Data Mining

COMPLEXITY MEASURES IN MANUFACTURING SYSTEMS

Alberto F. De Toni, Alessio Nardini, Fabio Nonino, Gianluca Zanutto
Laboratory of Management Engineering, Department of Electrical, Management and Mechanical Engineering, University of Udine, Via delle Scienze 208 33100 Udine (UD), Italy

Corresponding Author: Gianluca Zanutto Office: ([phone] Fax: [phone] e-mail: [e-mail]

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Abstract This study analyzes the most widewpread methodologies available in literature used to measure complexity. The research moves from a theoretical physic perspective, through the Complexity Theory, to a manufacturing system. On these subjects, two classification frameworks are proposed in order to categorize the most widespread measures. In particular, the second classification framework regards entropic measures widely used to measure complexity in manufacturing systems. With reference to this second framework, two indexes were selected (static and dynamic complexity index) and a Business Dynamic model was developed. This model was used with empirical data collected in a job shop manufacturing system in order to test the usefulness and validity of the dynamic complex index. The Business Dynamic model analyzed the trend of the index in function of different inputs in a selected work center. The results showed that the maximum value of the dynamic complexity index represents the so called “edge of chaos”, where the amount of information needed to manage the system is maximum and where there is the trade off between flexibility and efficiency of the production system. In conclusion, the main result reached in this study regards the “edge of chaos” that is the target configuration for a company, in a particular system and under the same external conditions. Key Words Complexity Measures, Entropic Measures, Manufacturing Systems, Job-shop, Business Dynamics

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1. Introduction The origins of the studies of the Complexity Theory come from the

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