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Course Outline

Introduction to AI in Supply Chain and Logistics

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

AI-Powered Demand Forecasting

  • Applying machine learning for time-series forecasting.
  • Managing seasonality and trend elements.
  • Enhancing forecast accuracy by leveraging historical data.

Inventory Optimization and Replenishment Strategies

  • Predicting stock levels using AI algorithms.
  • 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 dynamic route planning with traffic awareness.
  • Scheduling transport operations enabled by AI.

Warehouse Automation and Robotics Integration

  • Applying AI to automate 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.
  • Monitoring KPIs through real-time data streams.
  • Generating alerts and managing exceptions.

Case Study and Capstone Project

  • Analyzing a complex, multi-node supply chain scenario.
  • Applying forecasting and routing models to the scenario.
  • Presenting a comprehensive, data-driven logistics optimization plan.

Summary and Future Directions

Requirements

  • Familiarity with supply chain or logistics operational processes.
  • Proficiency with data analysis or business intelligence platforms.
  • Fundamental knowledge of programming or scripting languages.

Target Audience

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

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