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.

[1]  Tor Guimaraes,et al.  Empirically testing the impact of manufacturing system complexity on performance , 1999 .

[2]  Zongben Xu,et al.  Three improved neural network models for air quality forecasting , 2003 .

[3]  S. D. Jong,et al.  Three-way principal component analysis applied to food analysis: an example , 2002 .

[4]  Mikkel Thorup,et al.  On the approximability of numerical taxonomy (fitting distances by tree metrics) , 1996, SODA '96.

[5]  Vasanthakumar N. Bhat,et al.  A multivariate analysis of airline flight delays , 1995 .

[6]  Cem Canel,et al.  Underlying dimensions of business location decisions , 1998 .

[7]  Tat Y. Choi,et al.  Levels of satisfaction among Asian and Western travellers , 2000 .

[8]  J. E. Birnie,et al.  A matched plant comparison of productivity in East and West Germany: Transition to the market economy , 1996 .

[9]  Jaime Cohen,et al.  Numerical taxonomy on data: experimental results , 1997, SODA '97.

[10]  Thomas S. Abler,et al.  DENDROGRAM AND CELESTIAL TREE: NUMERICAL TAXONOMY AND VARIANTS OF THE IROQUOIAN CREATION MYTH , 1987 .

[11]  T.C.E. Cheng,et al.  Identifying potential barriers to total quality management using principal component analysis and correspondence analysis , 1997 .

[12]  Mile Terziovski,et al.  Achieving performance excellence through an integrated strategy of radical innovation and continuous improvement , 2002 .

[13]  Han Kook Hong,et al.  Evaluating the efficiency of system integration projects using data envelopment analysis (DEA) and machine learning , 1999 .

[14]  T. Du,et al.  Using principal component analysis in process performance for multivariate data , 2000 .

[15]  Qin He.  A Review of Clustering Algorithms as Applied in IR , 1999 .

[16]  Qin He,et al.  A Review of Clustering Algorithms as Applied in IR , 1999 .

[17]  Marie Chavent,et al.  A monothetic clustering method , 1998, Pattern Recognit. Lett..

[18]  Tage Koed Madsen,et al.  Successful Export Marketing Management: Some Empirical Evidence , 1989 .

[19]  Taylor H. Ricketts,et al.  Who's Where in North America? , 1999 .

[20]  Frank Rossi,et al.  Analysis of the beverage data using cluster analysis, rotated principal components analysis and LOESS curves , 2001 .

[21]  D. Selkoe.  International Standard Industrial Classification of All Economic Activities , 1987, Measuring Cuban Economic Performance.

[22]  Christian N. Madu.  Reliability and quality interface , 1999 .

[23]  C. T. Seppala,et al.  A review of performance monitoring and assessment techniques for univariate and multivariate control systems , 1999 .

[24]  Cláudia S. Sarrico,et al.  Data envelopment analysis and university selection , 1997 .

[25]  R. Belfield.  The process of innovation. , 1977, Science.

[26]  Peter Galbraith,et al.  Conceptual mis(understandings) of beginning undergraduates , 2000 .

[27]  Derek L. Waller,et al.  Operations Management: A Supply Chain Approach , 1999 .

[28]  M. Ali Azadeh,et al.  CREATING HIGHLY RELIABLE MANUFACTURING SYSTEMS: AN INTEGRATED APPROACH , 2000 .

[29]  R. Minhas,et al.  Benefit segmentation by factor analysis: an improved method of targeting customers for financial services , 1996 .

[30]  Ian P. McCarthy,et al.  Manufacturing classification: Lessons from organizational systematics and biological taxonomy , 1995 .

[31]  Kaoru Tone,et al.  Data Envelopment Analysis , 1996 .

[32]  Nigel Meade,et al.  Strategic Positioning in the UK Car Market , 1987 .

[33]  Andreas Buja,et al.  Interactive High-Dimensional Data Visualization , 1996 .

[34]  Joe Zhu,et al.  Data envelopment analysis vs. principal component analysis: An illustrative study of economic performance of Chinese cities , 1998, Eur. J. Oper. Res..

[35]  Kuldeep Kumar,et al.  Detection of financial distress via multivariate statistical analysis , 2001 .

[36]  Avraham Shtub,et al.  Production classification system: concepts, models and strategies , 1985 .

[37]  Tai-Yue Wang,et al.  Productivity diagnosis via fuzzy clustering and classification: An application to machinery industry , 1996 .

[38]  A. J. Morris,et al.  Manufacturing performance enhancement through multivariate statistical process control , 1999 .

[39]  K. D. Barber,et al.  The use of numerical analysis to classify companies according to production control complexity , 1986 .

[40]  John F. Proud,et al.  Master Scheduling: A Practical Guide to Competitive Manufacturing , 1994 .

[41]  P. Jonsson.  An empirical taxonomy of advanced manufacturing technology , 2000 .

[42]  Chris W. Clegg,et al.  A new taxonomy of modern manufacturing practices , 1997 .

[43]  Thomas H. Davenport,et al.  Process Innovation: Reengineering Work Through Information Technology , 1992 .

[44]  P. Poore,et al.  World Bank's world development report. , 1993, Lancet.

[45]  Azadeh,et al.  IDENTIFYING THE ECONOMIC IMPORTANCE OF INDUSTRIAL SECTORS BY MULTIVARIATE ANALYSIS. , 2001 .

[46]  Abraham Mehrez,et al.  Multivariate economic analysis of robot performance repeatability and accuracy , 1996 .

[47]  Tudor Rickards,et al.  Assessing and comparing the innovativeness and creative climate of firms , 1996 .

[48]  C. Hulten,et al.  New Developments in Productivity Analysis , 2001 .

[49]  Antonio Davila,et al.  Performance Measurement and Control Systems for Implementing Strategy: Text and Cases , 1999 .

[50]  Emel Turker,et al.  The socio-economic analysis of fuelwood consumption with the principal components analysis in Turkey. , 1997 .

[51]  Mitsuo Matsudaira,et al.  Features and Characteristic Values of Fabric Compressional Curves , 1994 .