%0 Journal Article %1 Swaminathan:1998:ds %A Swaminathan, Jayashankar M. %A Smith, Stephen F. %A Sadeh, Norman M. %D 1998 %J Decision Sciences %K Artificial Chain Decision Intelligence, Management. Simulation, Supply Support System, and thesis %N 3 %P 607--632 %R 10.1111/j.1540-5915.1998.tb01356.x %T Modeling Supply Chain Dynamics: A Multiagent Approach %V 29 %X A global economy and increase in customer expectations in terms of cost and services have put a premium on effective supply chain reengineering. It is essential to perform risk-benefit analysis of reengineering alternatives before making a final decision. Simulation provides an effective pragmatic approach to detailed analysis and evaluation of supply chain design and management alternatives. However, the utility of this methodology is hampered by the time and effort required to develop models with sufficient fidelity to the actual supply chain of interest. In this paper, we describe a supply chain modeling framework designed to overcome this difficulty. Using our approach, supply chain models are composed from software components that represent types of supply chain agents (e.g., retailers, manufacturers, transporters), their constituent control elements (e.g., inventory policy), and their interaction protocols (e.g., message types). The underlying library of supply chain modeling components has been derived from analysis of several different supply chains. It provides a reusable base of domain-specific primitives that enables rapid development of customized decision support tools.

@article{Swaminathan:1998:ds, abstract = {A global economy and increase in customer expectations in terms of cost and services have put a premium on effective supply chain reengineering. It is essential to perform risk-benefit analysis of reengineering alternatives before making a final decision. Simulation provides an effective pragmatic approach to detailed analysis and evaluation of supply chain design and management alternatives. However, the utility of this methodology is hampered by the time and effort required to develop models with sufficient fidelity to the actual supply chain of interest. In this paper, we describe a supply chain modeling framework designed to overcome this difficulty. Using our approach, supply chain models are composed from software components that represent types of supply chain agents (e.g., retailers, manufacturers, transporters), their constituent control elements (e.g., inventory policy), and their interaction protocols (e.g., message types). The underlying library of supply chain modeling components has been derived from analysis of several different supply chains. It provides a reusable base of domain-specific primitives that enables rapid development of customized decision support tools.}, added-at = {2017-03-16T11:50:55.000+0100}, author = {Swaminathan, Jayashankar M. and Smith, Stephen F. and Sadeh, Norman M.}, biburl = {https://www.bibsonomy.org/bibtex/237819e590196f2a56d42094fe3bea88b/krevelen}, doi = {10.1111/j.1540-5915.1998.tb01356.x}, interhash = {fa3bbb79333140890d25b4558db59bcc}, intrahash = {37819e590196f2a56d42094fe3bea88b}, issn = {1540-5915}, journal = {Decision Sciences}, keywords = {Artificial Chain Decision Intelligence, Management. Simulation, Supply Support System, and thesis}, number = 3, owner = {Rick}, pages = {607--632}, timestamp = {2017-03-16T11:54:14.000+0100}, title = {Modeling Supply Chain Dynamics: A Multiagent Approach}, volume = 29, year = 1998 }