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International Conference on Machine Learning for Supply Chain Analytics - (ICMLSCA-26)

31st - 1st January 2027 , Copenhagen - Denmark

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Call For Papers

The (ICMLSCA-26) emphasizes interdisciplinary collaboration by bringing together experts from diverse fields. It encourages research that integrates multiple perspectives to address complex global challenges.

Key areas such as Artificial Intelligence,Data Science,Machine Learning are explored to promote cross-domain knowledge exchange and collaborative innovation.

Authors are invited to submit papers addressing, but not limited to, the following areas:

  • Machine learning for supply chain optimization
  • Data analytics in logistics management
  • Predictive analytics for inventory management
  • AI applications in demand forecasting
  • Challenges in supply chain data integration
  • Real-time data processing in supply chains
  • AI-driven solutions for transportation logistics
  • Data visualization for supply chain insights
  • Machine learning for risk management in supply chains
  • Ethics of AI in supply chain practices
  • Collaborative supply chain data sharing
  • AI for enhancing supplier relationship management
  • Future trends in supply chain analytics
  • Data-driven decision making in logistics
  • Machine learning for quality control in supply chains
  • AI applications in last-mile delivery
  • Impact of IoT on supply chain data science
  • Machine learning for production scheduling
  • Data privacy concerns in supply chain analytics
  • AI for sustainable supply chain practices

Assessment

All submissions will undergo peer review to ensure quality and interdisciplinary relevance. Accepted papers will be presented and considered for publication in journals and conference proceedings.

Registration

Registering for the conference provides access to keynote sessions, technical presentations, and networking opportunities with global experts.

Publication

Publishing through the conference enhances the visibility of your research and connects your work with a broader academic audience.