Optimizing Supply Chain Agility and Sustainability: Machine Learning Approaches for Inventory and Logistics Efficiency
DOI:
https://doi.org/10.5281/Keywords:
Supply chain, machine learning, agility, sustainability, inventory management, logistics, efficiency, predictive analytics, deep learning, and carbon footprint.Abstract
The modern supply chain landscape has evolved significantly, driven by increasing customer expectations, volatile markets, and the pressing need for sustainability. As global commerce expands, organizations face the challenge of maintaining agility while ensuring environmental responsibility. Machine learning (ML) presents a compelling solution to this dilemma, offering the potential to optimize both agility and sustainability across the supply chain. This research paper explores the role of ML in enhancing supply chain agility and sustainability, focusing on inventory and logistics efficiency. By reviewing state-of-the-art machine learning techniques, such as predictive analytics, reinforcement learning, and deep learning, we analyze how these approaches can streamline inventory management, reduce carbon footprints, and improve decision-making in real time. Furthermore, the study examines the synergistic relationship between agility and sustainability, demonstrating how ML can bridge the gap between rapid adaptability and long-term environmental objectives.
References
[1] C. S. Kodete, B. Thuraka, V. Pasupuleti, and S. Malisetty, "Determining the efficacy of machine learning strategies in quelling cyber security threats: Evidence from selected literatures," Asian Journal of Research in Computer Science, vol. 17, no. 8, pp. 24-33, 2024.
[2] C. S. Kodete, B. Thuraka, V. Pasupuleti, and S. Malisetty, "Hormonal Influences on Skeletal Muscle Function in Women across Life Stages: A Systematic Review," Muscles, vol. 3, no. 3, pp. 271-286, 2024.
[3] M. Di Capua, A. Ciaramella, and A. De Prisco, "Machine learning and computer vision for the automation of processes in advanced logistics: The integrated logistic platform (ILP) 4.0," Procedia Computer Science, vol. 217, pp. 326-338, 2023.
[4] V. Pasupuleti, B. Thuraka, C. S. Kodete, and S. Malisetty, "Enhancing supply chain agility and sustainability through machine learning: Optimization techniques for logistics and inventory management," Logistics, vol. 8, no. 3, p. 73, 2024.
[5] B. Thuraka, V. Pasupuleti, S. Malisetty, and K. O. Ogirri, "Leveraging artificial intelligence and strategic management for success in inter/national projects in US and beyond," Journal of Engineering Research and Reports, vol. 26, no. 8, pp. 49-59, 2024.
[6] K. Tsolaki, T. Vafeiadis, A. Nizamis, D. Ioannidis, and D. Tzovaras, "Utilizing machine learning on freight transportation and logistics applications: A review," ICT Express, vol. 9, no. 3, pp. 284-295, 2023.
[7] H. Lin, J. Lin, and F. Wang, "An innovative machine learning model for supply chain management," Journal of Innovation & Knowledge, vol. 7, no. 4, p. 100276, 2022.
[8] M.-I. Mahraz, L. Benabbou, and A. Berrado, "Machine learning in supply chain management: A systematic literature review," International Journal of Supply and Operations Management, vol. 9, no. 4, pp. 398-416, 2022.
[9] X. Yao, Y. Cheng, L. Zhou, and M. Song, "Green efficiency performance analysis of the logistics industry in China: based on a kind of machine learning methods," Annals of Operations Research, vol. 308, pp. 727-752, 2022.
[10] S. Taghiyeh, D. C. Lengacher, A. H. Sadeghi, A. Sahebi-Fakhrabad, and R. B. Handfield, "A novel multi-phase hierarchical forecasting approach with machine learning in supply chain management," Supply Chain Analytics, vol. 3, p. 100032, 2023.
[11] A. C. Odimarha, S. A. Ayodeji, and E. A. Abaku, "Machine learning's influence on supply chain and logistics optimization in the oil and gas sector: a comprehensive analysis," Computer Science & IT Research Journal, vol. 5, no. 3, pp. 725-740, 2024.
[12] R. Pramodhini, S. Kumar, S. Bhardwaj, N. Agrahari, S. Pandey, and S. S. Harakannanavar, "E-Commerce Inventory Management System Using Machine Learning Approach," in 2023 International Conference on Data Science and Network Security (ICDSNS), 2023: IEEE, pp. 1-7.
