AI-Driven Financial Analytics for Supply Chain Resilience in U.S. Manufacturing

AI-Driven Financial Analytics for Supply Chain Resilience in U.S. Manufacturing | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 3 November 2025 V1 Latest version Share on AI-Driven Financial Analytics for Supply Chain Resilience in U.S. Manufacturing Authors : Adesola Abigeal Ogunnubi 0009-0007-3383-4879 [email protected] , Crystal Frema Davis , and Freda Otilia Agbadzete Authors Info & Affiliations https://doi.org/10.22541/au.176220122.21347945/v1 215 views 227 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This paper investigates how AI-driven financial analytics can enhance supply chain resilience in U.S. manufacturing by integrating financial intelligence with operational decision-making. The study emphasizes measure output, including capacity utilization, productivity ratios, and output variability, as a critical factor that links production efficiency to financial stability. By leveraging machine learningbased forecasting and financial stress-testing models, the proposed framework enables manufacturers to anticipate disruptions, optimize liquidity management, and evaluate the cost-effectiveness of resilience strategies (Gao, 2024; Thomas, 2024). Empirical studies suggest that the integration of predictive analytics into supply chain finance reduces volatility, strengthens supplier relations, and minimizes recovery time after disruptions (Chen & Patel, 2023; Zheng, 2025). This research contributes to ongoing debates on manufacturing competitiveness, offering a dual lens that balances financial sustainability with operational continuity. The findings provide actionable insights for executives, policymakers, and supply chain managers seeking to align financial resilience with longterm output stability and innovation in the U.S. manufacturing sector (Deloitte, Supplementary Material File (new ai-driven financial analytics.pdf) Download 873.13 KB Information & Authors Information Version history V1 Version 1 03 November 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords ai-driven financial analytics competitiveness financial forecasting liquidity management machine learning measure output operational performance output stability productivity ratios stress testing supply chain finance supply chain resilience u.s. manufacturing Authors Affiliations Adesola Abigeal Ogunnubi 0009-0007-3383-4879 [email protected] View all articles by this author Crystal Frema Davis View all articles by this author Freda Otilia Agbadzete View all articles by this author Metrics & Citations Metrics Article Usage 215 views 227 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Adesola Abigeal Ogunnubi, Crystal Frema Davis, Freda Otilia Agbadzete. AI-Driven Financial Analytics for Supply Chain Resilience in U.S. Manufacturing. Authorea . 03 November 2025. DOI: https://doi.org/10.22541/au.176220122.21347945/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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