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Course Outline

Introduction to AI in Supply Chain and Logistics

  • Emerging trends in smart logistics
  • Comparing AI with traditional analytics in supply chain management
  • Key technologies and platforms

AI-Driven Demand Forecasting

  • Time-series forecasting using machine learning
  • Managing seasonality and trend components
  • Enhancing forecast accuracy through historical data analysis

Inventory Optimization and Replenishment Strategies

  • Predicting stock levels with AI
  • Calculating safety stock and reorder points
  • Integrating AI solutions with ERP and WMS systems

Route Optimization and Fleet Intelligence

  • Shortest path algorithms and delivery routing
  • Dynamic route planning with traffic awareness
  • AI-enabled transport scheduling

Warehouse Automation and Robotics

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

Real-Time Analytics and Dashboarding

  • Creating live dashboards with Tableau and Python
  • Monitoring KPIs via real-time data streams
  • Generating alerts and managing exceptions

Case Study and Capstone Project

  • Analyzing a multi-node supply chain scenario
  • Applying forecasting and routing models
  • Presenting a data-driven logistics optimization plan

Conclusion and Future Directions

Requirements

  • Familiarity with supply chain or logistics operations
  • Experience using data analysis or business intelligence tools
  • Basic knowledge of programming or scripting

Target Audience

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

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