Forecasting Wafer Fab Outputs using Lot Remaining Cycle Time Prediction in Semiconductor Manufacturing

Semiconductor manufacturing is a crucial component of modern supply chains, yet it faces significant challenges due to the complexity and variability of production processes. This study addresses the problem of forecasting wafer fab outputs by predicting the remaining cycle time of lots in semi-conductor manufacturing. We propose a statistical modeling approach that leverages historical data to estimate lot departures and output quantities for a given forecasting horizon. The study focuses on the 20 most highest-volume products in a high-mix, low-volume production environment, using data from a major wafer fab in southeast France. A total of 26 linear regression models are evaluated, considering different contexts input and output variables, to predict RCTs and forecast lot departures. The results indicate that the best-performing model achieves a forecasting accuracy of 41.55% for an 8-week forecasting horizon and 33.39% for a 12-week forecasting horizon, comparable to what is found in the literature. However, the study also highlights the challenges of extrapolating training data to future periods, particularly due to variability in product prioritization and system congestion. Future work will focus on integrating additional variables, such as congestion metrics and due dates, as well as exploring non-linear models to enhance forecasting accuracy and robustness. This research provides a foundational framework for wafer fab output forecasting, essential for effective production management and supply chain stability in the semiconductor industry.