article · Logistics
Global uncertainty and complex disruptions require supply chains to adapt and recover rapidly. Integrating artificial intelligence, including machine learning, predictive analytics, and real-time data processing, provides a strategic approach to anticipate and respond to these challenges. Artificial intelligence enhances demand forecasting accuracy, optimizes inventory control, and boosts real-time visibility across supply networks, mitigating the risks of shortages and excess stock. In addition, automation and robotics guided by these technologies streamline operations and reduce human error. A conceptual framework maps out this integration, focusing on core components of supply chain resilience such as risk management and operational efficiency. The approach also highlights how data-sharing and communication tools powered by artificial intelligence can cultivate trust and coordination between partner organisations, helping them manage modern supply chain complexities and pursue greater sustainability.
Supply chain disruptions can trigger widespread shortages, financial losses, and operational delays. By showing how artificial intelligence can predict vulnerabilities, streamline logistics, and foster closer partner collaboration, this work highlights methods for organisations to safeguard their networks and maintain continuous operations amid ongoing global economic uncertainty.
The work presents a conceptual framework targeting enterprise managers, logistics operators, and supply chain planners seeking to integrate artificial intelligence into forecasting and risk management. Because the work outlines a conceptual model rather than a tested software product or empirical deployment, it remains at an early stage. Developers and industry partners can use the concepts to structure commercial data-sharing platforms and decision-support systems.
AI-generated from the published abstract. Always read the original work before citing.
Background: Amid growing global uncertainty and increasingly complex disruptions, the ability of supply chains to rapidly adapt and recover is critical. The incorporation of artificial intelligence (AI) into supply chain management represents a transformative strategy for enhancing resilience. By harnessing advanced AI technologies, such as machine learning, predictive analytics, and real-time data processing, organizations can more effectively anticipate, respond to, and recover from disruptions.AI improves demand forecasting accuracy, optimizes inventory management, and increases real-time visibility across the supply chain, reducing the risks of stockouts and surplus inventory. Furthermore, I-driven automation and robotics enhance operational efficiency by minimizing human error and streamlining processes. Methodology/Approach: This paper proposes a conceptual framework for strengthening supply chain resilience through AI integration. The framework leverages AI technologies to improve key aspects of supply chain resilience, including risk management, operational efficiency, and real-time visibility. Result/Conclusions: Additionally, it underscores the importance of collaborative relationships with supply chain partners, enabled by AI-powered data-sharing and communication tools that foster trust and coordination within the network. Originality/Value: This comprehensive framework offers a strategic approach to integrating AI into supply chain management, highlighting its potential to significantly enhance resilience, operational efficiency, and sustainability, thereby empowering organizations to navigate the complexities of modern supply chains more effectively.
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DOI: 10.3390/logistics8040111
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