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

Module 1: AI Foundations in Logistics and Supply

• Exploring Artificial Intelligence: key concepts and practical uses
• The impact of AI on logistics and fuel distribution: identifying opportunities
• Overview of no-code AI tools including Excel AI, ChatGPT, and Power BI
• Real-world examples from the transportation and fuel sectors

Module 2: Organizing and Analyzing Operational Data

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

Module 3: AI-Powered Forecasting for Fuel Demand

• Key drivers of demand forecasting and relevant variables
• Leveraging Excel’s AI capabilities and ChatGPT for predictive analysis
• Predicting short-term (1–2 week) trends in fuel demand
• Exercise: Developing a simple forecast model using available data

Module 4: Optimizing Routes and Resources

• Core principles of route optimization and scheduling
• Utilizing AI tools to recommend best routes and delivery orders
• Applying Excel and ChatGPT for route planning considering real-world constraints
• Activity: Generating route alternatives for delivery units

Module 5: Estimating Costs and Optimizing Logistics

• Recognizing cost factors such as distance, tolls, fuel usage, and freight
• Employing AI models to calculate logistics expenses
• Contrasting traditional manual cost planning with AI-assisted methods
• Creating cost calculation templates with variable inputs

Module 6: Visualizing Dashboards and KPIs

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

Module 7: Embedding AI into Logistics Processes

• Streamlining repetitive reporting and data aggregation tasks
• Automating workflows using Power Automate or Excel macros
• Setting up alert systems for inventory levels or delivery thresholds
• Case study: Implementing AI alerts for tank refill scheduling

Module 8: A 90-Day Roadmap for AI Adoption in Logistics

• Crafting a phased plan for AI implementation
• Defining pilot use cases and success indicators
• Expanding AI-assisted workflows across different teams
• Fostering continuous improvement and knowledge sharing

Recap and Future Actions