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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
  • Essential technologies and platforms

AI-Driven Demand Forecasting

  • Time-series forecasting using machine learning
  • Managing seasonality and trend elements
  • Enhancing forecast precision through historical data

Inventory Optimization and Replenishment Strategies

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

Route Optimization and Fleet Intelligence

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

Warehouse Automation and Robotics

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

Real-Time Analytics and Dashboarding

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

Case Study and Capstone Project

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

Recap and Future Steps

Requirements

  • Solid comprehension of supply chain or logistics operations
  • Practical experience with data analysis or business intelligence tools
  • Foundational knowledge of programming or scripting

Target Audience

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

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