Work in Progress Level Prediction with Long Short-Term Memory Recurrent Neural Network

Abstract

Abstract Since the reliability of production plans drops largely within several days after plan creation, production control faces huge challenges, when trying to foresee the work in progress (WIP) level at bottleneck machines and trying to react appropriately. Whereas several researchers applied artificial intelligence to predict lead times or transition times to improve the planning reliability, only small efforts have been taken on time series prediction in the field of production control, especially on the topic WIP prediction. In this paper univarate times series approaches are used for predicting the work in progress for a bottleneck machine for one and more step ahead. Long short-term memory recurrent neural networks, LSMT models show higher accuracy than classical methods. For more step ahead forecasting four different approaches are investigated. Systematical model tuning and comparison of various error measures are presented for a real industrial use case from the steal manufacturing industry.