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Duration 14 hours
Course Outline
Module 1: Introduction to AI in Logistics and Supply Chain
- Foundations of Artificial Intelligence: core concepts and practical uses
- The role of AI in logistics and fuel distribution: opportunities and impact
- Overview of no-code AI tools: Excel AI, ChatGPT, Power BI, and others
- Real-world examples from the transport and fuel industry
Module 2: Organizing and Interpreting Operational Data
- Identifying critical logistics and supply datasets (routes, tanks, deliveries)
- Preparing volume control and inventory data for AI integration
- Data cleaning, formatting, and validation techniques in Excel
- Generating insights through dynamic tables and pivot charts
Module 3: AI-Enhanced Fuel Demand Forecasting
- Understanding demand forecasting and key influencing factors
- Utilizing Excel’s AI capabilities and ChatGPT for predictive analysis
- Predicting short-term (1–2 week) fuel demand trends
- Practical exercise: Developing a basic forecast model using existing data
Module 4: Route Planning and Resource Optimization
- Core principles of route optimization and scheduling
- Using AI tools to recommend optimal routes and delivery sequences
- Applying Excel and ChatGPT for realistic route planning with constraints
- Practical activity: Generating route options for delivery units
Module 5: Cost Estimation and Logistics Optimization
- Identifying cost drivers: distance, tolls, fuel consumption, and freight
- Employing AI models to calculate logistics costs
- Comparing traditional manual planning with AI-assisted approaches
- Creating 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 volume control systems
- Hands-on session: Building a real-time logistics performance dashboard
Module 7: Integrating AI into Logistics Workflows
- Automating repetitive reporting and data aggregation tasks
- Leveraging Power Automate or Excel macros for process automation
- Setting up alert systems for inventory or delivery thresholds
- Practical example: Implementing AI-based alerts for tank refill scheduling
Module 8: 90-Day AI Adoption Plan for Logistics and Supply Chain
- Developing a phased AI implementation roadmap
- Selecting pilot use cases and defining success metrics
- Scaling AI-assisted workflows across teams
- Fostering continuous improvement and knowledge sharing
Summary and Next Steps
Requirements
- Familiarity with Microsoft Excel or Google Sheets
- No previous background in Artificial Intelligence is necessary
Target Audience
- Professionals in logistics and supply chain within the fuel transport and retail sectors
- Operations and inventory coordinators
- Supervisors and planners responsible for fleet routing and fuel distribution
