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Duration 14 hours
Course Outline
Module 1: Introduction to AI in Logistics and Supply
- Fundamentals of Artificial Intelligence: key concepts and use cases
- The role of AI in logistics and fuel distribution: opportunities and impact
- No-code AI solutions: Excel AI, ChatGPT, Power BI, and other tools
- Real-world examples from the transportation and fuel sectors
Module 2: Structuring and Analyzing Operational Data
- Identifying critical logistics and supply datasets (routes, tanks, deliveries)
- Organizing volumetric control and inventory data for AI integration
- Data cleaning, formatting, and validation techniques in Excel
- Generating insights through dynamic tables and pivot charts
Module 3: AI-Assisted Forecasting for Fuel Demand
- 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 trends
- Practical exercise: creating a simple 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 under real-world 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
- Utilizing AI models for logistics cost estimation
- Comparing manual versus AI-assisted cost planning methods
- Creating cost calculation templates with dynamic inputs
Module 6: Dashboards and KPI Visualization
- Introduction to Power BI and Excel dashboard creation
- Designing visual reports for logistics and supply KPIs
- Integrating data from volumetric control systems
- Hands-on: building a real-time logistics performance dashboard
Module 7: Integrating AI into Logistics Workflows
- Automating repetitive reporting and data consolidation tasks
- Leveraging Power Automate or Excel macros for process automation
- Setting up alert systems for inventory or delivery thresholds
- Practical example: AI-driven alerts 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 metrics
- Scaling AI-assisted workflows across teams
- Fostering continuous improvement and knowledge-sharing practices
Summary and Next Steps
Requirements
- Proficiency in Microsoft Excel or Google Sheets
- No previous experience with Artificial Intelligence is necessary
Target Audience
- Logistics and supply professionals in the fuel transportation and retail sector
- Operations and inventory coordinators
- Supervisors and planners responsible for fleet routes and fuel distribution
