Organizational simulation in support of global manufacturing enterprises

In the past several decades, manufacturing has begun the process of transforming from a production firm centric enterprise into one in which a lead firm engages multiple partner and supplier firms in a networked enterprise to produce complex systems and products. Inherent in this transformation is the notion of global manufacturing as a socio-technical enterprise, co-emphasizing socio-interaction among firms with the more traditional technical focus on meeting production quotas, minimizing costs and maximizing profits. This chapter presents the methodology of organizational simulation as applied to the study and analysis of global manufacturing. Computer simulation has a rich history of application in analyzing the behavior and performance of manufacturing systems from a technical perspective, focusing on such concepts as forecasted product demand, production capacities and lead times, transportation capacities and lead times, inventory locations and levels, and production schedules, as well uncertainties associated with these factors. This perspective fails to consider socio-behaviors associated with firms and their interactions. Organizational simulation is a new paradigm whereby technical process modeling and social behavior modeling are combined to represent the behavior and predict the performance of the socio-technical enterprise. It has been applied in military acquisition, product design and health care. This chapter presents its application in global manufacturing enterprises, particularly to the issues of stakeholder alignment during change, alignment of stakeholder expectations and allocation of limited shared resources.

[1]  Randall P. Sadowski,et al.  Simulation with Arena , 1998 .

[2]  Sophie D'Amours,et al.  Study of the performance of multi-behaviour agents for supply chain planning , 2009, Comput. Ind..

[3]  Sophie D'Amours,et al.  Modeling agent-based simulations for supply chain planning: The FAMASS methodological framework , 2010, 2010 IEEE International Conference on Systems, Man and Cybernetics.

[4]  Mark E. Nissen,et al.  Computational experimentation on new organizational forms: Exploring behavior and performance of Edge organizations , 2007, Comput. Math. Organ. Theory.

[5]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994 .

[6]  Kenneth R. Boff,et al.  Organizational Simulation (Wiley Series in Systems Engineering and Management) , 2005 .

[7]  Douglas A. Bodner,et al.  Understanding R&D value creation with organizational simulation , 2007 .

[8]  William B. Rouse,et al.  Complex engineered, organizational and natural systems , 2007, Syst. Eng..

[9]  Soundar Kumara,et al.  Comparison of mitigation strategies for supplier risks: A multi agent-based simulation approach , 2009, 2009 IEEE/INFORMS International Conference on Service Operations, Logistics and Informatics.

[10]  J. Sterman.  Business Dynamics , 2000 .

[11]  Leon F. McGinnis,et al.  Models of Complex Enterprise Networks , 2011 .

[12]  Marvin D. Troutt,et al.  Agent based modeling for supply chain management: examining the impact of information sharing , 2008 .

[13]  John D. Sterman,et al.  System Dynamics: Systems Thinking and Modeling for a Complex World , 2002 .

[14]  Michael J. Prietula,et al.  Simulating organizations: computational models of institutions and groups , 1998 .

[15]  Soonhung Han,et al.  A multichannel visualization module for virtual manufacturing , 2006, Comput. Ind..

[16]  Robert M. Cranwell,et al.  SEM: Enterprise Modeling of JSF Global Sustainment , 2006, Proceedings of the 2006 Winter Simulation Conference.

[17]  Matthew W. Rohrer.  AutoMod tutorial , 1996, Winter Simulation Conference.

[18]  Lori Rosenkopf,et al.  Social Network Effects on the Extent of Innovation Diffusion: A Computer Simulation , 1997 .

[19]  J. Stender,et al.  Many Agent-Simulation and Artificial Life , 1994 .

[20]  Kathleen M. Carley,et al.  Modeling and Simulating Terrorist Networks in Social and Geospatial Dimensions , 2007, IEEE Intelligent Systems.

[21]  F. Bass.  A new product growth model for consumer durables , 1976 .

[22]  D. A. Bodner,et al.  The Effect of Processes and Incentives on Acquisition Cost Growth , 2011 .

[23]  Doug Bodner,et al.  A Framework and Tools for Organizational Simulation , 2009 .

[24]  Jeffrey S. Smith,et al.  Survey on the use of simulation for manufacturing system design and operation , 2003 .

[25]  Farhana Rahman,et al.  Addressing Cost Increases in Evolutionary Acquisition , 2010 .

[26]  Bernd Schmidt,et al.  Cooperative Agents: Applications in the Social Sciences , 2002 .

[27]  Ilaria Giannoccaro,et al.  Supply chain cooperation in industrial districts: A simulation analysis , 2007, Eur. J. Oper. Res..

[28]  Ivo Vondrák,et al.  Business Process Modeling , 2007, Encyclopedia of Database Systems.

[29]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[30]  William B. Rouse.  Complex engineered, organizational and natural systems: Issues Underlying the Complexity of Systems and Fundamental Research Needed To Address These Issues Contract: National Science Foundation; grant number 0538768 , 2007 .

[31]  John T. Dillard,et al.  Employing organizational modeling and simulation of the KC-135 depot's flight controls repair cell , 2008 .

[32]  Andrew Stern,et al.  A Behavior Language: Joint Action and Behavioral Idioms , 2004, Life-like characters.

[33]  Robert H. Waterman,et al.  Structure is not organization , 1980 .

[34]  Manuel Laguna,et al.  Business Process Modeling, Simulation and Design , 2004 .

[35]  A. Borshchev,et al.  From System Dynamics and Discrete Event to Practical Agent Based Modeling : Reasons , Techniques , Tools , 2004 .

[36]  Frank M. Bass,et al.  A New Product Growth for Model Consumer Durables , 2004, Manag. Sci..

[37]  J. Banks,et al.  Discrete-Event System Simulation , 1995 .