IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v233y2014i3p566-579.html
   My bibliography  Save this article

Interpreting supply chain dynamics: A quasi-chaos perspective

Author

Listed:
  • Hwarng, H. Brian
  • Yuan, Xuchuan

Abstract

Chaotic phenomena, chaos amplification and other interesting nonlinear behaviors have been observed in supply chain systems. Chaos can be defined theoretically if the dynamics under study are produced only by deterministic factors. However, deterministic settings rarely present themselves in reality. In fact, real data are typically unknown. How can the chaos theory and its related methodology be applied in the real world? When the demand is stochastic, the interpretation and distribution of the Lyapunov exponents derived from the effective inventory at different supply chain levels are not similar to those under deterministic demand settings. Are the observed dynamics of the effective inventory random, chaotic, or simply quasi-chaos? In this study, we investigate a situation whereby the chaos analysis is applied to a time series as if its underlying structure, deterministic or stochastic, is unknown. The result shows clear distinction in chaos characterization between the two categories of demand process, deterministic vs. stochastic. It also highlights the complexity of the interplay between stochastic demand processes and nonlinear dynamics. Therefore, caution should be exercised in interpreting system dynamics when applying chaos analysis to a system of unknown underlying structure. By understanding this delicate interplay, decision makers have the better chance to tackle the problem correctly or more effectively at the demand end or the supply end.

Suggested Citation

  • Hwarng, H. Brian & Yuan, Xuchuan, 2014. "Interpreting supply chain dynamics: A quasi-chaos perspective," European Journal of Operational Research, Elsevier, vol. 233(3), pages 566-579.
    • Handle: RePEc:eee:ejores:v:233:y:2014:i:3:p:566-579
    DOI: 10.1016/j.ejor.2013.09.025
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221713007807
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2013.09.025?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Hau L. Lee & Kut C. So & Christopher S. Tang, 2000. "The Value of Information Sharing in a Two-Level Supply Chain," Management Science, INFORMS, vol. 46(5), pages 626-643, May.
    2. Kim, Jeon G. & Chatfield, Dean & Harrison, Terry P. & Hayya, Jack C., 2006. "Quantifying the bullwhip effect in a supply chain with stochastic lead time," European Journal of Operational Research, Elsevier, vol. 173(2), pages 617-636, September.
    3. Gérard P. Cachon, 1999. "Managing Supply Chain Demand Variability with Scheduled Ordering Policies," Management Science, INFORMS, vol. 45(6), pages 843-856, June.
    4. John D. Sterman, 1989. "Modeling Managerial Behavior: Misperceptions of Feedback in a Dynamic Decision Making Experiment," Management Science, INFORMS, vol. 35(3), pages 321-339, March.
    5. Gérard P. Cachon & Marshall Fisher, 2000. "Supply Chain Inventory Management and the Value of Shared Information," Management Science, INFORMS, vol. 46(8), pages 1032-1048, August.
    6. Hau L. Lee & V. Padmanabhan & Seungjin Whang, 1997. "Information Distortion in a Supply Chain: The Bullwhip Effect," Management Science, INFORMS, vol. 43(4), pages 546-558, April.
    7. 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.
    8. Hwarng, H. Brian & Xie, Na, 2008. "Understanding supply chain dynamics: A chaos perspective," European Journal of Operational Research, Elsevier, vol. 184(3), pages 1163-1178, February.
    9. Larsen, Erik R. & Morecroft, John D. W. & Thomsen, Jesper S., 1999. "Complex behaviour in a production-distribution model," European Journal of Operational Research, Elsevier, vol. 119(1), pages 61-74, November.
    10. Sterman, John D., 1989. "Misperceptions of feedback in dynamic decision making," Organizational Behavior and Human Decision Processes, Elsevier, vol. 43(3), pages 301-335, June.
    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. Pappas, Nikolaos & Papatheodorou, Andreas, 2017. "Tourism and the refugee crisis in Greece: Perceptions and decision-making of accommodation providers," Tourism Management, Elsevier, vol. 63(C), pages 31-41.
    2. Yuan, Xuchuan & Brian Hwarng, H., 2023. "Examining the dynamics of reactive capacity allocation through a chaos lens," European Journal of Operational Research, Elsevier, vol. 308(2), pages 912-928.
    3. Spiegler, Virginia L.M. & Naim, Mohamed M., 2017. "Investigating sustained oscillations in nonlinear production and inventory control models," European Journal of Operational Research, Elsevier, vol. 261(2), pages 572-583.
    4. Zhao, LiuWei & Chang, Jianwei & DU, Jianguo, 2019. "Dynamics analysis on competition between manufacturing and remanufacturing in context of government subsidies," Chaos, Solitons & Fractals, Elsevier, vol. 121(C), pages 119-128.
    5. Katsoras, Efthymios & Georgiadis, Patroklos, 2022. "An integrated System Dynamics model for Closed Loop Supply Chains under disaster effects: The case of COVID-19," International Journal of Production Economics, Elsevier, vol. 253(C).
    6. Efthymios Katsoras & Patroklos Georgiadis, 2022. "A Dynamic Analysis for Mitigating Disaster Effects in Closed Loop Supply Chains," Sustainability, MDPI, vol. 14(9), pages 1-20, 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. Hau L. Lee & V. Padmanabhan & Seungjin Whang, 2004. "Comments on "Information Distortion in a Supply Chain: The Bullwhip Effect"," Management Science, INFORMS, vol. 50(12_supple), pages 1887-1893, December.
    3. QU, Zhan & RAFF, Horst, 2023. "Two-part tariffs, inventory stockpiling, and the bullwhip effect," European Journal of Operational Research, Elsevier, vol. 308(1), pages 201-214.
    4. Anantaram Balakrishnan & Joseph Geunes & Michael S. Pangburn, 2004. "Coordinating Supply Chains by Controlling Upstream Variability Propagation," Manufacturing & Service Operations Management, INFORMS, vol. 6(2), pages 163-183, July.
    5. Hwarng, H. Brian & Xie, Na, 2008. "Understanding supply chain dynamics: A chaos perspective," European Journal of Operational Research, Elsevier, vol. 184(3), pages 1163-1178, February.
    6. 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.
    7. Carole Camisullis & Vincent Giard & Gisele Mendy-Bilek, 2010. "The information to share in upstream supply chains dedicated to mass production of customized products for allowing a decentralized management," Working Papers hal-00876993, HAL.
    8. 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.
    9. Enrique Holgado de Frutos & Juan R Trapero & Francisco Ramos, 2020. "A literature review on operational decisions applied to collaborative supply chains," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-28, March.
    10. 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.
    11. Chatfield, Dean C. & Pritchard, Alan M., 2013. "Returns and the bullwhip effect," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 49(1), pages 159-175.
    12. Tang, Christopher S., 2006. "Perspectives in supply chain risk management," International Journal of Production Economics, Elsevier, vol. 103(2), pages 451-488, October.
    13. Wen-Lung Shiau & Yogesh K. Dwivedi & Chia-Han Tsai, 2015. "Supply chain management: exploring the intellectual structure," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(1), pages 215-230, October.
    14. repec:dau:papers:123456789/3719 is not listed on IDEAS
    15. Li Chen & Hau L. Lee, 2012. "Bullwhip Effect Measurement and Its Implications," Operations Research, INFORMS, vol. 60(4), pages 771-784, August.
    16. 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.
    17. Wang, Xun & Disney, Stephen M. & Wang, Jing, 2014. "Exploring the oscillatory dynamics of a forbidden returns inventory system," International Journal of Production Economics, Elsevier, vol. 147(PA), pages 3-12.
    18. Ahmed Shaban & Mohamed A. Shalaby & Giulio Di Gravio & Riccardo Patriarca, 2020. "Analysis of Variance Amplification and Service Level in a Supply Chain with Correlated Demand," Sustainability, MDPI, vol. 12(16), pages 1-27, August.
    19. Kim, Ilhyung & Springer, Mark, 2008. "Measuring endogenous supply chain volatility: Beyond the bullwhip effect," European Journal of Operational Research, Elsevier, vol. 189(1), pages 172-193, August.
    20. 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.
    21. 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.

    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:ejores:v:233:y:2014:i:3:p:566-579. 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/eor .

    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.