Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.ejor.2013.09.025
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dc.titleInterpreting supply chain dynamics: A quasi-chaos perspective
dc.contributor.authorHwarng, H.B.
dc.contributor.authorYuan, X.
dc.date.accessioned2014-12-12T07:32:23Z
dc.date.available2014-12-12T07:32:23Z
dc.date.issued2014-03-16
dc.identifier.citationHwarng, H.B., Yuan, X. (2014-03-16). Interpreting supply chain dynamics: A quasi-chaos perspective. European Journal of Operational Research 233 (3) : 566-579. ScholarBank@NUS Repository. https://doi.org/10.1016/j.ejor.2013.09.025
dc.identifier.issn03772217
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/115779
dc.description.abstractChaotic 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. © 2013 Elsevier B.V. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.ejor.2013.09.025
dc.sourceScopus
dc.subjectChaos theory
dc.subjectLyapunov exponent
dc.subjectQuasi-chaos
dc.subjectSupply chain management
dc.subjectSystem dynamics
dc.typeArticle
dc.contributor.departmentDECISION SCIENCES
dc.description.doi10.1016/j.ejor.2013.09.025
dc.description.sourcetitleEuropean Journal of Operational Research
dc.description.volume233
dc.description.issue3
dc.description.page566-579
dc.description.codenEJORD
dc.identifier.isiut000328180200010
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