La dualidad de la inteligencia artificial en la sostenibilidad de las cadenas de suministro: una revisión narrativa
DOI:
https://doi.org/10.31637/epsir-2024-552Palavras-chave:
Inteligencia Artificial, Cadenas de suministro, sostenibilidad, huella de carbono, optimización logística, predicción de demanda, gestión de inventarios, eficiencia operativa, actitudesResumo
Introducción: En la era digital actual, la inteligencia artificial (IA) se posiciona como una herramienta crucial para avanzar hacia cadenas de suministro sostenibles, abordando ineficiencias y reduciendo emisiones de carbono derivadas de la creciente demanda energética. Metodología: Se realizó una revisión narrativa de la literatura, evaluando artículos publicados en las bases de datos Scopus y Science Direct entre 2022 y 2024, para capturar los avances recientes del impacto de la IA en la sostenibilidad de las cadenas de suministro. Resultados: Los hallazgos subrayan la capacidad de la IA para optimizar procesos logísticos, mejorar la predicción de la demanda y gestionar inventarios de manera eficiente, reduciendo la huella de carbono y optimizando el uso de recursos. Discusión: Aunque los beneficios son significativos, la implementación de la IA enfrenta desafíos como el alto consumo energético y la complejidad en la integración de datos. Es esencial considerar las implicaciones éticas y sociales para maximizar los beneficios y minimizar los impactos negativos. Conclusiones: La integración de la IA en la gestión de la cadena de suministro representa un avance significativo en sostenibilidad y eficiencia operativa. Se requieren tecnologías más eficientes y políticas que apoyen la adopción de IA sostenible para superar los desafíos y maximizar los beneficios.
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Referências
Abate, Y., Ukpabi, C. y Karjaluoto, H. (2023). AI -Sustainability Nexus: A Framework for Future Research. 23.
ABB. (2022, June 29). ABB Smart Building’s AI-Powered SaaS Increases Energy Efficiency, Reduces Carbon Footprint. ABB. https://bit.ly/4cHjp1R
Akter, S., Babu, M. M., Hani, U., Sultana, S., Bandara, R. y Grant, D. (2024). Unleashing the power of artificial intelligence for climate action in industrial markets. Industrial Marketing Management, 117, 92-113. https://doi.org/10.1016/j.indmarman.2023.12.011 DOI: https://doi.org/10.1016/j.indmarman.2023.12.011
Allbirds. (2022). Allbirds 2022 Flight Status. Sustainability Report. https://www.allbirds.com/pages/sustainable-practices#reality
Al-Sakkari, E. G., Ragab, A., Dagdougui, H., Boffito, D. C. y Amazouz, M. (2024). Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities. Science of The Total Environment, 917, 170085. https://doi.org/10.1016/j.scitotenv.2024.170085 DOI: https://doi.org/10.1016/j.scitotenv.2024.170085
Amirteimoori, A., Allahviranloo, T., Zadmirzaei, M. y Hasanzadeh, F. (2023). On the environmental performance analysis: A combined fuzzy data envelopment analysis and artificial intelligence algorithms. Expert Systems with Applications, 224, 119953. https://doi.org/10.1016/j.eswa.2023.119953 DOI: https://doi.org/10.1016/j.eswa.2023.119953
Ammanath, B. (2024). How to manage AI’s energy demand—Today and in the future. World Economic Forum. https://bit.ly/3XV4IUr
Badghish, S. y Soomro, Y. A. (2024). Artificial Intelligence Adoption by SMEs to Achieve Sustainable Business Performance: Application of Technology–Organization–Environment Framework. Sustainability, 16(5). https://doi.org/10.3390/su16051864 DOI: https://doi.org/10.3390/su16051864
Bains, A., Sridhar, K., Dhull, S. B., Chawla, P., Sharma, M., Sarangi, P. K. y Gupta, V. K. (2024). Circular bioeconomy in carbon footprint components of nonthermal processing technologies towards sustainable food system: A review. Trends in Food Science & Technology, 149, 104520. https://doi.org/10.1016/j.tifs.2024.104520 DOI: https://doi.org/10.1016/j.tifs.2024.104520
Baker, J. D. (2016). The Purpose, Process, and Methods of Writing a Literature Review. AORN Journal, 103(3), 265-269. https://doi.org/10.1016/j.aorn.2016.01.016 DOI: https://doi.org/10.1016/j.aorn.2016.01.016
Batra, N. (2023, November 1). Domino’s Pizza: Ensuring customer satisfaction with data-driven demand planning in Microsoft Dynamics 365. Microsoft Customers Stories. https://bit.ly/3xTo6Xc
Boualam, M. (2021, March 28). How IoT, AI, and Blockchain Can Create a Sustainable Supply Chain | SCM Globe. https://bit.ly/4fduRnN
Capgemini Research Institute. (2024). Data: A powerful ally in tackling Scope 3 emissions-reduction targets. Capgemini USA. https://bit.ly/3xTXUf0
Chan, H.-L. y Choi, T.-M. (2023). Logistics management for the future: The IJLRA framework. International Journal of Logistics Research and Applications, 1-19. https://doi.org/10.1080/13675567.2023.2286352 DOI: https://doi.org/10.1080/13675567.2023.2286352
Chen, P., Chu, Z. y Zhao, M. (2024). The Road to corporate sustainability: The importance of artificial intelligence. Technology in Society, 76, 102440. https://doi.org/10.1016/j.techsoc.2023.102440 DOI: https://doi.org/10.1016/j.techsoc.2023.102440
Colon, C., Brännström, Å., Rovenskaya, E. y Dieckmann, U. (2021). Fragmentation of production amplifies systemic risks from extreme events in supply-chain networks. PLOS ONE, 15(12), e0244196. https://doi.org/10.1371/journal.pone.0244196 DOI: https://doi.org/10.1371/journal.pone.0244196
Conrad, R. (2024, March 1). AI in Logistics: Driving Sustainability and Efficiency in Supply Chains. RTS Labs. https://rtslabs.com/ai-logistics-sustainability-efficiency/
Covariant. (n.d.). Otto Group strengthens its logistics network with hundreds of AI-powered robots. https://bit.ly/3xTo81g
Crawford, K. (2024). Generative AI’s environmental costs are soaring—And mostly secret. Nature, 626(8000), 693–693. https://doi.org/10.1038/d41586-024-00478-x DOI: https://doi.org/10.1038/d41586-024-00478-x
Delanoë, P., Tchuente, D. y Colin, G. (2023). Method and evaluations of the effective gain of artificial intelligence models for reducing CO2 emissions. Journal of Environmental Management, 331, 117261. https://doi.org/10.1016/j.jenvman.2023.117261 DOI: https://doi.org/10.1016/j.jenvman.2023.117261
Dhiman, R., Miteff, S., Wang, Y., Ma, S.-C., Amirikas, R. y Fabian, B. (2024). Artificial Intelligence and Sustainability—A Review. Analytics, 3(1), 140-164. https://doi.org/10.3390/analytics3010008 DOI: https://doi.org/10.3390/analytics3010008
Ding, T., Li, J., Shi, X., Li, X. y Chen, Y. (2023). Is artificial intelligence associated with carbon emissions reduction? Case of China. Resources Policy, 85, 103892. https://doi.org/10.1016/j.resourpol.2023.103892 DOI: https://doi.org/10.1016/j.resourpol.2023.103892
Dohale, V., Kamble, S., Ambilkar, P., Gold, S., y Belhadi, A. (2024). An integrated MCDM-ML approach for predicting the carbon neutrality index in manufacturing supply chains. Technological Forecasting and Social Change, 201, 123243. https://doi.org/10.1016/j.techfore.2024.123243 DOI: https://doi.org/10.1016/j.techfore.2024.123243
Fabri, L., Weissflog, J., y Wenninger, S. (2024). Unraveling the complexity: A taxonomy for characterizing and structuring smart energy services in the building sector. Journal of Cleaner Production, 461, 142522. https://doi.org/10.1016/j.jclepro.2024.142522 DOI: https://doi.org/10.1016/j.jclepro.2024.142522
Fernando, Y., Al-Madani, M. H. M. y Shaharudin, M. S. (2023). COVID-19 and global supply chain risks mitigation: Systematic review using a scientometric technique. Journal of Science and Technology Policy Management, ahead-of-print(ahead-of-print). https://doi.org/10.1108/JSTPM-01-2022-0013 DOI: https://doi.org/10.1108/JSTPM-01-2022-0013
Foy, K. (2023, October 5). New tools are available to help reduce the energy that AI models devour. MIT News | Massachusetts Institute of Technology. https://bit.ly/3Ll0qhG
Gaur, L., Afaq, A., Arora, G. K. y Khan, N. (2023). Artificial intelligence for carbon emissions using system of systems theory. Ecological Informatics, 76, 102165. https://doi.org/10.1016/j.ecoinf.2023.102165 DOI: https://doi.org/10.1016/j.ecoinf.2023.102165
Gbako, S., Paraskevadakis, D., Ren, J., Wang, J. y Radmilovic, Z. (2024). A systematic literature review of technological developments and challenges for inland waterways freight transport in intermodal supply chain management. Benchmarking: An International Journal, ahead-of-print(ahead-of-print). https://doi.org/10.1108/BIJ-03-2023-0164 DOI: https://doi.org/10.1108/BIJ-03-2023-0164
Gow, G. (2020, September 3). Environmental Sustainability And #AI [LinkedIn]. https://bit.ly/3Y3qo0z
Gray, C. (2022, May 5). HAVI: driving savings in the supply chain with AI. AAI Magazine, Data&Analytics. https://bit.ly/3xISlAh
Green, B., Johnson, C. y Adams, A. (2006). Writing Narrative Literature Reviews for Peer-Reviewed Journals: Secrets of the Trade. Journal of Chiropractic Medicine, 5, 101-117. https://doi.org/10.1016/S0899-3467(07)60142-6 DOI: https://doi.org/10.1016/S0899-3467(07)60142-6
Herold, D. M. y Marzantowicz, Ł. (2023). Supply chain responses to global disruptions and its ripple effects: An institutional complexity perspective. Operations Management Research, 16(4), 2213-2224. https://doi.org/10.1007/s12063-023-00404-w DOI: https://doi.org/10.1007/s12063-023-00404-w
Hewlett Packard. (2024). Climate Action. https://bit.ly/4f12LMo
Hong, Z. y Xiao, K. (2024). Digital economy structuring for sustainable development: The role of blockchain and artificial intelligence in improving supply chain and reducing negative environmental impacts. Scientific Reports, 14(1), 3912. https://doi.org/10.1038/s41598-024-53760-3 DOI: https://doi.org/10.1038/s41598-024-53760-3
IBM. (2024, June 24). IBM Supply Chain Intelligence Suite. https://ibm.co/4cBVIYQ
Juárez, C. (2024, May 17). Nueva IA de Walmart reduce desperdicio de perecederos y textiles en anaqueles. The Logistics World. https://bit.ly/3LnUkwZ
Kishan, S. y Saul, J. (2024, January 25). AI Needs So Much Power That Old Coal Plants Are Sticking Around. Bloomberg.Com. https://bloom.bg/4eVEUNT
Kolasani, S. (2024). Revolutionizing manufacturing, making it more efficient, flexible, and intelligent with Industry 4.0 innovations. International Journal of Sustainable Development Through AI, ML and IoT, 3(1), 1-17. https://ijsdai.com/index.php/IJSDAI/article/view/46
Kusznir, F. (n.d.). How AI is accelerating the Energy Transition and carbon negative. GE Digital. https://invent.ge/4cAelfP
Lynn, J. (2024, May 28). Nvidia’s Record Earnings Overshadow New Standard in Chip Energy Efficiency. Carbon Credits. https://bit.ly/4csZrI5
Law, M. (2024, January 22). How Unilever Uses AI & Digital Solutions in its Operations. Technology Magazine. https://bit.ly/4f2iD0U
Lemos, S. I. C., Ferreira, F. A. F., Zopounidis, C., Galariotis, E. y Ferreira, N. C. M. Q. F. (2022). Artificial intelligence and change management in small and medium-sized enterprises: An analysis of dynamics within adaptation initiatives. Annals of Operations Research. https://doi.org/10.1007/s10479-022-05159-4 DOI: https://doi.org/10.1007/s10479-022-05159-4
Lin, J., Zeng, Y., Wu, S. y Luo, X. (Robert). (2024). How does artificial intelligence affect the environmental performance of organizations? The role of green innovation and green culture. Information & Management, 61(2), 103924. https://doi.org/10.1016/j.im.2024.103924 DOI: https://doi.org/10.1016/j.im.2024.103924
Maghsoudi, M., Shokouhyar, S., Ataei, A., Ahmadi, S. y Shokoohyar, S. (2023). Co-authorship network analysis of AI applications in sustainable supply chains: Key players and themes. Journal of Cleaner Production, 422, 138472. https://doi.org/10.1016/j.jclepro.2023.138472 DOI: https://doi.org/10.1016/j.jclepro.2023.138472
Markets and Markets. (2023). Carbon Footprint Management Market Growth Drivers & Opportunities. https://bit.ly/3zGYFbZ
Marr, B. (2019, April 8). The Fascinating Ways PepsiCo Uses Artificial Intelligence And Machine Learning To Deliver Success. Forbes. https://bit.ly/4cCJu2b
Matias, Y. (2023, October 10). Project Green Light’s work to reduce urban emissions using AI. Google. https://bit.ly/3xWl709
Nahar, S. (2024). Modeling the effects of artificial intelligence (AI)-based innovation on sustainable development goals (SDGs): Applying a system dynamics perspective in a cross-country setting. Technological Forecasting and Social Change, 201, 123203. https://doi.org/10.1016/j.techfore.2023.123203 DOI: https://doi.org/10.1016/j.techfore.2023.123203
Nemitallah, M. A., Nabhan, M. A., Alowaifeer, M., Haeruman, A., Alzahrani, F., Habib, M. A., Elshafei, M., Abouheaf, M. I., Aliyu, M. y Alfarraj, M. (2023). Artificial intelligence for control and optimization of boilers’ performance and emissions: A review. JournalofCleanerProduction, 417, 138109. https://doi.org/10.1016/j.jclepro.2023.138109 DOI: https://doi.org/10.1016/j.jclepro.2023.138109
Ochoa-Barragán, R., Serrano-Arévalo, T. I., Pulido-Ocegueda, J. C., Cerda-Flores, S. C., Ramírez-Márquez, C., Nápoles-Rivera, F. y Ponce-Ortega, J. M. (2024). Sustainable lime production in Michoacan Mexico: An optimal and equitable approach with machine learning. Journal of Cleaner Production, 442, 141017. https://doi.org/10.1016/j.jclepro.2024.141017 DOI: https://doi.org/10.1016/j.jclepro.2024.141017
Pasquini, N. (2024, March 20). How to Reduce AI’s Energy Consumption. Harvard Magazine, Science&Technology. https://www.harvardmagazine.com/node/85960
Pesz, B. (2023, June 12). Optimizing energy efficiency with AI-powered energy management software | Nokia. https://nokia.ly/3VY5kWJ
Qi, B., Shen, Y. y Xu, T. (2023). An artificial-intelligence-enabled sustainable supply chain model for B2C E-commerce business in the international trade. Technological Forecasting and Social Change, 191, 122491. https://doi.org/10.1016/j.techfore.2023.122491 DOI: https://doi.org/10.1016/j.techfore.2023.122491
Rico, A. (2024, May 7). Inversiones de las tecnológicas como Apple, Google y Amazon en inteligencia artificial. Diario La República. https://bit.ly/3LlbkUp
Robotics 24/7 Staff. (2023, May 10). Otto Group Works With Covariant to Apply AI Robotics to Its Logistics Network. Robotics 24/7. https://bit.ly/4bEG51m
Siemens. (2023). Sustainability report [Report]. Siemens. https://bit.ly/4f1NPNI
Timmer, M. P., Los, B., Stehrer, R. y de Vries, G. J. (2021). Correction to: Supply Chain Fragmentation and the Global Trade Elasticity: A New Accounting Framework. IMF Economic Review, 69(4), 681–681. https://doi.org/10.1057/s41308-021-00139-3 DOI: https://doi.org/10.1057/s41308-021-00139-3
Verma, S. (2019, November 19). How blockchain and IoT is making supply chain smarter. IBM Blog. https://ibm.co/3zMyCju
Wang, Q., Zhang, F., Li, R. y Sun, J. (2024). Does artificial intelligence promote energy transition and curb carbon emissions? The role of trade openness. Journal of Cleaner Production, 447, 141298. https://doi.org/10.1016/j.jclepro.2024.141298 DOI: https://doi.org/10.1016/j.jclepro.2024.141298
World Economic Forum. (2021, April 1). 4 steps to using AI in an environmentally responsible way. World Economic Forum. https://bit.ly/4eZkYK4
World Wide Technology WWT. (2023, May 26). Using AI to Reduce Energy Consumption, Cost and Carbon Emissions in Data Centers. ATC Insight. https://bit.ly/3VZklaI
Zaytsev, A. (2023, September 23). Case Study: Zara’s Comprehensive Approach to AI and Supply Chain Management. AIX | AI Expert Network. https://bit.ly/4bL2IBk
Zhou, W., Zhang, Y. y Li, X. (2024). Artificial intelligence, green technological progress, energy conservation, and carbon emission reduction in China: An examination based on dynamic spatial Durbin modeling. JournalofCleanerProduction, 446, 141142. https://doi.org/10.1016/j.jclepro.2024.141142 DOI: https://doi.org/10.1016/j.jclepro.2024.141142
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