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Article
Publication date: 1 April 2001

S. Gamesalingam and Kuldeep Kumar

Describes the ability of modern computer‐driven multivariate statistical analysis to deal with complex data and the development of statistical models for predicting financial…

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Abstract

Describes the ability of modern computer‐driven multivariate statistical analysis to deal with complex data and the development of statistical models for predicting financial distress. Applies multivariate techniques to 1986‐1991 financial ratio data for Australian failed (29) and nonfailed (42) companies; and explains the techniques used (principal components analysis, factor analysis, discriminant analysis and cluster analysis) and the different types of information they can provide to help identify the distress levels of companies. Predicts that multivariate methods will change the way researchers think about problems and design their research. An unusually clear exposition of the application of multivariate methods.

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Managerial Finance, vol. 27 no. 4
Type: Research Article
ISSN: 0307-4358

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Article
Publication date: 5 June 2007

A. Azadeh, S.F. Ghaderi and V. Ebrahimipour

This paper seeks to present an integrated principal component analysis (PCA) data envelopment analysis (DEA) framework for assessment and ranking of manufacturing systems based on…

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Abstract

Purpose

This paper seeks to present an integrated principal component analysis (PCA) data envelopment analysis (DEA) framework for assessment and ranking of manufacturing systems based on equipment performance indicators.

Design/methodology/approach

The integrated framework discussed in this paper is based on PCA and DEA. The validity of the integrated model is further verified and validated by numerical taxonomy (NT) methods.

Findings

The results of the integrated PCA DEA framework show the ranking of sectors and weak and strong points of each sector with regard to equipment and machinery. Moreover, a non‐parametric correlation method, namely, Spearman correlation experiment shows high level of correlation among the findings of PCA, DEA and NT. Furthermore, it identifies which indicators have major impacts on the performance of manufacturing sectors.

Practical implications

To achieve the objectives of this study, a comprehensive study was conducted to locate all economic and technical indicators which influence equipment performance. These indicators are related to equipment productivity, efficiency, effectiveness and profitability. Standard factors such as down time, time to repair, mean time between failure, operating time, value added and production value were considered as shaping factors. The manufacturing sectors are selected according to the format of International Standard for Industrial Classification.

Originality/value

The modeling approach of this paper could be used for ranking and analysis of other sectors in particular or countries in general.

Details

Engineering Computations, vol. 24 no. 4
Type: Research Article
ISSN: 0264-4401

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