Data-Driven Workforce Optimization in Weaving Manufacturing Systems: An Integrated Machine Learning and Queueing Framework

This study proposes a predictive–prescriptive framework for workforce planning and queueing-based capacity assessment in weaving manufacturing systems by integrating statistical analysis, machine learning, and queueing theory. The dataset comprised 12 months of hall-level observations combining four operational indicators recorded by the LoomData system—warp and weft breakage frequencies and their corresponding repair durations—with workforce requirements obtained from the company’s operational planning records. Statistical analyses identified operational differences among production halls and time periods. Workforce demand was modeled using multiple linear regression and tree-based machine learning algorithms, including M5P, REPTree, Random Forest, and Random Tree. Model performance was evaluated through cross-validation and independent testing, where multiple linear regression demonstrated the highest predictive accuracy and stability (R2 > 0.93). The predicted workforce estimates were subsequently converted into integer staffing levels and incorporated into an M/M/c queueing model to analytically assess system utilization, expected waiting times, expected queue lengths, and capacity pressure. The results revealed hall-specific differences in queueing performance and capacity utilization. These findings showed that predictive model performance should be complemented by a queueing-based capacity assessment to support workforce planning. By integrating predictive modeling with analytical queueing assessment, the proposed framework bridges machine learning and operations research while providing practical decision support for data-driven workforce planning in manufacturing environments.