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

Module 1: AI in Logistics and Supply: An Introduction

• Exploring Artificial Intelligence: key concepts and applications
• AI in logistics and fuel distribution: potential and impact
• No-code AI tools: Excel AI, ChatGPT, Power BI, and other platforms
• Real-world examples from the transport and fuel industries

Module 2: Structuring and Analysing Operational Data

• Pinpointing critical logistics and supply datasets (routes, tanks, deliveries)
• Preparing volumetric control and inventory data for AI processing
• Data cleansing, formatting, and validation within Excel
• Constructing dynamic tables and pivot charts to generate insights

Module 3: AI-Driven Forecasting for Fuel Demand

• Comprehending demand forecasting and key influencing factors
• Leveraging Excel’s AI capabilities and ChatGPT for predictive analysis
• Predicting short-term (1–2 week) fuel demand patterns
• Practical task: creating a simple forecast model using existing data

Module 4: Route Planning and Resource Optimisation

• Core principles of route optimisation and scheduling
• Using AI tools to recommend optimal routes and delivery sequences
• Applying Excel and ChatGPT for route planning with real-world constraints
• Practical activity: generating route options for delivery units

Module 5: Cost Estimation and Logistics Optimisation

• Identifying cost factors: distance, tolls, fuel consumption, freight
• Employing AI models to estimate logistics costs
• Comparing manual versus AI-assisted cost planning approaches
• Developing cost calculation templates with dynamic inputs

Module 6: Dashboards and KPI Visualisation

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

Module 7: Embedding AI into Logistics Workflows

• Automating routine reporting and data aggregation tasks
• Utilising Power Automate or Excel macros for task automation
• Setting up alert systems for inventory or delivery thresholds
• Practical example: AI-based alerts for tank refill scheduling

Module 8: A 90-Day Plan for AI Adoption in Logistics and Supply

• Developing a phased AI implementation roadmap
• Selecting pilot use cases and defining success metrics
• Expanding AI-assisted workflows across teams
• Establishing continuous improvement and knowledge-sharing protocols

Summary and Next Steps