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

Module 1: Introduction to AI in Logistics and Supply

• Understanding Artificial Intelligence: core concepts and applications
• AI in logistics and fuel distribution: identifying opportunities and impact
• Exploring no-code AI tools: Excel AI, ChatGPT, Power BI, and others
• Real-world examples from the transportation and fuel industry

Module 2: Structuring and Analyzing Operational Data

• Identifying key logistics and supply datasets (routes, tanks, deliveries)
• Organizing volumetric control and inventory data for AI processing
• Data cleaning, formatting, and validation techniques in Excel
• Creating dynamic tables and pivot charts to generate insights

Module 3: AI-Assisted Forecasting for Fuel Demand

• Understanding demand forecasting and key influencing variables
• Leveraging Excel’s AI features and ChatGPT for predictive analysis
• Forecasting short-term (1–2 week) fuel demand trends
• Practical exercise: building a simple forecast model using existing data

Module 4: Route Planning and Resource Optimization

• Key concepts in route optimization and scheduling
• Using AI tools to suggest optimal routes and delivery sequences
• Applying Excel and ChatGPT for route planning considering real constraints
• Hands-on activity: generating route options for delivery units

Module 5: Cost Estimation and Logistics Optimization

• Identifying cost drivers: distance, tolls, fuel consumption, and freight
• Using AI models to estimate logistics costs accurately
• Comparing manual planning versus AI-assisted cost planning
• Building cost calculation templates with dynamic inputs

Module 6: Dashboards and KPI Visualization

• Introduction to Power BI and Excel dashboards
• Designing visual reports for logistics and supply KPIs
• Integrating data from volumetric control systems
• Hands-on: creating a real-time logistics performance dashboard

Module 7: Integrating AI into Logistics Workflows

• Automating repetitive reporting and data consolidation tasks
• Using Power Automate or Excel macros for task automation
• Creating alert systems for inventory or delivery thresholds
• Practical example: implementing an AI-based alert for tank refill scheduling

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

• Building a step-by-step AI implementation roadmap
• Identifying pilot use cases and defining success metrics
• Scaling AI-assisted workflows across teams
• Establishing continuous improvement and knowledge-sharing practices

Summary and Next Steps