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On order smoothing interpolating the order-up-to and constant order policies

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  • Cui, Geng
  • Imura, Naoto
  • Nishinari, Katsuhiro
  • Ezaki, Takahiro

Abstract

We investigate order smoothing by interpolating two well-known inventory replenishment policies: the order-up-to policy and the constant order policy, as a means to improve supply chain performance. Order smoothing mitigates the bullwhip effect, reducing the risk of order fluctuations and inventory fluctuations for upstream supply chain echelons. Based on a two-echelon supply chain model, we explore the conditions under which order smoothing becomes desirable compared to situations without order smoothing, focusing on customer demand characteristics and smoothing strength. Our analysis examines both sinusoidal and stochastic responses to provide two distinct yet interrelated perspectives: amplitude in the frequency domain and the time domain. We highlight the significant impact of auto-correlated demand processes, which are prevalent in practice and relevant literature, on the economic benefits of order smoothing. Furthermore, we demonstrate that our proposed policy is an extension of the well-studied proportional order-up-to policy and can outperform it in certain scenarios.

Suggested Citation

  • Cui, Geng & Imura, Naoto & Nishinari, Katsuhiro & Ezaki, Takahiro, 2025. "On order smoothing interpolating the order-up-to and constant order policies," Omega, Elsevier, vol. 136(C).
  • Handle: RePEc:eee:jomega:v:136:y:2025:i:c:s0305048325000520
    DOI: 10.1016/j.omega.2025.103326
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    1. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    2. De Moor, Bram J. & Gijsbrechts, Joren & Boute, Robert N., 2022. "Reward shaping to improve the performance of deep reinforcement learning in perishable inventory management," European Journal of Operational Research, Elsevier, vol. 301(2), pages 535-545.
    3. Hosoda, Takamichi & Disney, Stephen M., 2012. "On the replenishment policy when the market demand information is lagged," International Journal of Production Economics, Elsevier, vol. 135(1), pages 458-467.
    4. Zhou, Li & Naim, Mohamed M. & Disney, Stephen M., 2017. "The impact of product returns and remanufacturing uncertainties on the dynamic performance of a multi-echelon closed-loop supply chain," International Journal of Production Economics, Elsevier, vol. 183(PB), pages 487-502.
    5. Disney, S. M. & Towill, D. R., 2003. "The effect of vendor managed inventory (VMI) dynamics on the Bullwhip Effect in supply chains," International Journal of Production Economics, Elsevier, vol. 85(2), pages 199-215, August.
    6. Dejonckheere, J. & Disney, S. M. & Lambrecht, M. R. & Towill, D. R., 2003. "Measuring and avoiding the bullwhip effect: A control theoretic approach," European Journal of Operational Research, Elsevier, vol. 147(3), pages 567-590, June.
    7. Gaalman, Gerard & Disney, Stephen M. & Wang, Xun, 2022. "When bullwhip increases in the lead time: An eigenvalue analysis of ARMA demand," International Journal of Production Economics, Elsevier, vol. 250(C).
    8. Afshin Oroojlooyjadid & MohammadReza Nazari & Lawrence V. Snyder & Martin Takáč, 2022. "A Deep Q-Network for the Beer Game: Deep Reinforcement Learning for Inventory Optimization," Manufacturing & Service Operations Management, INFORMS, vol. 24(1), pages 285-304, January.
    9. Li Chen & Hau L. Lee, 2012. "Bullwhip Effect Measurement and Its Implications," Operations Research, INFORMS, vol. 60(4), pages 771-784, August.
    10. Nielsen, Christina & Larsen, Christian, 2005. "An analytical study of the Q(s,S) policy applied to the joint replenishment problem," European Journal of Operational Research, Elsevier, vol. 163(3), pages 721-732, June.
    11. Isaksson, Olov H.D. & Seifert, Ralf W., 2016. "Quantifying the bullwhip effect using two-echelon data: A cross-industry empirical investigation," International Journal of Production Economics, Elsevier, vol. 171(P3), pages 311-320.
    12. Kenneth Gilbert, 2005. "An ARIMA Supply Chain Model," Management Science, INFORMS, vol. 51(2), pages 305-310, February.
    13. Hosoda, Takamichi & Disney, Stephen M., 2009. "Impact of market demand mis-specification on a two-level supply chain," International Journal of Production Economics, Elsevier, vol. 121(2), pages 739-751, October.
    14. Disney, S. M. & Towill, D. R. & van de Velde, W., 2004. "Variance amplification and the golden ratio in production and inventory control," International Journal of Production Economics, Elsevier, vol. 90(3), pages 295-309, August.
    15. 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.
    16. Wang, Xun & Disney, Stephen M., 2016. "The bullwhip effect: Progress, trends and directions," European Journal of Operational Research, Elsevier, vol. 250(3), pages 691-701.
    17. Yu-Sheng Zheng & A. Federgruen, 1991. "Finding Optimal (s, S) Policies Is About As Simple As Evaluating a Single Policy," Operations Research, INFORMS, vol. 39(4), pages 654-665, August.
    18. Hosoda, Takamichi & Disney, Stephen M. & Gavirneni, Srinagesh, 2015. "The impact of information sharing, random yield, correlation, and lead times in closed loop supply chains," European Journal of Operational Research, Elsevier, vol. 246(3), pages 827-836.
    19. 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.
    20. Carbonneau, Real & Laframboise, Kevin & Vahidov, Rustam, 2008. "Application of machine learning techniques for supply chain demand forecasting," European Journal of Operational Research, Elsevier, vol. 184(3), pages 1140-1154, February.
    21. Dehaybe, Henri & Catanzaro, Daniele & Chevalier, Philippe, 2024. "Deep Reinforcement Learning for inventory optimization with non-stationary uncertain demand," European Journal of Operational Research, Elsevier, vol. 314(2), pages 433-445.
    22. 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.
    23. 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.
    24. Dejonckheere, J. & Disney, S. M. & Lambrecht, M. R. & Towill, D. R., 2004. "The impact of information enrichment on the Bullwhip effect in supply chains: A control engineering perspective," European Journal of Operational Research, Elsevier, vol. 153(3), pages 727-750, March.
    25. Boute, Robert N. & Gijsbrechts, Joren & van Jaarsveld, Willem & Vanvuchelen, Nathalie, 2022. "Deep reinforcement learning for inventory control: A roadmap," European Journal of Operational Research, Elsevier, vol. 298(2), pages 401-412.
    26. Agrawal, Sunil & Sengupta, Raghu Nandan & Shanker, Kripa, 2009. "Impact of information sharing and lead time on bullwhip effect and on-hand inventory," European Journal of Operational Research, Elsevier, vol. 192(2), pages 576-593, January.
    27. Li, Qinyun & Disney, Stephen M. & Gaalman, Gerard, 2014. "Avoiding the bullwhip effect using Damped Trend forecasting and the Order-Up-To replenishment policy," International Journal of Production Economics, Elsevier, vol. 149(C), pages 3-16.
    28. Svetunkov, Ivan & Boylan, John E., 2023. "iETS: State space model for intermittent demand forecasting," International Journal of Production Economics, Elsevier, vol. 265(C).
    29. Frank Chen & Jennifer K. Ryan & David Simchi‐Levi, 2000. "The impact of exponential smoothing forecasts on the bullwhip effect," Naval Research Logistics (NRL), John Wiley & Sons, vol. 47(4), pages 269-286, June.
    30. Joren Gijsbrechts & Robert N. Boute & Jan A. Van Mieghem & Dennis J. Zhang, 2022. "Can Deep Reinforcement Learning Improve Inventory Management? Performance on Lost Sales, Dual-Sourcing, and Multi-Echelon Problems," Manufacturing & Service Operations Management, INFORMS, vol. 24(3), pages 1349-1368, May.
    31. Robert N. Boute & Jan A. Van Mieghem, 2015. "Global Dual Sourcing and Order Smoothing: The Impact of Capacity and Lead Times," Management Science, INFORMS, vol. 61(9), pages 2080-2099, September.
    32. Rachel Croson & Karen Donohue, 2006. "Behavioral Causes of the Bullwhip Effect and the Observed Value of Inventory Information," Management Science, INFORMS, vol. 52(3), pages 323-336, March.
    33. Anantaram Balakrishnan & Joseph Geunes & Michael S. Pangburn, 2004. "Coordinating Supply Chains by Controlling Upstream Variability Propagation," Manufacturing & Service Operations Management, INFORMS, vol. 6(2), pages 163-183, July.
    34. Li, Qinyun & Gaalman, Gerard & Disney, Stephen M., 2023. "On the equivalence of the proportional and damped trend order-up-to policies: An eigenvalue analysis," International Journal of Production Economics, Elsevier, vol. 265(C).
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