Route delays, overstocked warehouses, and reactive maintenance quietly drain profit from logistics operations long before anyone traces the cause back to outdated planning methods.

This course equips logistics managers, supply chain experts, and operations professionals with the practical skills to apply artificial intelligence across the logistics function, from demand forecasting and route optimisation to warehouse automation and supplier risk detection.

Participants move between the theoretical foundations of AI in logistics and its real-world applications, building the confidence to identify where automation and predictive tools genuinely improve performance rather than add complexity.

By the end, participants will be equipped to design and evaluate AI-supported logistics solutions that keep their operations competitive as the industry evolves.

Who Should Attend?

• Logistics, supply chain, procurement, and vendor management professionals.
• Warehouse managers and supervisors, and transportation and fleet managers.
• Operations and quality control managers, and anyone interested in how AI is reshaping logistics.

Knowledge and Benefits:

By the end of the programme, participants will be able to:

• Understand the fundamentals of logistics and how AI technologies can be integrated into its core processes.
• Apply AI to demand forecasting, inventory management, and route planning to reduce costs and improve efficiency.
• Implement AI-driven solutions for vehicle tracking, fleet management, and warehouse automation, including picking and sorting.
• Use predictive tools to optimise inventory levels and prevent stockouts.
• Understand the role of robotics and automation in modern warehousing.
• Strengthen supplier relationship management and detect vendor risk and fraud using AI-driven tools.

• Module 1: Smarter Routes, Real-Time Fleets
  • Optimisation algorithms for route planning and delivery.
  • Real-time vehicle tracking and monitoring powered by AI.
  • Case studies of successful AI-driven fleet management.
• Module 2: Why Logistics Needs AI Now
  • Fundamentals of logistics and its role in the supply chain.
  • Machine learning, deep learning, and NLP applications in supply chain data.
  • Benefits and challenges of integrating AI into logistics.
• Module 3: Predictive Inventory and Demand Management
  • Inventory optimisation using predictive forecasting.
  • Dynamic pricing and demand-based inventory strategies.
  • Implementing AI-based demand forecasting to prevent stockouts.
• Module 4: Robotics and Automation on the Warehouse Floor
  • Robotics and autonomous systems in modern warehouses.
  • AI-driven picking and sorting.
  • Safety and ethical considerations in automated warehouses.
• Module 5: AI in Supplier Relationships and Quality Assurance
  • Enhancing supplier relationship management and detecting vendor risk and fraud with AI.
  • Quality control and defect detection using computer vision.
  • Predictive maintenance to reduce downtime.
• Module 6: Ethics, Privacy, and Responsible AI Deployment
  • Bias and transparency in AI-driven logistics.
  • Compliance with data privacy and security regulations.
  • Ensuring responsible AI deployment.
• Module 7: Learning from Real Deployments
  • Analysing real-world AI logistics case studies.
  • Group problem-solving and developing AI-based solutions for specific business needs.
• Module 8: What’s Next - Industry 4.0 and Continuous Adaptation
  • The future of AI in logistics.
  • Industry 4.0 and the integration of IoT with AI.
  • Staying current as technologies continue to evolve.