Predicting future production system bottlenecks with a graph neural network approach

Predicting throughput bottlenecks has received an increasing research attention in the recent years. Several statistical and machine learning models have been developed, with the objective of identifying future bottleneck locations for more efficient and cost-saving productions. However, bottleneck prediction remains a challenging task due to the complexity of manufacturing systems and the interactive effects from many impacting factors. This paper proposed the BSTAN (bottleneck spatial-temporal attention network) - an interpretable modeling framework to improve system bottlenecks prediction capability for complex manufacturing systems by: (1) characterizing line topological features using graph representations; (2) modeling spatial-temporal relationships and interactions between stations using graph attention network with gated recurrent units. In this proposed framework, the production line is first transformed into the representation of a weighted undirected graph to depict network structural information of a production line. An attention-based graph neural network with gated recurrent units is applied to capture both the temporal trends and station interactions to predict each station's blockage and starvation. Future bottleneck stations are identified by analyzing the forecasted blockage and starvation distribution. To evaluate the model performance, an industrial case study is performed with data collected from a one-year production at an automotive powertrain assembly line. With the proper model con-struction, BSTAN has outperformed both statistical and machine learning benchmark models, achieving up to 15% lower root mean square error compared to the best benchmark method in predicting overall system blockage and starvation time.

Automated summary

Predicting throughput bottlenecks has become an important area of research with the goal of improving production efficiency and cost-saving in manufacturing systems. This paper proposes a modeling framework called BSTAN, which characterizes line topological features using graph representations and models spatial-temporal relationships and interactions between stations using a graph attention network. The framework outperforms benchmark models in predicting system blockage and starvation time, achieving up to 15% lower root mean square error.