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Course Outline
Overview of AI in Supply Chain and Logistics
- Emerging trends in smart logistics
- AI vs. traditional analytics in supply chain management
- Key technologies and platforms
AI for Demand Forecasting
- Time-series forecasting with machine learning
- Handling seasonality and trend components
- Improving forecast accuracy with historical data
Inventory Optimization and Replenishment
- AI-driven stock level prediction
- Safety stock and reorder point calculations
- Integrating AI with ERP and WMS
Route Optimization and Fleet Intelligence
- Shortest path algorithms and delivery routing
- Traffic-aware dynamic route planning
- AI-enabled transport scheduling
Warehouse Automation and Robotics
- AI in picking, sorting, and storage automation
- Computer vision for shelf monitoring
- Coordinating with AGVs and robotic arms
Real-Time Analytics and Dashboarding
- Live dashboards with Tableau and Python
- Monitoring KPIs with real-time data streams
- Generating alerts and exception handling
Case Study and Capstone Project
- Analyzing a multi-node supply chain scenario
- Applying forecasting and routing models
- Presenting a data-driven logistics optimization plan
Summary and Next Steps
Requirements
- An understanding of supply chain or logistics operations
- Experience with data analysis or business intelligence tools
- Basic familiarity with programming or scripting
Audience
- Supply chain analysts
- Logistics managers
- Industrial planners
21 Hours
Testimonials (3)
a broad approach to the topic, practical knowledge
Jakub Wieczorek - Politechnika Slaska
Course - Introduction to AI in Smart Factories and Industrial Automation
Machine Translated
The input fm other industries through the trainer.
Lars Schacht - Scandlines Danmark ApS
Course - Advanced Sales and Operations Planning (S&OP) for Demand Forecasting
Technological Advancement
Piotr Sliwa - Petrosoft.pl Technologie Informatyczne sp. z o.o.
Course - Blockchain in Supply Chain Management
Machine Translated