Abstract
In this work, we investigate the implications of commodity price uncertainty for optimal procurement and inventory control decisions. While the existing literature typically relies on the full information paradigm, i.e., optimizing procurement and inventory decisions under full information of the underlying stochastic price process, we develop and test different data-driven approaches that optimize decisions under very limited statistical model assumptions. Our results are data-driven policies and decision rules that can support commodity procurement managers, inventory managers as well as commodity merchants. We furthermore test all optimization models based on real data from different commodity classes (i.e., metals, energy and agricultural).
This paper is a summary of the author’s dissertation (Mandl C. (2019). Optimal Procurement and Inventory Control in Volatile Commodity Markets - Advances in Stochastic and Data-Driven Optimization, [1]).
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References
Mandl, C.: Optimal Procurement and Inventory Control in Volatile Commodity Markets - Advances in Stochastic and Data-Driven Optimization. Dissertation, TUM School of Management, Technische Universität München (2019)
Refinitiv Eikon. https://eikon.thomsonreuters.com/index.html
Beschaffungsmanagement: Rohstoffkosten-Management. Einkaufsexperten fordern jetzt aktives Management von Rohstoffkosten für schlechtere Zeiten. Beschaffungsmanagement 04/2009, pp. 12–13 (2009)
Beutel, A.-L., Minner, S.: Safety stock planning under causal demand forecasting. Int. J. Prod. Econ. 140(2), 637–645 (2012)
Ban, G.-Y., Rudin, C.: The big data newsvendor: practical insights from machine learning. Oper. Res. 67(1), 90–108 (2019)
Hamilton, J.D.: A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57(2), 357–384 (1989)
Haköz, C., Seshadri, S.: Supply chain operations in the presence of a spot market: a review with discussion. J. Oper. Res. Soc. 58(11), 1412–1429 (2007)
Secomandi, N., Seppi, D.J.: Real options and merchant operations of energy and other commodities. Found. Trends Technol. Inf. Oper. Manag. 6(3–4), 161–331 (2012)
Mandl, C., Minner, S.: Data-driven optimization for commodity procurement under price uncertainty. Manuf. Serv. Oper. Manag. (2021). https://doi.org/10.1287/msom.2020.0890
Mandl, C., Minner, S.: When Do Commodity Spot Price Regimes Matter for Inventory Managers? Working Paper (2021). https://ssrn.com/abstract=3011340
Mandl, C., Nadarajah, S., Minner, S., Gavirneni, N.: Data-driven storage operations: Cross-commodity backtest and structured policies. Prod. Oper. Manage. (2022). https://onlinelibrary.wiley.com/doi/full/10.1111/poms.13683
Acknowledgments
While working on this thesis, the author was doctoral student at the chair of logistics and supply chain management (Prof. Dr. Stefan Minner) in the School of Management at Technische Universität München.
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Mandl, C. (2022). Prescriptive Analytics for Commodity Procurement Applications. In: Trautmann, N., Gnägi, M. (eds) Operations Research Proceedings 2021. OR 2021. Lecture Notes in Operations Research. Springer, Cham. https://doi.org/10.1007/978-3-031-08623-6_5
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DOI: https://doi.org/10.1007/978-3-031-08623-6_5
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