Computing Multivariate Process Capability Indices With Microsoft Excel

In manufacturing industry there is growing interest in measures of process capability under multivariate setting. While there are many statistical packages to assess univariate capability, a current problem with the multivariate measures of capability is the shortage of user friendly software. In this paper a Visual Basic program has been developed to realize an Excel spreadsheet that may be used to compute two multivariate measures of capability. Our aim is to provide a useful tool for practitioners dealing with multivariate capability assessment problems. The features of the program include easy data entry and clear report formatMultivariate Process capability indices, statistical quality control, Visual Basic, Excel Indici di capacità multivariati, Controllo statistico della qualità

Control Charts and the Effect of the Two-Component Measurement Error Model

Monitoring algorithms, such as the Shewhart and Cusum control charts, are often used for monitoring purposes in the chemical industry or within an environmental context. The statistical properties of these algorithms are known to be highly responsive to measurement errors. Recent studies have underlined the important role played by the twocomponent measurement error model in chemical and environmental monitoring. In the present work, we study the effects of the twocomponent error model on the performance of the X and S Shewhart control charts. Results reveal that gauge imprecision may seriously alter the statistical properties of the control charts. We propose how to reduce the effects of measurement errors, and illustrate how to take errors into account in the design of monitoring algorithmsAverage run length, calibration curve, constant measurement error, Monte Carlo study, proportional measurement error, repeated measurements, Shewhart control charts

A sequential hypothesis testing procedure for the process capability index Cpk

In this study we propose a sequential procedure for hypothesis testing on the Cpk process capability index. We compare the properties of the sequential test with the performances of non-sequential tests by performing an extensive simulation study. The results indicate that the proposed sequential procedure makes it possible to save a large amount of sample size, which can be translated into reduced costs, time and resources

Control Charts and the Effect of the Two-Component Measurement Error Model

Monitoring algorithms, such as the Shewhart and Cusum control charts, are often used for monitoring purposes in the chemical industry or within an environmental context. The statistical properties of these algorithms are known to be highly responsive to measurement errors. Recent studies have underlined the important role played by the twocomponent measurement error model in chemical and environmental monitoring. In the present work, we study the effects of the twocomponent error model on the performance of the X and S Shewhart control charts. Results reveal that gauge imprecision may seriously alter the statistical properties of the control charts. We propose how to reduce the effects of measurement errors, and illustrate how to take errors into account in the design of monitoring algorithms

Computing Multivariate Process Capability Indices (Excel)

In manufacturing industry there is growing interest in measures of process capability under multivariate setting. Although there are many statistical packages to assess univariate capability, a current problem with the multivariate measures of capability is the shortage of user friendly software. In this article a Visual Basic program has been developed to realize an Excel spreadsheet that may be used to compute two multivariate measures of capability. The aim of this article is to provide a useful tool for practitioners dealing with multivariate capability assessment problems. The features of the program include easy data entry and clear report format

Computing Multivariate Process Capability Indices With Microsoft Excel

In manufacturing industry there is growing interest in measures of process capability under multivariate setting. While there are many statistical packages to assess univariate capability, a current problem with the multivariate measures of capability is the shortage of user friendly software. In this paper a Visual Basic program has been developed to realize an Excel spreadsheet that may be used to compute two multivariate measures of capability. Our aim is to provide a useful tool for practitioners dealing with multivariate capability assessment problems. The features of the program include easy data entry and clear report format

MPCI : An R Package for Computing Multivariate Process Capability Indices

Manufacturing processes are often based on more than one quality characteristic. When these variables are correlated the process capability analysis should be performed using multivariate statistical methodologies. Although there is a growing interest in methods for evaluating the capability of multivariate processes, little attention has been given to developing user friendly software for supporting multivariate capability analysis. In this work we introduce the package MPCI for R, which allows to compute multivariateprocess capability indices. MPCI aims to provide a useful tool for dealing with multivariate capability assessment problems. We illustrate the use of MPCI package through both simulated and real examples

Statistical process control for improving healthcare processes. A case study in an Italian teaching hospital

This study aims to investigate the utility and potentialities of statistical process control for monitoring performances of healthcare organizations. We retrospectively applied the statistical process control for monitoring perioperative system performance, represented in this study by the operating room turnaround time. The results showed that the control charts are able to identify the steady-state behavior of the process and to detect improvements or deteriorations in process performance over tim