AI for Logistics and Supply: Practical No-Code Applications for the Fuel Industry Training Course

Module 1: Overview of AI in Logistics and Supply

• Grasping Artificial Intelligence: Key concepts and real-world applications
• AI in logistics and fuel distribution: Potential benefits and impact
• No-code AI utilities: Excel AI, ChatGPT, Power BI, and similar platforms
• Practical examples drawn from the transport and fuel industries

Module 2: Organising and Interpreting Operational Data

• Pinpointing essential logistics and supply datasets (routes, tanks, deliveries)
• Structuring volumetric control and inventory data for AI integration
• Cleaning, formatting, and validating data within Excel
• Developing dynamic tables and pivot charts to generate insights

Module 3: AI-Enhanced Forecasting for Fuel Demand

• Comprehending demand forecasting and the variables that influence it
• Employing Excel’s AI capabilities and ChatGPT for predictive analysis
• Forecasting short-term (1–2 week) trends in fuel demand
• Practical task: Constructing a simple forecast model using available data

Module 4: Route Planning and Resource Optimisation

• Core principles of route optimisation and scheduling
• Leveraging AI tools to propose optimal routes and delivery orders
• Applying Excel and ChatGPT for route planning under real-world constraints
• Practical exercise: Generating route alternatives for delivery vehicles

Module 5: Cost Estimation and Logistics Efficiency

• Identifying key cost factors: distance, tolls, fuel usage, and freight
• Using AI models to calculate logistics costs
• Contrasting manual planning with AI-assisted cost estimation
• Creating cost calculation templates with adjustable inputs

Module 6: Dashboards and KPI Visualisation

• Introduction to Power BI and Excel dashboard creation
• Designing visual reports for logistics and supply chain KPIs
• Connecting data from volumetric control systems
• Practical session: Building a real-time logistics performance dashboard

Module 7: Embedding AI into Logistics Workflows

• Automating routine reporting and data aggregation tasks
• Utilising Power Automate or Excel macros for task automation
• Setting up alert systems for inventory levels or delivery milestones
• Real-world example: AI-based alerts for tank refilling schedules

Module 8: 90-Day AI Integration Plan for Logistics and Supply

• Creating a phased roadmap for AI implementation
• Selecting pilot projects and defining success indicators
• Expanding AI-assisted processes across teams
• Fostering continuous improvement and knowledge sharing practices

Conclusion and Future Steps