A manufacturing systems network model for the evaluation of complex manufacturing systems

Purpose - – The topology of manufacturing systems is specified during the design phase and can afterwards only be adjusted at high expense. The purpose of this paper is to exploit the availability of large-scale data sets in manufacturing by applying measures from complex network theory and from classical performance evaluation to investigate the relation between structure and performance. Design/methodology/approach - – The paper develops a manufacturing system network model that is composed of measures from complex network theory. The analysis is based on six company data sets containing up to half a million operation records. The paper uses the network model as a straightforward approach to assess the manufacturing systems and to evaluate the impact of topological measures on fundamental performance figures, e.g., work in process or lateness. Findings - – The paper able to show that the manufacturing systems network model is a low-effort approach to quickly assess a manufacturing system. Additionally, the paper demonstrates that manufacturing networks display distinct, non-random network characteristics on a network-wide scale and that the relations between topological and performance key figures are non-linear. Research limitations/implications - – The sample consists of six data sets from Germany-based manufacturing companies. As the model is universal, it can easily be applied to further data sets from any industry. Practical implications - – The model can be utilized to quickly analyze large data sets without employing classical methods (e.g. simulation studies) which require time-intensive modeling and execution. Originality/value - – This paper explores for the first time the application of network figures in manufacturing systems in relation to performance figures by using real data from manufacturing companies.

[1]  Dirk Helbing,et al.  Scaling laws in the spatial structure of urban road networks , 2006 .

[2]  M. Kleiber.  Body size and metabolic rate. , 1947, Physiological reviews.

[3]  Peter Butala,et al.  Anomaly detection in shop floor material flow: A network theory approach , 2013 .

[4]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[5]  Jianmin Gao,et al.  System failure analysis based on complex network theory , 2009, 2009 Annual Reliability and Maintainability Symposium.

[6]  D. Helbing,et al.  Growth, innovation, scaling, and the pace of life in cities , 2007, Proceedings of the National Academy of Sciences.

[7]  Sudarshan S. Chawathe,et al.  Managing RFID Data , 2004, VLDB.

[8]  Dirk Helbing,et al.  Flow control by periodic devices: a unifying language for the description of traffic, production, and metabolic systems , 2011 .

[9]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[10]  Dirk Helbing,et al.  Scaling laws in urban supply networks , 2006 .

[11]  Philip M. Dixon,et al.  Bootstrapping the gini coefficient of inequality , 1987 .

[12]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[13]  Yanjun Li,et al.  A Framework to Model the Topological Structure of Supply Networks , 2011, IEEE Transactions on Automation Science and Engineering.

[14]  Elwyn Graham John,et al.  A weighted similarity coefficient technique for manufacturing facility design , 2011 .

[15]  Geoffrey B. West,et al.  Scaling in Biology , 2000 .

[16]  K. Sneppen,et al.  Specificity and Stability in Topology of Protein Networks , 2002, Science.

[17]  Peter Butala,et al.  Discovering autonomous structures within complex networks of work systems , 2012 .

[18]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[19]  Nilay Shah,et al.  Logistical network design with robustness and complexity considerations , 2007 .

[20]  Dirk Helbing,et al.  Logistics Networks: Coping with Nonlinearity and Complexity , 2014 .

[21]  H. Pierreval *,et al.  A simulation-based optimization approach to size manufacturing systems , 2005 .

[22]  A. Barabasi,et al.  Network biology: understanding the cell's functional organization , 2004, Nature Reviews Genetics.

[23]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[24]  Xue Li,et al.  RFID Data Management: Challenges and Opportunities , 2007, 2007 IEEE International Conference on RFID.

[25]  Neo D. Martinez,et al.  Two degrees of separation in complex food webs , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[26]  V. Latora,et al.  Complex networks: Structure and dynamics , 2006 .

[27]  Elwyn Graham John,et al.  A flow direction weighting scheme for facility layout in cellular manufacturing , 2013 .

[28]  H. ElMaraghy,et al.  Product Variety Management in Design and Manufacturing: Challenges and Strategies , 2012 .

[29]  Aref Y. Maalej,et al.  A simulation‐expert‐system‐based approach for machine sizing of production systems , 2006 .