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Managing Supply Chain Demand Variability with Scheduled Ordering Policies

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  • Gérard P. Cachon

    (Duke University, Fuqua School of Business, Durham, North Carolina 27708)

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    Abstract

    This paper studies supply chain demand variability in a model with one supplier and Nretailers that face stochastic demand. Retailers implement scheduled ordering policies: Orders occur at fixed intervals and are equal to some multiple of a fixed batch size. A method is presented that exactly evaluates costs. Previous research demonstrates that the supplier's demand variance declines as the retailers' order intervals are balanced, i.e., the same number of retailers order each period. This research shows that the supplier's demand variance will (generally) decline as the retailers' order interval is lengthened or as their batch size is increased. Lower supplier demand variance can certainly lead to lower inventory at the supplier. This paper finds that reducing supplier demand variance with scheduled ordering policies can also lower total supply chain costs.

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    File URL: http://dx.doi.org/10.1287/mnsc.45.6.843
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 45 (1999)
    Issue (Month): 6 (June)
    Pages: 843-856

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    Handle: RePEc:inm:ormnsc:v:45:y:1999:i:6:p:843-856

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    Related research

    Keywords: supply chain management; multi-echelon inventory; bullwhip effect;

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    Cited by:
    1. Potter, Andrew & Disney, Stephen M., 2006. "Bullwhip and batching: An exploration," International Journal of Production Economics, Elsevier, vol. 104(2), pages 408-418, December.
    2. Riddalls, C. E. & Bennett, S., 2001. "The optimal control of batched production and its effect on demand amplification," International Journal of Production Economics, Elsevier, vol. 72(2), pages 159-168, July.
    3. Lai, Richard, 2005. "Bullwhip in a Spanish Shop," MPRA Paper 4758, University Library of Munich, Germany.
    4. Noblesse, Ann M. & Boute, Robert N. & Lambrecht, Marc R. & Van Houdt, Benny, 2014. "Characterizing order processes of continuous review (s,S) and (r,nQ) policies," European Journal of Operational Research, Elsevier, vol. 236(2), pages 534-547.
    5. Yao, Jianming & Liu, Liwen, 2009. "Optimization analysis of supply chain scheduling in mass customization," International Journal of Production Economics, Elsevier, vol. 117(1), pages 197-211, January.
    6. Wang, Qinan, 2013. "A periodic-review inventory control policy for a two-level supply chain with multiple retailers and stochastic demand," European Journal of Operational Research, Elsevier, vol. 230(1), pages 53-62.
    7. Yan, Xi Steven & Robb, David J. & Silver, Edward A., 2009. "Inventory performance under pack size constraints and spatially-correlated demand," International Journal of Production Economics, Elsevier, vol. 117(2), pages 330-337, February.
    8. Casaburi, Lorenzo & Minerva, G. Alfredo, 2011. "Production in advance versus production to order: The role of downstream spatial clustering and product differentiation," Journal of Urban Economics, Elsevier, vol. 70(1), pages 32-46, July.
    9. Ewing, Bradley T. & Thompson, Mark A., 2008. "Industrial production, volatility, and the supply chain," International Journal of Production Economics, Elsevier, vol. 115(2), pages 553-558, October.
    10. Hwarng, H. Brian & Yuan, Xuchuan, 2014. "Interpreting supply chain dynamics: A quasi-chaos perspective," European Journal of Operational Research, Elsevier, vol. 233(3), pages 566-579.
    11. Jain, Apurva, 2007. "Value of capacity pooling in supply chains with heterogeneous customers," European Journal of Operational Research, Elsevier, vol. 177(1), pages 239-260, February.
    12. Camisullis, Carole & Giard, Vincent & Mendy-Bilek, Gisèle, 2010. "The information to share in upstream supply chains dedicated to mass production of customized products for allowing a decentralized management," Economics Papers from University Paris Dauphine 123456789/3719, Paris Dauphine University.
    13. Miragliotta, Giovanni, 2006. "Layers and mechanisms: A new taxonomy for the Bullwhip Effect," International Journal of Production Economics, Elsevier, vol. 104(2), pages 365-381, December.
    14. Carole Camisullis & Vincent Giard & Gisele Mendy-Bilek, 2010. "The information to share in upstream supply chains dedicated to mass production of customized products for allowing a decentralized management," Working Papers hal-00876993, HAL.
    15. Dobos, Imre, 2011. "The analysis of bullwhip effect in a HMMS-type supply chain," International Journal of Production Economics, Elsevier, vol. 131(1), pages 250-256, May.
    16. Chen, Lucy Gongtao & Gavirneni, Srinagesh, 2013. "A note on the effectiveness of scheduled balanced ordering in a one-supplier two-retailer system with uniform end-customer demands," International Journal of Production Economics, Elsevier, vol. 146(1), pages 240-245.
    17. Chandra, Charu & Grabis, Janis, 2005. "Application of multi-steps forecasting for restraining the bullwhip effect and improving inventory performance under autoregressive demand," European Journal of Operational Research, Elsevier, vol. 166(2), pages 337-350, October.

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