Queue Length Forecasting in Complex Manufacturing Job Shops

Currently, manufacturing is characterized by increasing complexity both on the technical and organizational levels. Thus, more complex intelligent production control methods are developed in order to remain competitive achieve operational excellence. Operations management described early influence among target metrics, such as queuing times, queue length, speed. However, accurate predictions of lengths have long been overlooked a means better understanding systems. In provide length forecasts, this paper introduced methodology identify retrospect based transitional data, well comparison easy-to-deploy machine learning-based forecasting models. Forecasting, static data sets, time series models can be shown successfully applied an exemplary semiconductor case study. The main findings concluded that prediction, even with minimal available feasible applying variety techniques, which enable further research predictions.