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Duration 14 hours
Course Outline
Module 1: Foundational Concepts of AI in Logistics and Supply
- Exploring Artificial Intelligence: Key concepts and practical applications
- The role of AI in logistics and fuel distribution: Potential benefits and impact
- Overview of no-code AI solutions: Excel AI, ChatGPT, Power BI, and related tools
- Real-world examples from the transportation and fuel industries
Module 2: Organizing and Interpreting Operational Data
- Identifying critical logistics and supply datasets, including routes, tank levels, and delivery records
- Structuring volumetric control and inventory data to prepare it for AI analysis
- Performing data cleaning, formatting, and validation using Excel
- Generating insights through dynamic tables and pivot charts
Module 3: AI-Driven Forecasting for Fuel Demand
- Analyzing demand forecasting methods and key influencing factors
- Leveraging Excel’s AI capabilities and ChatGPT for predictive modeling
- Predicting short-term (1–2 week) trends in fuel demand
- Practical task: Developing a basic forecast model using existing data
Module 4: Optimizing Route Planning and Resources
- Core principles of route optimization and scheduling
- Employing AI tools to recommend efficient routes and delivery sequences
- Using Excel and ChatGPT to plan routes under real-world constraints
- Hands-on session: Generating route alternatives for delivery vehicles
Module 5: Estimating Costs and Optimizing Logistics
- Recognizing primary cost factors: distance, tolls, fuel usage, and freight charges
- Applying AI models to calculate logistics expenditures
- Evaluating the differences between manual and AI-assisted cost planning
- Developing cost calculation templates with adjustable inputs
Module 6: Creating Dashboards and Visualizing KPIs
- Getting started with Power BI and Excel dashboards
- Designing visual reports for tracking 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
- Utilizing Power Automate or Excel macros for workflow automation
- Setting up alert mechanisms for inventory levels or delivery milestones
- Case study: Implementing an AI-based alert for tank replenishment scheduling
Module 8: A 90-Day Roadmap for AI Adoption in Logistics and Supply
- Creating a phased plan for AI implementation
- Selecting pilot projects and defining success indicators
- Expanding AI-assisted workflows across different teams
- Cultivating a culture of continuous improvement and knowledge exchange
Wrap-up and Future Actions
Requirements
- Familiarity with Microsoft Excel or Google Sheets
- No prior background in Artificial Intelligence is necessary
Target Audience
- Professionals in logistics and supply within the fuel transportation and retail sectors
- Operations and inventory coordinators
- Supervisors and planners responsible for fleet management and fuel distribution
