News
Statistics
| Number of Journals | 34 |
| Number of Issues | 1,306 |
| Number of Articles | 9,428 |
| Article View | 9,191,247 |
| PDF Download | 5,622,152 |
Evaluation of centralized distribution system in clothing industry with risk-pooling approach (a case study) | ||
| مدیریت زنجیره تأمین | ||
| Article 3, Volume 21, Issue 65, June 2020, Pages 24-46 PDF (1.02 M) | ||
| Document Type: Research/ Original/ Regular Article | ||
| Authors | ||
| R. Ghasemy Yaghin* 1; Golshan Ahmadi2 | ||
| 1Textile Engineering Department, AUT | ||
| 2BSc, Department of Textile Engineering, Amirkabir University of Technology | ||
| Receive Date: 21 December 2019, Revise Date: 08 April 2020, Accept Date: 29 April 2020 | ||
| Abstract | ||
| As supply chains and its distribution centres expand, risk increases and can have very different impacts on the optimal inventory management policies to meet consumer demand. The basic idea of risk pooling in supply chains is that if the customer demands from all sources are pooled by a centralized system, then not only will expected profits increase, but also inventory levels and costs will decrease. Regarding to customer demand variability of textile clothing products, efficient inventory management in clothing supply chain leads to total inventory cost reduction, and then retaining competitive edge. In this paper, the effect of customer demand uncertainty in multiple warehouses system under the two centralized and decentralized systems is taken into consideration in the presence of deterministic supply. In order to investigate the effect of risk pooling and its result under two scenarios in (s,S) inventory control namely, 1) stochastic demand 2) stochastic demand and lead-time, a two-echelon clothing supply chain is taken into account under the aforementioned scenarios. We consider customer demand variability in a single manufacturer, multiple distribution centers and multiple retailers (demand points). We also assume that inventory is stored at only a single echelon, distribution centers, in order to make decisions about centralization versus decentralization of supply chain under consideration. In the latter part of this paper, aggregate warehouse is then located by logistical costs minimization through a real case study. In doing so, different transportation modes, their related costs and distances from the centralized distribution center are involved by comparing potential location sites for the obtained pooled inventory center. Numerical results show that if customer demand distribution for products is normal, average inventory in centralized distribution system is less than that of decentralized distribution systems. Finally, sensitivity analyses are numerically conducted to investigate critical inventory parameters on service level of the local company. | ||
| Keywords | ||
| Supply chain inventory management; (s; S) inventory policy; Risk pooling; Aggregate systems; Clothing industry; Logistical decisions | ||
| References | ||
|
[1] Ballou, R.H.; Business logistics/ Supply chain management; Pushp Print, Fifth Edition, 2006. [2] Simchi-levi, D.; Designing and Managing The Supply Chain-Concept, Strategies, and Cases; McGraw-hill Higher Education, 2000. [3] Gelders,L.F.; Van Looy; “An Inventory Policy For Slow And Fast Movers in a Petrochemical Plant: A case study”; Journal of Operational Research Society, 29(9), 867-874, 1978. [4] Eppen, G; “Effects of Centralization on Expected Costs in a Multi- Location Newsvendor Problem”, Management Science 25, 498-50, 1979. [5] Benjaafar, S., Cooper, W. “On the Benefit Of Inventory Pooling In Production-Inventory Systems”, Management Science 51, 548-565, 2005. [6] Chopra, S., Sodhi, M. “Managing Risk to Avoid Supply Chain Breakdown”, Sloan Management Review 46, 53-61, 2004. [7] Yang, H., Schrage, L. “Conditions That Cause Risk Pooling to Increase Inventory”; European Journal of Operational Research, 192, 837-851, 2009. [8] Çömez-Dolgana, N., Tanyerib, B. “Inventory Performance with Pooling: Evidence from Mergers and Acquisitions”; International Journal of Production Economics, 168, 331-339, 2015. [9] Ballou, R.H.; “Evaluating Inventory Management Performance Using a Turnover Curve”; International Journal of Physical Distribution & Logistics Management, 30(1), 72-85, 1999. [10] Schmitt, A.J., Anthony, S., Siyuan, Synder, V., Max Shen, Z. “Centralization Versus Decentralization: Risk Pooling, Risk Diversification, and Supply Chain Disruptions”, International Journal of Production Economics, 59, 643-650, 2014. [11] C. Edirisinghe, D. Atkins, “Lower bounding inventory allocations for risk pooling in two-echelon supply chains”, International Journal of Production Economics, 187, 159-167, 2017. [12] E. Arikan and L. Silbermayr, “Risk pooling via unidirectional inventory transshipments in a decentralized supply chain”, International Journal of Production Research, 56(17), 5593-5610, 2018. [13] G. Oeser, “What’s the penalty for using the Square Root Law of inventory centralisation?”, International Journal of Retail & Distribution Management, 47(3), 292-310, 2019. [14] Kumar, S., Tiwari, M.K. “Supply Chain System Design Integrated With Risk Pooling”; Computers & Industrial Engineering, 64, 580-588, 2013. [15] Berman, O., Krass D., Tajbakhsh, M.M. “On The Benefits of Risk Pooling in Inventory Management” Production and Operations Management, 20(1), 57-71, 2011. [16] Browne, J; Production Management Systems: An Integrated Perspective; Addison-Wesley Publishing Company, 1996. [17] Dong-lei, “On The Effects of Risk Pooling in Supply Chain Management: Review and Extensions” Acta Mathematicae Applicatae Sinica, English Series, 4, 709–722, 2009. [18] Freund, J E, Miller, I,; Mathematical Statistics with Applications; 8th Edition, 2014. [19] Boyd, S., Vandenberghe, L., Convex Optimization; Cambridge University Press, 2004. | ||
|
Statistics Article View: 936 PDF Download: 425 |
||