Browse > Article
http://dx.doi.org/10.5394/KINPR.2010.34.2.131

Inventory policy comparison on supply chain network by simulation technique  

Park, Nam-Kyu (Department of Distribution Management, The Tong myung University)
Choi, Woo-Young (Department of Distribution Management, The Tong myung University)
Abstract
The aim of the paper is to solve the problem of customer reduction due to the difficulty of parts sourcing which impacts production delay and delivery delay in SC networks. Furthermore, this paper is to suggest the new inventory policy of MTS in order to solve the problem of current inventory policy. In order to compare two policies, a LCD maker is selected as a case study and the real data for 2007 years is used for simulation input. The maker uses MTO policy for parts sourcing which has the problem of lead time even if it has some advantage of inventory cost. Based on current process. The simulation program of AS-IS model and TO-BE model using ARENA 10 version is developed for evaluation. In a result, the order number of two policies shows that MTO is 52 and MTS is 53. However the quantity of order shows big difference such that MTO is 168,460 and MTS is 225,106. Particularly, the lead time of new inventory policy shows much shorter that that of MTO such that MTO 100 is days and MTS is 16 days. In spite of short lead time by MTS policy, new policy has to take burden of inventory cost per year. Total inventory cost per year by MTS policy is US$ 11,254 and each part inventory cost is that POL is US$ 1,807, LDI is US$ 2,166 and Panel is US$ 7,281. The implication of the research is that the company has to consider the cost and the service simultaneously in deciding the inventory policy. In the paper, even if the optimal point of deciding is put into tactical area, the ground of decision is suggested in order to improve the problem in SC networks.
Keywords
SCM; Simulation; Arena; MTO; MTS;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Sphicas, G.P. (2006), "EOQ and EPQ with linear and fixed backorder costs: Two cases identified and models analyzed without calculus", International Journal of Production Economics, Vol 100, pp. 59-64.   DOI   ScienceOn
2 Altiok, T. and Melamed, B. (2007). "Simulation Modeling and Analysis with ARENA", Elsevier Science.
3 Ballou, R.H. (1998). "Business Logistics Management, 4nd Edition", Prentice Hall.
4 Carravilla, M.A. and Sousaa, J.P. (1995), "Hierarchical production planning in a Make-To-Order company : a case study", European journal of operational research, Vol. 86, pp. 43-56.   DOI   ScienceOn
5 Corti, D., Pozzetti, A., and Zorzini, M. (2006), "A capacity-driven approach to establish reliable due dates in a MTO environment ", International Journal of Production Economics, Vol. 104, pp. 536-554.   DOI   ScienceOn
6 Giri, B.C. and Yun, W.Y. (2005), " Optimal design of unreliable production–inventory systems with variable production rate", European Journal of Operational Research, Vol 162, pp. 372-386.   DOI   ScienceOn
7 Haskose, A., Kingsman, B.G., and Worthington, D. (2004), "Performance analysis of make-to-order manufacturing systems under different workload control regimes" International Journal of Production Economics, Vol 90, pp. 169-186.   DOI   ScienceOn
8 Simchi-Levi, D., Kaminsky, P., and Simchi-Levi, E. (2003). "Designing & Managing the Supply Chain, 2nd Edition", McGraw-Hill.