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Course Outline

Overview of 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 for Demand Forecasting

  • Time-series forecasting using machine learning techniques.
  • Managing seasonality and trend variables.
  • Enhancing forecast precision through historical data analysis.

Inventory Optimization and Replenishment

  • AI-based prediction of stock levels.
  • Calculations for safety stock and reorder points.
  • Integrating AI systems with ERP and WMS platforms.

Route Optimization and Fleet Intelligence

  • Shortest path algorithms and delivery routing strategies.
  • Dynamic route planning that accounts for traffic conditions.
  • AI-supported transport scheduling.

Warehouse Automation and Robotics

  • Applying AI to picking, sorting, and storage automation.
  • Utilizing computer vision for shelf monitoring.
  • Coordination with AGVs and robotic arms.

Real-Time Analytics and Dashboarding

  • Creating live dashboards using Tableau and Python.
  • Tracking KPIs via real-time data streams.
  • Setting up alerts and handling exceptions.

Case Study and Capstone Project

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

Summary and Next Steps

Requirements

  • Familiarity with supply chain or logistics operations.
  • Experience in data analysis or business intelligence tools.
  • Basic proficiency in programming or scripting.

Target Audience

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

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