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Course Outline

Introduction to AI in Supply Chain and Logistics

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

AI-Driven Demand Forecasting

  • Implementing time-series forecasting using machine learning
  • Addressing 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

  • Utilizing shortest path algorithms and delivery routing
  • Executing traffic-aware dynamic route planning
  • Managing transport schedules via AI-enabled tools

Warehouse Automation and Robotics

  • Applying AI to picking, sorting, and storage automation
  • Using computer vision for shelf monitoring
  • Coordinating operations with AGVs and robotic arms

Real-Time Analytics and Dashboarding

  • Building live dashboards using Tableau and Python
  • Monitoring KPIs through real-time data streams
  • Generating alerts and managing exception handling

Case Study and Capstone Project

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

Conclusion and Future Pathways

Requirements

  • A solid grasp of supply chain or logistics operations
  • Practical experience with data analysis or business intelligence tools
  • Foundational knowledge of programming or scripting languages

Target Audience

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

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