An integrated framework for continuous assessment and improvement of manufacturing systems

This paper presents an integrated framework for assessment and ranking of manufacturing systems based on management and organizational performance indicators. The integrated approach of this paper is based on principal component analysis. The validity of the model is verified and validated by numerical taxonomy and clustering analysis approach. Furthermore, the non-parametric correlation methods, namely, Spearman and Kendall-Tau correlation experiments should show high level of correlation between the findings of PCA and taxonomy. 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. Two actual test problems and a random sample of 12 indicators were selected to show the applicability of the integrated approach. The results of PCA 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.