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Annals of Operations Research

, Volume 257, Issue 1–2, pp 121–165 | Cite as

Joint procurement and demand-side bidding strategies under price volatility

  • Xiaofeng Nie
  • Tamer Boyacı
  • Mehmet Gümüş
  • Saibal Ray
  • Dan Zhang
Article

Abstract

We consider a firm buying a commodity from a spot market as raw material and selling a final product by submitting bids. Bidding opportunities (i.e., demand arrivals) are random, and the likelihood of winning bids (i.e., selling the product) depends on the bid price. The price of the commodity raw material is also stochastic. The objective of the firm is to jointly decide on the procurement and bidding strategies to maximize its expected total discounted profit in the face of this demand and supply randomness. We model the commodity prices in the spot market as a Markov chain and the bidding opportunities as a Poisson process. Subsequently, we formulate the decision-making problem of the firm as an infinite-horizon stochastic dynamic program and analytically characterize its structural properties. We prove that the optimal procurement strategy follows a price-dependent base-stock policy and the optimal bidding price is decreasing with respect to the inventory level. We also formulate and analyze three intuitively appealing heuristic strategies, which either do not allow for carrying inventory or adopt simpler bidding policies (e.g., a constant bid price or myopically set bid prices). Using historical daily prices of several commodities, we then calibrate our models and conduct an extensive numerical study to compare the performances of the different strategies. Our study reveals the importance of adopting the optimal integrative procurement and bidding strategy, which is particularly rewarding when the raw material prices are more volatile and/or when there is significant competition on the demand side (the probability of winning is much smaller when submitting the same bid price). We establish that the relative performances of the three heuristic strategies depend critically on the holding cost of raw material inventory and the competitive environment, and identify conditions under which the shortfalls in profits from adopting such strategies are relatively less significant.

Keywords

Supply chain management Procurement Bidding  Supply risk  Price volatility Price-dependent base-stock policy 

References

  1. Akella, R., Araman, V. F., & Kleinknecht, J. (2002). B2B markets: Procurement and supplier risk management in e-business. In J. Geunes, P. M. Pardalos, & H. E. Romeijn (Eds.), Supply chain management: Models, applications, and research directions (pp. 33–66). Dordrecht: Kluwer Academic Publishers.Google Scholar
  2. Berling, P., & Martínez de Albéniz, V. (2011). Optimal inventory policies when purchase price and demand are stochastic. Operations Research, 59(1), 109–124.CrossRefGoogle Scholar
  3. Billingsley, P. (1968). Statistical inference for Markov processes. Chicago: University of Chicago Press.Google Scholar
  4. Bladt, M., & Sørensen, M. (2009). Efficient estimation of transition rates between credit ratings from observations at discrete time points. Quantitative Finance, 9(2), 147–160.CrossRefGoogle Scholar
  5. Cohen, M. A., & Agrawal, N. (1999). An analytical comparison of long and short term contracts. IIE Transactions, 31(8), 783–796.Google Scholar
  6. Economist Intelligence Unit. (2009). Managing supply-chain risk for reward. Technical report, Economist Intelligence Unit.Google Scholar
  7. Fabian, T., Fisher, J. L., Sasieni, M. W., & Yardeni, A. (1959). Purchasing raw material on a fluctuating market. Operations Research, 7(1), 107–122.CrossRefGoogle Scholar
  8. Federgruen, A., & Heching, A. (1999). Combined pricing and inventory control under uncertainty. Operations Research, 47(3), 454–475.CrossRefGoogle Scholar
  9. Fu, Q., Lee, C. Y., & Teo, C. P. (2010). Procurement risk management using options: Random spot price and the portfolio effect. IIE Transactions, 42(11), 793–811.CrossRefGoogle Scholar
  10. Gavirneni, S. (2004). Periodic review inventory control with fluctuating purchasing costs. Operations Research Letters, 32(4), 374–379.CrossRefGoogle Scholar
  11. Gayon, J. P., Benjaafar, S., & de Vericourt, F. (2009a). Using imperfect advance demand information in production–inventory systems with multiple customer classes. Manufacturing and Service Operations Management, 11(1), 128–143.CrossRefGoogle Scholar
  12. Gayon, J. P., Talay-Değirmenci, I., Karaesmen, F., & Örmeci, E. (2009b). Optimal pricing and production policies of a make-to-stock system with fluctuating demand. Probability in the Engineering and Informational Sciences, 23(2), 205–230.CrossRefGoogle Scholar
  13. Goel, A., & Gutierrez, G. (2007). Integrating commodity markets in the optimal procurement policies of a stochastic inventory system. Technical report, University of Texas-Austin.Google Scholar
  14. Golabi, K. (1985). Optimal inventory policies when ordering prices are random. Operations Research, 33(3), 575–588.CrossRefGoogle Scholar
  15. Gümüş, M., Ray, S., & Gurnani, H. (2012). Supply-side story: Risks, guarantees, competition, and information asymmetry. Management Science, 58(9), 1694–1714.CrossRefGoogle Scholar
  16. Guo, X., Kaminsky, P., Tomecek, P., & Yuen, M. (2011). Optimal spot market inventory strategies in the presence of cost and price risk. Mathematical Methods of Operations Research, 73(1), 109–137.CrossRefGoogle Scholar
  17. Guttorp, P. (1995). Stochastic modeling of scientific data. London: Chapman & Hall.CrossRefGoogle Scholar
  18. Ha, A. Y. (1997). Inventory rationing in a make-to-stock production system with several demand classes and lost sales. Management Science, 43(8), 1093–1103.CrossRefGoogle Scholar
  19. Haksöz, Ç., & Seshadri, S. (2007). Supply chain operations in the presence of a spot market: A review with discussion. Journal of the Operational Research Society, 58(11), 1412–1429.CrossRefGoogle Scholar
  20. Kalymon, B. A. (1971). Stochastic prices in a single-item inventory purchasing model. Operations Research, 19(6), 1434–1458.CrossRefGoogle Scholar
  21. Kingsman, B. G. (1986). Purchasing raw materials with uncertain fluctuating prices. European Journal of Operational Research, 25(3), 358–372.CrossRefGoogle Scholar
  22. Lippman, S. A. (1975). Applying a new device in the optimization of exponential queuing systems. Operations Research, 23(4), 687–710.CrossRefGoogle Scholar
  23. Martínez de Albéniz, V., & Simchi-Levi, D. (2005). A portfolio approach to procurement contracts. Production and Operations Management, 14(1), 90–114.CrossRefGoogle Scholar
  24. O’Marah, K. (2009). Supply chain risk: Kanban won’t cut it. Technical report, AMR Research.Google Scholar
  25. Özekici, S., & Parlar, M. (1999). Inventory models with unreliable suppliers in a random environment. Annals of Operations Research, 91(1), 123–136.CrossRefGoogle Scholar
  26. Porteus, E. (1982). Conditions for characterizing the structure of optimal strategies in infinite-horizon dynamic programs. Journal of Optimization Theory and Applications, 36(3), 419–432.CrossRefGoogle Scholar
  27. Porteus, E. L. (2002). Foundations of stochastic inventory theory. Stanford: Stanford University Press.Google Scholar
  28. Puterman, M. L. (1994). Markov decision processes: Discrete stochastic dynamic programming. New York: Wiley.CrossRefGoogle Scholar
  29. Ross, S. M. (1999). An introduction to mathematical finance: Options and other topics. New York: Cambridge University Press.Google Scholar
  30. Schwartz, E. S. (1997). The stochastic behavior of commodity prices: Implications for valuation and hedging. Journal of Finance, 52(3), 923–973.CrossRefGoogle Scholar
  31. Seifert, R. W., Thonemann, U. W., & Hausman, W. H. (2004). Optimal procurement strategies for online spot markets. European Journal of Operational Research, 152(3), 781–799.CrossRefGoogle Scholar
  32. Tang, C. S. (2006). Perspectives in supply chain risk management. International Journal of Production Economics, 103(2), 451–488.CrossRefGoogle Scholar
  33. Vakharia, A. J., & Yenipazarlı, A. (2009). Managing supply chain disruptions. Foundations and Trends in Technology, Information and Operations Management, 2(4), 243–325.CrossRefGoogle Scholar
  34. Wang, Y. (2001). The optimality of myopic stocking policies for systems with decreasing purchasing prices. European Journal of Operational Research, 133(1), 153–159.CrossRefGoogle Scholar
  35. Yang, J., & Xia, Y. (2009). Acquisition management under fluctuating raw material prices. Production and Operations Management, 18(2), 212–225.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Xiaofeng Nie
    • 1
  • Tamer Boyacı
    • 2
  • Mehmet Gümüş
    • 2
  • Saibal Ray
    • 2
  • Dan Zhang
    • 3
  1. 1.School of Mechanical and Aerospace EngineeringNanyang Technological UniversitySingaporeSingapore
  2. 2.Desautels Faculty of ManagementMcGill UniversityMontrealCanada
  3. 3.Leeds School of BusinessUniversity of Colorado at BoulderBoulderUSA

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