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

14th - 15th November 2026 Berlin, Germany (Hybrid Event)

Call for Paper

The ICMLSCA is committed to addressing global challenges through impactful research and sustainable solutions. It brings together researchers dedicated to advancing knowledge for societal benefit. Focusing on Artificial Intelligence,Data Science,Machine Learning, the conference promotes research aligned with global development goals and long-term sustainability. 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
Review & Publication

All submissions will be reviewed for their contribution to global impact and research quality. Accepted papers will be presented and considered for publication in reputed platforms.

Registration

Join participants from around the world by completing your registration and becoming part of a global research community.

Publication

Accepted papers will gain international exposure through conference presentations and publication opportunities.

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