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Course Outline

Introduction to AI in Supply Chain and Logistics

  • Emerging trends in smart logistics
  • AI versus traditional analytics in supply chain management
  • Essential technologies and platforms

AI for Demand Forecasting

  • Time-series forecasting utilizing machine learning
  • Managing seasonality and trend components
  • Enhancing forecast precision with historical data

Inventory Optimization and Replenishment

  • AI-driven prediction of stock levels
  • 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 strategies
  • Traffic-aware dynamic route planning
  • AI-enabled transport scheduling

Warehouse Automation and Robotics

  • AI applications in picking, sorting, and storage automation
  • Computer vision techniques for shelf monitoring
  • Coordinating with AGVs and robotic arms

Real-Time Analytics and Dashboarding

  • Live dashboards using Tableau and Python
  • Monitoring KPIs through real-time data streams
  • Generating alerts and managing exceptions

Case Study and Capstone Project

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

Summary and Recommended Next Steps

Requirements

  • Foundational knowledge of supply chain or logistics operations
  • Practical experience with data analysis or business intelligence tools
  • Basic proficiency in programming or scripting languages

Target Audience

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

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