Automated machine learning (AutoML) platforms are increasingly adopted in manufacturing to support data-driven decision-making. However, systematic and reproducible evaluations of their practical applicability remain limited. This study presents a controlled benchmarking framework for comparing three selected cloud-based AutoML platforms: Google Vertex AI, Microsoft Azure ML and IBM Watsonx, in the context of raw material demand forecasting for mold manufacturing. A synthetic dataset was generated to reflect essential operational characteristics of industrial production, including batch-based manufacturing, inventory-triggered replenishment and delivery lead times. While the underlying bill of materials logic is deterministic, the interaction of production variability and inventory dynamics introduces nonlinear and time-dependent behavior. All platforms were evaluated using identical data splits, chronological cross-validation and consistent performance metrics to ensure fair comparison and prevent information leakage. Results indicate moderate predictive performance, which is attributed to embedded operational complexity. Performance differences between platforms are marginal, highlighting that practical considerations such as feature handling, deployment readiness and computational effort may be more influential than raw accuracy. Although synthetic data limit external validity, the proposed framework provides a reproducible and transparent basis for applied evaluation of AutoML platforms. Future work will incorporate real industrial data and robustness testing under non-stationary and disrupted production conditions.
Grajewski et al. (Thu,) studied this question.