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Course Outline

Introduction to AI in Supply Chain and Logistics

  • Current trends in intelligent logistics
  • Comparative analysis of AI versus traditional analytics in supply chain management
  • Essential technologies and industry platforms

AI-Driven Demand Forecasting

  • Time-series forecasting techniques using machine learning
  • Managing seasonal variations and long-term trends
  • Enhancing forecast precision through historical data analysis

Inventory Optimization and Replenishment Strategies

  • Predicting optimal stock levels with AI
  • Calculating safety stock and reorder points
  • Integration of AI with ERP and WMS systems

Route Optimization and Fleet Intelligence

  • Applying shortest path algorithms for efficient delivery routing
  • Dynamic route planning with real-time traffic insights
  • AI-enabled transportation scheduling

Warehouse Automation and Robotics Integration

  • AI applications in picking, sorting, and storage processes
  • Utilizing computer vision for shelf monitoring
  • Coordination with AGVs and robotic arms

Real-Time Analytics and Dashboard Development

  • Building live dashboards using Tableau and Python
  • Tracking KPIs via real-time data streams
  • Implementing automated alerts and exception handling

Case Studies and Capstone Project

  • Analysis of a complex, multi-node supply chain scenario
  • Application of forecasting and routing models
  • Presentation of a data-driven logistics optimization strategy

Conclusion and Future Directions

Requirements

  • Foundational knowledge of supply chain or logistics workflows
  • Prior experience utilizing data analysis or business intelligence platforms
  • Basic proficiency in programming or scripting languages

Target Audience

  • Supply chain analysts
  • Logistics managers
  • Industrial planners
 21 Hours

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