Predicting transfer times across production lines using data pooling

Abstract

Reliable transfer time prediction is crucial for productivity in automated material handling systems within modern manufacturing environments. However, the complexity and dynamic behavior of manufacturing and logistics systems make accurate transfer time estimation highly challenging. This study proposes a transfer time prediction approach for automated material handling systems operating across production lines. To forecast inter-building and inter-floor transfer times, this study proposes a hybrid method, called time-series residual regression, that integrates linear time-series analysis with nonlinear machine learning. The framework further employs three data pooling strategies to effectively capture device heterogeneity and improve forecasting robustness. The hybrid method was validated using transfer records from inter-building stockers and inter-floor lifters in a Korean semiconductor fab. The experimental results show that the proposed model delivers superior performance, achieving R-squared values of 64.01 % for inter-building transfers and 72.00 % for inter-floor transfers.