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Duration 14 hours
Course Outline
Module 1: Introduction to AI in Logistics and Supply
- Exploring Artificial Intelligence: core concepts and applications
- AI in logistics and fuel distribution: potential opportunities and impact
- No-code AI tools: Excel AI, ChatGPT, Power BI, and other platforms
- Practical examples drawn from the transport and fuel industries
Module 2: Structuring and Analysing Operational Data
- Identifying key logistics and supply datasets (routes, tanks, deliveries)
- Organising volumetric control and inventory data for AI application
- Data cleaning, formatting, and validation in Excel
- Creating dynamic tables and pivot charts to generate insights
Module 3: AI-Assisted Forecasting for Fuel Demand
- Understanding demand forecasting and key influencing variables
- Using Excel’s AI features and ChatGPT for predictive analysis
- Predicting short-term (1–2 week) fuel demand trends
- Practical exercise: developing a simple forecast model using existing data
Module 4: Route Planning and Resource Optimisation
- Key concepts in route optimisation and scheduling
- Utilising AI tools to recommend optimal routes and delivery sequences
- Applying Excel and ChatGPT for route planning with real-world constraints
- Hands-on activity: generating route options for delivery vehicles
Module 5: Cost Estimation and Logistics Optimisation
- Identifying cost drivers: distance, tolls, fuel consumption, freight
- Using AI models to estimate logistics costs
- Comparing manual versus AI-assisted cost planning
- Building cost calculation templates with dynamic inputs
Module 6: Dashboards and KPI Visualisation
- 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: setting up 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
- 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
- Basic proficiency with Microsoft Excel or Google Sheets
- No previous experience with Artificial Intelligence is necessary
Target Audience
- Logistics and supply specialists in the fuel transport and retail industry
- Operations and inventory coordinators
- Supervisors and planners responsible for fleet routes and fuel distribution
