Agent Based Modeling and Simulation of Structural Hole Based Order Allocation Strategy

Order allocation is one of the most important decision-making problems of firms having significant influences on performances of themselves and the whole supply chain. Existing researches about order allocation have mainly focused on evaluating capabilities of directly connected suppliers so that it is hard to consider effects and interactions from undirected connections over multiple lower-layers. To alleviate the limitation, this paper proposed a novel approach to order allocation using structural hole. By applying the concept of structural hole to the supply network, we could evaluate the structural supplying powers of firms with respect to both of direct and indirect connections. In the proposed approach, we derived a methodology to measure the potential supplying power of each firm by modifying the effective size as one of the measurements of structural hole and then, proposed its application, the structural hole based order allocation strategy. Furthermore, we conducted the agent based modeling of supply chain to perform the decision-making process of order allocation and simulated the proposed strategy. As a results, by coping with the variance of demand more stably, it could improve the performance of supply chain from the aspects of fill rate, inventory level and demand-supply balance.

[1]  Bin Hu,et al.  Agent-based simulation of competitive and collaborative mechanisms for mobile service chains , 2010, Inf. Sci..

[2]  Simon Rodan,et al.  Structural holes and managerial performance: Identifying the underlying mechanisms , 2010, Soc. Networks.

[3]  Liu Wei-hua,et al.  An emergency order allocation model based on multi‐provider in two‐echelon logistics service supply chain , 2011 .

[4]  C. Rutherford,et al.  Disruptions and supply networks: a multi‐level, multi‐theoretical relational perspective , 2011 .

[5]  Atakan Yücel,et al.  A weighted additive fuzzy programming approach for multi-criteria supplier selection , 2011, Expert Syst. Appl..

[6]  Arun Kumar,et al.  An agent-based framework for collaborative negotiation in the global manufacturing supply chain network , 2006 .

[7]  Zugang Liu,et al.  Modeling and analysis of the multiperiod effects of social relationship on supply chain networks , 2011, Eur. J. Oper. Res..

[8]  Sai Ho Chung,et al.  A heuristic methodology for order distribution in a demand driven collaborative supply chain , 2004 .

[9]  Ali Azadeh,et al.  An integrated framework for supplier evaluation and order allocation in a non-crisp environment , 2010 .

[10]  Whan-Seon Kim,et al.  Effects of a Trust Mechanism on Complex Adaptive Supply Networks: An Agent-Based Social Simulation Study , 2009, J. Artif. Soc. Soc. Simul..

[11]  Simon Rodan,et al.  More than Network Structure: How Knowledge Heterogeneity Influences Managerial Performance and Innovativeness , 2004 .

[12]  P. Wangphanich,et al.  Analysis of the bullwhip effect in multi-product, multi-stage supply chain systems–a simulation approach , 2010 .

[13]  Steven B. Andrews,et al.  Structural Holes: The Social Structure of Competition , 1995, The SAGE Encyclopedia of Research Design.

[14]  S. Borgatti,et al.  On Social Network Analysis in a Supply Chain Context , 2009 .

[15]  Yuh-Jen Chen,et al.  Structured methodology for supplier selection and evaluation in a supply chain , 2011, Inf. Sci..

[16]  Jafar Razmi,et al.  Supplier selection and order allocation based on fuzzy SWOT analysis and fuzzy linear programming , 2011, Expert Syst. Appl..

[17]  Seyed Hassan Ghodsypour,et al.  Vendor selection and order allocation using an integrated fuzzy case-based reasoning and mathematical programming model , 2009 .

[18]  K. Tan A framework of supply chain management literature , 2001 .

[19]  Jinsheng Roan,et al.  The Application of Structural Holes Theory to Supply Chain Network Information Flow Analysis , 2011 .

[20]  Weijun Xia,et al.  Supplier selection with multiple criteria in volume discount environments , 2007 .

[21]  M. Breton,et al.  Supplier selection-order allocation: A two stage multiple criteria dynamic programming approach , 2011 .

[22]  Chang Ouk Kim,et al.  Multi-agent systems applications in manufacturing systems and supply chain management: a review paper , 2008 .

[23]  Boris V. Sokolov,et al.  A multi-structural framework for adaptive supply chain planning and operations control with structure dynamics considerations , 2010, Eur. J. Oper. Res..

[24]  Sai Ho Chung,et al.  Optimization of Order Fulfillment in Distribution Network Problems , 2006, J. Intell. Manuf..

[25]  Ezgi Aktar Demirtaş,et al.  An integrated multiobjective decision making process for supplier selection and order allocation , 2008 .

[26]  David Z. Zhang,et al.  Agent-based model for optimising supply-chain configurations , 2008 .

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

[28]  Abbas Rafii Study of the performance of RPS , 1976, PERV.

[29]  Thomas Y. Choi,et al.  Structural investigation of supply networks: A social network analysis approach , 2011 .

[30]  N. Hop,et al.  Order allocation in a multiple-supplier environment , 2005 .

[31]  Geoffrey G. Bell,et al.  Benefiting from network position: firm capabilities, structural holes, and performance , 2005 .