Artificial Intelligence Empowers Sustainable Supply Chains
DOI:
https://doi.org/10.61453/INTIj.202555Keywords:
Artificial intelligence empowerment, Sustainable supply chains, Low-carbon emission reductionAbstract
With the continuous turbulence in the global market and the rapid development of artificial intelligence (AI), the sustainable development of supply chains has attracted significant attention. Addressing the "efficiency-environmental protection-equity" challenges faced by current sustainable supply chains, this paper attempts to analyze how AI can empower sustainable supply chains and explores AI's ability to handle the dynamic complexity of supply chains, including real-time data monitoring, accurate prediction, intelligent decision-making, risk management, data sharing, and continuous learning. The study finds that AI can empower sustainable supply chains through the following aspects: demand forecasting and inventory optimization, logistics network optimization, supply chain risk management, supplier management, production and manufacturing optimization, real-time monitoring and transparency, as well as carbon footprint management and emission reduction optimization. Through these means, AI helps improve supply chain efficiency, reduce costs, enhance forecasting and demand management capabilities, strengthen risk management and emergency response capabilities, and boost supply chain resilience.
References
Arulchakkaravarthi, A., Santhanaraghavan, P., Kumar, R., Muralithar, S., Ramasamy, P., & Nagarajan, T. (2003). Detection characteristics of vertical Bridgman grown stilbene crystals for gamma rays using ⁶⁰Co, ¹³⁷Cs and ²²Na gamma-ray sources. Materials Chemistry and Physics, 77(1), 77–80. https://doi.org/10.1016/S0254-0584(01)00561-2
ASCM. (2025). 2025 supply chain trends report. Association for Supply Chain Management.
Bertrand, G. H. V., Hamel, M., & Sguerra, F. (2014). Current status on plastic scintillators modifications. Chemistry – A European Journal, 20(48), 15660–15685. https://doi.org/10.1002/chem.201404093
Cai, H., & Xu, X. Q. (1996). Industrial logistics (2nd ed.). China Machine Press.
Carter, C. R., & Rogers, D. S. (2008). A framework of sustainable supply chain management: Moving toward new theory. International Journal of Physical Distribution & Logistics Management, 38(5), 360–387. https://doi.org/10.1108/09600030810882816
Chen, Z. (2023). Supply chain risk management under digital transformation. China Financial Publishing House.
Dumazert, J., Coulon, R., Hamel, M., et al. (2016). Gadolinium-loaded plastic scintillators for thermal neutron detection using compensation. IEEE Transactions on Nuclear Science, 63(3), 1551–1564. https://doi.org/10.1109/TNS.2016.2535278
Elkington, J. (1998). Cannibals with forks: The triple bottom line of 21st century business. New Society Publishers.
Febbraro, M. T. (2014). A deuterated neutron detector array for the study of nuclear reactions with stable and rare isotope beams (Doctoral dissertation).
Garcia, A. R., Mendoza, E., Cano-Ott, D., et al. (2017). New physics model in GEANT4 for the simulation of neutron interactions with organic scintillation detectors. Nuclear Instruments and Methods in Physics Research Section A, 868, 73–81. https://doi.org/10.1016/j.nima.2017.06.021
Glenn, A. M., Mabe, A. N., Zaitseva, N. P., et al. (2018). Recent developments in plastic scintillators with pulse shape discrimination. Nuclear Instruments and Methods in Physics Research Section A, 889, 97–104. https://doi.org/10.1016/j.nima.2018.01.093
Glenn, A., Martinez, H. P., Zaitseva, N., et al. (2013). Pulse shape discrimination with lithium-containing organic scintillators. Nuclear Instruments and Methods in Physics Research Section A, 729, 747–754. https://doi.org/10.1016/j.nima.2013.08.048
Grodzicka-Kobylka, M., Szczesniak, T., Moszynski, M., et al. (2020). Fast neutron and gamma-ray pulse shape discrimination in EJ-276 and EJ-276G plastic scintillators. Journal of Instrumentation, 15(3), P03030. https://doi.org/10.1088/1748-0221/15/03/P03030
Hajagos, T. J., Liu, C., Cherepy, N. J., et al. (2018). High-Z sensitized plastic scintillators: A review. Advanced Materials, 30(27), e1706956. https://doi.org/10.1002/adma.201706956
Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172. https://psycnet.apa.org/doi/10.1177/1094670517752459
International Energy Agency. (2025). Global energy review 2025.
Ji, C. S. (2014). Neutron detection. China Atomic Energy Press.
Li, Q. (2024). Report on the work of the government. People’s Publishing House.
Liu, B. Q. (2019). Key technologies research on neutron-gamma discrimination based on plastic scintillators (Doctoral dissertation, Chengdu University of Technology).
Liu, H. R. (2004). Modern logistics management (3rd ed.). Tsinghua University Press.
Luo, X., Huang, J., & Zhou, X. (2023). Artificial intelligence-empowered supply chain innovation. Journal of Industrial Engineering and Engineering Management, 37(3), 1–12. https://doi.org/10.1016/j.compind.2024.104132
Maddalena, F., Tjahjana, L., et al. (2019). Inorganic, organic, and perovskite halides with nanotechnology for high-light yield X- and γ-ray scintillators. Crystals, 9(2), 88. https://doi.org/10.3390/cryst9020088
McCarthy, T. M., Davis, D. F., Golicic, S. L., & Mentzer, J. T. (2006). The evolution of logistics and supply chain management. International Journal of Physical Distribution & Logistics Management, 36(8), 621–642. https://doi.org/10.1108/09555340710760152
Mike M. (2024). ASCM’s top 10 supply chain trends for 2024. Association for Supply Chain Management.
Particle Data Group. (2012). Review of particle physics. Physical Review D, 86(1), 010001. https://doi.org/10.1103/PhysRevD.86.010001
Pino, F., Stevanato, L., Cester, D., et al. (2014a). The light output and the detection efficiency of the liquid scintillator EJ-309. Applied Radiation and Isotopes, 89, 79–84. https://doi.org/10.1016/j.apradiso.2014.02.016
Pino, F., Stevanato, L., Cester, D., et al. (2014b). Detecting fast and thermal neutrons with a boron-loaded liquid scintillator, EJ-339A. Applied Radiation and Isotopes, 92, 6–11. https://doi.org/10.1016/j.apradiso.2014.05.025
Porter, F., Freedman, M., Wagner, F., & Sherman, I. (1966). Response of NaI, anthracene and plastic scintillators to electrons. Nuclear Instruments and Methods in Physics Research, 39, 35–44. https://doi.org/10.1016/0029-554X(66)90041-3
Qin, Q., Li, W., Jiao, T. Y., et al. (2021). Research on neutron/gamma discrimination capability of plastic scintillator detector. Aerospace Metrology & Measurement, 41(3), 91–96.
Riahi, K., Schmitz, A., Yilmaz, S., et al. (2021). Long-term global energy scenarios. Global Energy Assessment Report, 123–160.
Sabot, B., Dutsov, C., Cassette, P., et al. (2024). A compact detector system for simultaneous measurements of the light-yield non-linearity and timing properties of scintillators. Scientific Reports, 14(1), 6960. https://doi.org/10.1038/s41598-024-57186-9
Seuring, S., & Müller, M. (2008). From a literature review to a conceptual framework for sustainable supply chain management. Journal of Cleaner Production, 16(15), 1699–1710. https://doi.org/10.1016/j.jclepro.2008.04.020
Shu, H., Wang, J., & Liu, S. (2025). AI-enabled smart logistics for supply chain resilience. Journal of Management Science, 38(1), 50–65.
Song, Y. (2024). Digital supply chain transformation and enterprise upgrading. Economic Science Press.
Watanabe, K., Fujimoto, Y., & Yanagida, T. (2015). Comparative study of neutron and gamma-ray pulse shape discrimination of anthracene, stilbene, and p-terphenyl. Nuclear Instruments and Methods in Physics Research Section A, 784, 111–114. https://doi.org/10.1016/j.nima.2014.12.031
Zaitseva, N., Glenn, A., Carman, L., et al. (2015). Scintillation properties of solution-grown trans-stilbene single crystals. Nuclear Instruments and Methods in Physics Research Section A, 789, 8–15. https://doi.org/10.1016/j.nima.2015.03.090
Zhang, H. (2025). Digital procurement and supply chain transparency. China Journal of Logistics, 45(2), 30–42.
Zhang, W., & Shen, K. (2025). AI-driven optimization in global supply networks. Journal of Operations and Management Research, 12(1), 23–36.
Zhang, Z. Q., Li, Q., Zhang, Z. J., & Zhao, J. (2022). Research progress of scintillation materials for neutron detection. Chinese Journal of Nature, 44(4), 301–315. https://doi.org/10.3969/j.issn.0253-9608.2022.04.005
Zmeškal, M., Thulliez, L., & Dumonteil, E. (2023). Improvement of Geant4 Neutron-HP package: Doppler broadening of the neutron elastic scattering kernel and cross sections. https://doi.org/10.48550/arXiv.2303.07300
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 INTI Journal

This work is licensed under a Creative Commons Attribution 4.0 International License.