Integrating Artificial Intelligence and Geospatial Technologies for Supply Chain Optimisation

Nistor Andrei, Cezar Scarlat

Abstract


This paper investigates the integration of Artificial Intelligence (AI) and geospatial technologies in optimizing supply chain operations, emphasizing enhanced sustainability and operational efficiency. It addresses the increasingly important role of AI in predictive demand analysis and the utilization of geospatial data for strategic route optimization, particularly in maritime logistics. The research examines the impact of these technologies on supply chain visibility and warehouse management, revealing improvements in real-time tracking, inventory management, and eco-friendly operations. The study explores the technical challenges, investment considerations, and environmental implications of adopting these advanced technologies. By analyzing current applications and potential developments, the paper contributes to a deeper understanding of how AI and geospatial integration can lead to more resilient, efficient, and sustainable supply chains in the face of global economic and environmental challenges. 

Keywords


Supply Chain, Artificial Intelligence, Geospatial Technologies, Predictive Demand, Blockchain, Sustainability.

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References


Brau, Rebekah I., Nada R. Sanders, John Aloysius, and Donnie Williams. 2023. “Utilizing People, Analytics, and AI for Decision Making in the Digitalized Retail Supply Chain.” Journal of Business Logistics, June. https://doi.org/10.1111/jbl.12355.

Cherif, Ghassen, Benoit Trouillet, and Abdoul K.A. Toguyeni. 2022. “Modeling and Routing Problems of Automated Port Using T-TPN and Beam Search.” In 2022 8th International Conference on Control, Decision and Information Technologies (CoDIT), 1201–6. Istanbul, Turkey: IEEE. https://doi.org/10.1109/CoDIT55151.2022.9803942.

Fu, Qinghua, Abdul Aziz Abdul Rahman, Hui Jiang, Jawad Abbas, and Ubaldo Comite. 2022. “Sustainable Supply Chain and Business Performance: The Impact of Strategy, Network Design, Information Systems, and Organizational Structure.” Sustainability 14 (3): 1080. https://doi.org/10.3390/su14031080.

Imran, Md Mahadi Hasan, Ahmad Faisal Mohamad Ayob, and Shahrizan Jamaludin. 2021. “Applications of Artificial Intelligence in Ship Berthing: A Review.” Indian Journal of Geo-Marine Sciences 50 (11). https://doi.org/10.56042/ijms.v50i11.66761.

Junaid, Muhammad, Qingyu Zhang, and Muzzammil Wasim Syed. 2022. “Effects of Sustainable Supply Chain Integration on Green Innovation and Firm Performance.” Sustainable Production and Consumption 30 (March): 145–57. https://doi.org/10.1016/j.spc.2021.11.031.

Kamel Boulos, Maged N., James T. Wilson, and Kevin A. Clauson. 2018. “Geospatial Blockchain: Promises, Challenges, and Scenarios in Health and Healthcare.” International Journal of Health Geographics 17 (1): 25, s12942-018-0144–x. https://doi.org/10.1186/s12942-018-0144-x.

Khan, Sharfuddin Ahmed, Muhammad Shujaat Mubarik, Simonov Kusi-Sarpong, Himanshu Gupta, Syed Imran Zaman, and Mobashar Mubarik. 2022. “Blockchain Technologies as Enablers of Supply Chain Mapping for Sustainable Supply Chains.” Business Strategy and the Environment 31 (8): 3742–56. https://doi.org/10.1002/bse.3029.

Kim, Yuseon, and Kyongseok Park. 2023. “Outlier-Aware Demand Prediction Using Recurrent Neural Network-Based Models and Statistical Approach.” IEEE Access 11: 129285–99. https://doi.org/10.1109/ACCESS.2023.3333030.

Kumar, Saureng, and S. C. Sharma. 2023. “Integrated Model for Predicting Supply Chain Risk Through MachineLearning Algorithms.” International Journal of Mathematical, Engineering and Management Sciences 8 (3): 353–73. https://doi.org/10.33889/IJMEMS.2023.8.3.021.

Kusi-Sarpong, Simonov, Muhammad Shujaat Mubarik, Sharfuddin Ahmed Khan, Steve Brown, and Muhammad Faraz Mubarak. 2022. “Intellectual Capital, Blockchain-Driven Supply Chain and Sustainable Production: Role of Supply Chain Mapping.” Technological Forecasting and Social Change 175 (February): 121331. https://doi.org/10.1016/j.techfore.2021.121331.

Lehtola, Ville V., Jakub Montewka, and Johanna Salokannel. 2020. “Sea Captains’ Views on Automated Ship Route Optimization in Ice-Covered Waters.” Journal of Navigation 73 (2): 364–83. https://doi.org/10.1017/S0373463319000651.

Li, Qiang. 2023. “A Research on Autonomous Collision Avoidance under the Constraint of COLREGs.” Sustainability 15 (3): 2446. https://doi.org/10.3390/su15032446.

Liu, Ryan Wen, Maohan Liang, Jiangtian Nie, Wei Yang Bryan Lim, Yang Zhang, and Mohsen Guizani. 2022. “Deep Learning-Powered Vessel Trajectory Prediction for Improving Smart Traffic Services in Maritime Internet of Things.” IEEE Transactions on Network Science and Engineering 9 (5): 3080–94. https://doi.org/10.1109/TNSE.2022.3140529.

Lyu, Hongguang, Zengrui Hao, Jiawei Li, Guang Li, Xiaofeng Sun, Guoqing Zhang, Yong Yin, Yanjie Zhao, and Lunping Zhang. 2023. “Ship Autonomous Collision-Avoidance Strategies—A Comprehensive Review.” Journal of Marine Science and Engineering 11 (4): 830. https://doi.org/10.3390/jmse11040830.

Ma, Xiaoya, Mengxiu Li, Jin Tong, and Xiaying Feng. 2023. “Deep Learning Combinatorial Models for Intelligent Supply Chain Demand Forecasting.” Biomimetics 8 (3): 312. https://doi.org/10.3390/biomimetics8030312.

Panda, Sandeep Kumar, and Sachi Nandan Mohanty. 2023. “Time Series Forecasting and Modeling of Food Demand Supply Chain Based on Regressors Analysis.” IEEE Access 11: 42679–700. https://doi.org/10.1109/ACCESS.2023.3266275.

Pathan, Muhammad Salman, Edana Richardson, Edgar Galvan, and Peter Mooney. 2023. “The Role of Artificial Intelligence within Circular Economy Activities—A View from Ireland.” Sustainability 15 (12): 9451. https://doi.org/10.3390/su15129451.

Qin, Meng, Chi-Wei Su, Muhammad Umar, Oana-Ramona Lobonţ, and Alina Georgiana Manta. 2023. “Are Climate and Geopolitics the Challenges to Sustainable Development? Novel Evidence from the Global Supply Chain.” Economic Analysis and Policy 77 (March): 748–63. https://doi.org/10.1016/j.eap.2023.01.002.

Rashid, Aamir, Syed Baber Ali, Rizwana Rasheed, Noor Aina Amirah, and Abdul Hafaz Ngah. 2023. “A Paradigm of Blockchain and Supply Chain Performance: A Mediated Model Using Structural Equation Modeling.” Kybernetes 52 (12): 6163–78. https://doi.org/10.1108/K-04-2022-0543.

Scarlat, Cezar, Alexandra Ioanid, and Nistor Andrei. 2023. “Use of The Geospatial Technologies and Its Implications in The Maritime Transport and Logistics.”

“Sensors in the Rotterdam Container Terminal.” n.d. Accessed February 6, 2024. https://www.ifm.com/de/en/shared/landingpages/port-automation/container-terminal-rotterdam.

Thombre, Sarang, Zheng Zhao, Henrik Ramm-Schmidt, Jose M. Vallet Garcia, Tuomo Malkamaki, Sergey Nikolskiy, Toni Hammarberg, et al. 2022. “Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review.” IEEE Transactions on Intelligent Transportation Systems 23 (1): 64–83. https://doi.org/10.1109/TITS.2020.3023957.

Tsolakis, Naoum, Dimitris Zissis, Spiros Papaefthimiou, and Nikolaos Korfiatis. 2022. “Towards AI Driven Environmental Sustainability: An Application of Automated Logistics in Container Port Terminals.” International Journal of Production Research 60 (14): 4508–28. https://doi.org/10.1080/00207543.2021.1914355.

Varriale, Vincenzo, Antonello Cammarano, Francesca Michelino, and Mauro Caputo. 2023. “Industry 5.0 and Triple Bottom Line Approach in Supply Chain Management: The State-of-the-Art.” Sustainability 15 (7): 5712. https://doi.org/10.3390/su15075712.

Woo, Sung Hun, Hyun Ji Park, Sung Won Cho, and Ki Hong Kim. 2023. “Proactive Berth Scheduling with Data-Driven Buffer Time in Container Terminals.” INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH, December. https://doi.org/10.1111/itor.13412.

Wu, Jiangpeng, Hongxu Yin, Dan Huang, and Yang Shao. 2020. “Application Analysis of Artificial Intelligence in Port Shore Power.” Journal of Physics: Conference Series 1575 (1): 012210. https://doi.org/10.1088/1742-6596/1575/1/012210.

Yuan, Yuan. 2022. “Cognitive Heterogeneous Wireless Network and Artificial Intelligence-Based Supply Chain Efficiency Optimization Application.” Edited by Jun Ye. Computational Intelligence and Neuroscience 2022 (July): 1–10. https://doi.org/10.1155/2022/8482365.

Zhang, Xianyu, Xinguo Ming, and Zhihua Chen. 2018. “Integration of AI Technologies and Logistics Robots in Unmanned Port: A Framework and Application.” In Proceedings of the 2018 4th International Conference on Robotics and Artificial Intelligence, 82–86. Guangzhou China: ACM. https://doi.org/10.1145/3297097.3297101.

Zheng, Jian, Wenjun Sun, Yun Li, and Jiayin Hu. 2023. “A Receding Horizon Navigation and Control System for Autonomous Merchant Ships: Reducing Fuel Costs and Carbon Emissions under the Premise of Safety.” Journal of Marine Science and Engineering 11 (1): 127. https://doi.org/10.3390/jmse11010127.




DOI: https://dx.doi.org/10.21622/MARLOG.2024.13.1.110

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The International Maritime Transport and Logistics Journal (MARLOG)

E-ISSN: 2974-3141
P-ISSN: 2974-3133

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Arab Academy for Science, Technology and Maritime Transport (AASTMT)

Alexandria, Egypt