Modeling the Impact of Information on Inventories

  • Ananth V. Iyer
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 17)


In this chapter, we study models of supply chains that focus on the impact of demand information on demand uncertainty and the consequent impact on the inventory levels required to maximize expected profit. We will also focus on the different impacts of information on the manufacturer and the buyer expected profits. This permits us to study the effect of contractual agreements between the buyer and the supplier that may be required to share the benefits of information on a supply chain. The bulk of the material in this chapter is derived from Eppen and Iyer (1997a), (1997b) and Iyer and Bergen (1997).


Optimal Policy Service Level Inventory Level Service Level Agreement Expected Profit 
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. Agrawal, N, and Smith, S.C., “Estimation Methods for Retail Inventory Management with Unobservable Lost Sales”, Working Paper, Santa Clara University, 1994.Google Scholar
  2. Anupindi, R., and Akella, R., “An Inventory Model with Commitments”, Working Paper, Carnegie Mellon University, GSIA, 1993.Google Scholar
  3. Arrow, K., Harris, T., and Marschak, J., “Optimal Inventory Policy”, Econometrica, Vol. 9, No. 3, July 1951, pg 250–272.CrossRefGoogle Scholar
  4. Azoury, K., “Bayes Solution to Dynamic Inventory Models under Unknown Demand Distribution”, Management Science, Vol. 31 No. 9, September 1985, pp. 1150 – 1160.CrossRefGoogle Scholar
  5. Azoury, K. S., and Miller, B. L., “A Comparison of the Optimal Ordering Levels of Bayesian and Non-Bayesian Inventory Models”, Management Science, Vol. 30, No. 8, August 1984, pp. 993 – 1003.CrossRefGoogle Scholar
  6. Bassok, Y, and Anupindi, R., “Analysis of Supply Contracts with Total Minimum Commitment”, IIE Transactions, 1994.Google Scholar
  7. Berger, James O., Statistical Decision Theory and Bayesian Analysis, Springer-Verlag, Second edition, New York, 1985.Google Scholar
  8. Blackburn, J.D., Time-Based Competition, Business One Irwin, Homewood, IL, 1991, pg 246–269.Google Scholar
  9. Blinder, A., “Inventories and Sticky Prices: More on the Microfoundations of Macroeconomics”, American Economic Review, Vol. 72, 1982, pg 332–348.Google Scholar
  10. Chang, S.H. and Fyffe, D. E., “Estimation of Forecast Errors for Seasonal-Style-Goods Sales”, Management Science, Vol 18, No.2, October 1971, pp B-89–B-96.CrossRefGoogle Scholar
  11. Chung, K.L., A First Course in Probability Theory, Harcourt, Brace and World, New York, 1968.Google Scholar
  12. Crowston, W.B., Hausman, W. H., and Kampe, W.R., “Multistage Production for Stochastic Seasonal Demand”, Management Science, Vol 19, No. 8,April 1973.Google Scholar
  13. Eppen, G.D., and Fama, E.F., “Solutions for Cash Balance and Simple Dynamic Portfolio Problems”, Journal of Business, January 1968.Google Scholar
  14. Eppen, G.D., and Fama, E.F., “Cash Balance and Simple Dynamic Portfolio Problems with Proportional Costs”, International Economic Review, Vol. 10, No. 2, June 1969, pg 119–133.CrossRefGoogle Scholar
  15. Eppen, G.D., and Iyer, A.V.,“Improved Fashion Buying Using Bayesian Updates”, Operations Research, November-December 1997Google Scholar
  16. Eppen, G.D., and Iyer,A.V.,“Backup Agreements in Fashion Buying — The Value of Upstream Flexibility 11”, Management Science, November 1997.Google Scholar
  17. Fisher, M.A, and Raman, A., “Reducing the Cost of Demand Uncertainty Through Accurate Response to Early Sales”, Operations Research, Vol. 44, No. 1, January-February 1996.Google Scholar
  18. Fukuda, Y., “Optimal Disposal Policies”, Naval Research Logistics Quarterly, Vol. 8, No. 3, September 1961, Pg 221–227.CrossRefGoogle Scholar
  19. Harpaz, G., Lee, W.Y. and Winkler, R.L., “Optimal Output Decisions of a Competitive Firm”, Management Science, Vol. 28, 1982, pg 589–602.CrossRefGoogle Scholar
  20. Hausman, W. H., “Sequential Decision Problems: A Model to Exploit Existing Forecasters”, Management Science, Vol. 16. No.2, October 1969, pp 93–111.CrossRefGoogle Scholar
  21. Hausman, W. H. and Peterson, R., “Multiproduct Production Scheduling for Style Goods with Limited Capacity, Forecast Revisions and Terminal Delivery”, Management Science, Vol. 18. No. 7, March 1972, pp 370–383.CrossRefGoogle Scholar
  22. Hausman, W.H., and Sides, R., “Mail-Order Demands for Style Goods: Theory and Data Analysis”, Management Science, Vol. 20. No. 2, October 1973, pp 191–202.CrossRefGoogle Scholar
  23. Hertz, D. B. and Schaffir, K. H.,“A Forecasting Method for Management of Seasonal Style-Goods Inventories”, Operations Research, Vol. 8, 1960, pp45–52.CrossRefGoogle Scholar
  24. Iglehart, D., “The Dynamic Inventory Problem with Unknown Demand Distribution”, Management Science, Vol. 10, 1964, pp. 429–440.CrossRefGoogle Scholar
  25. Iyer, A.V., and Bergen, M., “Quick Response in Manufacturer-Retailer Channels”, Management Science, April 1997.Google Scholar
  26. Kumar, A., Akella, R., and Cornuejols, G., “Supply Contracts Under Bounded Order Quantities”, Working Paper, Carnegie Mellon University, GSIA, 1992.Google Scholar
  27. Lovejoy, W. S., “Myopic Policies for Some Inventory MOdels with Uncertain Demand Distributions”, Management Science, Vol. 36, No. 6, June 1990, pp. 724 – 738.CrossRefGoogle Scholar
  28. Matsuo, H.,“A Stochastic Sequencing Problem for Style Goods with Forecast Revisions and Hierarchical Structure”, Management Science, Vol. 36. N0. 3, March 1990, pp. 332 – 347.CrossRefGoogle Scholar
  29. Miller, B., “Scarf’s State Reduction Method, Flexibility and a Dependent Demand Inventory Model”, Operations Research, Vol. 34, No. 1, Jan-Feb 1986, pp. 83–90.CrossRefGoogle Scholar
  30. Morrison, D.G., and Schmittlein, D.C., “Generalizing the NBD Model for Customer Purchases: What are the Implications and Is it Worth the Effort”, Journal of Business and Economic Statistics, Vol. 6, No. 2, April 1988, pp. 145–159.Google Scholar
  31. Moses, M., and Seshadri, S., “Policy Mechanisms for Supply Chain Co-ordination”, Working Paper, Stern School of Business, New York University, 1995.Google Scholar
  32. Murray, G. R., Jr. and Silver, E.A.,“A Bayesian Analysis of the Style Goods Inventory Problem”, Management Science, Vol. 12, No. 11, July 1966, pp. 785–797.CrossRefGoogle Scholar
  33. Neave, E.H.,“The Stochastic Cash Balance Problem with Fixed Costs for Increases and Decreases”, Management Science, Vol. 16, March 1970, pg 472–490.CrossRefGoogle Scholar
  34. Pindycj, R., “Adjustment Costs, Uncertainty and the Behavior of the Firm”, American Economic Review, Vol. 72, 1982, pg 415–427.Google Scholar
  35. Ravindran, A.,“Management of Seasonal style-Goods Inventories”, Operations Research, Vol. 20, No. 2, March-April 1972, pp. 265–275.CrossRefGoogle Scholar
  36. Scarf, H., “Some Remarks on Bayes Solutions to the Inventory Problem”, Naval Research Logistics Quarterly, Vol. 7, 1958, pg 591–596.CrossRefGoogle Scholar
  37. Scarf, H., “Bayes Solutions of the Statistical Inventory Problem”, Annals of Mathematical Statistics, Vol. 30, 1959, pg 490–508.CrossRefGoogle Scholar
  38. Stern, L.W., and El-Ansary, A.I., Marketing Channels, Prentice Hall, Englewood Cliffs, N.J. 07632, 1988.Google Scholar
  39. Tsay, A., and Lovejoy, W.S., “Supply Chain Control with Quantity Flexibility”, Working Paper, Santa Clara University, Santa Clara, CA. 95053, 1995.Google Scholar
  40. Veinott, A.F. Jr., “The Status of Mathematical Inventory Theory”, Management Science, Vol. 12, No. 11, July 1966, pg 745–777.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • Ananth V. Iyer
    • 1
  1. 1.Krannert School of ManagementPurdue UniversityWest LafayetteUSA

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