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Duration 14 hours
Course Outline
Module 1: Foundations of AI in Logistics and Supply
- Exploring Artificial Intelligence: key concepts and use cases
- AI in logistics and fuel distribution: potential benefits and industry impact
- No-code AI solutions: Excel AI features, ChatGPT, Power BI, and more
- Real-world examples from the transport and fuel sectors
Module 2: Preparing and Analyzing Operational Data
- Recognizing critical logistics datasets (routes, tanks, deliveries)
- Structuring volumetric control and inventory data for AI processing
- Data refinement, formatting, and validation within Excel
- Generating insights through dynamic tables and pivot charts
Module 3: AI-Enhanced Fuel Demand Forecasting
- Comprehending demand forecasting and its driving factors
- Applying Excel’s AI capabilities and ChatGPT for predictive analysis
- Predicting short-term (1–2 week) fuel demand patterns
- Practical task: developing a basic forecast model using available data
Module 4: Route Planning and Resource Optimization
- Fundamentals of route optimization and scheduling
- Using AI tools to recommend optimal routes and delivery orders
- Leveraging Excel and ChatGPT for realistic route planning under constraints
- Hands-on exercise: generating route alternatives for delivery vehicles
Module 5: Cost Estimation and Logistics Efficiency
- Identifying key cost factors: distance, tolls, fuel usage, freight
- Employing AI models to project logistics expenses
- Evaluating manual versus AI-assisted cost planning methods
- Developing cost calculation templates with flexible inputs
Module 6: Dashboards and KPI Visualization
- Overview of Power BI and Excel dashboard creation
- Designing visual reports for logistics and supply KPIs
- Connecting data from volumetric control systems
- Practical session: building a real-time logistics performance dashboard
Module 7: Embedding AI into Logistics Workflows
- Streamlining repetitive reporting and data aggregation tasks
- Automating tasks using Power Automate or Excel macros
- Setting up alert systems for inventory or delivery milestones
- Case study: implementing AI-based alerts for tank refill schedules
Module 8: 90-Day AI Adoption Strategy for Logistics and Supply
- Creating a phased roadmap for AI implementation
- Selecting pilot projects and defining success criteria
- Expanding AI-assisted processes across teams
- Fostering continuous improvement and knowledge exchange practices
Conclusions and Forward Path
Requirements
- Familiarity with Microsoft Excel or Google Sheets
- No previous background in Artificial Intelligence is necessary
Target Audience
- Logistics and supply specialists in the fuel transportation and retail sector
- Operations and inventory coordinators
- Supervisors and planners responsible for fleet routing and fuel delivery
