Abstract
Inventory management at a single or multiple levels of a supply chain is usually performed with computations such as Economic Order Quantity or Markov Decision Processes. The former makes many unrealistic assumptions and the later requires specialist Operations Research knowledge to implement. Dynamic Bayesian networks provide an alternative framework which is accessible to non-specialist managers through off-the-shelf graphical software systems. We show how such systems may be deployed to model a simple inventory problem, and learn an improved solution over EOQ. We discuss how these systems can allow managers to model additional risk factors throughout a supply chain through intuitive, incremental extensions to the Bayesian networks.
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References
Bayesian Networks. In: Arbib, M.A. (ed.) The Handbook of Brain Theory and Neural Networks, MIT Press, Cambridge (2003)
Buffet, S.: A Markov model for inventory level optimisation in supply chain management. In: Eighteenth Canadian Conference on Artificial Intelligence, pp. 133–144 (2005)
Erlenkotter, D.: Ford Whittman Harris and the economic order quantity model. Operations Research 38(6), 937–946 (1990)
Giannoccaro, I., Pontrandolfo, P.: Inventory management in supply chains: a reinforcement learning approach. International Journal of Production Economics 78, 153–161 (2002)
Howard, R.A., Matheson, J.E.: Influence diagrams. Decision Analysis 2(3), 127–143 (2005)
Kayne, D.: Managing Risk and Resilience in the Supply Chain. BSI British Standards Institutio (2008)
Kofjač, D., Kljajić, M., Škraba, A., Rodič, B.: Adaptive fuzzy inventory control algorithm for replenishment process optimization in an uncertain environment. In: Abramowicz, W. (ed.) BIS 2007. LNCS, vol. 4439, pp. 536–548. Springer, Heidelberg (2007)
Lysons, K., Gillingham, M.: Purchasing and Supply Chain Management, 6th edn. Prentice-Hall, Englewood Cliffs (2003)
Murphy, K.P.: Dynamic Bayesian networks: Representation, inference and learning. Technical report, University of California, Berkeley (2002)
Pearl, J.: Probabilistic reasoning in intelligent systems: networks of plausible inference, 2nd edn. Morgan Kaufmann Publishers, San Francisco (1988)
Porteus, E.L.: Foundations of stochastic inventory theory. Standford University Press (2002)
Slack, N., Chambers, S., Johnston, R.: Operations Management, 5th edn. Prentice Hall, Financial Times (2007)
Sutton, R., Barto, A.: Reinforcement Learning. MIT Press, Cambridge (1998)
Zhang, N.L., Qi, R., Poole, D.: A computational theory of decision networks. International Journal of Approximate Reasoning 11, 83–158 (1994)
Zhao, Q., Chen, S., Leung, S., Lai, K.: Integration of inventory and transportation decisions in a logistics system. Transportation Research Part E 46, 913–925 (2010)
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Taylor, M., Fox, C. (2011). Inventory Management with Dynamic Bayesian Network Software Systems. In: Abramowicz, W. (eds) Business Information Systems. BIS 2011. Lecture Notes in Business Information Processing, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21863-7_25
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DOI: https://doi.org/10.1007/978-3-642-21863-7_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21829-3
Online ISBN: 978-3-642-21863-7
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