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Duration 14 hours
Course Outline
Module 1: AI Fundamentals in Logistics and Supply
- Exploring Artificial Intelligence: key concepts and use cases
- The role of AI in logistics and fuel distribution: potential benefits and impact
- No-code AI solutions: Excel AI, ChatGPT, Power BI, and other platforms
- Real-world examples from the transportation and fuel industries
Module 2: Organizing and Interpreting Operational Data
- Identifying critical logistics and supply datasets (routes, tanks, deliveries)
- Preparing volumetric control and inventory data for AI processing
- Data cleansing, formatting, and validation techniques in Excel
- Generating insights through dynamic tables and pivot charts
Module 3: AI-Enhanced Fuel Demand Forecasting
- Understanding demand forecasting and key influencing factors
- Applying Excel’s AI capabilities and ChatGPT for predictive analysis
- Predicting short-term (1–2 week) fuel demand patterns
- Practical task: constructing a simple forecast model using available data
Module 4: Route Planning and Resource Efficiency
- Core principles of route optimization and scheduling
- Using AI tools to recommend optimal routes and delivery orders
- Employing Excel and ChatGPT for route planning with actual constraints
- Hands-on session: generating route alternatives for delivery vehicles
Module 5: Cost Analysis and Logistics Improvement
- Recognizing cost factors: distance, tolls, fuel usage, and freight
- Estimating logistics costs with AI models
- Comparing traditional vs. AI-supported cost planning methods
- Developing cost calculation templates with dynamic parameters
Module 6: Dashboards and KPI Visualization
- Overview of Power BI and Excel dashboard creation
- Designing visual reports for logistics and supply chain KPIs
- Integrating data from volumetric control systems
- Practical exercise: building a real-time logistics performance dashboard
Module 7: Embedding AI in Logistics Processes
- Automating routine reporting and data aggregation tasks
- Leveraging Power Automate or Excel macros for process automation
- Setting up alert systems for inventory levels or delivery thresholds
- Case study: AI-driven alerts for tank refill scheduling
Module 8: 90-Day AI Implementation Strategy for Logistics
- Creating a step-by-step roadmap for AI deployment
- Selecting pilot use cases and defining success criteria
- Expanding AI-supported workflows across team structures
- Fostering continuous improvement and knowledge exchange practices
Conclusion and Future Actions
Requirements
- Familiarity with Microsoft Excel or Google Sheets is required
- No previous experience with Artificial Intelligence is necessary
Target Audience
- Logistics and supply chain specialists in the fuel transport and sales sector
- Operations and inventory coordinators
- Supervisors and planners responsible for fleet routes and fuel delivery
