Abstract
Globally operating suppliers face the rising challenge of wholesale pricing under scarce data about retail demand, in contrast to better informed, locally operating retailers. At the same time, as local businesses proliferate, markets congest and retail competition increases. To capture these strategic considerations, we employ the classic Cournot model and extend it to a two-stage supply chain with an upstream supplier who operates under demand uncertainty and multiple downstream retailers who compete over quantity. The supplier’s belief about retail demand is modeled via a continuous probability distribution function F. If F has the decreasing generalized mean residual life property, then the supplier’s optimal pricing policy exists and is the unique fixed point of the mean residual life function. We evaluate the realized Price of Uncertainty and show that there exist demand levels for which market performs better when the supplier prices under demand uncertainty. In general, performance worsens for lower values of realized demand. We examine the effects of increasing competition on supply chain efficiency via the realized Price of Anarchy and complement our findings with numerical results.
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References
Balcan, M.-F., Blum, A., Mansour, Y.: The price of uncertainty. ACM Trans. Econ. Comput. 1(3), 15:1–15:29 (2013). https://doi.org/10.1145/2509413.2509415
Banciu, M., Mirchandani, P.: Technical note - new results concerning probability distributions with increasing generalized failure rates. Oper. Res. 61(4), 925–931 (2013). https://doi.org/10.1287/opre.2013.1198
Belzunce, F., Martinez-Riquelme, C., Mulero J.: Univariate stochastic orders. In: An Introduction to Stochastic Orders, Chap. 2. Academic Press (2016). https://doi.org/10.1016/B978-0-12-803768-3.00002-8
Herweg, F.: The expectation-based loss-averse newsvendor. Econ. Lett. 120(3), 429–432 (2013). https://doi.org/10.1016/j.econlet.2013.05.035
Lariviere, M., Porteus, E.: Selling to the newsvendor: an analysis of price-only contracts. Manuf. Serv. Oper. Manag. 3(4), 293–305 (2001). https://doi.org/10.1287/msom.3.4.293.9971
Lariviere, M.: A note on probability distributions with increasing generalized failure rates. Oper. Res. 54(3), 602–604 (2006). https://doi.org/10.1007/978-1-4615-4949-9_8
Leonardos, S., Melolidakis, C.: Selling to cournot oligopolists: pricing under uncertainty & generalized mean residual life (2017). https://arxiv.org/abs/1709.09618
Leonardos, S., Melolidakis, C.: Comparative Statics via Stochastic Orderings in a Two-Echelon Market with Upstream Demand Uncertainty. AIRO Springer Series (2018, forthcoming). https://arxiv.org/abs/1803.03451
Lu, Y., Simchi-Levi, D.: On the unimodality of the profit function of the pricing newsvendor. Prod. Oper. Manag. 22, 615–625 (2013). https://doi.org/10.1111/j.1937-5956.2012.01419.x
Mandal, P., Kaul, R., Jain, T.: Stocking and pricing decisions under endogenous demand and reference point effects. Eur. J. Oper. Res. 264(1), 181–199 (2018). https://doi.org/10.1016/j.ejor.2017.05.053
Perakis, G., Roels, G.: The price of anarchy in supply chains: quantifying the efficiency of price-only contracts. Manag. Sci. 53(8), 1249–1268 (2007). https://doi.org/10.1287/mnsc.1060.0656
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Melolidakis, C., Leonardos, S., Koki, C. (2018). Measuring Market Performance with Stochastic Demand: Price of Anarchy and Price of Uncertainty. In: Destercke, S., Denoeux, T., Cuzzolin, F., Martin, A. (eds) Belief Functions: Theory and Applications. BELIEF 2018. Lecture Notes in Computer Science(), vol 11069. Springer, Cham. https://doi.org/10.1007/978-3-319-99383-6_21
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DOI: https://doi.org/10.1007/978-3-319-99383-6_21
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