A MULTIVARIATE APPROACH FOR ASSESSMENT AND IMPROVEMENT OF MACHINERY AND EQUIPMENT MANUFACTURERS BASED ON MACHINE PERFORMANCE

摘要

This paper presents a framework for assessment and ranking of machinery and equipment manufacturers based on machinery productivity indicators. The integrated approach discussed in this paper is based on Principle Component Analysis (PCA). The validity of the model is verified and validated by Numerical Taxonomy (NT) approach. Furthermore, a non-parametric correlation method, namely, Spearman correlation experiment shows high level of correlation between the findings of PCA and NT. To achieve the objectives of this study, a comprehensive study was conducted to locate all economic and technical indicators (indexes) which influence machinery performance. These indicators are related to machine productivity, efficiency, effectiveness and profitability. 10 indicators were identified as major indexes impacting machinery conditions in manufacturing systems. 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 industrial sectors are selected according to the format of International Standard for Industrial Classification of all economic activities (ISIC). Furthermore, Iranian industries are classified as 4 digit ISIC codes. At each stage, a comparative study is conducted through PCA by considering the selected 10 indicators. PCA ranked the industrial sectors based on 10 indexes discussed in this paper. This in turn shows the weak and strong points of machinery and equipment manufacturers sector (ISIC 29) in regard to machinery and equipment. Furthermore, PCA identified which machinery indicators have the major impacts on the performance of industrial sectors. The modeling approach of this paper could be used for ranking and analysis of other sectors in particular or countries in general