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Course Outline

Introduction to AI in Supply Chain and Logistics

  • Current trends in intelligent logistics systems
  • Comparing AI approaches with traditional analytics in supply chain management
  • Overview of essential technologies and platforms

AI Applications in Demand Forecasting

  • Implementing time-series forecasting using machine learning algorithms
  • Managing seasonal patterns and trend components
  • Enhancing forecast precision through the analysis of historical data

Optimizing Inventory and Replenishment Strategies

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

Route Optimization and Fleet Management Intelligence

  • Utilizing shortest path algorithms for delivery routing
  • Planning dynamic routes with traffic-aware capabilities
  • Scheduling transport operations using AI

Warehouse Automation and Robotics Integration

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

Real-Time Analytics and Dashboard Development

  • Creating live dashboards using Tableau and Python
  • Tracking KPIs through real-time data streams
  • Configuring alerts and exception handling mechanisms

Case Studies and Capstone Project

  • Evaluating a multi-node supply chain scenario
  • Implementing forecasting and routing models
  • Presenting a data-informed logistics optimization strategy

Conclusion and Future Directions

Requirements

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

Target Audience

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

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