Optimal Use of Demand Information in Supply Chain Management

  • Guillermo Gallego
  • Özalp Özer
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 42)


Alan Greenspan was puzzled by the data on his computer screen. Capital expenditures in high technology were rising sharply, unemployment was declining, prices were holding steady and profits were rising. At the same time, the Labor Department’s statistics showed that productivity had decreased by one percent during the second quarter. Greenspan did not agree with the productivity data. He found the missing link in the Survey of Current Business from the Bureau of Labor Statistics, Woodward [44], which indicated that business inventories were shrinking significantly while the economy was growing. This suggested that new computer technology was allowing just-in-time orders. Instead of stocking products weeks or months in advance, businesses could keep detailed track of what was needed and order within days, while competitive pressures were forcing quality control. Greenspan eventually became convinced by this argument and voiced it publicly in the State of the Economy address before the Committee on Ways and Means, U.S. House of Representatives, January 20, 1999.


Optimal Policy Supply Chain Management Penalty Cost Base Stock Demand Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [1]
    Appell, B., B. Gressens, and C. Brousseau. (2000) The Value Propositions of Dynamic Pricing in Business-to-Business E-Commerce. http://www.CRMproject.com/crm/wp/appell.html
  2. [2]
    Aviv, Y. and A. Federgruen. (1999) Capacitated Multi-Item Inventory Systems with Random and Seasonally Fluctuating Demands: Implication for Postponement Strategies. To Appear in Management Science.Google Scholar
  3. [3]
    Axsäter, S. (1993) Using the Deterministic EOQ Formula in Stochastic Inventory Control. Management Science, 831–834.Google Scholar
  4. [4]
    Bourland, K.E., S.G. Powell, and D.F. Pyke. (1996) Exploiting Timely Demand Information to Reduce Inventories. European Journal of Operations Research, Vol. 92, 239–253.CrossRefGoogle Scholar
  5. [5]
    Brown, G., T.M. Corcoran, and R.M. Lloyd. (1971) Inventory Models with Forecasting and Dependent Demand. Management Science, Vol. 17, 498–499.CrossRefGoogle Scholar
  6. [6]
    Chen, F. (1998) Echelon Reorder Points, Installation Reorder Points, and Value of Centralized Demand Information. Management Science, Vol. 44, No. 12, part 2 of 2, s221 - s234.CrossRefGoogle Scholar
  7. [7]
    Chen, F. (1999) Market Segmentation, Advanced Demand Information, and Supply Chain Performance. To appear in MSandOM.Google Scholar
  8. [8]
    Chen, F. and J. Song. (1999) Optimal Policies for Multi-echelon Inventory Problems with Markov Modulated Demand. Working Paper, Columbia Graduate School of Business.Google Scholar
  9. [9]
    Chen, F. and Y.S. Zheng. (1994) Lower Bounds for Multi-Echelon Stochastic Inventory Systems. Management Science, Vol. 40, 1426–1443.CrossRefGoogle Scholar
  10. [10]
    Chen, F., Z. Drezner, J.K. Ryan, and D. Simchi-Levi. (2000) Quantifying the Bullwhip Effect: The Impact of Forecasting, Lead-time and Information. Management Science, Vol. 46, 436–443.CrossRefGoogle Scholar
  11. [11]
    Clark, A. and H. Scarf. (1960) Optimal Policies for a Multi-Echelon Inventory Problem. Management Sciences, Vol. 6, 475–490.CrossRefGoogle Scholar
  12. [12]
    Federgruen, A. (1993) Centralized Planning Models for Multi-Echelon Inventory Systems under Uncertainty. Chapter 3 in Handbook in Operations Research and Management Science, Vol. 4.Google Scholar
  13. [13]
    Federgruen, A. and P. Zipkin. (1984A) Computational Issues in an Infinite Horizon, Multi-Echelon Inventory Model. Operations Research, Vol. 32, 818–836.Google Scholar
  14. [14]
    Federgruen, A. and P. Zipkin. (1984B) Approximations of Dynamic, Multi location Production and Inventory Problems. Management Science, Vol. 30, 69–84.Google Scholar
  15. [15]
    Friedman, M. (1953) Essays in the Theory of Positive Economics. University of Chicago Press, 1953, p. 15.Google Scholar
  16. [16]
    Gallego, G., Y. Huang, K. Katircioglu, AND Y.T. Leung. (2000) When to Share Demand Information in a Simple Supply Chain? Submitted to Management Science.Google Scholar
  17. [17]
    Gallego, G. and O. Ozer. (1999) Integrating Replenishment Decisions With Advance Demand Information. Submitted to Management Science.Google Scholar
  18. [18]
    Gallego, G. and O. Ozer. (2000) Optimal Replenishment Policies for Multi-Echelon Inventory Problems under Advance Demand Information. Submitted to Operations Research.Google Scholar
  19. [19]
    Gallego, G. and B. Toktay. (1999) All-or-Nothing Ordering under a Capacity Constraint and Forecasts of Stationary Demand. Working Paper, Columbia University, New York, NY.Google Scholar
  20. [20]
    Gallego, G. and G. Van Ryzin. (1997) A Multi-Product Dynamic Pricing Model with Applications to Network Yield Management. Operations Research, Vol. 45, 24–41.CrossRefGoogle Scholar
  21. [21]
    Gavirneni, S., R. Kapuscinski, and S. Tayur. (1999) Value of Information in Capacitated Supply Chains. Management Science, Vol. 45, 16–24.CrossRefGoogle Scholar
  22. [22]
    Graves, S.C., H.C. Meal, S. Dasu, and Y. Qui. (1986) Two Stage Production Planning In a Dynamic Environment, in Multi Stage Production Planning and Inventory Control, S. Axsäter, C. Schneeweiss and E. Silver (Eds.), Lecture Notes In Economics and Mathematical Systems, Springer-Verlag, Berlin, Vol. 266, 9–43.CrossRefGoogle Scholar
  23. [23]
    Güllü, R. (1996) On the Value of Information in Dynamic Production/Inventory Problems Under Forecast Evolution. Naval Research Logistics, Vol. 43, 289–303.CrossRefGoogle Scholar
  24. [24]
    Güllü, R. (1998) Optimal Production/Inventory Policies Under Forecasts and Limited Production Capacity. Working Paper, Middle East Technical University.Google Scholar
  25. [25]
    Katircioglu, K. and D. Atkins. (1998) New Optimal Policies for A Unit Demand Inventory Problem. Working paper, IBM Research Division, Yorktown Heights, NY.Google Scholar
  26. [26]
    Hariharan, R. and P. Zipkin. (1995) Customer Order Information, Lead Times, and Inventories. Management Science, Vol. 41, 1599–1607.CrossRefGoogle Scholar
  27. [27]
    Hausman, W.H. (1969) Sequential Decision Problems: A model to Exploit Existing Forecasts. Management Science, Vol. 16, B93 - B111.CrossRefGoogle Scholar
  28. [28]
    Heat, D. and P. Jackson. (1994) Modeling the Evolution of Demand Forecasts with Application to Safety Stock Analysis in Production/Distribution Systems. IIE Transactions, Vol. 26, 1730.Google Scholar
  29. [29]
    Lee, H.L., C. Billington, and B. Carter. (1993) Hewlett-Packard Gains Control of Inventory and Service Through Design for Localization. Interfaces, Vol. 23, 4 July-August 1–11.Google Scholar
  30. [30]
    Lee, H.L., K. So, and C. Tang. (2000) Information Sharing in a Two-Level Supply Chain. Management Science, Vol. 46, 626–643.CrossRefGoogle Scholar
  31. [31]
    Moinzadeh, K. (1999) An Improved Ordering Policy for Continuous Review Inventory Systems with Arbitrary Inter-Demand Time Distributions. Working paper, School of Business. University of Seattle, WA 98195.Google Scholar
  32. [32]
    Moinzadeh, K. and S. Nahmias. (1988) A Continuous Review Model for an Inventory System with Two Supply Modes. Management Science, Vol. 34, 761–773.CrossRefGoogle Scholar
  33. [33]
    Ozer, O. (2000) Replenishment Strategies for Distribution Systems under Advance Demand Information. Working Paper, Stanford University, Department of MSandE.Google Scholar
  34. [34]
    Ozer, O. (2000) Supply Chain Management With Advance Demand Information. Ph.D. Dissertation, Columbia University.Google Scholar
  35. [35]
    Porteus, E.L. (1990) Stochastic Inventory Theory. Chapter 12 in Handbooks in Operations Research and Management Science, Vol. 2, 605–652.CrossRefGoogle Scholar
  36. [36]
    Rosling, K. (1989) Optimal Inventory Policies for Assembly Systems Under Random Demands. Operations Research, Vol. 37, 565579.Google Scholar
  37. [37]
    Scarf, H. (1960) The Optimality of (s, S) Policies in the Dynamic Inventory Problem. in: K.A. Arrow, S. Karlin, and P. Suppes (Eds.), Mathematical Methods in the Social Sciences, Stanford University Press, Stanford, CA.Google Scholar
  38. [38]
    Scarf, H. (1963) A Survey of Analytic Techniques in Inventory Theory. Chapter 7 in: Scarf, H., D.M. Gilford, and M.W. Shelly (Eds.), in Multistage Inventory Models and Techniques, 185–225, Stanford University Press, Stanford, CA.Google Scholar
  39. [39]
    Scarf, H. (2000) Optimal Inventory Policies when Sales are Discretionary. Working Paper, Yale University.Google Scholar
  40. [40]
    Schwarz, L.B., Petruzzi, N.C., and K. Wee. (1998) The Value of Advance-Order Information and the Implication for Managing the Supply Chain: An Information/Control/Buffer Portfolio Perspective. Working paper, Kranert Graduate School of Management, Purdue University.Google Scholar
  41. [41]
    Sethi, S.P., H. Yan, and H. Zhang. (2000) Peeling Layers of an Onion: An Inventory Model with Multiple Delivery Modes and Forecast Updates. Working Paper, University of Texas at Dallas, Richardson, Texas.Google Scholar
  42. [42]
    Tontay, L.B. and L.W. Wein. (1999) Analysis of a ForecastingProduction-Inventory System with Stationary Demand. Working Paper, Sloan School of Management, MIT.Google Scholar
  43. [43]
    Veinott, A. (1966) The Status of Mathematical Inventory Theory. Management Science, Vol. 12, 745–777.CrossRefGoogle Scholar
  44. [44]
    Woodward, Robert. (2000) Maestro: Greenspan’s Fed and the American Boom. Simon and Schuster, New York.Google Scholar
  45. [45]
    Zheng, Y.S. (1992) On Properties of Stochastic Inventory Systems Management Science, Vol. 38, 87–103.CrossRefGoogle Scholar
  46. [46]
    Zipkin, P. (1982) Exact and Approximate Cost Functions for Product Aggregates. Management Science, Vol. 28, 1002–1012.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • Guillermo Gallego
    • 1
  • Özalp Özer
    • 2
  1. 1.Industrial Engineering and Operations ResearchColumbia UniversityNew YorkUSA
  2. 2.Management Science and EngineeringStanford UniversityStanfordUSA

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