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Simultaneous structural–operational control of supply chain dynamics and resilience

  • S.I. : Applications of OR in Disaster Relief Operations, Part II
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Abstract

This study develops a resilience control model and computational algorithm for simultaneous structural–operational design of supply chain (SC) structural dynamics and recovery policy control. Our model integrates both structural recovery control in the SC as a whole and the corresponding functional recovery control at individual firms in the SC. Such a comprehensive combination is unique in literature and affords more realistic application to SC resilience control decisions. The focus of our study is to advance insights into feedback-driven understanding of resilience within open system control context. We construct a model that allows theorizing the notion of SC resilience within a disruption dynamics profile as a product of degradation and recovery control loops and examine the conditions for changes of disruption profile states. We show that the deviations from the resilient trajectory are associated with structural and performance degradation, and the recovery operations in structural adaptation yield the performance recovery. We contribute to existing works by comprehensively modelling structural dynamics and operational dynamics within an integrated feedback-driven framework to enable proactive SC resilience control. Our approach conceptualizes a new perspective as compared to the more common closed system view where SC resilience is treated from the performance equilibrium point of view. The proposed approach can help explain and improve the firms’ operations in multiple ways. First, the combination of structural and functional dynamics can help revealing the latent supply–demand allocations which would be disrupted in case of particular changes in the SC design and suggest re-allocations of supply and demand Second, the model can be used to perform the dynamic analysis of SC disruption and recovery and to explain the reasons of SC performance degradation and restoration. This analysis can be further used to improve SC risk mitigation policies and recovery plans.

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References

  • Altay, N., Gunasekaran, A., Dubey, R., & Childe, S. J. (2018). Agility and resilience as antecedents of supply chain performance under moderating effects of organizational culture within humanitarian setting: A dynamic capability view. Production Planning and Control,29(14), 1158–1174.

    Google Scholar 

  • Banker, S. (2016). PepsiCo’s practical application of supply chain resilience strategies. [online] Forbes.com. https://www.forbes.com/sites/stevebanker/2016/10/01/pepsicos-practical-application-of-supply-chain-resilience-strategies/#7121d6df6293. Accessed 09 March 2019.

  • Basole, R. C., & Bellamy, M. A. (2014). Supply network structure, visibility, and risk diffusion: A computational approach. Decision Sciences,45(4), 1–49.

    Google Scholar 

  • Blackhurst, J., Dunn, J., & Craighead, C. (2011). An empirically derived framework of global supply re-siliency. Journal of Business Logistics,32(4), 347–391.

    Google Scholar 

  • Blackhurst, J., Rungtusanatham, M. J., Scheibe, K., & Ambulkar, S. (2018). Supply chain vulnerability assessment: A network based visualization and clustering analysis approach. Journal of Purchasing and Supply Management,24(1), 21–30.

    Google Scholar 

  • Bode, C., & Macdonald, J. R. (2017). Stages of supply chain disruption response: Direct, constraining, and mediating factors for impact mitigation. Decision Sciences,48(5), 836–874.

    Google Scholar 

  • Boltyanskiy, B. (1973). Optimal control of discrete systems. Moscow: Nauka.

    Google Scholar 

  • Cavalcantea, I. M., Frazzon E. M., Forcellinia, F. A., & Ivanov, D. (2019). A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management, 49, 86–97.

    Google Scholar 

  • Chen, X., Xi, Z., & Jing, P. (2017). A unified framework for evaluating supply chain reliability and resilience. IEEE Transactions on Reliability,66(4), 1144–1156.

    Google Scholar 

  • Chernousko, F. L., & Lyubushin, A. A. (1982). Method of successive approximations for solution of optimal control problems. Optimal Control Applications and Methods,3(2), 101–114.

    Google Scholar 

  • Christopher, M., & Peck, H. (2004). Building the resilient supply chain. International Journal of Logistics Management,15(2), 1–13.

    Google Scholar 

  • Dolgui, A., Ivanov, D., Sethi, S. P., & Sokolov, B. (2019). Scheduling in production, supply chain and Industry 4.0 systems by optimal control. International Journal of Production Research,57(2), 411–432.

    Google Scholar 

  • Dolgui, A., Ivanov, D., & Sokolov, B. (2018). Ripple effect in the supply chain: An analysis and recent literature. International Journal of Production Research,56(1–2), 414–430.

    Google Scholar 

  • Dubey, R., Altay, N., Gunasekaran, A., Blome, C., Papadopoulos, T., & Childe, S. J. (2018). Supply chain agility, adaptability and alignment: Empirical evidence from the Indian auto components industry. International Journal of Operations & Production Management,38(1), 129–148.

    Google Scholar 

  • Dubey, R., Gunasekaran, A., Childe, S. J., Wamba, S. F., Roubaud, D., & Foropon, C. (2019). Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience. International Journal of Production Research. https://doi.org/10.1080/00207543.2019.1582820.

    Article  Google Scholar 

  • Elluru, S., Gupta, H., Karu, H., & Prakash Singh, S. (2017). Proactive and reactive models for disaster resilient supply chain. Annals of Operations Research. https://doi.org/10.1007/s10479-017-2681-2.

  • Giannoccaro, I., Nair, A., & Choi, T. (2018). The impact of control and complexity on supply network performance: An empirically informed investigation using NK simulation analysis. Decision Science,49(4), 625–659.

    Google Scholar 

  • Govindan, G., Jafarian, A., Azbari, M. E., & Choi, T. M. (2016). Optimal bi-objective redundancy allocation for systems reliability and risk management. IEEE Transactions on Cybernetics,46, 1735–1748.

    Google Scholar 

  • Gunasekaran, A., Subramanian, N., & Rahman, S. (2015). Supply chain resilience: Role of complexities and strategies. International Journal of Production Research,53(22), 6809–6819.

    Google Scholar 

  • He, J., Alavifard, F., Ivanov, D., & Jahani H. (2018). A real-option approach to mitigate disruption risk in the supply chain. Omega. https://doi.org/10.1016/j.omega.2018.08.008.

    Article  Google Scholar 

  • Ho, W., Zheng, T., Yildiz, H., & Talluri, S. (2015). Supply chain risk management: A literature review. International Journal of Production Research,53(16), 5031–5069.

    Google Scholar 

  • Hosseini, S., Barker, K., & Ramirez-Marquez, J. E. (2016). A review of definitions and measure of system resilience. Reliability Engineering and System Safety,145, 47–61.

    Google Scholar 

  • Hosseini, S., Ivanov, D., & Dolgui, A. (2019a). Review of quantitative methods for supply chain resilience analysis. Transportation Research Part E, 125, 285–307.

    Google Scholar 

  • Hosseini, S., Morshedlou, N., Ivanov D., Sarder, MD., Barker, K., & Al Khaled, A. (2019b). Resilient supplier selection and optimal order allocation under disruption risks. International Journal of Production Economics, 213, 124–137.

    Google Scholar 

  • Ivanov, D. (2018). Structural dynamics and resilience in supply chain risk management. New York: Springer.

    Google Scholar 

  • Ivanov, D., & Dolgui, A. (2019). Low-Certainty-Need (LCN) supply chains: A new perspective in managing disruption risks and resilience. International Journal of Production Research. https://doi.org/10.1080/00207543.2018.1521025.

    Article  Google Scholar 

  • Ivanov, D., Dolgui, A., & Sokolov, B. (2016a). Robust dynamic schedule coordination control in the supply chain. Computers & Industrial Engineering,94, 18–31.

    Google Scholar 

  • Ivanov, D., Dolgui, A., & Sokolov, B. (2018a). Scheduling of recovery actions in the supply chain with resilience analysis considerations. International Journal of Production Research,56(19), 6473–6490.

    Google Scholar 

  • Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research,57(3), 829–846.

    Google Scholar 

  • Ivanov, D., Dolgui, A., Sokolov, B., & Ivanova, M. (2017a). Literature review on disruption recovery in the supply chain. International Journal of Production Research,55(20), 6158–6174.

    Google Scholar 

  • Ivanov, D., Dolgui, A., Sokolov, B., & Werner, F. (2016b). Schedule robustness analysis with the help of attainable sets in continuous flow problem under capacity disruptions. International Journal of Production Research,54(1), 3397–3413.

    Google Scholar 

  • Ivanov, D., Pavlov, A., Pavlov, D., & Sokolov, B. (2017b). Minimization of disruption-related return flows in the supply chain. International Journal of Production Economics,183, 503–513.

    Google Scholar 

  • Ivanov D., & Rozhkov M. (2017). Coordination of production and ordering policies under capacity disruption and product write-off risk: An analytical study with real-data based simulations of a fast moving consumer goods company. Annals of Operations Research. https://doi.org/10.1007/s10479-017-2643-8.

    Article  Google Scholar 

  • Ivanov, D., Sethi, S., Dolgui, A., & Sokolov, B. (2018b). A survey on the control theory applications to operational systems, supply chain management and Industry 4.0. Annual Reviews in Control,46, 134–147.

    Google Scholar 

  • Ivanov, D., & Sokolov, B. (2012). Dynamic supply chain scheduling. Journal of Scheduling,15(2), 201–216.

    Google Scholar 

  • Ivanov, D., & Sokolov, B. (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,224(2), 313–323.

    Google Scholar 

  • Ivanov, D., Sokolov, B., & Dolgui, A. (2014a). The Ripple effect in supply chains: Trade-off ‘efficiency-flexibility-resilience’ in disruption management. International Journal of Production Research,52(7), 2154–2172.

    Google Scholar 

  • Ivanov, D., Sokolov, B., & Dolgui, A. (2014b). Multi-stage supply chain scheduling in petrochemistry with non-preemptive operations and execution control. International Journal of Production Research,52(13), 4059–4077.

    Google Scholar 

  • Ivanov, D., Sokolov, B., Dolgui, A., Werner, F., & Ivanova, M. (2016c). A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory Industry 4.0. International Journal of Production Research,54(2), 386–402.

    Google Scholar 

  • Ivanov, D., Sokolov, B., & Kaeschel, J. (2010). A multi-structural framework for adaptive supply chain planning and operations with structure dynamics considerations. European Journal of Operational Research,200, 409–420.

    Google Scholar 

  • Ivanov, D., Sokolov, B., & Pavlov, A. (2013). Dual problem formulation and its application to optimal re-design of an integrated production-distribution network with structure dynamics and ripple effect considerations. International Journal of Production Research,51(18), 5386–5403.

    Google Scholar 

  • Ivanov, D., Sokolov, B., & Pavlov, A. (2014c). Optimal distribution (re)planning in a centralized multi-stage network under conditions of ripple effect and structure dynamics. European Journal of Operational Research,237(2), 758–770.

    Google Scholar 

  • Ivanov, D., Sokolov, B., Pavlov, A., Dolgui, A., & Pavlov, D. (2016d). Disruption-driven supply chain (re)-planning and performance impact assessment with consideration of pro-active and recovery policies. Transportation Research Part E,90, 7–24.

    Google Scholar 

  • Jain, V., Kumar, S., Soni, U., & Chandra, C. (2017). Supply chain resilience: Model development and empirical analysis. International Journal of Production Research,55(22), 6779–6800.

    Google Scholar 

  • Kamalahmadi, M., & Mellat-Parast, M. (2016). Developing a resilient supply chain through supplier flexibility and reliability assessment. International Journal of Production Research,54(1), 302–321.

    Google Scholar 

  • Khmelnitsky, E., Kogan, K., & Maimom, O. (1997). Maximum principle-based methods for production scheduling with partially sequence-dependent setups. International Journal of Production Research,35(10), 2701–2712.

    Google Scholar 

  • Khojasteh, Y. (Ed.). (2018). Supply chain risk management. Singapore: Springer.

    Google Scholar 

  • Krylov, I. A., & Chernousko, F. L. (1972). An algorithm for the method of successive approximations in optimal control problems. Zh. Vychisl. Mat. Mat. Fiz.,12(1), 14–34.

    Google Scholar 

  • Lee, E. B., & Markus, L. (1967). Foundations of optimal control theory. New York: Wiley.

    Google Scholar 

  • Levner, E., & Ptuskin, A. (2018). Entropy-based model for the ripple effect: Managing environmental risks in supply chains. International Journal of Production Research,56(7), 2539–2551.

    Google Scholar 

  • Liberatore, F., Scaparra, M. P., & Daskin, M. S. (2012). Hedging against disruptions with ripple effects in location analysis. Omega,40, 21–30.

    Google Scholar 

  • Lücker, F., & Seifert, R. W. (2017). Building up resilience in a pharmaceutical supply chain through inventory, dual sourcing and agility capacity. Omega,73, 114–124.

    Google Scholar 

  • Lücker, F., Seifert, R. W., & Biçer, I. (2018). Roles of inventory and reserve capacity in mitigating supply chain disruption risk. International Journal of Production Research. https://doi.org/10.1080/00207543.2018.1504173.

    Article  Google Scholar 

  • Macdonald, J. R., Zobel, C. W., Melnyk, S. A., & Griffis, S. E. (2018). Supply chain risk and resilience: Theory building through structured experiments and simulation. International Journal of Production Research,56(12), 4337–4355.

    Google Scholar 

  • Mizgier, K. J. (2017). Global sensitivity analysis and aggregation of risk in multi-product supply chain networks. International Journal of Production Research,55(1), 130–144.

    Google Scholar 

  • Mizgier, K. J., Jüttner, M., & Wagner, S. M. (2013). Bottleneck identification in supply chain networks. International Journal of Production Research,51(5), 1477–1490.

    Google Scholar 

  • Mizgier, K. J., Wagner, S. M., & Jüttner, M. (2015). Disentangling diversification in supply chain networks. International Journal of Production Economics,162, 115–124.

    Google Scholar 

  • Moiseev, N. N. (1974). Element of the optimal systems theory. Moscow: Nauka. (in Russian).

    Google Scholar 

  • Nair, A., & Vidal, J. M. (2011). Supply network topology and robustness against disruptions—An investigation using a multi-agent model. International Journal of Production Research,49(5), 1391–1404.

    Google Scholar 

  • Namdar, J., Li, X., Sawhney, R., & Pradhan, N. (2018). Supply chain resilience for single and multiple sourcing in the presence of disruption risks. International Journal of Production Research,56(6), 2339–2360.

    Google Scholar 

  • Pavlov, A., Ivanov, D., Dolgui, A., & Sokolov, B. (2018). Hybrid fuzzy-probabilistic approach to supply chain resilience assessment. IEEE Transactions on Engineering Management,65(2), 303–315.

    Google Scholar 

  • Pavlov, A., Ivanov, D., Pavlov, D., & Slinko, A. (2019). Optimization of network redundancy and contingency planning in sustainable and resilient supply chain resource management under conditions of structural dynamics. Annals of Operations Research. https://doi.org/10.1007/s10479-019-03182-6.

    Article  Google Scholar 

  • Pontryagin, L. S., Boltyanskiy, V. G., Gamkrelidze, R. V., & Mishchenko, E. F. (1964). The mathematical theory of optimal processes. Oxford: Pergamon Press.

    Google Scholar 

  • Rangel, D. A., de Oliveira, T. K., & Alexandre, M. S. (2015). Supply chain risk classification: Discussion and proposal. International Journal of Production Research,53(22), 6868–6887.

    Google Scholar 

  • Reyes Levalle, R., & Nof, S. Y. (2017). Resilience in supply networks: Definition, dimensions, and levels. Annual Reviews in Control,43, 224–236.

    Google Scholar 

  • Sawik, T. (2017). A portfolio approach to supply chain disruption management. International Journal of Production Research,55(7), 1970–1991.

    Google Scholar 

  • Schmidt, W., & Simchi-Levi, D. (2013). Nissan Motor Company Ltd.: Building operational resiliency. MIT Sloan Management, August, 13–149.

  • Sheffi, Y. (2005). The resilient enterprise: Overcoming vulnerability for competitive advantage. Cambridge, MA: MIT Press.

    Google Scholar 

  • Simchi-Levi, D., Schmidt, W., Wei, Y., Zhang, P. Y., Combs, K., Ge, Y., et al. (2015). Identifying risks and mitigating disruptions in the automotive supply chain. Interfaces,45(5), 375–390.

    Google Scholar 

  • Sokolov, B., Ivanov, D., Dolgui, A., & Pavlov, A. (2016). Structural quantification of the ripple effect in the supply chain. International Journal of Production Research,54(1), 152–169.

    Google Scholar 

  • Spiegler, V., Naim, M., & Wikner, J. (2012). A control engineering approach to the assessment of supply chain resilience. International Journal of Production Research,50, 6162–6187.

    Google Scholar 

  • Spiegler, V. L. M., Naim, M. M., Towill, D. R., & Wikner, J. (2016). The value of nonlinear control theory in investigating the underlying dynamics and resilience of a grocery supply chain. International Journal of Production Research,54(1), 265–286.

    Google Scholar 

  • Tan, W. J., Zhang, A. N., & Cai, W. (2019). A graph-based model to measure structural redundancy for supply chain resilience. International Journal of Production Research. https://doi.org/10.1080/00207543.2019.1566666.

    Article  Google Scholar 

  • Tang, C. S. (2006). Perspectives in supply chain risk management. International Journal of Production Economics,103, 451–488.

    Google Scholar 

  • Tang, L., Jing, K., He, J., & Stanley, H. E. (2016). Complex interdependent supply chain networks: Cascading failure and robustness. Physica A,443, 58–69.

    Google Scholar 

  • Tukamuhabwa, B. R., Stevenson, M., Busby, J., & Zorzini, M. (2015). Supply chain resilience: Definition, review and theoretical foundations for further study. International Journal of Production Research,53(18), 5592–5623.

    Google Scholar 

  • Wang, H. L. (2008). Supply chain control model: A cybernetics-based approach. In IEEE international conference on service operations and logistics, and informatics.

  • Xia, Y., Yang, M. H., Golany, B., Gilbert, S. M., & Yu, G. (2004). Real-time disruption management in a two-stage production and inventory system. IIE Transactions,36(2), 111–125.

    Google Scholar 

  • Yadav, S. R., Mishra, N., Kumar, V., & Tiwari, M. K. (2011). A framework for designing robust supply chains considering product development issues. International Journal of Production Research,49(20), 6065–6088.

    Google Scholar 

  • Yoon, J., Talluri, S., Yildiz, H., & Ho, W. (2018). Models for supplier selection and risk mitigation: A holistic approach. International Journal of Production Research,56(1), 3636–3661.

    Google Scholar 

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Acknowledgements

The authors thank the associate editor and three anonymous referees for their invaluable comments that helped us in manuscript improvement immensely.

Funding

The research described in this paper is partially supported by the Russian Foundation for Basic Research (Grants 16-29-09482-ofi-m, 17-29-07073-ofi-i, 19-08-00989), state order of the Ministry of Education and Science of the Russian Federation No. 2.3135.2017/4.6, state research 0073–2019–0004.

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Ivanov, D., Sokolov, B. Simultaneous structural–operational control of supply chain dynamics and resilience. Ann Oper Res 283, 1191–1210 (2019). https://doi.org/10.1007/s10479-019-03231-0

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