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Replenishment Policy in a Two-Echelon Supply Chain:An Analysis Using Discrete-Event Simulation
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The present study identifies optimal inventory policies for two-echelon systems under the effects of supply disruptions and stochastic demand. Previous research has incorporated and addressed stochastic demand quite extensively but the study of supply disruptions is relatively new. Presently, supply disruptions are studied heuristically and through theoretical analysis to identify optimum inventory levels. The current study uses discrete event simulation to arrive at optimal policies under varied levels of supply disruptions and stochastic demand. It uses a designed experiment to vary disruption length and disruption frequency. We find that under conditions of supply disruption, a decentralised policy is more likely to yield lower costs than a centralised policy when disruption levels are high but a centralised policy is better otherwise.
Keywords
Supply Disruptions, Discrete-Event Simulation, Two-Echelon System, Experimental Design.
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- Al-Rifai, M., & Rossetti, M. (2007). An efficient heuristic optimization algorithm for a two-echelon (R, Q) inventory system. International Journal of Production Economics, 109(1-2), 195-213. Retrieved from http://dx.doi.org/10.1016/j.ijpe.2006.12.052
- Altiok, T., & Melamed, B. (2007). Simulation modeling and analysis with Arena (1st ed.). Amsterdam: Academic Press.
- Arreola-Risa, A., & DeCroix, G. (1998). Inventory management under random supply disruptions and partial backorders. Naval Research Logistics, 45(7), 687-703. Retrieved from http://dx.doi.org/10.1002/ (sici)1520-6750(199810)45:7<687::aidnav3> 3.3.co;2-#
- Atan, Z., & Snyder, L. (2012). Disruptions in one-warehouse multiple-retailer systems. SSRN Electronic Journal. Retrieved from http://dx.doi.org/10.2139/ ssrn.2171214
- Bollapragada, R., Rao, U. S., & Zhang, J. (2004). Managing inventory and supply performance in assembly systems with random supply capacity and demand. Management Science, 50(12), 1729-1743.
- Boute, R. N., Disney, S. M., Lambrecht, M. R., & Van Houdt, B. (2009). Designing replenishment rules in a two-echelon supply chain with a flexible or an inflexible capacity strategy. International Journal of Production Economics, 119, 187-198.
- Bulut, Z., & Snyder, L. V. (2009). Supply disruptions in a one-warehouse multiple-retailer system, Working Paper, P.C. Rossin College of Engineering and Applied Sciences, Lehigh University, Bethlehem, PA.
- Chopra, S., & Sodhi, M. S. (2004). Managing risk to avoid supply-chain breakdown. MIT Sloan Management Review, 46(1), 53-61.
- Deleris, L. A., & Erhun, F. (2005). Risk management in supply networks using Monte-carlo simulation, in Kuhl, M.E., Steiger, N.M., Armstrong, F.B, Joines, J.A. (Eds), Proceedings of the 2005 Winter Simulation Conference, pp. 1643-49.
- Giri, B., & Sarker, B. (2015). Coordinating a two-echelon supply chain under production disruption when retailers compete with price and service level. Operational Research, 16(1), 71-88. Retrieved from http://dx.doi.org/10.1007/s12351-015-0187-8
- Green, M. (2004). Loss/risk management notes: Survey: Executives rank fire, disruptions top threats. Best’s Review, 105(5), 105.
- Hopp, W. J., & Liu, Z. (2006). Protecting supply chain networks against catastrophic failure, Working Paper, Dept. of Industrial Engineering and Management Science, Northwestern University, Evanston, IL.
- Mak, H., & Shen, Z. (2012). Risk diversification and risk pooling in supply chain design. IIE Transactions, 44(8), 603-621. Retrieved from http://dx.doi.org/10.1080/0740817x.2011.635178
- Qi, L., Shen, Z. J. M., & Snyder, L. V. (2010). The effect of supply disruptions on supply chain design decisions. Transportation Science, 44(2), 274-289.
- Rong, Y., Atan, Z., & Snyder, L. V. (2015). Heuristics for base-stock levels in multi-echelon distribution networks. Working Paper, Lehigh University, Bethlehem, PA.
- Schmitt, A. J. (2011). Strategies for customer service level protection under multi-echelon supply chain disruption risk. Transportation Research Part B, 45, 1266-1283.
- Schmitt, A. J., & Singh, M. (2009). Quantifying supply chain disruption risk using Monte carlo and discrete-event simulation, In Rossetti, M.D., Hill, R.R., Johansson, B., Dunkin, A., Ingalls, R.G. (Eds.), Proceedings of the 2009 Winter Simulation Conference, pp. 1237-1248.
- Schmitt, A., & Singh, M. (2012). A quantitative analysis of disruption risk in a multi-echelon supply chain.
- International Journal of Production Economics, 139(1), 22-32. Retrieved from http://dx.doi.org/10.1016/j.ijpe.2012.01.004
- Schmitt, A., Snyder, L., & Shen, Z. (2010). Inventory systems with stochastic demand and supply: Properties and approximations. European Journal of Operational Research, 206(2), 313-328. Retrieved from http://dx.doi.org/10.1016/j.ejor.2010.02.029
- Schmitt, A., Sun, S., Snyder, L., & Shen, Z. (2014). Centralization versus decentralization: Risk pooling, risk diversification, and supply chain disruptions. Omega, 52, 201-212. Retrieved from http://dx.doi.org/10.1016/j.omega.2014.06.002
- Sheffi, Y. (2005). The resilient enterpise: Overcoming vulnerability for competitive advantage. MIT Press, Cambridge, MA.
- Sheffi, Y., & Rice, J. (2005). A supply chain view of the resilient enterprise. MIT Sloan Management Review, 47(1), 41-48.
- Sheffi, Y., (2001). Supply chain management under the threat of international terrorism. The International Journal of Logistic Management, 12(2), 1-11.
- Simchi-Levi D., Snyder, L., & Watson, M. (2002). Strategies for uncertain times. Supply Chain Management Review, 6(1), 11-12.
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