IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v113y2008i2p587-597.html
   My bibliography  Save this article

Mitigating the bullwhip effect by ordering policies and forecasting methods

Author

Listed:
  • Wright, David
  • Yuan, Xin

Abstract

The "bullwhip" effect, in which order variability increases as one moves up the supply chain, has been observed in a range of industries, modeled by several authors and various remedies suggested. This paper provides a simulation of the effect of improved forecasting methods, and finds that Holt's and Brown's methods substantially mitigate the bullwhip effect across a range of performance metrics. The end result is to identify ordering policies that perform particularly well in combination with these forecasting methods and indicate how they can be implemented in practice.

Suggested Citation

  • Wright, David & Yuan, Xin, 2008. "Mitigating the bullwhip effect by ordering policies and forecasting methods," International Journal of Production Economics, Elsevier, vol. 113(2), pages 587-597, June.
    • Handle: RePEc:eee:proeco:v:113:y:2008:i:2:p:587-597
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925-5273(08)00034-0
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Julia Miyaoka & Warren Hausman, 2004. "How a Base Stock Policy Using "Stale" Forecasts Provides Supply Chain Benefits," Manufacturing & Service Operations Management, INFORMS, vol. 6(2), pages 149-162, September.
    2. Naish, Howard F, 1994. "Production Smoothing in the Linear Quadratic Inventory Model," Economic Journal, Royal Economic Society, vol. 104(425), pages 864-875, July.
    3. Disney, S. M. & Towill, D. R., 2003. "The effect of vendor managed inventory (VMI) dynamics on the Bullwhip Effect in supply chains," International Journal of Production Economics, Elsevier, vol. 85(2), pages 199-215, August.
    4. Stephen C. Graves, 1999. "A Single-Item Inventory Model for a Nonstationary Demand Process," Manufacturing & Service Operations Management, INFORMS, vol. 1(1), pages 50-61.
    5. Marshall Fisher & Janice Hammond & Walter Obermeyer & Ananth Raman, 1997. "Configuring A Supply Chain To Reduce The Cost Of Demand Uncertainty," Production and Operations Management, Production and Operations Management Society, vol. 6(3), pages 211-225, September.
    6. Frank Chen & Zvi Drezner & Jennifer K. Ryan & David Simchi-Levi, 2000. "Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times, and Information," Management Science, INFORMS, vol. 46(3), pages 436-443, March.
    7. Stephen C. Graves, 1999. "Addendum to "A Single-Item Inventory Model for a Nonstationary Demand Process"," Manufacturing & Service Operations Management, INFORMS, vol. 1(2), pages 174-174.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Isaksson, Olov H.D. & Seifert, Ralf W., 2016. "Quantifying the bullwhip effect using two-echelon data: A cross-industry empirical investigation," International Journal of Production Economics, Elsevier, vol. 171(P3), pages 311-320.
    2. Cao, Qing & Baker, Jeff & Schniederjans, Dara, 2014. "Bullwhip effect reduction and improved business performance through guanxi: An empirical study," International Journal of Production Economics, Elsevier, vol. 158(C), pages 217-230.
    3. J. Zambujal-Oliveira, 2021. "Supply Chain Innovation Research: A Conceptual Approach of Information Management with Game Theory," Group Decision and Negotiation, Springer, vol. 30(2), pages 377-394, April.
    4. Roberto Dominguez & Salvatore Cannella & Borja Ponte & Jose M. Framinan, 2022. "Information sharing in decentralised supply chains with partial collaboration," Flexible Services and Manufacturing Journal, Springer, vol. 34(2), pages 263-292, June.
    5. Li, Xiaoming, 2008. "Demand evolution in stochastic inventory systems: Riskiness increase," International Journal of Production Economics, Elsevier, vol. 116(2), pages 182-189, December.
    6. de Lima, Daruichi Pereira & Fioriolli, José Carlos & Padula, Antonio Domingos & Pumi, Guilherme, 2018. "The impact of Chinese imports of soybean on port infrastructure in Brazil: A study based on the concept of the “Bullwhip Effect”," Journal of Commodity Markets, Elsevier, vol. 9(C), pages 55-76.
    7. Ciancimino, Elena & Cannella, Salvatore & Bruccoleri, Manfredi & Framinan, Jose M., 2012. "On the Bullwhip Avoidance Phase: The Synchronised Supply Chain," European Journal of Operational Research, Elsevier, vol. 221(1), pages 49-63.
    8. Udenio, Maximiliano & Vatamidou, Eleni & Fransoo, Jan C., 2023. "Exponential smoothing forecasts: Taming the Bullwhip Effect when demand is seasonal," Other publications TiSEM 8fca6329-83b9-4a49-a2aa-e, Tilburg University, School of Economics and Management.
    9. Cannella, Salvatore & Framinan, Jose M. & Bruccoleri, Manfredi & Barbosa-Póvoa, Ana Paula & Relvas, Susana, 2015. "The effect of Inventory Record Inaccuracy in Information Exchange Supply Chains," European Journal of Operational Research, Elsevier, vol. 243(1), pages 120-129.
    10. Chiang, Chung-Yean & Lin, Winston T. & Suresh, Nallan C., 2016. "An empirically-simulated investigation of the impact of demand forecasting on the bullwhip effect: Evidence from U.S. auto industry," International Journal of Production Economics, Elsevier, vol. 177(C), pages 53-65.
    11. Kiyoung Jeong & Jae-Dong Hong, 2019. "The impact of information sharing on bullwhip effect reduction in a supply chain," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1739-1751, April.
    12. 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.
    13. 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.
    14. Xu, Henry & Koh, Lenny & Parker, David, 2009. "Business processes inter-operation for supply network co-ordination," International Journal of Production Economics, Elsevier, vol. 122(1), pages 188-199, November.
    15. Erkan Bayraktar & Kazim Sari & Ekrem Tatoglu & Selim Zaim & Dursun Delen, 2020. "Assessing the supply chain performance: a causal analysis," Annals of Operations Research, Springer, vol. 287(1), pages 37-60, April.
    16. Dass, Mayukh & Fox, Gavin L., 2011. "A holistic network model for supply chain analysis," International Journal of Production Economics, Elsevier, vol. 131(2), pages 587-594, June.
    17. Xi Gang Yuan & Nan Zhu, 2016. "Bullwhip Effect Analysis in Two-Level Supply Chain Distribution Network Using Different Demand Forecasting Technology," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 33(03), pages 1-23, June.
    18. Ciancimino, Elena & Cannella, Salvatore & Canca Ortiz, José David & Framiñán Torres, José Manuel, 2009. "Análisis multinivel de cadenas de suministros: dos técnicas de resolución del efecto bullwhip // Supply Chain Multi-level Analysis: Two Bullwhip Dampening Approaches," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 8(1), pages 7-28, December.
    19. Ton Hien Duc, Truong & Luong, Huynh Trung & Kim, Yeong-Dae, 2010. "Effect of the third-party warehouse on bullwhip effect and inventory cost in supply chains," International Journal of Production Economics, Elsevier, vol. 124(2), pages 395-407, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Ma, Yungao & Wang, Nengmin & He, Zhengwen & Lu, Jizhou & Liang, Huigang, 2015. "Analysis of the bullwhip effect in two parallel supply chains with interacting price-sensitive demands," European Journal of Operational Research, Elsevier, vol. 243(3), pages 815-825.
    3. Ouyang, Yanfeng & Daganzo, Carlos, 2008. "Robust tests for the bullwhip effect in supply chains with stochastic dynamics," European Journal of Operational Research, Elsevier, vol. 185(1), pages 340-353, February.
    4. Zotteri, Giulio, 2013. "An empirical investigation on causes and effects of the Bullwhip-effect: Evidence from the personal care sector," International Journal of Production Economics, Elsevier, vol. 143(2), pages 489-498.
    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. Ouyang, Yanfeng & Li, Xiaopeng, 2010. "The bullwhip effect in supply chain networks," European Journal of Operational Research, Elsevier, vol. 201(3), pages 799-810, March.
    7. Ouyang, Yanfeng, 2007. "The effect of information sharing on supply chain stability and the bullwhip effect," European Journal of Operational Research, Elsevier, vol. 182(3), pages 1107-1121, November.
    8. Wang, Zhaodong & Wang, Xin & Ouyang, Yanfeng, 2015. "Bounded growth of the bullwhip effect under a class of nonlinear ordering policies," European Journal of Operational Research, Elsevier, vol. 247(1), pages 72-82.
    9. 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.
    10. Babai, M.Z. & Ali, M.M. & Boylan, J.E. & Syntetos, A.A., 2013. "Forecasting and inventory performance in a two-stage supply chain with ARIMA(0,1,1) demand: Theory and empirical analysis," International Journal of Production Economics, Elsevier, vol. 143(2), pages 463-471.
    11. Yanfeng Ouyang & Carlos Daganzo, 2006. "Characterization of the Bullwhip Effect in Linear, Time-Invariant Supply Chains: Some Formulae and Tests," Management Science, INFORMS, vol. 52(10), pages 1544-1556, October.
    12. Robert L. Bray & Haim Mendelson, 2012. "Information Transmission and the Bullwhip Effect: An Empirical Investigation," Management Science, INFORMS, vol. 58(5), pages 860-875, May.
    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. Layth C. Alwan & Christian H. Weiß, 2017. "INAR implementation of newsvendor model for serially dependent demand counts," International Journal of Production Research, Taylor & Francis Journals, vol. 55(4), pages 1085-1099, February.
    15. M M Ali & J E Boylan, 2011. "Feasibility principles for Downstream Demand Inference in supply chains," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 474-482, March.
    16. Emilio Carrizosa & Alba V. Olivares-Nadal & Pepa Ramírez-Cobo, 2020. "Embedding the production policy in location-allocation decisions," 4OR, Springer, vol. 18(3), pages 357-380, September.
    17. Isaksson, Olov H.D. & Seifert, Ralf W., 2016. "Quantifying the bullwhip effect using two-echelon data: A cross-industry empirical investigation," International Journal of Production Economics, Elsevier, vol. 171(P3), pages 311-320.
    18. Tliche, Y. & Taghipour, A. & Canel-Depitre, B., 2019. "Downstream Demand Inference in decentralized supply chains," European Journal of Operational Research, Elsevier, vol. 274(1), pages 65-77.
    19. Ali, Mohammad M. & Boylan, John E. & Syntetos, Aris A., 2012. "Forecast errors and inventory performance under forecast information sharing," International Journal of Forecasting, Elsevier, vol. 28(4), pages 830-841.
    20. Minner, Stefan & Transchel, Sandra, 2017. "Order variability in perishable product supply chains," European Journal of Operational Research, Elsevier, vol. 260(1), pages 93-107.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:proeco:v:113:y:2008:i:2:p:587-597. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.