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Course Outline

Overview of AI in Supply Chain and Logistics

  • Emerging trends in smart logistics.
  • AI compared to traditional analytics in supply chain management.
  • Key technologies and platforms.

AI for Demand Forecasting

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

Inventory Optimization and Replenishment

  • AI-driven prediction of stock levels.
  • Calculations for safety stock and reorder points.
  • Integrating AI 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 for shelf monitoring.
  • Coordination with AGVs and robotic arms.

Real-Time Analytics and Dashboarding

  • Creating live dashboards using Tableau and Python.
  • Monitoring KPIs via 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 Next Steps

Requirements

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

Target Audience

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

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