Automate supply chains with agentic AI systems
summary
AWS builds an agentic stack that connects to ERP and execution systems, running a A2A operating system with a supply‑chain digital twin, math solvers and ML demand forecasts to detect anomalies, propose actions and automated inventory rebalancing. Present examples target inventory excesses that average 20–25% and note 41% of orgs take four or more days to resolve urgent issues; a kiosk demo exists (no live demo on stage) and customers report pilot integrations for rerouting and stock transfers.
AI-written summary · about
chapters (8)
- 0:00Introduction and context
- 3:02Current supply chain challenges
- 6:35Autonomous supply chain vision
- 8:22From GenAI to agentic AI
- 10:30Future workforce and innovation
- 12:16Shifting effort-value balance
- 13:23Building the autonomous system
- 15:05Key use cases overview
related talks
27mLeveraging AI for Resilient Supply Chains
26mRiddles in Supply Chain Management - How AI solves them?
16mEnterprise AI Innovation: GenAI Experimentation
29mSolve common business problems with AI and ML
30mCustomer centricity in manufacturing with operational improvements
16mEnterprise AI Innovation: Gen AI Experimentation
42mThe Rise of AI
22mThe AI Frontier: Redefining Industrial Automation