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Duration 14 hours
Course Outline
Module 1: AI Foundations in Logistics and Supply
- Core concepts and real-world applications of Artificial Intelligence
- The role of AI in logistics and fuel distribution: identifying opportunities and impact
- Overview of no-code AI tools: Excel AI, ChatGPT, Power BI, and others
- Case studies from the transportation and fuel industries
Module 2: Organizing and Analyzing Operational Data
- Identifying critical logistics and supply datasets (routes, tanks, deliveries)
- Structuring volumetric control and inventory data for AI integration
- Techniques for data cleaning, formatting, and validation in Excel
- Generating insights through dynamic tables and pivot charts
Module 3: AI-Driven Forecasting for Fuel Demand
- Key variables influencing demand forecasting
- Leveraging Excel’s AI capabilities and ChatGPT for predictive analysis
- Predicting short-term (1–2 week) fuel demand trends
- Practical exercise: developing a basic forecast model using existing data
Module 4: Route Planning and Resource Optimization
- Fundamentals 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
- Hands-on activity: generating route options for delivery units
Module 5: Cost Estimation and Logistics Optimization
- Analyzing cost drivers: distance, tolls, fuel consumption, and freight
- Utilizing AI models to project logistics costs
- Comparing manual planning versus AI-assisted cost strategies
- Creating cost calculation templates with dynamic inputs
Module 6: Dashboards and KPI Visualization
- Introduction to Power BI and Excel dashboard features
- Designing visual reports for logistics and supply chain KPIs
- Integrating data from volumetric control systems
- Practical session: building 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 process automation
- Setting up alert systems for inventory levels or delivery milestones
- Practical example: implementing an AI-based alert for tank refill scheduling
Module 8: 90-Day AI Adoption Plan for Logistics and Supply
- Developing a step-by-step roadmap for AI implementation
- Defining pilot use cases and success metrics
- Expanding AI-assisted workflows across teams
- Establishing practices for continuous improvement and knowledge sharing
Summary and Next Steps
Requirements
- Familiarity with Microsoft Excel or Google Sheets
- No prior knowledge of Artificial Intelligence is necessary
Target Audience
- Professionals in logistics and supply chains within the fuel transportation and sales sectors
- Operations and inventory coordinators
- Supervisors and planners responsible for fleet routing and fuel delivery
