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 for the Lognormal Distribution

Control Charts are the main tools of Statistical Process Control. They are used for deciding whether a process is statistically stable or not. Much theory and many applications have been developed for the Gaussian (Normal) distribution in this area. However, in real data sets we usually face up nonnormal processes. Consequently, this theory does not apply. In the present paper, we focus attention on the lognormal distribution that can be considered as a special nonnormal case. In particular, we present the Shewhart Control Charts developed up to now, under such distributional assumptions and a new Control Chart based on the CUSUM theory.Control chart, Nonnormality, Shewhart, CUSUM, Average run length, Lognormal

A systematic way of achieving Total Continuous Process Improvement (TCPI) in an industry

This paper highlights the steps that are required to achieve total continuous process improvement in the production of products in a manufacturing industry. The importance of each step will be discussed and the strategies that need to be taken to achieve the objectives of each of the steps will be mentioned. The aim of this paper is to provide essential guidelines for operators, supervisors, engineers and managers so that they are aware of the various steps that need to be taken in achieving TCPI

On Certain Indices for Ordinal Data with Unequally Weighted Classes

In this paper, some new indices for ordinal data are introduced. These indices have been developed so as to measure the degree of concentration on the “small” or the “large” values of a variable whose level of measurement is ordinal. Their advantage in relation to other approaches is that they ascribe unequal weights to each class of values. Although, they constitute a useful tool in various fields of applications, the focus here is on their use in sample surveys and specifically in situations where one is interested in taking into account the “distance” of the responses from the “neutral” category in a given question. The properties of these indices are examined and methods for constructing confidence intervals for their actual values are discussed. The performance of these methods is evaluated through an extensive simulation study.

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

Effective Use of Process Capability Indices for Supplier Management

Process capability indices were originally invented to enable an organization to make economically sound decisions for process management. Process capability is a comparison of the voice of the process with the voice of the customer. Current practice is to use Cp and Cpk regardless of the validity of the underlying assumptions necessary for their use. Even if all necessary assumptions are satisfied, important problems can be missed if these indices are the sole process evaluation examined. Customer-supplier axioms are introduced to motivate more useful process evaluations and foster long-term harmonious relationships. This paper explores the alternative capability indices Cpm, Cpmk, Cjkp, Cθ, and Cs and loss function approaches including Taguchi\u27s unbounded quadratic loss function and the multivariate upside-down normal loss function. Illustrative case studies are presented

A Methodology to Improve PCI Use in Industry

This article presents the development of a methodology using decision trees to resolve issues in industry with using process capability indices (PCIs). The methodology forms the structure of a prototype decision support system (PDSS) for PCI selection, calculation, and interpretation. Download instructions for the PDSS are available at http://program.20m.com

The Use of Indices in Surveys

The paper deals with some new indices for ordinal data that arise from sample surveys. Their aim is to measure the degree of concentration to the “positive” or “negative” answers in a given question. The properties of these indices are examined. Moreover, methods for constructing confidence limits for the indices are discussed and their performance is evaluated through an extensive simulation study. Finally, the values of the indices defined and their confidence intervals are calculated for an example with real dataMultinomial proportions, Ordinal data, Indices, Confidence intervals, Sample surveys