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Course Outline

Introduction to AI in Supply Chain and Logistics

  • Emerging trends in smart logistics.
  • Contrasting AI with traditional analytics in supply chain management.
  • Key technologies and platforms driving transformation.

AI for Demand Forecasting

  • Applying machine learning to time-series forecasting.
  • Managing seasonality and trend components.
  • Enhancing forecast accuracy using historical data insights.

Inventory Optimisation and Replenishment

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

Route Optimisation and Fleet Intelligence

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

Warehouse Automation and Robotics

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

Real-Time Analytics and Dashboarding

  • Building live dashboards using Tableau and Python.
  • Monitoring KPIs through real-time data streams.
  • Configuring alerts and exception handling mechanisms.

Case Study and Capstone Project

  • Analysing complex, multi-node supply chain scenarios.
  • Applying forecasting and routing models to real-world problems.
  • Presenting a data-driven logistics optimisation plan.

Summary and Next Steps

Requirements

  • A solid understanding of supply chain or logistics operations.
  • Practical experience with data analysis or business intelligence tools.
  • Foundational knowledge of programming or scripting languages.

Audience

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

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