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Duration 14 hours
Course Outline
Module 1: Introduction to AI in Logistics and Supply
- Understanding Artificial Intelligence: key concepts and applications
- AI in logistics and fuel distribution: opportunities and impact
- No-code AI tools: Excel AI, ChatGPT, Power BI, and other solutions
- Practical examples drawn from the transportation and fuel industries
Module 2: Structuring and Analyzing Operational Data
- Identifying critical logistics and supply datasets (routes, tanks, deliveries)
- Organising volumetric control and inventory data for AI processing
- Data cleaning, formatting, and validation within Excel
- Creating dynamic tables and pivot charts to generate insights
Module 3: AI-Assisted Forecasting for Fuel Demand
- Understanding demand forecasting and the variables that influence it
- Utilising Excel’s AI features and ChatGPT for predictive analysis
- Forecasting short-term (1–2 week) fuel demand trends
- Practical exercise: building a simple forecast model using existing data
Module 4: Route Planning and Resource Optimization
- Core concepts in route optimisation and scheduling
- Using AI tools to suggest optimal routes and delivery sequences
- Applying Excel and ChatGPT for route planning with 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
- Using AI models to estimate logistics costs
- Comparing manual versus AI-assisted cost planning approaches
- Building cost calculation templates with dynamic 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
- Hands-on session: creating 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 task automation
- Creating alert systems for inventory or delivery thresholds
- Practical example: AI-based alerts for tank refill scheduling
Module 8: 90-Day AI Adoption Plan for Logistics and Supply
- Building a step-by-step AI implementation roadmap
- Identifying pilot use cases and success metrics
- Scaling AI-assisted workflows across teams
- Establishing practices for continuous improvement and knowledge sharing
Summary and Next Steps
Requirements
- Foundational proficiency with Microsoft Excel or Google Sheets
- No prior experience with Artificial Intelligence is necessary
Target Audience
- Logistics and supply professionals working in the fuel transportation and sales industry
- Operations and inventory coordinators
- Supervisors and planners responsible for managing fleet routes and fuel deliveries
