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A multi-structural framework for adaptive supply chain planning and operations control with structure dynamics considerations

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  • Ivanov, Dmitry
  • Sokolov, Boris
  • Kaeschel, Joachim

Abstract

A trend in up-to-date developments in supply chain management (SCM) is to make supply chains more agile, flexible, and responsive. In supply chains, different structures (functional, organizational, informational, financial, etc.) are (re)formed. These structures interrelate with each other and change in dynamics. The paper introduces a new conceptual framework for multi-structural planning and operations of adaptive supply chains with structure dynamics considerations. We elaborate a vision of adaptive supply chain management (A-SCM), a new dynamic model and tools for the planning and control of adaptive supply chains. SCM is addressed from perspectives of execution dynamics under uncertainty. Supply chains are modelled in terms of dynamic multi-structural macro-states, based on simultaneous consideration of the management as a function of both states and structures. The research approach is theoretically based on the combined application of control theory, operations research, and agent-based modelling. The findings suggest constructive ways to implement multi-structural supply chain management and to transit from a "one-way" partial optimization to the feedback-based, closed-loop adaptive supply chain optimization and execution management for value chain adaptability, stability and crisis-resistance. The proposed methodology enhances managerial insight into advanced supply chain management.

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  • Ivanov, Dmitry & Sokolov, Boris & Kaeschel, Joachim, 2010. "A multi-structural framework for adaptive supply chain planning and operations control with structure dynamics considerations," European Journal of Operational Research, Elsevier, vol. 200(2), pages 409-420, January.
  • Handle: RePEc:eee:ejores:v:200:y:2010:i:2:p:409-420
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    Cited by:

    1. Dmitry Ivanov & Boris Sokolov & Joachim Kaeschel, 2011. "Integrated supply chain planning based on a combined application of operations research and optimal control," 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. 19(3), pages 299-317, September.
    2. Mahmud A. Shareef & Yogesh K. Dwivedi & Vinod Kumar & D. Laurie Hughes & Ramakrishnan Raman, 2022. "Sustainable supply chain for disaster management: structural dynamics and disruptive risks," Annals of Operations Research, Springer, vol. 319(1), pages 1451-1475, December.
    3. Dmitry Ivanov & Boris Sokolov & Inna Solovyeva & Alexandre Dolgui & Ferry Jie, 2016. "Dynamic recovery policies for time-critical supply chains under conditions of ripple effect," International Journal of Production Research, Taylor & Francis Journals, vol. 54(23), pages 7245-7258, December.
    4. Jan HRON & Tomas MACAK, 2011. "Adaptive organization design based on system integration," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 57(12), pages 565-572.
    5. Chen, Shuo-Pei & Wu, Wann-Yih, 2010. "A systematic procedure to evaluate an automobile manufacturer-distributor partnership," European Journal of Operational Research, Elsevier, vol. 205(3), pages 687-698, September.
    6. Quijano, Pedro David Guti, 2015. "Decision Support Platform for Urban Freight Transport," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Blecker, Thorsten & Kersten, Wolfgang & Ringle, Christian M. (ed.), Operational Excellence in Logistics and Supply Chains: Optimization Methods, Data-driven Approaches and Security Insights. Proceedings of the Hamburg , volume 22, pages 419-440, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    7. Dmitry Ivanov, 2022. "Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic," Annals of Operations Research, Springer, vol. 319(1), pages 1411-1431, December.
    8. Alikhani, Reza & Ranjbar, Amirhossein & Jamali, Amir & Torabi, S. Ali & Zobel, Christopher W., 2023. "Towards increasing synergistic effects of resilience strategies in supply chain network design," Omega, Elsevier, vol. 116(C).
    9. Ivanov, Dmitry & Dolgui, Alexandre, 2021. "OR-methods for coping with the ripple effect in supply chains during COVID-19 pandemic: Managerial insights and research implications," International Journal of Production Economics, Elsevier, vol. 232(C).
    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. Ivanov, Dmitry & Dolgui, Alexandre & Sokolov, Boris, 2022. "Cloud supply chain: Integrating Industry 4.0 and digital platforms in the “Supply Chain-as-a-Service”," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    12. Zelentsov Viacheslav & Sokolov Boris & Brovkina Olga & Mochalov Victor, 2013. "Methodological and Technological Foundations of Remote Sensing Monitoring and Modelling of Natural and Technological Objects," Information Technology and Management Science, Sciendo, vol. 16(1), pages 37-41, December.
    13. 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.
    14. Weili Yin & Wenxue Ran, 2021. "Theoretical Exploration of Supply Chain Viability Utilizing Blockchain Technology," Sustainability, MDPI, vol. 13(15), pages 1-25, July.
    15. 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.
    16. Dmitry Ivanov & Richard Hartl & Alexandre Dolgui & Alexander Pavlov & Boris Sokolov, 2015. "Integration of aggregate distribution and dynamic transportation planning in a supply chain with capacity disruptions and the ripple effect consideration," International Journal of Production Research, Taylor & Francis Journals, vol. 53(23), pages 6963-6979, December.
    17. Grilec Kaurić, Alica & Miočević, Dario & Mikulić, Josip, 2014. "Dynamic Capabilities and Firm Effectiveness: The Mediating Role of Supply Chain Performance," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Blecker, Thorsten & Kersten, Wolfgang & Ringle, Christian M. (ed.), Innovative Methods in Logistics and Supply Chain Management: Current Issues and Emerging Practices. Proceedings of the Hamburg International Conferenc, volume 19, pages 391-412, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    18. Shiyu Chen & Wei Wang & Enrico Zio, 2021. "A Simulation-Based Multi-Objective Optimization Framework for the Production Planning in Energy Supply Chains," Energies, MDPI, vol. 14(9), pages 1-27, May.
    19. Dmitry Ivanov & Boris Sokolov, 2019. "Simultaneous structural–operational control of supply chain dynamics and resilience," Annals of Operations Research, Springer, vol. 283(1), pages 1191-1210, December.
    20. Navin K. Dev & Ravi Shankar & Angappa Gunasekaran & Lakshman S. Thakur, 2016. "A hybrid adaptive decision system for supply chain reconfiguration," International Journal of Production Research, Taylor & Francis Journals, vol. 54(23), pages 7100-7114, December.
    21. 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.
    22. Dirk Heyne & Lars Mönch, 2011. "An agent-based planning approach within the framework of distributed hierarchical enterprise management," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 22(2), pages 205-236, December.
    23. 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.
    24. Yang, Qihui & Scoglio, Caterina M. & Gruenbacher, Don M., 2021. "Robustness of supply chain networks against underload cascading failures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    25. Kaan Katircioglu & Robert Gooby & Mary Helander & Youssef Drissi & Pawan Chowdhary & Matt Johnson & Takashi Yonezawa, 2014. "Supply Chain Scenario Modeler: A Holistic Executive Decision Support Solution," Interfaces, INFORMS, vol. 44(1), pages 85-104, February.

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