Hostname: page-component-76fb5796d-dfsvx Total loading time: 0 Render date: 2024-04-26T23:18:36.842Z Has data issue: false hasContentIssue false
  • English
  • Français

OPTIMAL PRICING AND PRODUCTION POLICIES OF A MAKE-TO-STOCK SYSTEM WITH FLUCTUATING DEMAND

Published online by Cambridge University Press:  16 February 2009

Jean-Philippe Gayon ,
Işılay Talay-Değirmenci ,
Fikri Karaesmen  and
E. Lerzan Örmeci
Jean-Philippe Gayon
Affiliation:
Laboratoire G-SCOP, Grenoble INP 38031 Grenoble Cedex 1, France E-mail: jean-philippe.gayon@g-scop.fr
Işılay Talay-Değirmenci
Affiliation:
The Fuqua School of Business, Duke University, Durham, NC, 27708 E-mail: isilay.talay@duke.edu
Fikri Karaesmen
Affiliation:
Department of Industrial Engineering, Koç University, 34450, Sarıyer, İstanbul, Turkey E-mail: fkaraesmen@ku.edu.tr; lormeci@ku.edu.tr
E. Lerzan Örmeci
Affiliation:
Department of Industrial Engineering, Koç University, 34450, Sarıyer, İstanbul, Turkey E-mail: fkaraesmen@ku.edu.tr; lormeci@ku.edu.tr
Get access Rights & Permissions [Opens in a new window]

Abstract

We study the effects of different pricing strategies available to a production–inventory system with capacitated supply, which operates in a fluctuating demand environment. The demand depends on the environment and on the offered price. For such systems, three plausible pricing strategies are investigated: static pricing, for which only one price is used at all times, environment-dependent pricing, for which price changes with the environment, and dynamic pricing, for which price depends on both the current environment and the stock level. The objective is to find an optimal replenishment and pricing policy under each of these strategies. This article presents some structural properties of optimal replenishment policies and a numerical study that compares the performances of these three pricing strategies.

Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 23 , Issue 2 , April 2009 , pp. 205 - 230
Copyright
Copyright © Cambridge University Press 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Beyer, D. & Sethi, S.P. (1997). Average cost optimality in inventory models with markovian demands. Journal of Optimization Theory Applications 92: 497–526.CrossRefGoogle Scholar
2.Bitran, G. & Caldentey, R. (2003). An overview of pricing models for revenue management. Manufacturing and Service Operations Management 5(3): 203–229.CrossRefGoogle Scholar
3.Buzacott, J.A. & Shanthikumar, J.G. (1993). Stochastic models of manufacturing systems. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
4.Caldentey, R. & Wein, L.M. (2006). Revenue management of a make-to-stock queue. Operations Research 54(5): 859–875.CrossRefGoogle Scholar
5.Chan, L.M.A., Shen, Z.J.M., Simchi-Levi, D. & Swann, J. (2004). Coordination of pricing and inventory decisions: A survey and classification. In Simchi-Levi, D., Wu, S.D. & Shen, Z.J.M., (eds.) Handbook of quantitative supply chain analysis: Modeling in the e-business era. Amsterdam: Kluwer, pp. 335–392.CrossRefGoogle Scholar
6.Chan, L.M.A., Simchi-Levi, D. & Swann, J. (2006). Dynamic pricing strategies for manufacturing with stochastic demand and discretionary sales. Manufacturing and Service Operations Management 8: 149–168.CrossRefGoogle Scholar
7.Chen, H., Wu, O. & Yao, D.D. (2004). Optimal pricing and replenishment in a single-product inventory system. Working paper, Cheung Kong Graduate School of Business, University of British Columbia and Columbia University.Google Scholar
8.Chen, X. & Simchi-Levi, D. (2002). Coordinating inventory control and pricing strategies with random demand and fixed ordering cost: The continuous review model. Working paper, Operations Research Center, MIT, Cambridge, MA.Google Scholar
9.Chen, X. & Simchi-Levi, D. (2004). Coordinating inventory control and pricing strategies with random demand and fixed ordering cost: The finite horizon case. Operations Research 52: 887–896.CrossRefGoogle Scholar
10.Chen, X. & Simchi-Levi, D. (2004). Coodinating inventory control and pricing strategies with random demand and fixed ordering cost: The infinite horizon case. Mathematics of Operations Research 29(3): 698–723.CrossRefGoogle Scholar
11.Elmaghraby, W. & Keskinocak, P. (2003). Dynamic pricing in the presence of inventory considerations: Research overview, current practices and future directions. Management Science 49(10): 1287–1309.CrossRefGoogle Scholar
12.Federgruen, A. & Heching, A. (1999). Combined pricing and inventory control under uncertainty. Operations Research 47(3): 454–475.CrossRefGoogle Scholar
13.Feldman, R.M. (1978). A continuous review (s, S) inventory system in a random environment. Journal of Applied Probability 15: 654–659.CrossRefGoogle Scholar
14.Feng, Y. & Chen, F.Y. (2003). Joint pricing and inventory control with setup costs and demand uncertainty. Working paper, The Chinese University of Hong Kong.Google Scholar
15.Feng, Y. & Chen, F. (2004). Optimality and optimization of a joint pricing and inventory-control policy for a periodic-review system. Working paper, The Chinese University of Hong Kong.Google Scholar
16.Gallego, G. & van Ryzin, G. (1994). Optimal dynamic pricing of inventories with stochastic demand over finite horizons. Management Science 40(8): 999–1020.CrossRefGoogle Scholar
17.Kalpakam, S. & Arivarignan, G. (1989). A lost sales system in a random environment. Stochastic Analysis and Applications 7: 367–385.CrossRefGoogle Scholar
18.Koole, G. (1998). Structural results for the control of queueing systems using event-based dynamic programming. Queueing Systems 30: 323–339.CrossRefGoogle Scholar
19.Li, L. (1988). A stochastic theory of the firm. Mathematics of Operations Research 13(3): 447–466.CrossRefGoogle Scholar
20.McGill, J.I. & Van Ryzin, G.J. (1999). Revenue management: Research overview and prospects. Transportation Science 33(2): 233–256.CrossRefGoogle Scholar
21.Özekici, S. & Parlar, M. (1999). Inventory models with unreliable suppliers in a random environment. Annals of Operations Research 91: 123–136.CrossRefGoogle Scholar
22.Porteus, E.L. (2002). Foundations of stochastic inventory theory. Stanford, CA: Stanford University Press.CrossRefGoogle Scholar
23.Puterman, M.L. (1994). Markov decision processes. New York: John Wiley and Sons.CrossRefGoogle Scholar
24.Serfozo, R.F. (1979). An equivalence between continuous and discrete time markov decision processes. Operations Research 27(3): 616–620.CrossRefGoogle Scholar
25.Sethi, S.P. & Cheng, F. (1997). Optimality of (s, s) policies in inventory models with markovian demand. Operations Research 45: 931–939.CrossRefGoogle Scholar
26.Song, J.S. & Zipkin, P.H. (1993). Inventory control in a fluctuating demand environment. Operations Research 41: 351–370.CrossRefGoogle Scholar
27.Thomas, L.J. (1974). Price and production decisions with random demand. Operations Research 22: 513–518.CrossRefGoogle Scholar
28.Thowsen, G.T. (1975). A dynamic, nonstationary inventory problem for a Price/Quantity setting firm. Naval Research Logistics 22: 461–476.CrossRefGoogle Scholar
29.Yano, C.A. & Gilbert, S.M. (2002). Coordinated pricing and production/procurement decisions: A review. In Chakravarty, A. & Eliashbert, J. (eds.) Managing business interfaces: Marketing, engineering and manufacturing perspectives. Amsterdam: Kluwer Academic.Google Scholar
30.Zabel, E. (1970). Monopoly and uncertainty. The Review of Economic Studies 37: 205–219.CrossRefGoogle Scholar
31.Zabel, E. (1972). Multi-period monopoly under uncertainty. Economic Theory 5: 524–536.CrossRefGoogle Scholar
32Zipkin, P.H. (2000). Foundations of inventory management. New York: McGraw-Hill.Google Scholar