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Control and system-theoretic identification of the supply chain dynamics domain for planning, analysis and adaptation of performance under uncertainty

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  • Ivanov, Dmitry
  • Sokolov, Boris

Abstract

The analysis of how to achieve planned economic performance in a real-time, uncertain and perturbed execution environment is a vital and up-to-date issue in many supply chains. Although it is intuitive that uncertainty is likely to have impacts on performance, the research on systematic terminology and quantitative analysis in this domain is rather limited as compared with the well-established domain of supply chain optimal planning. This study is among the first to address the operative perspective of the supply chain dynamics domain. The methodology of this conceptual paper is based on the business and technical literature analysis and fundamentals of control and systems theory. In contributing to the existing studies in this domain, the paper proposes a possible systemization and classification of related terminology from different theoretical perspectives, and important practical problems. For the supply chain dynamics domain, the paper identifies and groups possible problem classes of research, corresponding quantitative methods, and describes the general mathematical formulations. The results of this study may be of interest to both academics and practitioners.

Suggested Citation

  • Ivanov, Dmitry & Sokolov, Boris, 2013. "Control and system-theoretic identification of the supply chain dynamics domain for planning, analysis and adaptation of performance under uncertainty," European Journal of Operational Research, Elsevier, vol. 224(2), pages 313-323.
  • Handle: RePEc:eee:ejores:v:224:y:2013:i:2:p:313-323
    DOI: 10.1016/j.ejor.2012.08.021
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    References listed on IDEAS

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    Cited by:

    1. Ladislav Lukáš & Miroslav Plevný, 2016. "Using entropy for quantitative measurement of operational complexity of supplier–customer system: case studies," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 24(2), pages 371-387, June.
    2. Ivanov, Dmitry & Pavlov, Alexander & Pavlov, Dmitry & Sokolov, Boris, 2017. "Minimization of disruption-related return flows in the supply chain," International Journal of Production Economics, Elsevier, vol. 183(PB), pages 503-513.
    3. Spiegler, Virginia L.M. & Naim, Mohamed M. & Towill, Denis R. & Wikner, Joakim, 2016. "A technique to develop simplified and linearised models of complex dynamic supply chain systems," European Journal of Operational Research, Elsevier, vol. 251(3), pages 888-903.
    4. 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.
    5. Hasani, Aliakbar & Khosrojerdi, Amirhossein, 2016. "Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 87(C), pages 20-52.
    6. repec:pal:jorsoc:v:68:y:2017:i:6:d:10.1057_s41274-016-0112-4 is not listed on IDEAS
    7. Lam, Jasmine Siu Lee & Bai, Xiwen, 2016. "A quality function deployment approach to improve maritime supply chain resilience," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 92(C), pages 16-27.
    8. Ivanov, Dmitry & Pavlov, Alexander & Sokolov, Boris, 2014. "Optimal distribution (re)planning in a centralized multi-stage supply network under conditions of the ripple effect and structure dynamics," European Journal of Operational Research, Elsevier, vol. 237(2), pages 758-770.
    9. Hu, Rui & Zhang, Qun, 2015. "Study of a low-carbon production strategy in the metallurgical industry in China," Energy, Elsevier, vol. 90(P2), pages 1456-1467.
    10. Gao, Long, 2015. "Collaborative forecasting, inventory hedging and contract coordination in dynamic supply risk management," European Journal of Operational Research, Elsevier, vol. 245(1), pages 133-145.
    11. repec:eee:proeco:v:191:y:2017:i:c:p:207-220 is not listed on IDEAS
    12. repec:eee:ejores:v:261:y:2017:i:2:p:572-583 is not listed on IDEAS
    13. Sagawa, Juliana Keiko & Nagano, Marcelo Seido, 2015. "Modeling the dynamics of a multi-product manufacturing system: A real case application," European Journal of Operational Research, Elsevier, vol. 244(2), pages 624-636.
    14. Ivanov, Dmitry & Pavlov, Alexander & Dolgui, Alexandre & Pavlov, Dmitry & Sokolov, Boris, 2016. "Disruption-driven supply chain (re)-planning and performance impact assessment with consideration of pro-active and recovery policies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 90(C), pages 7-24.
    15. Sagawa, Juliana Keiko & Nagano, Marcelo Seido & Speranza Neto, Mauro, 2017. "A closed-loop model of a multi-station and multi-product manufacturing system using bond graphs and hybrid controllers," European Journal of Operational Research, Elsevier, vol. 258(2), pages 677-691.
    16. repec:eee:phsmap:v:482:y:2017:i:c:p:407-421 is not listed on IDEAS

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