Bullwhip Effect of Multiple Products with Interdependent Product Demands

  • Srinivasan RaghunathanEmail author
  • Christopher S. Tang
  • Xiaohang Yue
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 276)


The bullwhip effect has been studied extensively by researchers using analytical and empirical models based on a single product. We extend the current theory to provide insights for a firm that manufactures multiple products in a single product category with interdependent demand streams. We find that interdependency between demand streams plays a critical role in determining the existence and magnitude of the bullwhip effect. More importantly, we show that interdependency impacts whether the firm should manage ordering and inventory decisions at the category level or at the product level, and whether the bullwhip effect measure computed at the category level is informative or not.


  1. Aichner, T., & Coletti, P. (2013). Customers’ online shopping preferences in mass customization. Journal of Direct, Data and Digital Marketing Practice, 15(1), 20–35.CrossRefGoogle Scholar
  2. Aviv, Y. (2003). A time-series framework for supply chain inventory management. Operations Research, 51(2), 210–227.CrossRefGoogle Scholar
  3. Box, G. E. P., & Tiao, G. C. (1977). A canonical analysis of multiple time series. Biometrika, 64(2), 355–365.CrossRefGoogle Scholar
  4. Bray, R., & Mendelson, H. (2012). Information transmission and the bullwhip effect: An empirical investigation. Management Science, 58(5), 860–875.CrossRefGoogle Scholar
  5. Cachon, G., Randall, T., & Schmidt, G. (2007). In search of the bullwhip effect. Manufacturing and Service Operations Management, 9(4), 457–479.CrossRefGoogle Scholar
  6. Chen, L., & Lee, H. L. (2009). Information sharing and order variability control under a generalized demand model. Management Science, 55(5), 781–797.CrossRefGoogle Scholar
  7. Chen, A., & Blue, J. (2010). Performance analysis of demand planning approaches for aggregating, forecasting and disaggregating interrelated demands. International Journal of Production Economics, 128, 586–602.CrossRefGoogle Scholar
  8. Chen, L., & Lee, H. L. (2012). Bullwhip effect measurement and its implications. Operations Research, 60(4), 771–784.CrossRefGoogle Scholar
  9. Chen, F., Drezner, Z., Ryan, J. K., & Simchi-Levi, D. (2000). Quantifying the bullwhip effect in a simple supply chain: the impact of forecasting, lead times, and information. Management Science, 46(3), 436–443.CrossRefGoogle Scholar
  10. Chopra, S., Reinhardt, G., & Mohan, U. (2007). The importance of decoupling recurrent and disruption risks in a supply chain. Naval Research Logistics, 54(5), 44–555.CrossRefGoogle Scholar
  11. Cox, M. W., & Alm, R. (1998). The right stuff. America’s Move to Mass Customization. Dallas: Federal Reserve Bank of Dallas.Google Scholar
  12. Dolgui, A., Ivanov, D., Sokolov, B. (2018). Ripple effect in the supply chain: an analysis and recent literature. International Journal of Production Research. (Published online).Google Scholar
  13. Fahimnia, B., Tang, C. S., Davarzani, H., & Sarkis, J. (2015). Quantitative models for managing supply chain risks: a review. European Journal of Operational Research, 247(1), 1–15.CrossRefGoogle Scholar
  14. Gaur, V., Giloni, A., & Seshadri, S. (2005). Information sharing in a supply chain under ARMA demand. Management Science, 51(6), 961–969.CrossRefGoogle Scholar
  15. Gilbert, K. (2005). An ARIMA supply chain model. Management Science, 51(2), 305–310.CrossRefGoogle Scholar
  16. Graves, S. C. (1999). A single-item inventory model for a nonstationary demand process. Manufacturing & Service Operations Management, 1(1), 50–61.CrossRefGoogle Scholar
  17. Gupta, S., Starr, M. K., Farahani, R. Z., & Matinrad, N. (2016). Disaster management from a POM perspective: mapping a new domain. Production and Operations Management, 25, 1611–1637.CrossRefGoogle Scholar
  18. Gurnani, H., Mehrotra, A., & Ray, S. (2012). Supply chain disruptions: theory and practice of managing risk. London: Springer.CrossRefGoogle Scholar
  19. Handfield, R. B., & McCormack, K. (2008). Supply chain risk management: minimizing disruptions in global sourcing. New York: Auerbach Publications.Google Scholar
  20. Heckmann, I. (2016). Towards supply chain risk analytics. Wiesbaden: Springer-Gabler.CrossRefGoogle Scholar
  21. Hendricks, K. B., & Singhal, V. R. (2005). An empirical analysis of the effect of supply chain disruptions on long‐run stock price performance and equity risk of the firm. Production and Operations management, 14(1), 35–52.Google Scholar
  22. Ho, W., Zheng, T., Yildiz, H., & Talluri, S. (2015). Supply chain risk management: A literature review. International Journal of Production Research, 53(16), 5031–5069.CrossRefGoogle Scholar
  23. Ivanov, D. (2017). Simulation-based ripple effect modelling in the supply chain. International Journal of Production Research, 55(7), 2083–2101.CrossRefGoogle Scholar
  24. Ivanov, D. (2018). Structural dynamics and resilience in supply chain risk management. New York: Springer.CrossRefGoogle Scholar
  25. Ivanov, D., Sokolov, B., Dolgui, A. (2014a). The ripple effect in supply chains: trade-off ‘efficiencyflexibility-resilience’ in disruption management. International Journal of Production Research, 52(7), 2154–2172.CrossRefGoogle Scholar
  26. Ivanov, D., Sokolov, B., Pavlov, A. (2014b). Optimal distribution (re)planning in a centralized multistage network under conditions of ripple effect and structure dynamics. European Journal of Operational Research, 237(2), 758–770.CrossRefGoogle Scholar
  27. Ivanov, D., Dolgui, A., Sokolov, B., & Ivanova, M. (2017). Literature review on disruption recovery in the supply chain. International Journal of Production Research, 55(20), 6158–6174.CrossRefGoogle Scholar
  28. Khojasteh, Y. (Ed.). (2017). Supply chain risk management. Singapore: Springer.Google Scholar
  29. Klibi, W., Martel, A., & Guitouni, A. (2010). The design of robust value-creating supply chain networks: A critical review. European Journal of Operational Research, 203(2), 283–293.CrossRefGoogle Scholar
  30. Kouvelis, P., & Dong, L. (2011). Handbook of integrated risk management in global supply chains. Hoboken: Wiley.CrossRefGoogle Scholar
  31. Lee, H. L., Padmanabhan, P., & Whang, S. (1997). Information distortion in a supply chain: The bullwhip effect. Management Science, 43, 546–558.Google Scholar
  32. Miyaoka, J., & Hausman, W. H. (2004). How a base stock policy using stale forecasts provides supply chain benefits. Manufacturing & Service Operations Management, 6(2), 149–162.CrossRefGoogle Scholar
  33. Raghunathan, S., Tang, C., & Yue, X. (2017). Analysis of the bullwhip effect in a multi-product setting with interdependent demands. Operations Research, 65(2), 424–432.CrossRefGoogle Scholar
  34. Sbrana, G., & Silvestrini, A. (2013). Forecasting aggregate demand: Analytical comparison of top-down and bottom-up approaches in a multivariate exponential smoothing framework. International Journal of Production Economics, 146, 185–198.CrossRefGoogle Scholar
  35. Simangunsong, E., Hendry, L. C., & Stevenson, M. (2012). Supply-chain uncertainty: a review and theoretical foundation for future research. International Journal of Production Research, 50(16), 4493–4523.CrossRefGoogle Scholar
  36. Sodhi, M. M. S., & Tang, C. S. (2012). Managing Supply Chain Risk. New York: Springer.CrossRefGoogle Scholar
  37. Tiao, G. C., & Box, G. E. P. (1981). Modeling multiple times series with applications. Journal of the American Statistical Association, 76(376), 802–816.Google Scholar
  38. Tiao, G. C., & Tsay, R. S. (1983). Multiple time series modeling and extended sample cross-correlations. Journal of Business and Economic Statistics, 1(1), 43–59.Google Scholar
  39. Wang, X., & Disney, S. (2016). The bullwhip effect: Progress, trends and directions. European Journal of Operations Research., 250(3), 691–701.CrossRefGoogle Scholar
  40. Waters, D. (2011). Supply chain risk management: Vulnerability and resilience in logistics (2nd ed.). London: Kogan Page.Google Scholar
  41. Zhang, X. (2004a). The impact of forecasting methods on the bullwhip effect. International Journal of Production Economics, 88(1), 15–27.CrossRefGoogle Scholar
  42. Zhang, X. (2004b). Evolution of ARMA demand in supply chains. Manufacturing and Services Operations Management, 6(2), 195–198.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Srinivasan Raghunathan
    • 1
    Email author
  • Christopher S. Tang
    • 2
  • Xiaohang Yue
    • 3
  1. 1.School of ManagementThe University of Texas at DallasRichardsonUSA
  2. 2.UCLA Anderson SchoolLos AngelesUSA
  3. 3.Lubar School of BusinessThe University of Wisconsin-MilwaukeeMilwaukeeUSA

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