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Duration 14 hours
Course Outline
Module 1: Foundations of AI in Logistics and Supply
- Core principles of Artificial Intelligence and its real-world applications
- The impact of AI on logistics and fuel distribution: key opportunities
- Exploring no-code AI solutions including Excel AI, ChatGPT, and Power BI
- Case studies from the transportation and fuel sectors
Module 2: Organizing and Interpreting Operational Data
- Identifying critical logistics datasets such as routes, tank levels, and delivery logs
- Structuring 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-Driven Fuel Demand Forecasting
- Analyzing demand forecasting models and key influencing factors
- Leveraging Excel’s AI capabilities and ChatGPT for predictive insights
- Predicting short-term (1–2 week) fuel demand patterns
- Practical application: developing a simple forecast model using existing data
Module 4: Optimizing Routes and Resource Allocation
- Fundamentals of route optimization and scheduling strategies
- Utilizing AI tools to recommend optimal paths and delivery orders
- Applying Excel and ChatGPT to plan routes under real-world constraints
- Interactive session: generating delivery route options for transport units
Module 5: Logistics Cost Estimation and Optimization
- Recognizing cost factors including distance, tolls, fuel usage, and freight charges
- Employing AI models to project logistics expenditures
- Evaluating manual cost planning against AI-assisted approaches
- Developing cost calculation templates with dynamic variables
Module 6: Building Dashboards and Visualizing KPIs
- Overview of Power BI and Excel dashboard capabilities
- Designing visual reports for logistics and supply chain performance indicators
- Incorporating data from volumetric control systems
- Hands-on project: developing a real-time logistics performance dashboard
Module 7: Embedding AI into Logistics Processes
- Automating routine reporting and data aggregation tasks
- Utilizing Power Automate or Excel macros for workflow automation
- Setting up alert mechanisms for inventory levels or delivery milestones
- Case example: implementing AI-based alerts for tank replenishment scheduling
Module 8: 90-Day Roadmap for AI Adoption in Logistics
- Creating a structured AI implementation strategy
- Selecting pilot use cases and defining success criteria
- Expanding AI-assisted workflows across organizational teams
- Establishing practices for continuous improvement and knowledge exchange
Summary and Future Directions
Requirements
- Familiarity with Microsoft Excel or Google Sheets
- No previous experience with Artificial Intelligence is necessary
Target Audience
- Logistics and supply chain specialists within the fuel transport and retail sector
- Coordinators for operations and inventory management
- Supervisors and planners responsible for fleet routing and fuel deliveries
