This track focuses on innovative approaches to predictive modeling within supply chain contexts. Researchers are invited to present methodologies that enhance demand forecasting and inventory optimization through advanced analytics.

This session explores the application of supervised learning algorithms to solve complex supply chain challenges. Contributions should demonstrate how these techniques can improve decision-making processes and operational efficiency.

This track addresses the use of unsupervised learning methods for identifying anomalies in logistics and supply chain operations. Papers should highlight novel approaches to feature extraction and risk assessment.

This session invites research on the integration of deep learning techniques in optimizing supply chain processes. Topics may include production planning, resource allocation, and predictive maintenance.

This track examines the role of IoT analytics in improving supply chain transparency and responsiveness. Submissions should focus on case studies or frameworks that leverage IoT data for better decision-making.

This session emphasizes the importance of data-driven strategies in supply chain management. Researchers are encouraged to present frameworks that facilitate effective decision-making through analytics.