Unified Data for Supply Chain Cost and Performance Visibility
This sponsored article by Easy Metrics, published via Emerj Artificial Intelligence Research, addresses the growing operational complexity in distribution and fulfillment networks. As performance expectations rise, leaders face challenges due to fragmented data across warehouse management, labor, and transportation systems. Citing reports from the Brookings Institution and the OECD, the text highlights how non-interoperable data delays disruption detection and increases costs. The article synthesizes insights from the AI in Business podcast, featuring Dan Keto of Easy Metrics and Jerod Hamilton of Tyson Foods. Key strategies discussed include unifying warehouse data to enable real-time visibility, sequencing AI investments to ensure data readiness, and measuring network-wide economics rather than isolated departmental performance. The goal is to provide leaders with a defensible view of cost and capacity consumption, preventing local efficiencies from undermining overall margins. By establishing a single, aligned model for robotics and automation data, companies can improve responsiveness and reduce execution risks in large-scale logistics operations.
Wire timeline
Unified Data for Supply Chain Cost and Performance Visibility
This sponsored article by Easy Metrics, published via Emerj Artificial Intelligence Research, addresses the growing operational complexity in distribution and fulfillment networks. As performance expectations rise, leaders face challenges due to fragmented data across warehouse management, labor, and transportation systems. Citing reports from the Brookings Institution and the OECD, the text highlights how non-interoperable data delays disruption detection and increases costs. The article synthesizes insights from the AI in Business podcast, featuring Dan Keto of Easy Metrics and Jerod Hamilton of Tyson Foods. Key strategies discussed include unifying warehouse data to enable real-time visibility, sequencing AI investments to ensure data readiness, and measuring network-wide economics rather than isolated departmental performance. The goal is to provide leaders with a defensible view of cost and capacity consumption, preventing local efficiencies from undermining overall margins. By establishing a single, aligned model for robotics and automation data, companies can improve responsiveness and reduce execution risks in large-scale logistics operations.
Emerj Artificial Intelligence Research