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Roadmap for Resilient Networks Building Through Artificial Intelligence

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Collaborative Networks in Digitalization and Society 5.0 (PRO-VE 2023)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 688))

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Abstract

The business environment around the world continues to face disruptive events of varying magnitude and origin, and as a result, many companies and supply chains often struggle to overcome them. As a solution, resilience has become necessary, not only to be competitive but profitable in the long term. To build resilience, it is critical to define stratagems to enhance its constituent capacities, anticipation, adaptation, and recovery. Recent research studies show that artificial intelligence techniques can be a solution to enhance all these constituent capacities, but implementations are still scarce, and research efforts are dispersed. This work presents a roadmap to help guide research efforts in the quest for resilience, based on a recent literature review.

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References

  1. Sanchis, R., Canetta, L., Poler, R.: A conceptual reference framework for enterprise resilience enhancement. Sustainability 12(4), 1464 (2020). https://doi.org/10.3390/su12041464

    Article  Google Scholar 

  2. Tranfield, D., Denyer, D., Smart, P.: Towards methodology for developing evidence-informed management knowledge by means of systematic review. Br. J. Manag. 14, 207–222 (2003)

    Article  Google Scholar 

  3. Ansari, F., Kohl, L.: AI-enhanced maintenance for building resilience and viability in supply chains. In: Dolgui, A., Ivanov, D., Sokolov, B. (eds.) Supply Network Dynamics and Control, vol. 20, pp. 163–185. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-09179-7_8

    Chapter  Google Scholar 

  4. Deiva Ganesh, A., Kalpana, P.: Supply chain risk identification: a real-time data-mining approach. Ind. Manag. Data Syst. 122(5), 1333–1354 (2022). https://doi.org/10.1108/IMDS-11-2021-0719

    Article  Google Scholar 

  5. Gu, F.: Exploring the application and optimization strategy of the LMBP algorithm in supply chain performance evaluation. Comput. Intell. Neurosci. 2022 (2022). https://doi.org/10.1155/2022/7977335

  6. Nguyen, A., Pellerin, R., Lamouri, S., Lekens, B.: Managing demand volatility of pharmaceutical products in times of disruption through news sentiment analysis. Int. J. Prod. Res. (2022). https://doi.org/10.1080/00207543.2022.2070044

  7. Ordibazar, A.H., Hussain, O., Saberi, M.: A recommender system and risk mitigation strategy for supply chain management using the counterfactual explanation algorithm. In: Hacid, H., et al. (eds.) Service-Oriented Computing – ICSOC 2021, pp. 103–116. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-14135-5_8

    Chapter  Google Scholar 

  8. Prathibha, S., et al.: Synthesizing data analytics towards intelligent enterprises. In: 2022 International Conference on Advanced Computing Technologies and Applications, ICACTA 2022 (2022). https://doi.org/10.1109/ICACTA54488.2022.9753427

  9. Belhadi, A., Kamble, S., Fosso Wamba, S., Queiroz, M.M.: Building supply-chain resilience: an artificial intelligence-based technique and decision-making framework. Int. J. Prod. Res. 60, 4487–4507 (2021). https://doi.org/10.1080/00207543.2021.1950935

    Article  Google Scholar 

  10. Narayanan, S., Samuel, P., Chacko, M.: Product pre-launch prediction. IEEE Access 1–14 (2020). https://doi.org/10.1109/ACCESS.2017

  11. Fu, W., Chien, C.F.: UNISON data-driven intermittent demand forecast framework to empower supply chain resilience and an empirical study in electronics distribution. Comput. Ind. Eng. 135, 940–949 (2019). https://doi.org/10.1016/j.cie.2019.07.002

    Article  Google Scholar 

  12. Hosseini, S., Al Khaled, A.: A hybrid ensemble and AHP approach for resilient supplier selection. J. Intell. Manuf. 30(1), 207–228 (2016). https://doi.org/10.1007/s10845-016-1241-y

    Article  Google Scholar 

  13. Xu, D., Tsang, I.W., Chew, E.K., Siclari, C., Kaul, V.: A data-analytics approach for enterprise resilience. IEEE Intell. Syst. 34(3), 6–18 (2019). https://doi.org/10.1109/MIS.2019.2918092

    Article  Google Scholar 

  14. Herrera-Enríquez, G., Toulkeridis, T., Castillo-Montesdeoca, E., Rodríguez-Rodríguez, G.: Critical factors of business adaptability during resilience in Baños de Agua Santa, Ecuador, due to volcanic hazards. In: Botto-Tobar, M., Cruz, H., Díaz Cadena, A. (eds.) CIT 2020. AISC, vol. 1327, pp. 283–297. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68083-1_22

    Chapter  Google Scholar 

  15. Rajesh, R.: A grey-layered ANP based decision support model for analyzing strategies of resilience in electronic supply chains. Eng. Appl. Artif. Intell. 87, 1–18 (2020). https://doi.org/10.1016/j.engappai.2019.103338

    Article  Google Scholar 

  16. Ramirez De La Huerga, M., Bañuls Silvera, V.A., Turoff, M.: A CIA-ISM scenario approach for analyzing complex cascading effects in Operational Risk Management. Eng. Appl. Artif. Intell. 46, 289–302 (2015). https://doi.org/10.1016/j.engappai.2015.07.016

    Article  Google Scholar 

  17. Bottani, E., Murino, T., Schiavo, M., Akkerman, R.: Resilient food supply chain design: Modelling framework and metaheuristic solution approach. Comput. Ind. Eng. 135, 177–198 (2019). https://doi.org/10.1016/j.cie.2019.05.011

    Article  Google Scholar 

  18. Tickle, R., Triguero, I., Figueredo, G.P., Mesgarpour, M., John, R.I.: PAS3-HSID: a dynamic bio-inspired approach for real-time hot spot identification in data streams. Cogn. Comput. 11(3), 434–458 (2019). https://doi.org/10.1007/s12559-019-09638-y

    Article  Google Scholar 

  19. Habib, S.J., Marimuthu, P.N.: A bio-inspired tool for managing resilience in enterprise networks with embedded intelligent formulation. Expert. Syst. 35(1), e12208 (2018). https://doi.org/10.1111/exsy.12208

    Article  Google Scholar 

  20. Habib, S., Marimuthu, P.N.: Managing enterprise network resilience through the mimicking of bio-organisms. In: Rocha, Á., Correia, A., Adeli, H., Reis, L., Mendonça Teixeira, M. (eds.) New Advances in Information Systems and Technologies. AISC, vol. 444, pp. 901–910. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31232-3_85

    Chapter  Google Scholar 

  21. Pintea, C.-M., Calinescu, A., Pop, P.C., Sabo, C.: Towards a secure two-stage supply chain network: a transportation-cost approach. In: Graña, M., López-Guede, J.M., Etxaniz, O., Herrero, Á., Quintián, H., Corchado, E. (eds.) SOCO/CISIS/ICEUTE 2016. AISC, vol. 527, pp. 547–554. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-47364-2_53

    Chapter  Google Scholar 

  22. Nunes, I.L., Figueira, S., Machado, V.C.: Combining FDSS and simulation to improve supply chain resilience. In: Hernández, J.E., Zarate, P., Dargam, F., Delibašić, B., Liu, S., Ribeiro, R. (eds.) EWG-DSS 2011. LNBIP, vol. 121, pp. 42–58. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32191-7_4

    Chapter  Google Scholar 

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Acknowledgements

This work was supported by the European Commission under the Erasmus+ Programme within the frame of CONTINUITY Project: Business Continuity Managers Training Platform with Reference No. 2021-1-IT01-KA220-VET-000033287 and the Regional Department of Innovation, Universities, Science and Digital Society of the Generalitat Valenciana within the frame of RESPECT Project: Resilient, Sustainable and People-Oriented Supply Chain 5.0 Optimization Using Hybrid Intelligence with Reference No. CIGE/2021/159.

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Authors and Affiliations

  1. Research Centre on Production Management and Engineering (CIGIP), Universitat Politècnica de València, Alcoy, Alicante, Spain

    Marco Arias-Vargas, Raquel Sanchis & Raúl Poler

  2. Industrial Engineering School, University of Costa Rica, San José, Costa Rica

    Marco Arias-Vargas

Authors
  1. Marco Arias-Vargas
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  2. Raquel Sanchis
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  3. Raúl Poler
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Corresponding author

Correspondence to Raquel Sanchis .

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Editors and Affiliations

  1. Universidade Nova de Lisboa, Monte Caparica, Portugal

    Luis M. Camarinha-Matos

  2. Mines Saint-Étienne, Univ Clermont, Saint-Étienne, France

    Xavier Boucher

  3. Polytechnic University of Valencia, Valencia, Spain

    Angel Ortiz

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Arias-Vargas, M., Sanchis, R., Poler, R. (2023). Roadmap for Resilient Networks Building Through Artificial Intelligence. In: Camarinha-Matos, L.M., Boucher, X., Ortiz, A. (eds) Collaborative Networks in Digitalization and Society 5.0. PRO-VE 2023. IFIP Advances in Information and Communication Technology, vol 688. Springer, Cham. https://doi.org/10.1007/978-3-031-42622-3_12

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  • DOI: https://doi.org/10.1007/978-3-031-42622-3_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42621-6

  • Online ISBN: 978-3-031-42622-3

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