Aligned with
This conference contributes to global sustainability by aligning its research discussions and academic sessions with key United Nations Sustainable Development Goals. It fosters knowledge exchange, innovation, and collaborative engagement.
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.
This track explores the latest trends and innovations in logistics analytics. Contributions should address how data analytics can enhance logistics performance and efficiency.
This session focuses on methodologies for risk assessment and management in supply chains using big data analytics. Papers should discuss frameworks that identify, analyze, and mitigate risks effectively.
This track invites research on advanced feature extraction techniques tailored for supply chain data. Submissions should demonstrate how these techniques can enhance model performance and insights.
This session highlights the role of predictive analytics in optimizing inventory management practices. Researchers are encouraged to share insights on improving stock levels and reducing costs through analytics.
This track examines the integration of big data analytics into overarching supply chain strategies. Contributions should focus on case studies that illustrate successful implementations and outcomes.