Throughput Bottleneck Prediction of Manufacturing Systems Using Time Series Analysis

Throughput bottlenecks define and constrain the productivity of a production line. The most cost-effective way to improve system throughput is to mitigate bottlenecks toward a balanced system. Most of the currently used bottleneck detection schemes found in literature utilize long-term analysis to identify the bottlenecks for a known period and ignore the operation dynamics leading to bottleneck shifts. This paper proposes a method for predicting the throughput bottlenecks of a production line using autoregressive moving average (ARMA) model. We consider the production blockage and starvation times of each station to be a time series used to predict throughput bottlenecks. It is realized that the blockage and starvation times of a production line are critical indicators reflecting the production system dynamics and its internal material flow. As the first attempt in literature for throughput bottleneck prediction, the results demonstrate that the ARMA model can accurately predict blockage and starvation information of each station and hence can accurately predict the system throughput bottleneck, which will lead to the most significant production improvement.

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   Comparison of bottleneck detection methods for AGV systems

   Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693)57 citationsDataRank 6.9