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Bullwhip Effect of Multiple Products with Interdependent Product Demands

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Handbook of Ripple Effects in the Supply Chain

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 276))

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Abstract

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.

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Notes

  1. 1.

    See Wang and Disney (2016) for a comprehensive review of the bullwhip effect literature.

  2. 2.

    There is extensive literature on aggregate and individual product-level forecasting, commonly known as top-down and bottom-up forecasting, in the operations management literature (e.g., Sbrana and Silvestrini 2013, Chen and Blue 2010 are some recent studies that compare the forecast accuracy of top-down and bottom-up approaches).

  3. 3.

    This work is based on Raghunathan et al. (2017). All proofs of propositions are available in Raghunathan et al. (2017).

  4. 4.

    While we do not have firm-level data to test whether the VAR(1) model fits brand demand streams, an examination of the data contained in the Compustat database shows that the VAR(1) model provides a statistically significant fit for the cost of goods sold (a widely used proxy for demand in the bullwhip effect literature) of firms within an industry. Furthermore, Chen and Blue (2010) gave an example of a semiconductor manufacturer where the VAR(1) model is appropriate to model the demand data of two product variants.

References

  • Aichner, T., & Coletti, P. (2013). Customers’ online shopping preferences in mass customization. Journal of Direct, Data and Digital Marketing Practice, 15(1), 20–35.

    Article  Google Scholar 

  • Aviv, Y. (2003). A time-series framework for supply chain inventory management. Operations Research, 51(2), 210–227.

    Article  Google Scholar 

  • Box, G. E. P., & Tiao, G. C. (1977). A canonical analysis of multiple time series. Biometrika, 64(2), 355–365.

    Article  Google Scholar 

  • Bray, R., & Mendelson, H. (2012). Information transmission and the bullwhip effect: An empirical investigation. Management Science, 58(5), 860–875.

    Article  Google Scholar 

  • Cachon, G., Randall, T., & Schmidt, G. (2007). In search of the bullwhip effect. Manufacturing and Service Operations Management, 9(4), 457–479.

    Article  Google Scholar 

  • Chen, L., & Lee, H. L. (2009). Information sharing and order variability control under a generalized demand model. Management Science, 55(5), 781–797.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Chen, L., & Lee, H. L. (2012). Bullwhip effect measurement and its implications. Operations Research, 60(4), 771–784.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Cox, M. W., & Alm, R. (1998). The right stuff. America’s Move to Mass Customization. Dallas: Federal Reserve Bank of Dallas.

    Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • Gaur, V., Giloni, A., & Seshadri, S. (2005). Information sharing in a supply chain under ARMA demand. Management Science, 51(6), 961–969.

    Article  Google Scholar 

  • Gilbert, K. (2005). An ARIMA supply chain model. Management Science, 51(2), 305–310.

    Article  Google Scholar 

  • Graves, S. C. (1999). A single-item inventory model for a nonstationary demand process. Manufacturing & Service Operations Management, 1(1), 50–61.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Gurnani, H., Mehrotra, A., & Ray, S. (2012). Supply chain disruptions: theory and practice of managing risk. London: Springer.

    Book  Google Scholar 

  • Handfield, R. B., & McCormack, K. (2008). Supply chain risk management: minimizing disruptions in global sourcing. New York: Auerbach Publications.

    Google Scholar 

  • Heckmann, I. (2016). Towards supply chain risk analytics. Wiesbaden: Springer-Gabler.

    Book  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • Ivanov, D. (2017). Simulation-based ripple effect modelling in the supply chain. International Journal of Production Research, 55(7), 2083–2101.

    Article  Google Scholar 

  • Ivanov, D. (2018). Structural dynamics and resilience in supply chain risk management. New York: Springer.

    Book  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Khojasteh, Y. (Ed.). (2017). Supply chain risk management. Singapore: Springer.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Kouvelis, P., & Dong, L. (2011). Handbook of integrated risk management in global supply chains. Hoboken: Wiley.

    Book  Google Scholar 

  • Lee, H. L., Padmanabhan, P., & Whang, S. (1997). Information distortion in a supply chain: The bullwhip effect. Management Science, 43, 546–558.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Sodhi, M. M. S., & Tang, C. S. (2012). Managing Supply Chain Risk. New York: Springer.

    Book  Google Scholar 

  • 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 

  • 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 

  • Wang, X., & Disney, S. (2016). The bullwhip effect: Progress, trends and directions. European Journal of Operations Research., 250(3), 691–701.

    Article  Google Scholar 

  • Waters, D. (2011). Supply chain risk management: Vulnerability and resilience in logistics (2nd ed.). London: Kogan Page.

    Google Scholar 

  • Zhang, X. (2004a). The impact of forecasting methods on the bullwhip effect. International Journal of Production Economics, 88(1), 15–27.

    Article  Google Scholar 

  • Zhang, X. (2004b). Evolution of ARMA demand in supply chains. Manufacturing and Services Operations Management, 6(2), 195–198.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Management, The University of Texas at Dallas, Richardson, TX, 75083, USA

    Srinivasan Raghunathan

  2. UCLA Anderson School, Los Angeles, CA, 90095, USA

    Christopher S. Tang

  3. Lubar School of Business, The University of Wisconsin-Milwaukee, Milwaukee, WI, 53201, USA

    Xiaohang Yue

Authors
  1. Srinivasan Raghunathan
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  2. Christopher S. Tang
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  3. Xiaohang Yue
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Corresponding author

Correspondence to Srinivasan Raghunathan .

Editor information

Editors and Affiliations

  1. Department of Business and Economics, Berlin School of Economics and Law, Berlin, Germany

    Dmitry Ivanov

  2. Department of Automation, Production and Computer Sciences, IMT Atlantique, LS2N - CNRS UMR, Nantes, France

    Alexandre Dolgui

  3. SPIIRAS, St. Petersburg, Russia

    Boris Sokolov

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Raghunathan, S., Tang, C.S., Yue, X. (2019). Bullwhip Effect of Multiple Products with Interdependent Product Demands. In: Ivanov, D., Dolgui, A., Sokolov, B. (eds) Handbook of Ripple Effects in the Supply Chain. International Series in Operations Research & Management Science, vol 276. Springer, Cham. https://doi.org/10.1007/978-3-030-14302-2_7

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