The performance of a smart manufacturing system is affected by not only the constituent processes but also their system-level interactions. However, in most current studies, indi- vidual process modeling and system-level performance evaluation are independent. This can substantially impact production efficiency. In this letter, utilizing available sensor data, an integrated data-enabled model is developed to seamlessly fuse two conventionally separated system-level and process-level models and analysis. A fast recursive method is developed to evaluate the system yield. The permanent production loss (PPL) concept is defined and evaluated based on the proposed integrated model. Furthermore, PPL attributions due to random downtime and quality issues have been identified. Case studies have shown that the integrated model is of high fidelity, and the PPL analysis can effectively identify the root cause of production yield loss.