The most efficient and scalable approach is to first develop a simple heuristic to label the unlabeled historical performance data. This labeled dataset can then be used to train a predictive model for anomalies. This method avoids the time and expense of manual labeling and is more efficient for predictive purposes. Option A is not scalable or cost-effective due to the manual effort required. Option B lacks the predictive capability needed for maintenance. Option D does not address the initial need for labeled data to train the model effectively.