Collecting and formatting diverse data required for deep learning is often expensive in both time and money, which is sometimes infeasible for smaller companies. Even if a manufacturer has the resources required to collect large amounts of data, historical data is often inadequate due to an evolving environment. For example, tools and process steps are constantly adapting to uncertainties found in supply chains, labor limitations, or change in part types. Evolving scenarios (i.e., adding more time constraint steps) in semiconductor manufacturing are especially common as technology nodes progress. Consequently, these rapid changes do not allow time for a diverse, historical dataset to develop to train models.However, what if there was a way to get more quality data? What if AI could be explored on business essential use cases without the resource limitations of technology advancements? With simulation, you can answer these ‘what-ifs’! You can model various scenarios and generate synthetic data to use in AI training. Projects can be accelerated without the cost of cleaning raw datasets and in significantly less time than it takes to collect a sufficient amount of data. Quantifying the impact of changes in a simulated environment prior to production implementation is also key to avoiding unnecessary, costly risks. Furthermore, training models on rich datasets develop more robust, resilient models. Evaluating on-edge cases or other diverse scenarios that seldom occur in historical data increases generalization abilities of models, which improves accuracy overall.