The optimal initial approach involves creating a simple heuristic to classify the machines’ historical performance data and then developing a model to detect anomalies based on this classified data. This method is advantageous due to its simplicity, the clear classification of data into normal and abnormal categories, and the flexibility it offers for iterative refinement. While option A provides a quick initial assessment, option D lays a more robust groundwork for a predictive maintenance system by facilitating the development of a model based on classified data. Employing a team of expert analysts (option B) is not scalable or cost-effective, and implementing a time-series model (option C) may overlook the need for initial data classification and simplicity.