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Information distortion in a supply chain and its mitigation using soft computing approach


  • Balan, S.
  • Vrat, Prem
  • Kumar, Pradeep


The information transferred in the form of orders between the nodes of a supply chain tends to be distorted when it moves from downstream to upstream. This phenomenon is called as bullwhip effect and this research is aimed to analyze this effect deeply in a single input single output (SISO) model. A discrete time series SISO model is developed for the analysis and it proves to be very useful in revealing the dynamics characteristics of the system. The bullwhip effect is measured from the transfer function model and the effect can be reduced by applying soft computing approach. A detailed sensitivity analysis is carried out to investigate the behavior of the model under various conditions. The applied fuzzy logic theory controls the errors and change in errors associated with forecasted demand between the nodes of a supply chain and it allows a smooth information flow in the chain. Tuning of fuzzy logic controller has been performed using adaptive neuro-fuzzy inference system (ANFIS). The method is illustrated with a numerical example. The application of soft computing approach addresses the real situation of human judgment with fuzziness helps the managers to forecast the demand with less distortion and to improve the supply chain effectiveness.

Suggested Citation

  • Balan, S. & Vrat, Prem & Kumar, Pradeep, 2009. "Information distortion in a supply chain and its mitigation using soft computing approach," Omega, Elsevier, vol. 37(2), pages 282-299, April.
  • Handle: RePEc:eee:jomega:v:37:y:2009:i:2:p:282-299

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    Cited by:

    1. Kyu Kim, Kyung & Yul Ryoo, Sung & Dug Jung, Myung, 2011. "Inter-organizational information systems visibility in buyer-supplier relationships: The case of telecommunication equipment component manufacturing industry," Omega, Elsevier, vol. 39(6), pages 667-676, December.
    2. Teo, Thompson S.H. & Lin, Sijie & Lai, Kee-hung, 2009. "Adopters and non-adopters of e-procurement in Singapore: An empirical study," Omega, Elsevier, vol. 37(5), pages 972-987, October.
    3. Tsai, Chih-Yang, 2011. "On delineating supply chain cash flow under collectionrisk," International Journal of Production Economics, Elsevier, vol. 129(1), pages 186-194, January.
    4. Thokozani Patmond Mbhele, 2014. "Antecedents of Quality Information Sharing in the FMCG Industry," Journal of Economics and Behavioral Studies, AMH International, vol. 6(12), pages 986-1003.
    5. Kristianto, Yohanes & Helo, Petri & Jiao, Jianxin (Roger) & Sandhu, Maqsood, 2012. "Adaptive fuzzy vendor managed inventory control for mitigating the Bullwhip effect in supply chains," European Journal of Operational Research, Elsevier, vol. 216(2), pages 346-355.
    6. Önkal, Dilek & Zeynep Sayım, K. & Lawrence, Michael, 2012. "Wisdom of group forecasts: Does role-playing play a role?," Omega, Elsevier, vol. 40(6), pages 693-702.
    7. Garcia, C.A. & Ibeas, A. & Herrera, J. & Vilanova, R., 2012. "Inventory control for the supply chain: An adaptive control approach based on the identification of the lead-time," Omega, Elsevier, vol. 40(3), pages 314-327.
    8. Garcia Salcedo, Carlos Andres & Ibeas Hernandez, Asier & Vilanova, Ramón & Herrera Cuartas, Jorge, 2013. "Inventory control of supply chains: Mitigating the bullwhip effect by centralized and decentralized Internal Model Control approaches," European Journal of Operational Research, Elsevier, vol. 224(2), pages 261-272.
    9. Wong, Bo K. & Lai, Vincent S., 2011. "A survey of the application of fuzzy set theory in production and operations management: 1998-2009," International Journal of Production Economics, Elsevier, vol. 129(1), pages 157-168, January.

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    Supply chain management Bullwhip effect Neuro-fuzzy SISO model Soft computing;


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