Abstract
A global industrial enterprise is a complex network of different distributed production plants that produce, inventory, and distribute products. An agent-based model can be used to solve complex network problems that involve independent actors. The global economy and the increase in both demand fluctuation and pressure to lower costs while satisfying customers have put a premium on smart supply chain management. It is important to undertake a risk benefit analysis of supply chain design alternatives before making decisions. Simulation is an effective approach to comparative analysis and evaluation of such alternatives. In this paper, we describe an agent-based simulation tool for the design of smart supply chain networks and logistics networks. In the agent-based approach, supply chain models comprise supply chain agents. The agent-based simulation tool is useful to predict the effects of local and system-level activities on multi-plant performance and to improve the tactical and strategic decision-making at the enterprise level. Specifically, this model can reveal the optimal transport method under demand fluctuation and network disruption conditions. We found that selecting transport methods according to maximum stock is effective and can reduce cut the amount of stock in the whole supply chain in Thailand.
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Okada, T., Namatame, A., Sato, H., Iwanaga, S. (2017). A Method to Reduce the Amount of Inventoried Stock in Thai Supply Chain. In: Leu, G., Singh, H., Elsayed, S. (eds) Intelligent and Evolutionary Systems. Proceedings in Adaptation, Learning and Optimization, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-49049-6_25
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DOI: https://doi.org/10.1007/978-3-319-49049-6_25
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