Abstract
In this paper we consider a procurement problem under purchase price uncertainty, which is the case encountered by companies who purchase from spot markets with fluctuating prices. We develop a procurement model by introducing the dynamics of information revelation via Bayesian learning, derive its optimal solution and identify some thresholds to improve purchase timing decisions. Using historical spot price data of crudes oils, we verify the effectiveness of proposed policies compared to the current policy of Chinese oil refineries, and find the Bayesian learning model does perform well—billions of dollars could be saved over the past several years.
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Acknowledgments
This study is supported in part by the National Natural Science Foundation of China (Grant No. 70771058/60834004), and the 863 Program of China (2008AA04Z102).
The authors thank H. S. Deng, X. F. Li, A. B. Pang, and G. P. Xiao from the China Petroleum & Chemical Corporation for the detailed information on refinery procurement operations. The authors also thank Prof. C. S. Park from Auburn University for his patient instructions on Bayesian learning approach.
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© 2013 Springer-Verlag Berlin Heidelberg
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Xie, Zx., Zheng, L. (2013). A Bayesian Learning Approach for Making Procurement Policies Under Price Uncertainty. In: Qi, E., Shen, J., Dou, R. (eds) The 19th International Conference on Industrial Engineering and Engineering Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38391-5_1
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DOI: https://doi.org/10.1007/978-3-642-38391-5_1
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