The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Supply-chain networks: a complex adaptive systems perspective
Abstract
In this era, information technology is revolutionizing almost every domain of technology and society, whereas the ‘complexity revolution’ is occurring in science at a silent pace. In this paper, we look at the impact of the two, in the context of supply-chain networks. With the advent of information technology, supply chains have acquired a complexity almost equivalent to that of biological systems. However, one of the major challenges that we are facing in supply-chain management is the deployment of coordination strategies that lead to adaptive, flexible and coherent collective behaviour in supply chains. The main hurdle has been the lack of the principles that govern how supply chains with complex organizational structure and function arise and develop, and what organizations and functionality are attainable, given specific kinds of lower-level constituent entities. The study of Complex Adaptive Systems (CAS), has been a research effort attempting to find common characteristics and/or formal distinctions among complex systems arising in diverse domains (like biology, social systems, ecology and technology) that might lead to a better understanding of how complexity occurs, whether it follows any general scientific laws of nature, and how it might be related to simplicity. In this paper, we argue that supply chains should be treated as a CAS. With this recognition, we propose how various concepts, tools and techniques used in the study of CAS can be exploited to characterize and model supply-chain networks. These tools and techniques are based on the fields of nonlinear dynamics, statistical physics and information theory.
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Cites
Detecting strange attractors in turbulence
Takens, F. (1981) · Lecture Notes in Mathematics · doi:10.1007/bfb0091924 · Cited by 10,459 · cited 1×
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Cited by
Artificial Intelligence in Supply Chain Management: A Systematic Literature Review
Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2020.09.009 · Cited by 1,020
“…studies and for different purposes, such as supply chain demand forecasting (Carbon-neau et al., 2008), time-series classification in supply chains (García et al., 2012), supplier selection (Guosheng and Guohong, 2008), and the designing of systems for supply chain networks (Surana et al., 2005).”
Ivanov, D., Tang, C. S., Dolgui, A., Battini, D., & Das, A. (2021) · International Journal of Production Research · doi:10.1080/00207543.2020.1798035 · Cited by 452
Towards a theory of multi-tier sustainable supply chains: A systematic literature review
M. Tachizawa, E., & Yew Wong, C. (2014) · Supply Chain Management: An International Journal · doi:10.1108/scm-02-2014-0070 · Cited by 419
Intelligent digital twin (iDT) for supply chain stress-testing, resilience, and viability
Ivanov, D. (2023) · International Journal of Production Economics · doi:10.1016/j.ijpe.2023.108938 · Cited by 270
How “smart cities” will change supply chain management
Tachizawa, E. M., Alvarez-Gil, M. J., & Montes-Sancho, M. J. (2015) · Supply Chain Management: An International Journal · doi:10.1108/scm-03-2014-0108 · Cited by 124
Metadata
- Authors
- Surana, A., Kumara *, S., Greaves, M., & Raghavan, U. N.
- Venue
- International Journal of Production Research
- Publisher
- Informa UK Limited
- Published
- 2005-10-15
- Volume
- 43
- Issue
- 20
- Pages
- 4235-4265
- ISSN
- 0020-7543 (ISSN-L)
- 1366-588X
- OpenAlex
- W1992597750
- Cited by
- 620
- References
- 4
- FWCI
- 35.41
- Type
- journal article
- Language
- en
- Open access
- closed
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