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The incremental bullwhip effect of operational deviations in an arborescent supply chain with requirements planning

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  • Sodhi, ManMohan S.
  • Tang, Christopher S.

Abstract

Lee et al. (1997) advocated the idea of sharing demand and order information among different supply chain entities to mitigate the bullwhip effect. Even with full supply chain visibility afforded by IT systems with requirements planning and with no information distortion, we identify a "core" bullwhip effect inherent to any supply chain because of the underlying demand characteristics and replenishment lead times. In addition, we quantify an incremental bullwhip effect as various operational deviations (inaccurate order placements, batching, lag in sharing demand forecast) contribute incrementally to the variance of the order quantity not only at the node where the deviation is taking place but also at all upstream supply chain nodes. We discuss some managerial implications of our results in the context of a UK manufacturer.

Suggested Citation

  • Sodhi, ManMohan S. & Tang, Christopher S., 2011. "The incremental bullwhip effect of operational deviations in an arborescent supply chain with requirements planning," European Journal of Operational Research, Elsevier, vol. 215(2), pages 374-382, December.
    • Handle: RePEc:eee:ejores:v:215:y:2011:i:2:p:374-382
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    Cited by:

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    3. Dai, Hongyan & Li, Jianbin & Yan, Nina & Zhou, Weihua, 2016. "Bullwhip effect and supply chain costs with low- and high-quality information on inventory shrinkage," European Journal of Operational Research, Elsevier, vol. 250(2), pages 457-469.
    4. Ponte, Borja & Dominguez, Roberto & Cannella, Salvatore & Framinan, Jose M., 2022. "The implications of batching in the bullwhip effect and customer service of closed-loop supply chains," International Journal of Production Economics, Elsevier, vol. 244(C).
    5. K. Devika & A. Jafarian & A. Hassanzadeh & R. Khodaverdi, 2016. "Optimizing of bullwhip effect and net stock amplification in three-echelon supply chains using evolutionary multi-objective metaheuristics," Annals of Operations Research, Springer, vol. 242(2), pages 457-487, July.
    6. Williams, Brent D. & Waller, Matthew A. & Ahire, Sanjay & Ferrier, Gary D., 2014. "Predicting retailer orders with POS and order data: The inventory balance effect," European Journal of Operational Research, Elsevier, vol. 232(3), pages 593-600.
    7. 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.
    8. Ojha, Divesh & Sahin, Funda & Shockley, Jeff & Sridharan, Sri V., 2019. "Is there a performance tradeoff in managing order fulfillment and the bullwhip effect in supply chains? The role of information sharing and information type," International Journal of Production Economics, Elsevier, vol. 208(C), pages 529-543.
    9. Sodhi, ManMohan S. & Sodhi, Navdeep S. & Tang, Christopher S., 2014. "An EOQ model for MRO customers under stochastic price to quantify bullwhip effect for the manufacturer," International Journal of Production Economics, Elsevier, vol. 155(C), pages 132-142.
    10. Huang, Tao & Fildes, Robert & Soopramanien, Didier, 2014. "The value of competitive information in forecasting FMCG retail product sales and the variable selection problem," European Journal of Operational Research, Elsevier, vol. 237(2), pages 738-748.
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