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