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

Module 1: Overview of AI in Logistics and Supply

• Grasping Artificial Intelligence: key concepts and practical uses
• AI in logistics and fuel distribution: potential benefits and industry impact
• No-code AI solutions: Excel AI, ChatGPT, Power BI, and additional tools
• Real-world examples from the transportation and fuel industries

Module 2: Organizing and Examining Operational Data

• Recognizing critical logistics and supply datasets (including routes, tanks, and deliveries)
• Preparing volumetric control and inventory records for AI processing
• Performing data cleaning, formatting, and verification within Excel
• Generating insights through dynamic tables and pivot charts

Module 3: AI-Enhanced Fuel Demand Forecasting

• Understanding demand prediction and the variables that influence it
• Leveraging Excel’s AI features and ChatGPT for predictive analysis
• Projecting short-term (1–2 week) trends in fuel demand
• Practical task: developing a basic forecast model using available data

Module 4: Route Planning and Resource Efficiency

• Core principles of route optimization and scheduling
• Utilizing AI tools to propose optimal routes and delivery orders
• Applying Excel and ChatGPT for route planning under real-world constraints
• Practical activity: generating route alternatives for delivery vehicles

Module 5: Cost Projection and Logistics Refinement

• Identifying cost factors: distance, tolls, fuel usage, and freight
• Employing AI models to calculate logistics expenses
• Contrasting manual planning with AI-assisted cost estimation
• Creating cost calculation templates with adjustable inputs

Module 6: Dashboards and KPI Display

• Introduction to Power BI and Excel-based dashboards
• Designing visual reports for logistics and supply chain KPIs
• Integrating data from volumetric control systems
• Practical task: building a real-time logistics performance dashboard

Module 7: Embedding AI into Logistics Processes

• Automating repetitive reporting and data aggregation tasks
• Using Power Automate or Excel macros for workflow automation
• Setting up alert mechanisms for inventory levels or delivery milestones
• Real-world example: AI-triggered alerts for tank replenishment scheduling

Module 8: 90-Day AI Integration Plan for Logistics and Supply

• Developing a phased roadmap for AI implementation
• Selecting pilot use cases and defining success indicators
• Expanding AI-assisted workflows across team members
• Implementing continuous improvement and knowledge exchange practices

Recap and Future Directions