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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64583
Volume:
10 |
6 |
Jun 2026
Pages:
1-15
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Independent researcher
Independent researcher
Modern business operations depend on global supply chains which enable companies to procure raw materials and produce goods before transporting products to international markets. The systems have become more exposed to disruptive events which include pandemics and geopolitical conflicts and climate events and economic instability. The COVID-19 pandemic demonstrated major flaws in existing supply chain systems because they lacked visibility and took too long to respond while relying too much on human decision-making. The result has led companies to concentrate their efforts on building strong supply chain systems that can withstand disruptions while they develop solutions to minimize operational hazards. Artificial Intelligence (AI) has emerged as a transformative technology that offers new ways to address these challenges. AI allows businesses to forecast potential disruptions through its advanced data analytics and machine learning capabilities and real-time monitoring systems. AI-driven supply chains use historical data to create predictive models which enable them to detect potential risks beforehand. Companies need to establish preventive measures to protect their operational activities because these security protocols will help them build protective systems against operational threats. The essay demonstrates how supply chains achieve better resilience through advanced AI protective systems which enable organizations to identify and respond to potential threats. The research investigates the effects of artificial intelligence application in supply chain management on business resilience and risk management procedures that organizations use to protect their operations. The research investigates traditional supply chain systems which show their limitations and AI technologies which provide enhanced operational capabilities through increased visibility and more accurate forecasting and better decision-making. The research shows that organizations face two primary obstacles which include high implementation expenses and difficulties related to managing data. The essay presents arguments to establish that AI functions as a partial solution which does not address all business requirements (McKinsey & Company, 2020).
KEYWORDS
Artificial Intelligence (AI) integration with supply chain management systems has created fundamental changes in how international companies handle their operational resilience and risk management needs. Today's supply chains require advanced solutions because their increasing complexity causes operational breakdowns that traditional methods cannot handle. AI technology enables organizations to solve operational challenges through its capacity to deliver real-time system monitoring and predictive capacity and fast operational decision processes. Businesses strengthen their supply chain resilience through AI because it enables them to forecast potential interruptions and create effective responses and operational modifications. The system enhances protection against risks through its capability to identify emerging threats and advance forecasting methods and procedural enhancements. The real-world applications show that AI-based companies achieve superior results in dealing with unpredictable situations while sustaining their business activities. The implementation of AI technology brings multiple challenges because organizations face expenses related to system deployment and data management difficulties and they risk becoming too dependent on their automated processes. The existing limitations demonstrate that organizations require a middle ground solution which permits them to combine AI technologies with their staff members’ capabilities and their established operational controls. AI functions as an effective instrument which helps businesses build supply chains that combine resilience with risk management capabilities. The continuous progression of digital technologies will increase the significance of AI within supply chain operations which official business practices will use as core elements in their global strategies (Deloitte, 2022).
Deloitte. (2022). AI in modern supply chain management. https://www.deloitte.com/us/en/services/consulting/articles/ai-in-modern-supply-chain-management.html Gartner. (2025). Supply chain resilience. https://www.gartner.com/en/articles/supply-chain-resilience Harvard Business Review. (2025). How generative AI improves supply chain management. http://hbr.org/2025/01/how-generative-ai-improves-supply-chain-management IBM. (2023). Supply chain resiliency. https://www.ibm.com/think/topics/supply-chain-resiliency KPMG. (2022). Six key trends impacting global supply chains in 2022. https://kpmg.com/sg/en/insights/strategy-and-growth/six-key-trends-impacting-global-supply-chains-in-2022.html McKinsey & Company. (2020). Risk, resilience, and rebalancing in global value chains. https://www.mckinsey.com/capabilities/operations/our-insights/risk-resilience-and-rebalancing-in-global-value-chains World Economic Forum. (2023). Shared intelligence for resilient supply systems. https://www.weforum.org/publications/shared-intelligence-for-resilient-supply-systems/ World Economic Forum. (2026). How we can best enable the new era of supply chains. https://www.weforum.org/stories/2026/01/how-we-can-best-enable-the-new-era-of-supply-chains/ World Economic Forum. (2026). AI-powered supply chains and regional ecosystems shaping globalization. https://www.weforum.org/stories/2026/02/ai-powered-supply-chains-and-regional-ecosystems-shaping-globalization/
Publication Date
6/30/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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