Leveraging AI for Resilient Supply Chains
summary
Claims concrete gains: 60% forecast uplift, 20–30% inventory reduction and up to 95% order fill accuracy, tied to specific AI uses — demand sensing, cost-prediction models, master-data cleansing, supplier-network optimization, and anomaly/fraud detection. Examples mention cloud autoscaling, microservices data flows, generative tools like chat GPD for data summarization, and autonomous picking/packing pilots; transcript reports these approaches deployed in regulated manufacturing and logistics and framing a $184M disruption cost per org.
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chapters (10)
- 0:00Introduction and Welcome
- 0:58Data Complexity in Modern Supply Chains
- 1:44Global Disruptions and AI Solutions
- 2:40AI's Role in Supply Chain Management
- 3:09Cost Management and Supplier Optimization
- 5:28Forecasting and Demand Planning
- 7:09Supply Chain Challenges
- 10:18AI Applications in Supply Chains
- 19:25Impact of AI on Supply Chain and Enterprise Operations
- 25:20Conclusion and Future Potential of AI
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