Industry 4.0 has transformed manufacturing models and the Internet of Things (IoT) has become a central aspect of future factories to be smart and connected. However, most industries do not have an elaborate mechanism to determine the impact of IoT readiness and its implementation in the performance of the entire factory due to its transformational nature. This paper advances a cross-strategy that combines the IoT adoption indicators and Partial Least Squares Structural Equation Modeling (PLS-SEM) to estimate key enablers and results of smart factory performance. The framework also draws on the data of surveys given by manufacturing experts across multiple areas of industry and it is based on the relationships between Digital Readiness, IoT Maturity, Production Flexibility, Data-Driven Decision-Making, and Smart Factory Performance. The research also incorporates the Analytic Hierarchy Process (AHP) to determine interdependencies of the essential adoption drivers and focus on the improvements. The results of the PLS-SEM analysis indicate that digital readiness and the level of IoT maturity have positive effects on the agility of productions and performance outcomes with a high impact. The framework can be also used to provide a realistic diagnostic to manufacturers that want to gauge their Industry 4.0 preparedness and formulate informed investments decisions on IoT-based technologies.