Multi-task learning with multi-gate mixture of transformer-based experts for predictive process monitoring in manufacturing

Manufacturing industries involve both business processes and complex manufacturing processes. Predictive process monitoring techniques are effective for managing process executions by making multi-perspective real-time predictions, preventing issues such as delivery delays. Conventional predictive process monitoring for business processes focuses on predicting the next activity, next event time, and remaining time using single-task learning, which is costly and complex. For complex manufacturing processes, predictive process monitoring primarily aims to predict the remaining time, that is, product cycle time. However, single-task learning methods fail to capture all the variations within the historical process executions. To address them, we propose the multi-gate mixture of transformer-based experts framework, which leverages a transformer network within the multi-gate mixture-of-experts multi-task learning architecture to extract sequential features and employs gated expert networks to model task commonalities and differences. Empirical results demonstrate that multi-gate mixture of transformer-based experts outperforms three alternative architectures across five real-life event logs, highlighting its generalization, effectiveness, and efficiency in predictive process monitoring.