AI-Driven Autonomous Decision Systems for Global Manufacturing Supply Chains
DOI:
https://doi.org/10.22399/ijcesen.5333Keywords:
Autonomous Supply Chains, Artificial Intelligence, Supply Chain Decision, Intelligence Digital, Manufacturing, Production OptimizationAbstract
The article proposes an Autonomous Supply Chain Decision Architecture (ASCDA) that leverages machine learning, digital twin modeling, industrial IoT, and constraint-aware optimization to enable real-time autonomous decision-making in global manufacturing supply chains. Operational complexity—driven by expanding product portfolios, distributed supplier networks, and demand volatility—overwhelms traditional batch planning cycles. The ASCDA integrates four layers of decision logic (predictive analytics, operational constraint encoding, scenario simulation, and autonomous execution) with Integrated Business Planning governance frameworks to ensure algorithmic decisions remain aligned with financial and organizational objectives. The article demonstrates through numerical case analysis how this architecture improves throughput, reduces inventory buffers, and accelerates disruption response. The transition requires organizational restructuring: supply chain professionals evolve from plan generators to system architects and governance stewards, necessitating hybrid skill sets bridging domain expertise and data literacy. The ASCDA relative is situated to existing frameworks (SCOR, RAMI 4.0, ISA-95) and outline governance boundaries, human escalation thresholds, and accountability mechanisms essential for trustworthy autonomous supply chain systems.
References
[1] Pvpsiddhartha. (n.d.). Logistics and Supply Chain Management. Available: https://www.pvpsiddhartha.ac.in/dep_it/lecture%20notes/4-2-23/LSCM/LSCM%20Unit-1.pdf
[2] Hau L. Lee, V. Padmanabhan, & Seungjin Whang. (2004). Information distortion in a supply chain: The bullwhip effect. Available: https://www.researchgate.net/publication/227446993
[3] Tonya Boone, Ram Ganeshan, Aditya Jain, & Nada R. Sanders. (2019). Forecasting sales in the supply chain: Consumer analytics in the big data era. International Journal of Forecasting. 35(1):170–180. Available: https://www.sciencedirect.com/science/article/abs/pii/S0169207018301523
[4] Reza Toorajipour, Vahid Sohrabpour, Ali Nazarpour, Pejvak Oghazi, & Maria Fischl. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research. 122:502–517. Available: https://www.sciencedirect.com/science/article/pii/S014829632030583X
[5] Emanuel João Martins & Fernando Paulo Belfo. (2023). Major concerns about Enterprise Resource Planning (ERP) systems: A systematic review of a decade of research (2011–2021). ScienceDirect. Available: https://www.sciencedirect.com/science/article/pii/S1877050923003125
[6] Hartmut Stadtler & Christoph Kilger. (2008). Supply chain management and advanced planning: Concepts, models, software and case studies. Springer. Available: https://www.cur.ac.rw/mis/main/library/documents/book_file/2015_Book_SupplyChainManagementAndAdvanc.pdf
[7] Li Ninglin, Suraya Miskon, Noriza Mohd. Jamal, & Shanshan Yue. (2024). Supply chain visibility through a control tower: A systematic literature review. International Journal of Academic Research in Business and Social Sciences. Available: https://www.researchgate.net/publication/387196950
[8] Han Wang, Chenze Wang, Qing Liu, Xiaohan Zhang, Min Liu, Yumin Ma, Feng Yan, & Weiming Shen. (2024). A data and knowledge-driven autonomous intelligent manufacturing system for intelligent factories. Journal of Manufacturing Systems. Available: https://www.sciencedirect.com/science/article/abs/pii/S0278612524000785
[9] Real Carbonneau, Kevin Laframboise, & Rustam Vahidov. (2008). Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research. 184(3):1140–1154. Available: https://www.sciencedirect.com/science/article/abs/pii/S0377221706012057
[10] Rajkanwar Singh, Pratik Mandal, & Sukanta Nayak. (2024). Reinforcement Learning Approach in Supply Chain Management: A Review. ResearchGate. Available: https://www.researchgate.net/publication/378775545
[11] Michel Gendreau & Jean-Yves Potvin. (2010). Handbook of Metaheuristics. ACM Digital Library. Available: https://dl.acm.org/doi/10.5555/1941310
[12] Angira Sharma, Edward Kosasih, Jie Zhang, Alexandra Brintrup, & Anisoara Calinescu. (2022). Digital Twins: State of the art theory and practice, challenges, and open research questions. Journal of Industrial Information Integration. Available: https://www.sciencedirect.com/science/article/pii/S2452414X22000516
[13] Li Da Xu, Wu He, & Shancang Li. (n.d.). Internet of Things in industries: A survey. IEEEXplore. Available: https://ieeexplore.ieee.org/abstract/document/6714496
[14] Herbert Jodlbauer, Manuel Brunner, Nadine Bachmann, Shailesh Tripathi, & Matthias Thürer. (2023). Supply Chain Management: A Structured Literature Review and Implications for Future Research. MDPI. Available: https://www.mdpi.com/2305-6290/7/4/70
[15] Svitlana Smerichevska, Larysa Prodanova, & Oleksandr Yakushev. (2024). Digitization of logistics and supply chain management. Electronic Scientific and Practical Publication in Economic Sciences. Available: https://www.researchgate.net/publication/383902315
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