Reduction in the balanced scorecard performance measurement systems in manufacturing organizations by PCA

Abstract

In this paper, we compare PCA and ordinal logistic regression in ranking the manufacturing systems. In this regard we present an integrated framework for assessment and ranking of manufacturing systems based on management and organizational performance indicators. To achieve the objectives of this study, a comprehensive study was conducted to locate all economic and technical indicators which influence organizational performance. Sixty one indicators were identified and classified in .five categories, namely, (1) financial, (2) customer satisfaction, (3) process innovation, (4) production process and (5) organizational learning and growth. These indicators are related to organizational and managerial productivity and efficiency. One actual test problem and a random sample of 12 indicators were selected to show the applicability of the integrated approach. The results of PCA and OLR showed the weak and strong points of each sector in regard to the selected indicators. Furthermore, it identifies which indicators have the major impacts on the overall performance of industrial sectors. The modeling approach of this paper could be easily utilized for managerial and organizational ranking and analysis of other sectors. The results of such studies would help top managers to have better understanding and improve existing systems with respect to managerial and organizational performance.

Keywords: Productivity and competitiveness; Multivariate statistics integrated assessment, BCA