Abstract
Disruptive events keep happening; while the business world has not fully recovered from the pandemic and is still dealing with the Russia – Ukraine war, two U.S. banks were recently intervened due to financial troubles. International news mentions a potential financial recession and a potential mid-term crisis. These latent events would affect most organisations, so they need to be more resilient to face up to this turbulent context. One of the main constituent capacities of resilience is anticipation. In this work, this is assessed through a developed web tool that monitors keywords in international news sources, analyses the sentiment of each news based on artificial intelligence, guides preventive actions through access to technical articles, and offers the option to ask a smart chatbot about the manner. The guidance provided through this tool would strengthen the organizations’ anticipation capabilities and enhance enterprise and supply chain resilience.
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References
Arias-Vargas, M., Sanchis, R., Poler, R.: Impact of predicting disruptive events in supply planning for enterprise resilience. IFAC-PapersOnLine 55(10), 1864–1869 (2022). https://doi.org/10.1016/j.ifacol.2022.09.670
Belhadi, A., Kamble, S., Wamba, S.F., Queiroz, M.M.: Building supply-chain resilience: an artificial intelligence-based technique and decision-making framework. Int. J. Prod. Res. 60(14), 4487–4507 (2021). https://doi.org/10.1080/00207543.2021.1950935
Naz, F., Kumar, A., Majumdar, A., Agrawal, R.: Is artificial intelligence an enabler of supply chain resiliency post COVID-19? An exploratory state-of-the-art review for future research. Oper. Manag. Res. 15, 378–398 (2021). https://doi.org/10.1007/s12063-021-00208-w
OpenAI. (n.d.). Introducing ChatGPT. https://openai.com/blog/chatgpt/
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
Sanchis, R., Duran-Heras, A., Poler, R.: Optimising the preparedness capacity of enterprise resilience using mathematical programming. Mathematics 8(9), 1596 (2020). https://doi.org/10.3390/math8091596
Xiao, P.: Artificial Intelligence Programming with Python. John Wiley & Sons Inc., New Jersey (2022)
Xie, M., Jiang, J., Shen, T., Wang, Y., Gerrard, L., Clarke, A.: A green pipeline for out-of-domain public sentiment analysis. In: Li, B., et al. (eds.) Advanced Data Mining and Applications: 17th International Conference, ADMA 2021, Sydney, NSW, Australia, February 2–4, 2022, Proceedings, Part I, pp. 190–202. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-030-95405-5_14
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 Inteligence with Reference No. CIGE/2021/159.
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Arias-Vargas, M., Sanchis, R., Poler, R. (2024). The Smart Resilience Adviser, an Anticipation Tool Powered by Artificial Intelligence. In: Bautista-Valhondo, J., Mateo-Doll, M., Lusa, A., Pastor-Moreno, R. (eds) Proceedings of the 17th International Conference on Industrial Engineering and Industrial Management (ICIEIM) – XXVII Congreso de Ingeniería de Organización (CIO2023). CIO 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 206. Springer, Cham. https://doi.org/10.1007/978-3-031-57996-7_67
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