Year: 2025
Type: article
Abstract: This academic article focuses on studying the application of Artificial Intelligence (AI) in supply chain management as a transformative strategy to enhance resilience. The research method involves the literature review, including academic textbooks, research papers, theories, and models, followed by analysis, synthesis, compilation, and summarization of the results. The study finds that enhancing supply chain resilience includes improving supply chain efficiency and risk management including supplier relationship management, scenario planning & simulation and customer service enhancement among others. The study suggests that organizations can better predict, respond to, and recover from disruptions by leveraging advanced AI technologies such as machine learning, predictive analytics, and real-time data processing. AI helps mitigate risks associated with out-of-stock and overstocking by improving demand forecasting accuracy, optimizing inventory management, and providing real-time supply chain insights. Furthermore, AI-powered automation and robotics can enhance operational efficiency by reducing human errors and streamlining processes. The framework presented in this study integrates AI to improve supply chain resilience through technologies that enable real-time visibility, operational efficiency, and risk management. Additionally, the article emphasizes the importance of collaborative relationships with supply chain partners, supported by AI-driven communication and data exchange platforms. The strategic approach to integrating AI into supply chain management highlights its potential to boost resilience, operational performance, and sustainability. This approach enables organizations to adopt modern supply chain practices, fostering internal trust and internal coordination while offering an effective guideline for modern supply chain management.
Institution: Krirk University
Language: Thai
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W7215387506