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

Module 1: Introductory Overview of AI in Logistics and Supply

• Grasping Artificial Intelligence: key concepts and uses
• AI in logistics and fuel distribution: potential benefits and effects
• No-code AI platforms: Excel AI, ChatGPT, Power BI, and similar tools
• Real-world examples from the transport and fuel sectors

Module 2: Organizing and Analyzing Operational Data

• Recognizing key datasets in logistics and supply (routes, tanks, deliveries)
• Preparing volumetric control and inventory data for AI application
• Cleaning, formatting, and validating data in Excel
• Building dynamic tables and pivot charts to generate insights

Module 3: AI-Enhanced Forecasting for Fuel Demand

• Understanding demand prediction and its contributing factors
• Employing Excel’s AI capabilities and ChatGPT for predictive insights
• Predicting short-term (1–2 week) trends in fuel demand
• Practical task: constructing a simple forecasting model using available data

Module 4: Route Planning and Resource Optimization

• Core principles of route optimization and scheduling
• Utilizing AI tools to propose efficient routes and delivery orders
• Applying Excel and ChatGPT to route planning with actual constraints
• Practical activity: generating route alternatives for delivery vehicles

Module 5: Cost Estimation and Logistics Efficiency

• Identifying cost factors: distance, tolls, fuel usage, freight
• Using AI models to project logistics expenses
• Comparing manual versus AI-assisted cost planning methods
• Creating cost calculation templates with variable inputs

Module 6: Dashboards and KPI Visuals

• Getting started with Power BI and Excel dashboards
• Designing visual reports for logistics and supply KPIs
• Incorporating data from volumetric control systems
• Practical exercise: building a live logistics performance dashboard

Module 7: Embedding AI into Logistics Processes

• Automating routine reporting and data aggregation tasks
• Leveraging Power Automate or Excel macros for process automation
• Setting up alert systems for inventory or delivery limits
• Real-world example: AI-triggered alerts for tank refill schedules

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

• Developing a sequential AI adoption roadmap
• Defining pilot projects and success indicators
• Extending AI-supported workflows across different teams
• Fostering continuous improvement and knowledge exchange practices

Wrap-up and Future Actions