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Course Outline
Module 1: Foundations of AI in Logistics and Supply
- Grasping Artificial Intelligence: key concepts and use cases
- AI in logistics and fuel distribution: potential benefits and impact
- No-code AI solutions: Excel AI capabilities, ChatGPT, Power BI, and more
- Real-world examples from the transport and fuel industries
Module 2: Structuring and Interpreting Operational Data
- Pinpointing critical logistics and supply datasets (routes, tanks, deliveries)
- Preparing volumetric control and inventory records for AI processing
- Cleaning, formatting, and validating data within Excel
- Generating insights through dynamic tables and pivot charts
Module 3: AI-Enhanced Fuel Demand Forecasting
- Understanding demand prediction and key influencing factors
- Leveraging Excel’s AI features and ChatGPT for predictive analytics
- Projecting short-term (1–2 week) fuel demand patterns
- Practical task: constructing a basic forecast model using existing data
Module 4: Route Planning and Resource Efficiency
- Core principles of route optimisation and scheduling
- Utilising AI tools to recommend efficient routes and delivery sequences
- Applying Excel and ChatGPT for route planning under realistic constraints
- Interactive activity: creating route alternatives for delivery vehicles
Module 5: Cost Estimation and Logistics Efficiency
- Identifying key cost factors: distance, tolls, fuel usage, and freight charges
- Employing AI models to calculate logistics expenses
- Benchmarking manual planning against AI-assisted cost strategies
- Developing cost calculation templates with adjustable inputs
Module 6: Dashboards and KPI Visualisation
- Introduction to Power BI and Excel-based dashboards
- Designing visual reports for logistics and supply performance metrics
- Connecting data from volumetric control systems
- Practical session: building a live logistics performance dashboard
Module 7: Integrating AI into Logistics Processes
- Automating routine reporting and data aggregation tasks
- Using Power Automate or Excel macros for workflow automation
- Setting up alert mechanisms for inventory or delivery limits
- Real-world example: AI-triggered alerts for tank refilling schedules
Module 8: 90-Day AI Implementation Roadmap for Logistics and Supply
- Creating a phased AI adoption strategy
- Selecting pilot use cases and defining success indicators
- Expanding AI-assisted workflows across different teams
- Fostering a culture of continuous improvement and knowledge exchange
Wrap-up and Future Directions
Requirements
- Fundamental competence in Microsoft Excel or Google Sheets
- No previous exposure to Artificial Intelligence is necessary
Target Audience
- Professionals in logistics and supply within the fuel transport and retail sectors
- Coordinators managing operations and inventory levels
- Supervisors and planners responsible for fleet routing and fuel distribution
14 Hours
