The presentation is about a solution to improve and streamline industrial private wireless network performance predictions using generative digital twin models. There are several open problems in designing, delivering, and maintaining private wireless networks for autonomous industrial settings. In these dynamic environments with changing use cases and SLA profiles, immediate detection of performance problems becomes critical. The existing instrumentation methods proven useful for public networks are not as effective for maintaining these private industrial networks, where use-case-dependent performance prediction becomes a necessity. The described solution builds on the existing Nokia Digital Twin platform used for continuous testing and SLA management of 5G networks for industrial automation. The solution first builds an association between the field measurements and the digital representations of the environment by making use of Deep Neural Network (DNN) techniques. Then in subsequent steps, Generative Adversarial Network (GAN) methods are used to create what-if scenarios with alternate equipment and layout configurations to examine potential concerns, and to explore options towards more optimal performance outcomes.