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

Module 1: Intro to AI in Logistics and Supply

• Exploring Artificial Intelligence: key concepts and uses
• AI in logistics and fuel distribution: potential benefits and impact
• No-code AI solutions: Excel AI, ChatGPT, Power BI, and more
• Real-world examples from the transport and fuel industries

Module 2: Organizing and Analyzing Operational Data

• Identifying critical logistics and supply datasets (routes, tanks, deliveries)
• Structuring volumetric control and inventory data for AI processing
• Cleaning, formatting, and validating data within Excel
• Building dynamic tables and pivot charts to generate insights

Module 3: AI-Driven Forecasting for Fuel Demand

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

Module 4: Route Planning and Resource Efficiency

• Core principles of route optimization and scheduling
• Utilizing AI tools to recommend optimal routes and delivery orders
• Applying Excel and ChatGPT for route planning considering real-world constraints
• Hands-on activity: creating route alternatives for delivery units

Module 5: Cost Analysis and Logistics Efficiency

• Recognizing cost factors: distance, tolls, fuel usage, freight
• Employing AI models to predict logistics costs
• Evaluating manual versus AI-assisted cost planning
• Developing cost calculation templates with dynamic inputs

Module 6: Dashboards and KPI Visualization

• Overview of Power BI and Excel dashboard capabilities
• Creating visual reports for logistics and supply KPIs
• Incorporating data from volumetric control systems
• Hands-on session: building a real-time logistics performance dashboard

Module 7: Embedding AI into Logistics Processes

• Automating routine reporting and data aggregation tasks
• Using Power Automate or Excel macros for workflow automation
• Setting up alert systems for inventory levels or delivery milestones
• Practical example: AI-driven alerts for tank refill scheduling

Module 8: 90-Day AI Implementation Plan for Logistics

• Developing a phased AI integration roadmap
• Defining pilot use cases and success indicators
• Expanding AI-assisted workflows across teams
• Fostering continuous improvement and knowledge exchange practices

Summary and Next Steps