Bottleneck prediction and data-driven discrete-event simulation for a balanced manufacturing line

Abstract

Bottleneck identification is a relevant tool for continuous optimization of production lines. In this work, we implement a data-driven discrete-event simulator (DDS) based on experimental distributions, obtained from real historical data. The DDS allows to analyse the behavior of a balanced manufacturing line at Bosch Thermotechnology, under different hypotheses. It shows that some scenarios perceived as likely to increase output may actually decrease production metrics, reveals the importance of line injection rates, and leads to the need for adequate real time bottleneck forecasting tools, which allow shift managers intervention in a useful time frame. Eleven prediction models are tested, where a random forest and a multi-layer perceptron attain the best performances (above 95% in all metrics). This data flow is operationalized through a micro-services pipeline which is briefly discussed.