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Feasibility principles for Downstream Demand Inference in supply chains

Author

Listed:
  • M M Ali

    (Buckinghamshire New University)

  • J E Boylan

    (Buckinghamshire New University)

Abstract

Many companies are adopting strategies that enable Demand Information Sharing (DIS) between the supply chain links. Recently, a steady stream of research has identified mathematical relationships between demands and orders at any link in the supply chain. Based on these relationships and strict model assumptions, it has been suggested that the upstream member can infer the demand at the downstream member from their orders. If this is so, DIS will be of no value. In this paper, we argue that real-world modelling requires less restrictive assumptions. We present Feasibility Principles to show that it is not possible for an upstream member to accurately infer consumer demand under more realistic model assumptions. Thus, we conclude that DIS has value in supply chains. We then move our focus to the supply chain model assumptions in the papers arguing that there is value in sharing demand information. Using a simulation experiment, we show that the value of sharing demand information in terms of inventory reductions will increase under more realistic supply chain model assumptions.

Suggested Citation

  • M M Ali & J E Boylan, 2011. "Feasibility principles for Downstream Demand Inference in supply chains," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 474-482, March.
    • Handle: RePEc:pal:jorsoc:v:62:y:2011:i:3:d:10.1057_jors.2010.82
    DOI: 10.1057/jors.2010.82
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    References listed on IDEAS

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    1. Kenneth Gilbert, 2005. "An ARIMA Supply Chain Model," Management Science, INFORMS, vol. 51(2), pages 305-310, February.
    2. A A Syntetos & J E Boylan & S M Disney, 2009. "Forecasting for inventory planning: a 50-year review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 149-160, May.
    3. T C E Cheng & Y N Wu, 2005. "The impact of information sharing in a two-level supply chain with multiple retailers," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(10), pages 1159-1165, October.
    4. Kahn, James A, 1987. "Inventories and the Volatility of Production," American Economic Review, American Economic Association, vol. 77(4), pages 667-679, September.
    5. Frank Chen & Zvi Drezner & Jennifer K. Ryan & David Simchi-Levi, 2000. "Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times, and Information," Management Science, INFORMS, vol. 46(3), pages 436-443, March.
    6. Raghunathan, Srinivasan, 2003. "Impact of demand correlation on the value of and incentives for information sharing in a supply chain," European Journal of Operational Research, Elsevier, vol. 146(3), pages 634-649, May.
    7. Hau L. Lee & Kut C. So & Christopher S. Tang, 2000. "The Value of Information Sharing in a Two-Level Supply Chain," Management Science, INFORMS, vol. 46(5), pages 626-643, May.
    8. Z Yu & H Yan & T C E Cheng, 2002. "Modelling the benefits of information sharing-based partnerships in a two-level supply chain," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(4), pages 436-446, April.
    9. Hau L. Lee & V. Padmanabhan & Seungjin Whang, 1997. "Information Distortion in a Supply Chain: The Bullwhip Effect," Management Science, INFORMS, vol. 43(4), pages 546-558, April.
    10. Stephen C. Graves, 1999. "Addendum to "A Single-Item Inventory Model for a Nonstationary Demand Process"," Manufacturing & Service Operations Management, INFORMS, vol. 1(2), pages 174-174.
    11. Hosoda, Takamichi & Disney, Stephen M., 2006. "On variance amplification in a three-echelon supply chain with minimum mean square error forecasting," Omega, Elsevier, vol. 34(4), pages 344-358, August.
    12. Srinivasan Raghunathan, 2001. "Information Sharing in a Supply Chain: A Note on its Value when Demand Is Nonstationary," Management Science, INFORMS, vol. 47(4), pages 605-610, April.
    13. Stephen C. Graves, 1999. "A Single-Item Inventory Model for a Nonstationary Demand Process," Manufacturing & Service Operations Management, INFORMS, vol. 1(1), pages 50-61.
    14. Xiaolong Zhang, 2004. "Evolution of ARMA Demand in Supply Chains," Manufacturing & Service Operations Management, INFORMS, vol. 6(2), pages 195-198, April.
    15. Srinagesh Gavirneni & Roman Kapuscinski & Sridhar Tayur, 1999. "Value of Information in Capacitated Supply Chains," Management Science, INFORMS, vol. 45(1), pages 16-24, January.
    16. Caplin, Andrew S, 1985. "The Variability of Aggregate Demand with (S, s) Inventory Policies," Econometrica, Econometric Society, vol. 53(6), pages 1395-1409, November.
    17. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
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    Cited by:

    1. Syntetos, A.A. & Teunter, R.H., 2014. "On the calculation of safety stocks," Research Report 14003-OPERA, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    2. Prak, Dennis & Teunter, Ruud & Syntetos, Aris, 2017. "On the calculation of safety stocks when demand is forecasted," European Journal of Operational Research, Elsevier, vol. 256(2), pages 454-461.
    3. Tliche, Y. & Taghipour, A. & Canel-Depitre, B., 2019. "Downstream Demand Inference in decentralized supply chains," European Journal of Operational Research, Elsevier, vol. 274(1), pages 65-77.
    4. Ali, Mohammad M. & Boylan, John E. & Syntetos, Aris A., 2012. "Forecast errors and inventory performance under forecast information sharing," International Journal of Forecasting, Elsevier, vol. 28(4), pages 830-841.
    5. 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.
    6. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    7. Cannella, Salvatore & Framinan, Jose M. & Bruccoleri, Manfredi & Barbosa-Póvoa, Ana Paula & Relvas, Susana, 2015. "The effect of Inventory Record Inaccuracy in Information Exchange Supply Chains," European Journal of Operational Research, Elsevier, vol. 243(1), pages 120-129.
    8. Prak, Dennis & Teunter, Ruud, 2019. "A general method for addressing forecasting uncertainty in inventory models," International Journal of Forecasting, Elsevier, vol. 35(1), pages 224-238.
    9. Ali, Mohammad M. & Babai, Mohamed Zied & Boylan, John E. & Syntetos, A.A., 2017. "Supply chain forecasting when information is not shared," European Journal of Operational Research, Elsevier, vol. 260(3), pages 984-994.
    10. Babai, M.Z. & Boylan, J.E. & Syntetos, A.A. & Ali, M.M., 2016. "Reduction of the value of information sharing as demand becomes strongly auto-correlated," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 130-135.
    11. repec:dgr:rugsom:14003-opera is not listed on IDEAS
    12. Pastore, Erica & Alfieri, Arianna & Zotteri, Giulio & Boylan, John E., 2020. "The impact of demand parameter uncertainty on the bullwhip effect," European Journal of Operational Research, Elsevier, vol. 283(1), pages 94-107.
    13. Youssef Tliche & Atour Taghipour & Jomana Mahfod-Leroux & Mohammadali Vosooghidizaji, 2023. "Collaborative Bullwhip Effect-Oriented Bi-Objective Optimization for Inference-Based Weighted Moving Average Forecasting in Decentralized Supply Chain," International Journal of Information Systems and Supply Chain Management (IJISSCM), IGI Global, vol. 16(1), pages 1-37, January.
    14. Babai, M.Z. & Ali, M.M. & Boylan, J.E. & Syntetos, A.A., 2013. "Forecasting and inventory performance in a two-stage supply chain with ARIMA(0,1,1) demand: Theory and empirical analysis," International Journal of Production Economics, Elsevier, vol. 143(2), pages 463-471.

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