AI for Logistics and Supply: Practical No-Code Applications for the Fuel Industry Training Course

Module 1: Fundamentals of AI in Logistics and Supply

• Grasping Artificial Intelligence: key concepts and real-world uses
• AI in logistics and fuel distribution: potential benefits and impact
• No-code AI platforms: Excel AI features, ChatGPT, Power BI, and others
• Case studies from the transportation and fuel sectors

Module 2: Organizing and Analyzing Operational Data

• Recognizing critical logistics and supply datasets (routes, tanks, deliveries)
• Structuring volumetric control and inventory data for AI processing
• Data cleansing, formatting, and validation within Excel
• Generating insights using dynamic tables and pivot charts

Module 3: AI-Driven Forecasting for Fuel Demand

• Exploring demand forecasting and its influencing factors
• Leveraging Excel’s AI capabilities and ChatGPT for predictive analysis
• Projecting short-term (1–2 week) fuel demand trends
• Practical task: constructing a basic forecast model using existing data

Module 4: Route Planning and Resource Optimization

• Core concepts in route optimization and scheduling
• Utilizing AI tools to recommend optimal routes and delivery orders
• Applying Excel and ChatGPT for route planning with real-world constraints
• Practical activity: generating route alternatives for delivery units

Module 5: Cost Estimation and Logistics Optimization

• Identifying cost factors: distance, tolls, fuel usage, and freight
• Applying AI models to calculate logistics costs
• Contrasting manual versus AI-assisted cost planning methods
• Developing cost calculation templates with dynamic inputs

Module 6: Dashboards and KPI Visualization

• Overview of Power BI and Excel dashboards
• Designing visual reports for logistics and supply KPIs
• Integrating data from volumetric control systems
• Practical session: building a real-time logistics performance dashboard

Module 7: Integrating AI into Logistics Workflows

• Automating routine reporting and data consolidation tasks
• Employing Power Automate or Excel macros for task automation
• Setting up alert systems for inventory levels or delivery thresholds
• Case study: implementing an AI-based alert for tank refill scheduling

Module 8: 90-Day AI Adoption Strategy for Logistics and Supply

• Creating a phased AI implementation roadmap
• Defining pilot projects and success criteria
• Expanding AI-supported workflows across teams
• Establishing practices for continuous improvement and knowledge sharing

Summary and Future Directions