Design for Postponement: A Comprehensive Characterization of Its Benefits Under Unknown Demand Distributions

References

  • Alderson W. Marketing efficiency and the principle of postponement. Cost and Profit Outlook (1950) 3(SeptemberGoogle Scholar
  • Aviv Y., Federgruen A. Capacitated multi-item inventory systems with random and seasonally fluctuating demands: implications for postponement strategies. Management Sci. (2001) 47:512–531LinkGoogle Scholar
  • Azoury K. S. Bayes solution to dynamic inventory models under unknown demand distribution. Management Sci. (1985) 31:1150–1160LinkGoogle Scholar
  • Azoury K. S., Miller B. L. A comparison of the optimal ordering levels of Bayesian and non-Bayesian inventory models. Management Sci. (1984) 30:993–1003LinkGoogle Scholar
  • Bertsekas D. P., Shreve S. E.Stochastic Optimal Control: The Discrete Time Case (1978) (Prentice Hall, Englewood Cliffs, NJ) Google Scholar
  • DeGroot M. H.Optimal Statistical Decisions (1970) (McGraw Hill Inc., New York) Google Scholar
  • Denardo E. V.Dynamic Programming: Models and Applications (1982) (Prentice-Hall, Englewood Cliffs, NJ) Google Scholar
  • Dvoretzky A., Kiefer J., Wolfowitz J. The inventory problem: II. Case of unknown distributions of demand. Econometrica (1952) 19Google Scholar
  • Eppen G., Schrage L., Schwarz L. Centralized ordering policies in a multiwarehouse system with leadtimes and random demands. Multi-Level Production/Inventory Control Systems: Theory and Practice (1981) (North-Holland, Amsterdam) 51–69Google Scholar
  • Erhardt R. The power approximation for computing (s, S) inventory policies. Management Sci. (1979) 25:777–786LinkGoogle Scholar
  • Erkip N., Hausman W. H., Nahmias S. Optimal centralized ordering policies in multi-echelon inventory systems with correlated demands. Management Sci. (1990) 36:381–392LinkGoogle Scholar
  • Federgruen A., Sarin R. Recent advances in production and distribution management. Perspectives in Operations Management (1993) (Kluwer Academic Publishers, Norwell, MA) CrossrefGoogle Scholar
  • Federgruen A., Zipkin P. Approximations of dynamic, multilocation production and inventory problems. Management Sci. (1984a) 30:69–84LinkGoogle Scholar
  • Federgruen A., Zipkin P. Allocation policies and cost approximations for multilocation inventory systems. Naval Res. Logist. Quart. (1984b) 31:97–129CrossrefGoogle Scholar
  • Federgruen A., Zipkin P. Computational issues in an infinite horizon multi-echelon inventory model. Oper. Res. (1984c) 32:818–832LinkGoogle Scholar
  • Fisher M. L., Raman A. Reducing the cost of demand uncertainty through accurate response to early sales. Oper. Res. (1996) 44:87–99LinkGoogle Scholar
  • Fisher M. L., Obermeyer W. R., Hammond J., Raman A. Accurate response: the key to profiting from quick response. Bobbin Mag. (1994a) February):48–62Google Scholar
  • Fisher M. L., Hammond J., Obermeyer W. R., Raman A. Making supply meet demand in an uncertain world. Harvard Bus. Rev. (1994b) May-June):83–93Google Scholar
  • Fox B. Discrete optimization via marginal analysis. Management Sci. (1966) 4:36–153Google Scholar
  • Gallego G., Moon I. The distribution free newsboy problem: review and extensions. J. of Oper. Res. Soc. (1993) 44:825–834CrossrefGoogle Scholar
  • Garg A., Tang C. S. On postponement strategies for product families with multiple points of differentiation. IIE Trans. (1997) 29:641–650CrossrefGoogle Scholar
  • Gross O. A class of discrete-type minimization problems. (1956) . Working Paper RM-1644-PR, The RAND Corporation, Santa Monica, CAGoogle Scholar
  • Güllü R. A two-echelon allocation model and the value of information under correlated forecasts and demands. Eur. J. Oper. Res. (1997) 99:386–400CrossrefGoogle Scholar
  • Hamilton J. D.Time Series Analysis (1994) (Princeton University Press, Princeton, NJ) CrossrefGoogle Scholar
  • Heath C. D., Jackson P. L. Modeling the evolution of demand forecasts with applications to safety stock analysis in production/distribution systems. IIE Trans. (1994) 26:17–30CrossrefGoogle Scholar
  • Iglehart D. The dynamic inventory model with unknown demand distribution. Management Sci. (1964) 10:429–440LinkGoogle Scholar
  • Iyer A. V., Bergen M. E. Quick response in manufacturer-retailer channels. Management Sci. (1997) 43:559–570LinkGoogle Scholar
  • Jackson P. L. Stock allocation in a two echelon distribution system or “what to do until your ship comes in”. Management Sci. (1988) 34:880–895LinkGoogle Scholar
  • Johnson G., Thompson H. Optimality of myopic inventory policies for certain dependent demand processes. Management Sci. (1975) 21:1303–1307LinkGoogle Scholar
  • Jönsson H., Silver E. A. Analysis of a two-echelon inventory control system with complete redistribution. Management Sci. (1987a) 33:215–227LinkGoogle Scholar
  • Jönsson H., Silver E. A. Modification of demand distributions on the basis of aggregate information. IIE Trans. (1987b) 19:379–384CrossrefGoogle Scholar
  • Karlin S. Dynamic inventory policy with varying stochastic demands. Management Sci. (1960) 6:231–258LinkGoogle Scholar
  • Lee H., Sarin R. Design for supply chain management: concepts and examples. Perspectives in Operations Management (1993) (Kluwer Academic Publishers, Norwell, MA) CrossrefGoogle Scholar
  • Lee H. Effective management of inventory and service through product and process redesign. Oper. Res. (1996) 44:151–159LinkGoogle Scholar
  • Lee H., Billington M. C., Dasu S., Eastman C. Designing products and processes for postponement. Management of Design: Engineering and Management Perspectives (1994) (Kluwer Academic Publishers, Norwell) 105–122CrossrefGoogle Scholar
  • Lee H., Billington C., Carter B. Hewlett-Packard gains control of inventory and service through design for localization. Interfaces (1993) August):1–11LinkGoogle Scholar
  • Lee H., Tang C. S. Modeling the costs and benefits of delayed product differentiation. Management Sci. (1997) 43:40–53LinkGoogle Scholar
  • Lovejoy W. S. Myopic policies for some inventory models with uncertain demand. Management Sci. (1990) 36:724–738LinkGoogle Scholar
  • Miller B. Scarf's state reduction method, flexibility, and a dependent demand inventory model. Oper. Res. (1986) 34:83–90LinkGoogle Scholar
  • Mrena C. Supply chain strategies at Sun Microsystems. Presentation at the Supply Chain Management Conference (1997) May 15Santa Clara University, CAGoogle Scholar
  • New York Times Compaq Plans A Big Change in its PC Sales. (1997) May 5(L. Zuckerman)Google Scholar
  • Scarf H. A min-max solution of an inventory problem. Studies in the Mathematical Theory of Inventory and Production (1958) (Stanford University Press, Stanford, CA) . Chapter 2Google Scholar
  • Scarf H. Bayes solutions of the statistical inventory problem. Ann. Math. Statist. (1959) 30:490–508CrossrefGoogle Scholar
  • Scarf H. Some remarks on Bayes solutions to the inventory problem. Naval Logist. Res. Quart. (1960) 7:591–596CrossrefGoogle Scholar
  • Schwarz L. B. Model for assessing the value of warehouse risk-pooling: risk pooling over outside-supplier lead-times. Management Sci. (1989) 35:828–842LinkGoogle Scholar
  • Sethi S. P., Cheng F. Optimality of (s, S) policies in inventory models with Markovian demand. Oper. Res. (1997) 45:931–939LinkGoogle Scholar
  • Signorelli S., Heskett J. L. Benetton (A). Harvard Bus. School (1989) Google Scholar
  • Sobel M. Dynamic Affine Logistics Models. (1988) . Report. SUNY at Stony Brook, NYGoogle Scholar
  • Song J. S., Zipkin P. Inventory control in a fluctuating demand environment. Oper. Res. (1993) 41:351–370LinkGoogle Scholar
  • Stalk G., Evans P., Shulman L. E. Competing on capabilities: the new rules of corporate strategy. Harvard Bus. Rev. (1992) MarchGoogle Scholar
  • Tong Y. L.The Multivariate Normal Distribution (1990) (Springer-Verlag, New York) CrossrefGoogle Scholar
  • Topkis D. M. Minimizing a submodular function on a lattice. Oper. Res. (1978) 26(2):305–321LinkGoogle Scholar
  • Veinott A. Optimal policy for a multi-product, dynamic non-stationary inventory problem. Management Sci. (1965) 12:206–222LinkGoogle Scholar
  • Wall Street Journal GM expands plan to speed cars to buyers. (1996) October 21(G. Stern and R. Blumenstein)Google Scholar
  • Zipkin P. Simple ranking methods for allocation of one resource. Management Sci. (1980) 26:34–43LinkGoogle Scholar
  • Zipkin P. Exact and approximate cost functions for product aggregates. Management Sci. (1982) 28:1002–1012LinkGoogle Scholar
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