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Course Outline

Introduction to AI in Supply Chain and Logistics

  • Emerging trends in smart logistics.
  • Comparing AI with traditional analytics in supply chain management.
  • Overview of key technologies and platforms.

AI for Demand Forecasting

  • Time-series forecasting utilizing machine learning.
  • Addressing seasonality and trend components.
  • Enhancing forecast accuracy through historical data analysis.

Inventory Optimization and Replenishment

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

Route Optimization and Fleet Intelligence

  • Utilizing shortest path algorithms for delivery routing.
  • Implementing traffic-aware dynamic route planning.
  • Scheduling transport operations with AI assistance.

Warehouse Automation and Robotics

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

Real-Time Analytics and Dashboarding

  • Building live dashboards with Tableau and Python.
  • Monitoring 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 comprehensive, data-driven logistics optimization plan.

Summary and Next Steps

Requirements

  • A foundational understanding of supply chain or logistics operations.
  • Prior experience with data analysis or business intelligence tools.
  • Basic familiarity with programming or scripting concepts.

Target Audience

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

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