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Duration 14 hours
Course Outline
Module 1: Introduction to AI in Logistics and Supply
- Comprehending Artificial Intelligence: key concepts and use cases
- AI in logistics and fuel distribution: potential opportunities and impact
- No-code AI tools: Excel AI, ChatGPT, Power BI, and other platforms
- Real-world examples from the transport and fuel industries
Module 2: Structuring and Analysing Operational Data
- Recognising critical logistics and supply datasets (routes, tanks, deliveries)
- Organising volumetric control and inventory data for AI integration
- Data cleansing, formatting, and verification 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
- Utilising Excel’s AI features and ChatGPT for predictive analytics
- Predicting short-term (1–2 week) fuel demand trends
- Practical exercise: constructing a basic forecast model using existing data
Module 4: Route Planning and Resource Optimisation
- Fundamental principles of route optimisation and scheduling
- Employing AI tools to recommend optimal routes and delivery sequences
- Applying Excel and ChatGPT for route planning with real-world constraints
- Practical activity: generating route options for delivery vehicles
Module 5: Cost Estimation and Logistics Optimisation
- Identifying cost drivers: distance, tolls, fuel consumption, freight
- Utilising AI models to estimate logistics expenses
- Comparing manual versus AI-assisted cost planning
- Creating 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
- Practical work: 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: AI-based alerts 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 continuous improvement and knowledge-sharing practices
Summary and Next Steps
Requirements
- Basic familiarity with Microsoft Excel or Google Sheets
- No prior background in Artificial Intelligence is necessary
Target Audience
- Logistics and supply specialists in the fuel transport and retail sector
- Operations and inventory coordinators
- Supervisors and planners overseeing fleet routes and fuel delivery
