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Simulation of Order Fulfillment in Divergent Assembly Supply Chains

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Management of supply chains is a difficult task involving coordination and decision-making across organizational boundaries. Computational modeling using multi-agent simulation is a tool that can provide decision support for supply chain managers. We identify the components of a supply chain model and implement it in the Swarm multi-agent simulation platform. The model is used to study the impact of information sharing on order fulfillment in divergent assembly supply chains (commonly associated with the computer and electronics industries). We find that efficient information sharing enables inventory costs to be reduced while maintaining acceptable order fulfillment cycle times. This is true because information, which provides the basis for enhanced coordination and reduced uncertainty, can substitute for inventory.

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  • Troy J Strader & Fu-ren Lin & Michael J Shaw, 1998. "Simulation of Order Fulfillment in Divergent Assembly Supply Chains," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 1(2), pages 1-5.
    • Handle: RePEc:jas:jasssj:1998-3-1
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    Cited by:

    1. Uwe Cantner & Ivan Savin & Simone Vannuccini, 2019. "Replicator dynamics in value chains: explaining some puzzles of market selection," Industrial and Corporate Change, Oxford University Press, vol. 28(3), pages 589-611.
    2. Ojha, Divesh & Sahin, Funda & Shockley, Jeff & Sridharan, Sri V., 2019. "Is there a performance tradeoff in managing order fulfillment and the bullwhip effect in supply chains? The role of information sharing and information type," International Journal of Production Economics, Elsevier, vol. 208(C), pages 529-543.
    3. Fu-ren Lin & Shyh-ming Lin, 2006. "Enhancing the Supply Chain Performance by Integrating Simulated and Physical Agents into Organizational Information Systems," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 9(4), pages 1-1.
    4. Wei, Zelong & Song, Xi & Wang, Donghan, 2017. "Manufacturing flexibility, business model design, and firm performance," International Journal of Production Economics, Elsevier, vol. 193(C), pages 87-97.
    5. Roberto Dominguez & Salvatore Cannella, 2020. "Insights on Multi-Agent Systems Applications for Supply Chain Management," Sustainability, MDPI, vol. 12(5), pages 1-13, March.
    6. Luca Iandoli & Elio Marchione & Cristina Ponsiglione & Giuseppe Zollo, 2009. "Learning and Structural Properties in Small Firms’ Networks: A Computational Agent-Based Model," Research in Economics and Business: Central and Eastern Europe, Tallinn School of Economics and Business Administration, Tallinn University of Technology, vol. 1(1).
    7. Whan-Seon Kim, 2009. "Effects of a Trust Mechanism on Complex Adaptive Supply Networks: An Agent-Based Social Simulation Study," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(3), pages 1-4.
    8. Gek Woo Tan & Michael J. Shaw & Bill Fulkerson, 2000. "Web-based Supply Chain Management," Information Systems Frontiers, Springer, vol. 2(1), pages 41-55, January.
    9. Emerson, Denise & Zhou, Wei & Piramuthu, Selwyn, 2009. "Goodwill, inventory penalty, and adaptive supply chain management," European Journal of Operational Research, Elsevier, vol. 199(1), pages 130-138, November.
    10. Narjès Bellamine-Ben Saoud & Gloria Mark, 2007. "Complexity theory and collaboration: An agent-based simulator for a space mission design team," Computational and Mathematical Organization Theory, Springer, vol. 13(2), pages 113-146, June.

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