The Application of Artificial Intelligence in Industrial Supply Chain Optimization and Its Implications for Business Performance - Prananta, Arie Wahyu; Hayati, Amelia (Journal of Renewable Engineering, 2025)
The digital transformation occurring in the global industrial landscape has driven the need for more adaptive, efficient, and resilient supply chain systems. Amid increasing market complexity, demand fluctuations, and delivery time pressures, the integration of Artificial Intelligence (AI) technology has become a crucial strategy in addressing these challenges. This study aims to evaluate the strategic role of AI in industrial supply chain optimization and its impact on business performance. Using a qualitative approach using a systematic literature review method, various scientific publications from 2019 to 2024 were analyzed. The study results indicate that AI contributes significantly to demand planning, inventory management, distribution optimization, and data-driven decision-making. Positive impacts include increased operational efficiency, predictive accuracy, delivery reliability, and business productivity. However, implementation challenges remain, such as infrastructure limitations, a lack of expertise, organizational resistance, and regulatory and ethical issues. Therefore, successful AI adoption requires a comprehensive approach, encompassing technological readiness, human resource development, and sustainable data governance. This study recommends the need for a collaborative and inclusive digital transformation roadmap to realize an intelligent and competitive industrial supply chain in the digital economy era.