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Course Outline

Overview of AI in Supply Chain and Logistics

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

AI for Demand Forecasting

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

Inventory Optimisation and Replenishment

  • AI-driven stock level prediction.
  • Calculating safety stock and reorder points.
  • Integrating AI with ERP and WMS.

Route Optimisation and Fleet Intelligence

  • Shortest path algorithms and delivery routing.
  • Traffic-aware dynamic route planning.
  • AI-enabled transport scheduling.

Warehouse Automation and Robotics

  • AI in picking, sorting, and storage automation.
  • Computer vision for shelf monitoring.
  • Coordinating with AGVs and robotic arms.

Real-Time Analytics and Dashboarding

  • Live dashboards using Tableau and Python.
  • Monitoring KPIs via real-time data streams.
  • Generating alerts and handling exceptions.

Case Study and Capstone Project

  • Analysing a multi-node supply chain scenario.
  • Applying forecasting and routing models.
  • Presenting a data-driven logistics optimisation plan.

Summary and Next Steps

Requirements

  • A solid understanding of supply chain or logistics operations.
  • Experience using data analysis or business intelligence tools.
  • Basic familiarity with programming or scripting.

Audience

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

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