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On the replenishment policy when the market demand information is lagged

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  • Hosoda, Takamichi
  • Disney, Stephen M.

Abstract

We consider a situation where the most up-to-date information on the market demand and the inventory levels is not available to a replenishment decision maker in a single echelon of a supply chain. The objective of the decision maker is to minimise the sum of the inventory and the production costs. An intuitively attractive strategy under this setting might be to reduce the information time lag as much as possible by utilising information technologies such as RFID. We call this strategy the Time lag Elimination Strategy (TES). However, this course of action requires investment in information systems and will incur a running cost. We propose an alternative strategy that has similar economic consequences as the TES strategy, but it does not require new information systems. We call this strategy the Controlling Dynamics Strategy (CDS). The benefit coming from CDS is quantified and is compared to that from TES. We also quantify the benefits gained from the combined use of these two strategies. A new ordering policy is introduced that is easy to implement without any forecasting systems and can reduce the production cost significantly.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:proeco:v:135:y:2012:i:1:p:458-467
    DOI: 10.1016/j.ijpe.2011.08.022
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    References listed on IDEAS

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    1. 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.
    2. 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.
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    5. A. Bensoussan & M. Çakanyildirim & S. P. Sethi, 2006. "Optimality of Base-Stock and (s, S) Policies for Inventory Problems with Information Delays," Journal of Optimization Theory and Applications, Springer, vol. 130(2), pages 153-172, August.
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    7. Julia Miyaoka & Warren Hausman, 2004. "How a Base Stock Policy Using "Stale" Forecasts Provides Supply Chain Benefits," Manufacturing & Service Operations Management, INFORMS, vol. 6(2), pages 149-162, September.
    8. Gaalman, Gerard & Disney, Stephen M., 2006. "State space investigation of the bullwhip problem with ARMA(1,1) demand processes," International Journal of Production Economics, Elsevier, vol. 104(2), pages 327-339, December.
    9. Johnson, Mark & Mena, Carlos, 2008. "Supply chain management for servitised products: A multi-industry case study," International Journal of Production Economics, Elsevier, vol. 114(1), pages 27-39, July.
    10. Disney, S. M. & Towill, D. R., 2003. "On the bullwhip and inventory variance produced by an ordering policy," Omega, Elsevier, vol. 31(3), pages 157-167, June.
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    Cited by:

    1. Hoberg, Kai & Thonemann, Ulrich W., 2014. "Modeling and analyzing information delays in supply chains using transfer functions," International Journal of Production Economics, Elsevier, vol. 156(C), pages 132-145.
    2. Liu, Yunqiang & Liu, Sha & Ye, Deping & Tang, Hong & Wang, Fang, 2022. "Dynamic impact of negative public sentiment on agricultural product prices during COVID-19," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
    3. Qinyun Li & Stephen M. Disney, 2017. "Revisiting rescheduling: MRP nervousness and the bullwhip effect," International Journal of Production Research, Taylor & Francis Journals, vol. 55(7), pages 1992-2012, April.
    4. 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).
    5. Lin, J. & Naim, M.M. & Purvis, L. & Gosling, J., 2017. "The extension and exploitation of the inventory and order based production control system archetype from 1982 to 2015," International Journal of Production Economics, Elsevier, vol. 194(C), pages 135-152.
    6. 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.
    7. 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.
    8. Lin, Junyi & Naim, Mohamed M. & Spiegler, Virginia L.M., 2020. "Delivery time dynamics in an assemble-to-order inventory and order based production control system," International Journal of Production Economics, Elsevier, vol. 223(C).
    9. Claudia Aparecida de Mattos & Fernanda Caveiro Correia & Kumiko Oshio Kissimoto, 2024. "Artificial Intelligence Capabilities for Demand Planning Process," Logistics, MDPI, vol. 8(2), pages 1-16, May.
    10. Hedenstierna, Carl Philip T. & Disney, Stephen M., 2018. "Avoiding the capacity cost trap: Three means of smoothing under cyclical production planning," International Journal of Production Economics, Elsevier, vol. 201(C), pages 149-162.
    11. Anke, Jürgen, 2017. "IoT-coordinated logistics in product-service systems," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Digitalization in Supply Chain Management and Logistics: Smart and Digital Solutions for an Industry 4.0 Environment. Proceedings of the Hamburg Inter, volume 23, pages 35-54, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.

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