Data driven bottleneck detection of manufacturing systems

Abstract

Bottlenecks within a production line significantly reduce the productivity. Quick and correct identification of the bottleneck locations can lead to an improvement in the operation management of utilising finite manufacturing resources, increasing the system throughput, and minimising the total cost of production. Most of the current bottleneck detection schemes focus on the long-term bottleneck detection problem and an analytical or simulation model is usually needed. Due to recent developments, short-term process control and quick decision making on the plant floor have emerged as important qualities for operation management. This research proposes a new data driven method for throughput bottleneck detection in both the short and long term. The method utilises the production line blockage and starvation probabilities and buffer content records to identify the production constraints without building an analytical or simulation model. The method has been verified both analytically and by simulation. An industrial case study has also been used in order to demonstrate the implementation and validate the efficiency of the proposed bottleneck detection method.