Data-Driven Optimization for Commodity Procurement Under Price Uncertainty

Published Online:https://doi.org/10.1287/msom.2020.0890

References

  • Acworth W (2015) 2015 annual survey: Global derivatives volume. Accessed June 19, 2020, https://www.fia.org/index.php/articles/2015-annual-survey-global-derivatives-volume.Google Scholar
  • Andersen R (2008) Modern methods for robust regression. Liao TF, ed. Quantitative Applications in the Social Sciences (Sage Publications, Thousand Oaks, CA).CrossrefGoogle Scholar
  • Andersson H (2007) Are commodity prices mean reverting? Appl. Financial Econom. 17(10):769–783.CrossrefGoogle Scholar
  • Andrejczak M (2008) Against the grain: Food firms hedge costs. Wall Street Journal (March 24), http://www.wsj.com/articles/SB120632251149658521.Google Scholar
  • Ban G-Y (2020) Confidence intervals for data-driven inventory policies with demand censoring. Oper. Res. 68(2):309–654.LinkGoogle Scholar
  • Ban G-Y, Rudin C (2019) The big data newsvendor: Practical insights from machine learning. Oper. Res. 67(1):90–108.LinkGoogle Scholar
  • Ban G-Y, El Karoui N, Lim A (2018) Machine learning and portfolio optimization. Management Sci. 64(3):1136–1154.LinkGoogle Scholar
  • Ban G, Gallien J, Mersereau A (2019) Dynamic procurement of new products with covariate information: The residual tree method. Manufacturing Service Oper. Management 21(4):713–948.LinkGoogle Scholar
  • Bartram SM, Brown GW, Fehle FR (2009) International evidence on financial derivatives usage. Financial Management 38(1):185–206.CrossrefGoogle Scholar
  • Belt A, Boudier E (2017) Capturing commodity trading’s $70 billion prize—How digitalization is changing commodity trading. BCG (June 27), https://www.bcg.com/publications/2017/commodity-trading-risk-management-energy-environment-capturing-commodity-trading-billion-prize.aspx.Google Scholar
  • Berling P, Martínez-de-Albeníz V (2011) Optimal inventory policies when purchase price and demand are stochastic. Oper. Res. 59(1):109–124.LinkGoogle Scholar
  • Bertsimas D, Kallus N (2020) From predictive to prescriptive analytics. Management Sci. 66(3):1025–1044.Google Scholar
  • Beutel A-L, Minner S (2012) Safety stock planning under causal demand forecasting. Internat. J. Production Econom. 140(2):637–645.CrossrefGoogle Scholar
  • Brown S, Yücel K (2008) What drives natural gas prices? Energy J. 29(2):43–58.CrossrefGoogle Scholar
  • Carey S (2016) Airlines pull back on hedging fuel costs. Wall Street Journal (March 20), http://www.wsj.com/articles/airlines-pull-back-on-hedging-fuel-costs-1458514901.Google Scholar
  • Cohen T (2016) Israeli shipping data firm plans push into commodities markets. Reuters (March 16), https://www.reuters.com/article/us-tech-shipping-windward/israeli-shipping-data-firm-plans-push-into-commodities-markets.Google Scholar
  • Cohen MC, Lobel I, Leme RP (2016) Feature-based dynamic pricing. Working paper, New York University Stern School of Business, New YorkGoogle Scholar
  • Cortazar G, Millard C, Ortega H, Schwartz E (2019) Commodity price forecasts, futures prices and pricing models. Management Sci. 65(9):3949–4450.LinkGoogle Scholar
  • Curtis FE, Scheinberg K (2017) Optimization methods for supervised machine learning: From linear models to deep learning. Batta R, Peng J, eds. Leading Developments from INFORMS Communities, TutORials in Operations Research (INFORMS Catonsville, MD), 89–113.Google Scholar
  • Elmachtoub AN, Grigas P (2017) Smart “predict, then optimize.” Working paper, Columbia University, New York.Google Scholar
  • Froot KA, Scharfstein DS, Stein JC (1993) Risk management: Coordinating corporate investment and financing policies. J. Finance 47(5):1629–1658.CrossrefGoogle Scholar
  • Gaur V, Seshadri S (2005) Hedging inventory risk through market instruments. Manufacturing Service Oper. Management 7(2):103–120.LinkGoogle Scholar
  • Geman H (2005) Commodities and Commodity Derivatives (Wiley Finance, Chichester, UK).Google Scholar
  • Geman H (2007) Mean reversion vs. random walk in oil and natural gas prices. Fu MC, Jarrow RA, Yen J-Y, Elliott RJ, eds. Advances in Mathematical Finance (Springer, Boston), 219–228.CrossrefGoogle Scholar
  • Geman H, Nguyen V (2005) Soybean inventory and forward curve dynamics. Management Sci. 51(7):1076–1091.LinkGoogle Scholar
  • Georghiou A, Wiesemann W, Kuhn D (2005) The decision rule approach to ptimization under uncertainty: Methodology and applications in operations management. Working paper, McGill University, Montreal.Google Scholar
  • Goel A, Gutierrez GJ (2011) Multiechelon procurement and distribution policies for traded commodities. Management Sci. 57(12):2228–2244.LinkGoogle Scholar
  • Haksöz Ç, Seshadri S (2007) Supply chain operations in the presence of a spot market: A review with discussion. J. Oper. Res. Soc. 58(11):1412–1429.CrossrefGoogle Scholar
  • Hansen BE (2007) Least squares model averaging. Econometrica 75(4):1175–1189.CrossrefGoogle Scholar
  • Hastie T, Tibshirani R, Friedman J (2013) The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Series in Statistics (Springer, New York). Google Scholar
  • Hay GA, Holt CC (1975) A general solution for linear decision rules: An optimal dynamic strategy applicable under uncertainty. Econometrica 43(2):231–259.CrossrefGoogle Scholar
  • Heath D (2019) Macroeconomic factors in oil futures markets. Management Sci. 65(9):3949–4450.Google Scholar
  • Hull JC (2005) Options, Futures and Other Derivatives, 9th ed. (Pearson Prentice Hall, Upper Saddle River, NJ).Google Scholar
  • Lacima (2018) Lacima Analytics: Valuation and Optimization Suite. Accessed March 1, 2018, http://www.lacimagroup.com/page18759/ValuationampOptimisationSuite.aspx.Google Scholar
  • Lai G, Margot F, Secomandi N (2010) An approximate dynamic programming approach to benchmark practice-based heuristics for natural gas storage valuation. Oper. Res. 58(3):564–582.LinkGoogle Scholar
  • MathWorks (2018) Natural Gas Storage Valuation. Accessed March 1, 2018, http://www.mathworks.com/matlabcentral/fileexchange/47667-natural-gas-storage-valuation.Google Scholar
  • Modigliani F, Miller MH (1958) The cost of capital, corporation finance and the theory of investment. Amer. Econom. Rev. 48(3):261–297.Google Scholar
  • Mohri M, Rostamizadeh A, Talwalkar A (2012) Foundations of Machine Learning (MIT Press, Boston).Google Scholar
  • Nadarajah S, Margot F, Secomandi N (2015) Relaxations of approximate linear programs for the real option management of commodity storage. Management Sci. 61(12):3054–3076.LinkGoogle Scholar
  • OpenMarkets (2014) How satellite technology is predicting crop yields. OpenMarkets (December 22), http://openmarkets.cmegroup.com/9625/how-satellite-technology-is-predicting-crop-yields.Google Scholar
  • Pindyck R (2004) Volatility and commodity price dynamics. J. Futures Markets 24(11):1029–1047.CrossrefGoogle Scholar
  • Pindyck R, Rotemberg J (1990) The excess co-movement of commodity prices. Econom. J. 100(403):1173–1189.Google Scholar
  • Pirrong G (2011) Commodity Price Dynamics—A Structural Approach (Cambridge University Press, Cambridge, UK).CrossrefGoogle Scholar
  • Sauter P (2014) Big data in procurement. White paper, Arthur D. Little, Boston. Accessed June 19, 2020, https://www.adlittle.com/en/insights/viewpoints/big-data-procurement.Google Scholar
  • Schwartz E (1997) The stochastic behavior of commodity prices: Implications for valuation and hedging. J. Finance 52(3):923–973.CrossrefGoogle Scholar
  • Schwartz E, Smith J (2000) Short-term variations and long-term dynamics in commodity prices. Management Sci. 46(7):893–911.LinkGoogle Scholar
  • Secomandi N (2010) Optimal commodity trading with a capacitated storage asset. Management Sci. 56(3):449–467.LinkGoogle Scholar
  • Secomandi N (2015) Merchant commodity storage practice revisited. Oper. Res. 63(5):1131–1143.LinkGoogle Scholar
  • Secomandi N, Kekre S (2014) Optimal energy procurement in spot and forward markets. Manufacturing Service Oper. Management 16(2):270–282.LinkGoogle Scholar
  • Secomandi N, Lai G, Margot F, Scheller-Wolf A, Seppi DJ (2015) Merchant commodity storage and term-structure model error. Manufacturing Service Oper. Management 17(3):302–320.LinkGoogle Scholar
  • Shapiro A, Dentcheva D, Ruszczyński A (2009) Lectures on Stochastic Programming (SIAM, Philadelphia).CrossrefGoogle Scholar
  • Smith CW, Stulz RM (1985) The determinants of firms’ hedging policies. J. Financial Quant. Anal. 20(4):391–405.CrossrefGoogle Scholar
  • Stoll SO, Wiebauer K (2010) A spot price model for natural gas considering temperature as an exogenous factor and applications. J. Energy Markets 3(3):113–128.CrossrefGoogle Scholar
  • Vapnik VN (1998) Statistical Learning Theory (Wiley, New York).Google Scholar
  • Wang Y, Wu C, Yang L (2015) Hedging with futures: Does anything beat the naïve hedging strategy? Management Sci. 61(12):2870–2889.LinkGoogle Scholar
  • Wu O, Wang D, Qin Z (2012) Seasonal energy storage operations with limited flexibility: The price-adjusted rolling intrinsic policy. Manufacturing Service Oper. Management 14(3):455–471.LinkGoogle Scholar
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