Efficient monitoring and forecasting of vendor performance are critical to the scalability and reliability of Amazon’s Direct Fulfillment (DF) model, where third-party vendors ship products directly to customers without involving Amazon’s own warehouses. This model requires reliable integration systems and timely order fulfillment across a broad network of vendors spread out over different regions. This thesis aims to develop scalable methods to monitor vendor performance and integration health, and forecast vendor cancellations across Amazon’s DF supply network. To achieve this, we designed and implemented the Vendor Health Dashboard, a centralized tool that automates monitoring of thousands of vendors worldwide, significantly reducing manual oversight and extending coverage to long-tail vendors who were previously difficult to track.