Baruah, Pundarikaksha (2006): SUPPLY CHAINS FACING ATYPICAL DEMAND: OPTIMAL OPERATIONAL POLICIES AND BENEFITS UNDER INFORMATION SHARING. Published in: PhD Dissertation (30. December 2006)
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Demand patterns for products with seasonality and or short life-cycles do not follow a clear discernible pattern (to allow predictive time-series modeling of demand) for individual sales events or seasons due to such factors as considerable demand volatility, product promotions, and unforeseen marketplace events. Suppliers supporting such atypical demand patterns typically incur higher holding costs, lower capacity utilization, and lower order fill-rates, particularly under long lead-times and uncertainty in effective capacity. Retailers on the other hand struggle with product overages and supply shortages. On the other hand, atypical demand settings bring huge financial opportunity to supply chain players, and are pervasive. It is suggested in the literature that an effective means to reap these benefits is through increased information sharing between retailers and suppliers, superior forecasting with forecast update techniques, proper replenishment, and custom designed inventory/manufacturing policies. We also believe that sharing of order forecasts, also known as soft-orders, in advance by the buyer could be beneficial to both parties involved.
This dissertation in particular studies a two-player supply chain, facing atypical demand. Among the two-players is a buyer (retailer/distributor/vendor) that makes ordering decision(s) in the presence of upstream supply uncertainty and demand forecast revision(s). We propose a stochastic dynamic programming model to optimally deicide on soft-order(s) and a final firm-order under a deposit scheme for initial soft-order(s). While sharing of upstream soft-order inventory position information by the supplier before receiving a final order is not a common industrial practice, nor is it discussed in the literature, our analysis shows that such information sharing is beneficial under certain conditions.
Second player of the supply chain is a supplier (manufacturer) that makes production release decision(s) in the presence of limited and random effective capacity, and final order uncertainty. Our stochastic dynamic programming model for optimal production release decision making reveals that substantial savings in order fulfillment cost (that includes holding, overage, and underage costs) can be realized in the presence of advance soft-order(s). Soft-orders can also be shown to improve order fill-rate for the buyer.
This research explores complex interactions of factors that affect the operational decision making process, such as costs, demand uncertainty, supply uncertainty, effective capacity severity, information accuracy, information volatility, intentional manipulation of information etc. Through extensive analysis of the operational policies, we provide managerial insights, many of which are intuitively appealing, such as, additional information never increases cost of an optimal decision; many are also counterintuitive, for example, dynamic programming models cannot fully compensate for intentional soft-order inflation by the buyer, even under conditions of a stable and linear order inflation pattern, in the absence of deposits.
|Item Type:||MPRA Paper|
|Original Title:||SUPPLY CHAINS FACING ATYPICAL DEMAND: OPTIMAL OPERATIONAL POLICIES AND BENEFITS UNDER INFORMATION SHARING|
|English Title:||SUPPLY CHAINS FACING ATYPICAL DEMAND: OPTIMAL OPERATIONAL POLICIES AND BENEFITS UNDER INFORMATION SHARING|
|Keywords:||Supply Chain Economics, Information Sharing, Atypical Demand, Optimal Cost Model, Dynamic Program, Multi-player model|
|Subjects:||M - Business Administration and Business Economics; Marketing; Accounting > M1 - Business Administration > M11 - Production Management
Y - Miscellaneous Categories > Y4 - Dissertations (unclassified) > Y40 - Dissertations (unclassified)
C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C44 - Operations Research; Statistical Decision Theory
D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D82 - Asymmetric and Private Information; Mechanism Design
C - Mathematical and Quantitative Methods > C6 - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling > C61 - Optimization Techniques; Programming Models; Dynamic Analysis
D - Microeconomics > D2 - Production and Organizations > D24 - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
M - Business Administration and Business Economics; Marketing; Accounting > M2 - Business Economics > M21 - Business Economics
|Depositing User:||Pundarikaksha Baruah|
|Date Deposited:||08. Jul 2009 02:35|
|Last Modified:||12. Feb 2013 15:58|
Aviv, Y. 2003. A time-series framework for supply-chain inventory management. Operations Research. 51 210-227.
Baruah, P. and R. B. Chinnam. 2006. Benefits of Sharing Order Forecasts with Manufacturers facing Capacity Uncertainty. Working Paper. Wayne State University.
Bopllapragada, R., U. Rao, J. Zhang. 2004. Managing two-stage serial inventory systems under demand and supply uncertainty and customer service level requirements, IIE Transactions, 36, 73-85.
Bitran G. R., E. A. Haas, H. Matsuo. 1986. Production planning of style goods with high setup costs and forecast revisions. Operations Research 34, 226–236.
Blattberg, R. and S. Neslin. 1990. Sales promotion: concepts, methods, and strategy. Prentice-Hall Inc. New Jersey.
Bradford, J. W., and P. K. Sugrue. 1990. A Beyesian approach to the two-period style-goods inventory problem with single replenishment and heterogeneous Poisson demand. Journal of the Operational Research Society, 41(3) 211-218.
Cachon, G. P. and M. Fisher. 1997. Campbell Soup's Continuous Product Replenishment Program: evaluation and enhanced decision rules. Production and Operations Management. 6 266-275.
Cachon, G. P. and M. Fisher. 2000. Supply chain inventory management and the value of shared information. Management Science. 46(8) 1032-1048.
Cachon, G. P. and M. A. Lariviere. 2001. Contracting to Assure Supply: How to Share Demand Forecasts in a Supply Chain. Management Science, 47(5) 629-646.
Chen, F. 2003. Information sharing and supply chain coordination. Handbooks in Operations Research and Management Science: Supply Chain Management: Design, Coordination and Operation. 11. Elsevier Publishing Company.
Chen, F. 2001. Auctioning supply contracts. Working paper, Columbia University.
Chen, F. and B. Yu. 2005. Quantifying the Value of Lead-time Information in a Single-Location Inventory System. Manufacturing and Service Operations Management, 7(2), 144–151
Chopra, S. and P. Meindl. 2004, Supply Chain Management- Strategy, Plannin, and Operation., 2nd edition. Prentice Hall.
Christopher, M, R. Lowson and H. Peck. 2004. Creating agile supply chains in the fashion industry. International Journal of Retail & Distribution Management, 32(8/9), pp. 367-376.
Ciarallo, F. W., R. Akella, T. E. Morton. 1994. A periodic review, production planning model with uncertain capacity and uncertain demand - optimality of extended myopic policies. Management Science. 40(3) 320-332.
Clark, T.H. and J. Hammond. 1997. Reengineering Channel Reordering Processes to Improve Total Supply Chain Performance. Production and Operations Management. 6(3) 248-265.
Croson R. and K. Donohue. 2005. Information and its Impact on the Bullwhip Effect. System Dynamics Review, 21(3), 249–260.
Donohue, K. L. 2000. Efficient Supply Contracts for Fashion Goods with Forecast Updating and Two Production Modes. Management Science. 46(11) 1397-1411.
Eldridge, E. 2006. Motorists can soft-order new plug-in hybrid vehicle. The Examiner, Oct 17, 2006. URL: http://www.examiner.com
Ehrhardt, R. and L. Taube. 1987. An Inventory Model with Random Replenishment Quantities. International Journal of Production Research, 25, 1795-1803.
Eppen, G. D. and A. V. Iyer. 1997a. Improved Fashion Buying with Bayesian Updates. Operations Research. 45(6) 805-819.
Eppen, G. D. and A. V. Iyer. 1997b. Backup agreement in fashion buying- The value of upstream flexibility. Management Science. 43(11) 1469-1484.
Fearne, A., A. Donaldson, P. Norminton. 1999. The impact of alternative promotion strategies on the spirits category: evidence from the UK. The Journal of Product and Brand Management, 8(5) 430-442.
Fisher, M., K. Rajaram, A. Raman. 2001. Optimizing Inventory Replenishment of Retail Fashion Products, Manufacturing and Service Operations Management, 3(3), 230-241.
Fisher, M. and A. Raman. 1996. Reducing the cost of demand uncertainty through accurate re-sponse to early sales. Operations Research. 44(1) 87-99.
Gruen, T. W., D. S. Corsten, S. Bharadwaj. 2002. Retail Out-of-Stock: A Worldwide Examination of Extent, Causes and Consumer Response. Grocery Manufacturers of America, The Food Marketing Institute and CIES – The Food Business Forum.
Gurnani, H. and C. S. Tang. 1999. Optimal ordering decisions with uncertain cost and demand forecast updating. Management science. 45 1456-1462.
Gullu, R., E. Onol., N. Ekrim. 1999. Analysis of an inventory system under supply uncertainty. International Journal of Production Economics, 59, 377-385.
Heath, D. C. and P. L. Jackson. 1994. Modeling the evolution of demand forecasts with application to safety stock analysis in production/distribution systems. IIE Transactions. 26(3) 17-30.
Henig, M. and Y. Gerchak. 1990. The structure of periodic review policies in the presence of random yield, Operations Research, 38, 634–643.
Hausman, W. and R. Peterson. 1972. Multiproduct production scheduling for style goods with limited production capacity, forecast revisions and terminal delivery. Management Science. 18(7) 370-383.
Hausman, W. 1969. Sequential Decision Problems: A Model to Exploit Existing Forecasting. Management Science, 16(2), B93-B111.
Hu, H. 2003. Advanced demand information and safety capacity as a hedge against demand and capacity uncertainty. Manufacturing & Service Operations Management. 7(2) 144-151.
Hwang, J. and M. Singh. 1998. Optimal production policies for multi-stage systems with setup costs and uncertain capacity. Management Science. 44(9) 1279-1294.
Jemai, Z., N. K. Erkip, Y. Dallery. 2006. Contracting under uncertain capacity. Proceedings of International Conference on Information Systems, Logistics and Supply Chain, May 14-17, 2006, Lyon, France.
Johnson, M. E. 2001. Learning from toys: Lessons in managing supply chain risk from the toy industry. California Management Review. 43(3), pp 106-124.
Johnson, O. and H. Thomson. 1975. Optimality of myopic inventory policies for certain dependent demand process. Management Science. 21 1303-1307.
Kaminsky, P. and J.M. Swaminathan. 2001. Utilizing Forecast Band Refinement for Capacitated Production Planning. Manufacturing & Service Operations Management. 3(1) 68-82.
Kandel, E. 1996 The Right to Return, Journal of Law and Economics, 39(1), 329-356.
Karabuk, S. and S. D. Wu. 2003. Coordinating Strategic Capacity Planning in the Semiconductor Industry. Operations Research. 51(6) 839-849.
Karlin, S. 1958. Studies in the Mathematical Theory of Inventory and Production, University Press, Stanford, California.
Karenmen, F., G. Liberopoulos, Y. Dallery. 2004. The Value of Advance Demand Information in Production/Inventory Systems, Annals of Operations Research. 126, 135-157
Kouvellus, P., and J. M. Minler. 2002. Supply chain capacity and outsourcing decisions: the dynamic interplay of demand and supply uncertainty, IIE Transactions, 34, 717-728.
Kim, H., J. C. Lu, P. H. Kvam. 2004. Ordering Quantity Decisions Considering Uncertainty in Supply-Chain Logistics Operations, Working Paper, School of Industrial and Systems Engineering, Georgia Institute of Technology.
Koch, C. 2004. Nike Rebounds, CIO Magazine, Jul. 24, Issue 2004. Kurt Salmon Associates. 1993. Efficient Consumer Response: Enhancing Consumer Value in the Supply Chain. Washington D.C.: Kurt Salmon Associates.
Larson, R. B. 2004. Christmas Tree Marketing: Product, Price, Promotion, and Place Tactics. Journal of Forestry, 102(4), 40-45.
Lee, H. L., Padmanabhan, V., and Whang, S. 1997. The Bullwhip Effect in Supply Chains. Sloan Management Review, 38, pp. 93-102.
Lee, H., K. C. So., C. S. Tang. 2000. The Value of Information Sharing in a Two-Level Supply Chain. Management Science, 46(5), 626-643.
Lee. H. L. 2002. Aligning Supply Chain Strategies with Product Uncertainties. California Management Review, Spring, 105-119.
Lin, W. and V. Tardif. 1999. Component partitioning under demand and capacity uncertainty in printed circuit board assembly. International Journal of Flexible Manufacturing Systems. 11 159-176.
Lin, L. C., and K. L. Hou. 2005. An inventory system with investment to reduce yield variability and set-up costs, Journal of the Operational Research Society, 56, 67-74.
Liu, Q and G. Ryzin. 2005. Strategic Capacity Rationing to Induce Early Purchases. Working Paper. Columbia Business School.
Matsuo, H. 1990. A Stochastic Sequencing Problem for Style Goods with Forecast Revisions and Hierarchical Structure. Management Science, 36(3), 332-347.
Metters, R. 1998. General rules for production planning with seasonal demand. International Journal of Production Research. 36(5) 1387-1399.
Mohebbi, E. 2004. A replenishment model for the supply-uncertainty problem. International Journal of Production Economics, 87, 25-37.
Murray, G. R. and E. A. Silver. 1966. Bayesian analysis of the style goods inventory problem. Management Science 12: 785–797
NCTA. 2002a. Quic Tree Facts. www.realchristmastrees.org
NCTA. 2002b. Industry Statistics. www.realchristmastrees.org
Ozer, O. and W. Wei. 2004. Inventory Control with Limited Capacity and Advance Demand Information. Operations Research. 52(6), 988-1000. Parlar, M., and D. Berkin. 1991. Future supply uncertainty in EOQ models, Naval Research Logistics, 38. 107–121.
Parlar, M., Y. Wang, Y. Gerchak. 1995. A periodic review inventory model with Markovian supply availability: Optimality of (S, s) policies, International Journal of Production Economics, 42, 131–136.
Padmanabhan, V. and I. P. L. Png. 1995. Returns Policies: Make Money by Making Good, Sloan Management Review, 37(1), 65-72.
Raghunathan, S. 2001. Information sharing in a supply chain: A note on its value when demand is non-stationary. Management Science. 47(4) 605-610.
Raman, A. 1999. Managing inventory for fashion products. Quantitative Models for Supply Chain Management. S. Tayur, R. Ganeshan, and M. Magazine, eds. Kluwar Academic Publishers.
Raman, A. and B. Kim. 2002. Quantifying the impact of inventory holding cost and reactive capacity on an apparel supplier's profitability. Production and Operations Management. 11(3) 358-373.
Reinmuth, J. E. and M. D. Geurts. 1972. A Bayesian Approach to Forecasting the Effect of Atypical Situations. Journal of Marketing Research. 9(3) 292-297.
Rozhon, T. 2005. Before Christmas, Wal-Mart was stirring. http://www.nytimes.com/
Sahin, F. and E. W. Robinson. 2002. Flow coordination and information sharing in supply chains: Review, implications, and directions for future research. Decision Sciences. 33(4) 505-536.
Shih, W. 1980. Optimal Inventory Policies When Stock outs Result From Defective Products. International Journal of Production Research, 18(6), 677-685.
Silver, E. A. 1976. Establishing the order quantity when the amount received is uncertain. INFOR, 14(1), 32-39.
Simchi-Levi and Y. Zhao. 2004. The Value of Information Sharing in a Two-stage Supply Chain with Production Capacity Constraints: The Inﬁnite Horizon Case. Probability in the Engineering and Informational Sciences. 18(2), 247-274.
Smith, S. A. and D. D. Achabal. 1998. Clearance pricing and inventory policies for retail chains. Management Science. 44(3), 285-301.
Swaminathan, J., N. M. Sadeh, and S. F. Smith. 1997. Effect of Sharing Supplier Capacity Information. Working Paper. University of California, Berkeley and Carnegie Mellon University, Pittsburgh.
Tenser, J. 1997. Procter Gamble Revamps Retail Incentives (Streamlined '97 initiative to improve efficiencies), Supermarket News, 1.
Terwiesch, C., Z. J. Ren, T. H. Ho and M. A. Cohen. 2005. An Empirical Analysis of Forecast Sharing in the Semiconductor Equipment Supply Chain. Management Science. 51(2) 208-220.
Tokay, L. B. and L.. M. Wein. 2001. Analysis of a forecasting-production-inventory system with stationary demand. Management Science. 47(9) 1268-1281.
Vollmann, T, W. L. Berry, and D.C. Whybark. 1997. Manufacturing Planning and Control Systems, 4th edition. Irwin/McGRaw-Hill, Boston, Massachusetts.
Wang, Y. and G. Yigal. 1996. Periodic review production models with variable capacity, random yield, and uncertain demand. Management Science. 42(1) 130-137.
Wong, C. Y., J. S. Arlbjorn, and J. Johansen. 2005. Supply Chain Management Practices in toy supply chains. Supply Chain Management, 10(5), pp. 367-376.
Wu, K. and I. Lin. 2004. Extend (r, Q) Inventory Model Under Lead Time and Ordering Cost Reductions When the Receiving Quantity is Different from the Ordered. Quality & Quantity 38, 771–786.
Yang, S. and L. Malek. 2004. Multi-Supplier Sourcing with Random Yields: A Newsvendor Approach. Working Paper. Seton Hall University, NJ and New Jersey Institute of Technology, NJ, USA.
Yano, C. A., and H. L. Lee. 1995. Lot sizing with random yields: A review, Operations Research 43, 311–334.
VICS. 2004a. CPFR- An Overview. URL: http://www.vics.org/
VICS. 2004b. Retail Event Collaboration- Business Process Guide. URL: http://www.vics.org/