PERFORMANCE ASSESSMENT OF IRANIAN INDUSTRIAL SECTORS BASED ON PRINCIPAL COMPONENT ANALYSIS
Subject Areas :Keywords: Performance Measurement Productivity Indicators Principal component Analysis Numerical Taxonomy.,
Abstract :
The purpose of this study is to present an integrated framework for assessment and ranking of 23 Iranian industrial sectors, based on several productivity indicators during the years 80 to 85. The integrated approach of this study is based on "principal component analysis" (PCA).The validity of the model is verified and validated by numerical "taxonomy". Furthermore, the non- parametric correlation methods, namely, Kendall-Tau correlation experiments shows high level of correlation between the findings of PCA and “taxonomy” and the approach of this study may be used to assess the importance of each of the selected indicators for industrial units of interest. It is observed that the sectors: "Coke, refined petroleum products" (code 23), " Chemicals and chemical products"(code 24), and " Basic metals" (code 27), have the three top grades in the ranking while the sectors:" Wearing apparel, fur" (code 18), "Textiles" (code 17) and "Furniture, manufacturing" (code 36) have the weakest grades in the ranking. Furthermore, the most important productivity indicators are: Value added to salaries and wages- value added per employee-value added to energy cost- production value per employee-production value to salaries and wages and value added to total input.