To study these deviations, it performs a comparison of both methods considering simulated data distributions with the Weibull, Lognormal, Beta and Generalized Pareto, GP. In calculating the PCI the CCpk generates smaller deviations with the Weibull distribution, whereas that the BCpk, deviations are higher. On the other side with each distribution the BCP generates lower average deviation from the CCP. Finally a real case considering the distribution GP.

In the multivariate analysis of capacity many aspects even not exist resolved around some indexes and their application. For example, when the variables of quality are correlated and when they are not correlated. In this work we seek to propose the construction of a multivariate index of capacity that works for both previous cases, and to present an example of application of this alternative on a real dataset. A wide acting of our index is obtained proposed in front of other similar indexes of capacity.

In the multivariate analysis of capacity there are many aspects, not even resolved yet, around some indexes such as the non-normality of the data and if the variables of quality are correlated or not. In this work we intend to propose the development of a multivariate capacity index (ICPM, for its acronym in Spanish) that works for both previous cases through simulated data examples. It is worth mentioning that we find a wide performance of the proposed index in comparison with other similar capacity indexes.