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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 platforms utilized.

AI-Driven Demand Forecasting

  • Implementing time-series forecasting using machine learning.
  • Managing seasonality and trend components effectively.
  • Enhancing forecast accuracy through historical data analysis.

Inventory Optimization and Replenishment Strategies

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

Route Optimization and Fleet Intelligence

  • Utilizing shortest path algorithms for delivery routing.
  • Dynamic route planning that accounts for traffic conditions.
  • Scheduling transport via AI-enabled systems.

Warehouse Automation and Robotics

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

Real-Time Analytics and Dashboarding

  • Creating live dashboards using Tableau and Python.
  • Monitoring KPIs through real-time data streams.
  • Generating alerts and handling exceptions.

Case Study and Capstone Project

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

Summary and Next Steps

Requirements

  • A foundational understanding of supply chain or logistics operations.
  • Experience utilizing 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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