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Duration 14 hours
Course Outline
Module 1: Introduction to AI in Logistics and Supply
- Foundations of Artificial Intelligence: key concepts and use cases
- AI in logistics and fuel distribution: potential benefits and impact
- No-code AI solutions: Excel AI, ChatGPT, Power BI, and other tools
- Real-world examples from the transport and fuel industries
Module 2: Structuring and Analyzing Operational Data
- Identifying critical logistics and supply datasets (routes, tanks, deliveries)
- Preparing volumetric control and inventory data for AI processing
- Cleaning, formatting, and validating data in Excel
- Building dynamic tables and pivot charts to generate insights
Module 3: AI-Assisted Forecasting for Fuel Demand
- Understanding demand forecasting and key influencing factors
- Utilizing Excel’s AI features and ChatGPT for predictive analysis
- Predicting short-term (1–2 week) fuel demand trends
- Practical exercise: creating a basic forecast model using existing data
Module 4: Route Planning and Resource Optimization
- Core concepts in route optimization and scheduling
- Using AI tools to recommend optimal routes and delivery sequences
- Applying Excel and ChatGPT for route planning with real-world constraints
- Practical activity: generating route alternatives for delivery units
Module 5: Cost Estimation and Logistics Optimization
- Identifying cost factors: distance, tolls, fuel usage, and freight
- Employing AI models to estimate logistics expenses
- Comparing manual versus AI-supported cost planning
- Creating cost calculation templates with variable 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
- Practical exercise: building a real-time logistics performance dashboard
Module 7: Integrating AI into Logistics Workflows
- Automating routine reporting and data consolidation tasks
- Using Power Automate or Excel macros for task automation
- Setting up alert systems for inventory or delivery thresholds
- Practical example: creating an AI-based alert for tank refill scheduling
Module 8: 90-Day AI Adoption Plan for Logistics and Supply
- Developing a step-by-step AI implementation roadmap
- Defining pilot use cases and success indicators
- Expanding AI-supported workflows across teams
- Establishing practices for continuous improvement and knowledge sharing
Summary and Next Steps
Requirements
- Foundational skills in Microsoft Excel or Google Sheets
- No prior background in Artificial Intelligence is necessary
Target Audience
- Logistics and supply professionals in the fuel transport and sales industry
- Operations and inventory coordinators
- Supervisors and planners responsible for fleet routing and fuel delivery
