The incremental value of central control in serial supply chains

We consider a two-echelon serial supply chain where a single manufacturer replenishes a single downstream customer who faces random, stationary and discrete demand. In this setting, we compare the performance of a traditional supply chain having no information sharing to one where the customer shares demand and inventory information with the manufacturer. We also consider the case of central control where the manufacturer has full control over replenishments. This full information sharing with control by the manufacturer captures what has been called a vendor-managed inventory scenario in the literature. In order to estimate the performance of these three supply chain scenarios, we utilise renewal theory to develop probability models for each. A computational analysis of the models determines the benefit which information sharing offers as well as the incremental benefit central control provides beyond that of information sharing alone, a value which has not typically been considered in the literature despite its importance. Results indicate central control offers a 4.0% improvement over the no-information sharing setting; however, information sharing alone accounts for some of that benefit, offering an average cost savings of 1.8% compared to the no-information sharing setting. We then conclude that central control offers only an additional 2.2% benefit over the information sharing setting.

[1]  Fangruo Chen,et al.  Optimal Policies for Multi-Echelon Inventory Problems with Batch Ordering , 2000, Oper. Res..

[2]  S. Rajagopalan,et al.  Make to Order or Make to Stock: Model and Application , 2002, Manag. Sci..

[3]  Paul H. Zipkin,et al.  Estimating the Performance of Multi-Level Inventory Systems , 1988, Oper. Res..

[4]  Powell E. Robinson,et al.  Flow Coordination and Information Sharing in Supply Chains: Review, Implications, and Directions for Future Research , 2002, Decis. Sci..

[5]  Herbert E. Scarf,et al.  Optimal Policies for a Multi-Echelon Inventory Problem , 1960, Manag. Sci..

[6]  Ming Dong,et al.  Comparison of order-fulfilment performance in MTO and MTS systems with an inventory cost budget constraint , 2012 .

[7]  M. Gümüş,et al.  Calculating the benefits of vendor managed inventory in a manufacturer-retailer system , 2010 .

[8]  W. K. Chiang,et al.  The value of information sharing in the presence of supply uncertainty and demand volatility , 2007 .

[9]  E. Powell Robinson,et al.  Information sharing and coordination in make-to-order supply chains , 2005 .

[10]  Srinagesh Gavirneni,et al.  Benefits of co-operation in a production distribution environment , 2001, Eur. J. Oper. Res..

[11]  N. Shock Directions for Future Research. , 1980, Advances in pathobiology.

[12]  Sheldon M. Ross,et al.  Introduction to probability models , 1975 .

[13]  James Aaron Cooke,et al.  VMI: VERY MIXED IMPACT? , 1998 .

[14]  Sven Axsäter,et al.  Exact and Approximate Evaluation of Batch-Ordering Policies for Two-Level Inventory Systems , 1993, Oper. Res..

[15]  Hing Kai Chan,et al.  A review of coordination studies in the context of supply chain dynamics , 2010 .

[16]  R. Kapuściński,et al.  Value of Information in Capacitated Supply Chains , 1999 .

[17]  Mark E. Ferguson,et al.  Managing Slow‐Moving Perishables in the Grocery Industry , 2007 .

[18]  Michael J. Fry,et al.  Focused Issue: The Use of Information in Managing Supply Chains, Part 2 of 2: Coordinating Production and Delivery Under a (z, Z)-Type Vendor-Managed Inventory Contract , 2001, Manuf. Serv. Oper. Manag..

[19]  Qinan Wang,et al.  Coordination mechanisms of supply chain systems , 2007, Eur. J. Oper. Res..

[20]  Partha Priya Datta,et al.  Information sharing and coordination mechanisms for managing uncertainty in supply chains: a simulation study , 2011 .

[21]  R. Ehrhardt The Power Approximation for Computing (s, S) Inventory Policies , 1979 .

[22]  R. H. Hollier,et al.  Optimal inventory control of lumpy demand items using (s, S) policies with a maximum issue quantity restriction and opportunistic replenishments , 2005 .

[23]  W. Popp Simple And Combined Inventory Policies, Production to Stock or to Order? , 1965 .

[24]  Marshall L. Fisher,et al.  Supply Chain Inventory Management and the Value of Shared Information , 2000 .

[25]  A. Gunasekaran,et al.  Build‐to‐order supply chain management: a literature review and framework for development , 2005 .

[26]  Sven Axsäter,et al.  Supply Chain Operations: Serial and Distribution Inventory Systems , 2003, Supply Chain Management.

[27]  Kamran Moinzadeh,et al.  Lot Sizing with Randomly Graded Yields , 1997, Oper. Res..

[28]  Jing-Sheng Song,et al.  Coordination Mechanisms in Decentralized Serial Inventory Systems with Batch Ordering , 2009, Manag. Sci..

[29]  Li Zheng,et al.  A dynamic model for serial supply chain with periodic delivery policy , 2010 .

[30]  David F. Pyke,et al.  Inventory management and production planning and scheduling , 1998 .

[31]  M. Reiman,et al.  Echelon Reorder Points, Installation Reorder Points, and the Value of Centralized Demand Information , 1998 .

[32]  Lauren B. Davis,et al.  Information sharing in capacity constrained supply chains under lost sales , 2011 .

[33]  C.X. Wang Random yield and uncertain demand in decentralised supply chains under the traditional and VMI arrangements , 2009 .

[34]  A. F. Veinott,et al.  Computing Optimal (s, S) Inventory Policies , 1965 .