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Long-term forecasting of fuel demand at theater entry points

Author

Listed:
  • Lobo, Benjamin J.
  • Brown, Donald E.
  • Grazaitis, Peter J.

Abstract

Managing the distribution of fuel in theater requires Army fuel planners to forecast demand at the strategic level to ensure that fuel will be in the right place, at the right time, and in the amounts needed. This work presents a simulation approach to forecasting that accounts for the structure of the supply chain network when aggregating the demand of war fighters across the theater over the forecasting horizon. The resulting empirical distribution of demand at the theater entry point enables planners to identify forecast characteristics that impact their planning process, including the amplitudes and temporal positions of peaks in demand, and the estimated lead time to the point of use. Experimentation indicates that the forecasts are sensitive to the pattern of war fighter demand, the precise structure of the in-theater supply chain network, and the constraints and uncertainty present in the network, all of which are critical planning considerations.

Suggested Citation

  • Lobo, Benjamin J. & Brown, Donald E. & Grazaitis, Peter J., 2019. "Long-term forecasting of fuel demand at theater entry points," International Journal of Forecasting, Elsevier, vol. 35(2), pages 502-520.
    • Handle: RePEc:eee:intfor:v:35:y:2019:i:2:p:502-520
    DOI: 10.1016/j.ijforecast.2018.09.001
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    References listed on IDEAS

    as
    1. repec:dau:papers:123456789/6792 is not listed on IDEAS
    2. Mangalova, E. & Agafonov, E., 2014. "Wind power forecasting using the k-nearest neighbors algorithm," International Journal of Forecasting, Elsevier, vol. 30(2), pages 402-406.
    3. George I. Treyz & Dan S. Rickman & Gang Shao, 1991. "The REMI Economic-Demographic Forecasting and Simulation Model," International Regional Science Review, , vol. 14(3), pages 221-253, December.
    4. Schanne, N. & Wapler, R. & Weyh, A., 2010. "Regional unemployment forecasts with spatial interdependencies," International Journal of Forecasting, Elsevier, vol. 26(4), pages 908-926, October.
    5. Taylor, James W., 2008. "An evaluation of methods for very short-term load forecasting using minute-by-minute British data," International Journal of Forecasting, Elsevier, vol. 24(4), pages 645-658.
    6. Rego, José Roberto do & Mesquita, Marco Aurélio de, 2015. "Demand forecasting and inventory control: A simulation study on automotive spare parts," International Journal of Production Economics, Elsevier, vol. 161(C), pages 1-16.
    7. Greg H. Parlier, 2016. "Mission-Based Forecasting: Demand Forecasting for Military Operations," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 43, pages 32-37, Fall.
    8. Xie, Jingrui & Hong, Tao, 2016. "GEFCom2014 probabilistic electric load forecasting: An integrated solution with forecast combination and residual simulation," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1012-1016.
    9. Chèze, Benoît & Gastineau, Pascal & Chevallier, Julien, 2011. "Forecasting world and regional aviation jet fuel demands to the mid-term (2025)," Energy Policy, Elsevier, vol. 39(9), pages 5147-5158, September.
    10. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    11. Windisch, Johannes & Väätäinen, Kari & Anttila, Perttu & Nivala, Mikko & Laitila, Juha & Asikainen, Antti & Sikanen, Lauri, 2015. "Discrete-event simulation of an information-based raw material allocation process for increasing the efficiency of an energy wood supply chain," Applied Energy, Elsevier, vol. 149(C), pages 315-325.
    12. Taylor, James W. & de Menezes, Lilian M. & McSharry, Patrick E., 2006. "A comparison of univariate methods for forecasting electricity demand up to a day ahead," International Journal of Forecasting, Elsevier, vol. 22(1), pages 1-16.
    13. Beamon, Benita M., 1998. "Supply chain design and analysis:: Models and methods," International Journal of Production Economics, Elsevier, vol. 55(3), pages 281-294, August.
    14. Lobo, Benjamin J. & Brown, Donald E. & Gerber, Matthew S. & Grazaitis, Peter J., 2018. "A transient stochastic simulation–optimization model for operational fuel planning in-theater," European Journal of Operational Research, Elsevier, vol. 264(2), pages 637-652.
    15. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.
    Full references (including those not matched with items on IDEAS)

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