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

Module 1: Introduction to AI in Logistics and Supply

• Exploring Artificial Intelligence: core concepts and use cases
• AI in logistics and fuel distribution: potential benefits and industry impact
• No-code AI tools: Excel AI, ChatGPT, Power BI, and similar platforms
• Real-world examples from the transportation and fuel industries

Module 2: Organizing and Analyzing Operational Data

• Pinpointing critical logistics and supply datasets (routes, tanks, deliveries)
• Preparing volumetric control and inventory data for AI processing
• Data cleaning, formatting, and verification in Excel
• Building dynamic tables and pivot charts to generate insights

Module 3: AI-Enhanced Forecasting for Fuel Demand

• Understanding demand forecasting and key influencing factors
• Utilizing Excel’s AI capabilities and ChatGPT for predictive analysis
• Predicting short-term (1–2 week) fuel demand trends
• Practical task: constructing a simple forecast model using existing data

Module 4: Route Planning and Resource Optimization

• Core principles of route optimization and scheduling
• Using AI tools to recommend optimal routes and delivery sequences
• Applying Excel and ChatGPT for route planning under real-world constraints
• Practicum: generating route alternatives for delivery units

Module 5: Cost Estimation and Logistics Optimization

• Recognizing cost drivers: distance, tolls, fuel consumption, and freight
• Employing AI models to predict logistics costs
• Benchmarking manual vs. AI-assisted cost planning methods
• Creating cost calculation templates with dynamic inputs

Module 6: Dashboards and KPI Visualization

• Overview of Power BI and Excel dashboards
• Designing visual reports for logistics and supply KPIs
• Incorporating data from volumetric control systems
• Practicum: building a real-time logistics performance dashboard

Module 7: Integrating AI into Logistics Workflows

• Automating repetitive reporting and data aggregation tasks
• Leveraging Power Automate or Excel macros for process automation
• Establishing alert mechanisms for inventory or delivery thresholds
• Practical scenario: AI-driven alerts for tank refill scheduling

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

• Developing a phased AI implementation roadmap
• Selecting pilot use cases and defining success metrics
• Expanding AI-assisted workflows across teams
• Fostering continuous improvement and knowledge-sharing practices

Summary and Next Steps