## 4 Bottleneck Research Trends in Manufacturing System
### Use Neural Networks to Identify and Analyze Manufacturing System Bottlenecks  
Using machine learning technology to automatically identify and extract key features in a process is becoming an effective research method for process management and its decision support. Bottleneck identification is an important step for process improvement, but traditional approaches based on quantitative measures, such as machine operations and resource requirements, have limitations in knowledge-intensive processes.  
[PERSON] and his research team proposed an interpretable modeling framework called the Bottleneck Spatial-Temporal Attention Network (BSTAN) to enhance bottleneck prediction in complex manufacturing systems [105]. In this framework, the first step involves extracting the network structure information of the production line. The second step involves using a graph neural network based on gated recurrent units to capture temporal trends and workstation interactions, enabling the prediction of blockages and starvation at each workstation. Finally, the bottlenecks are predicted by analyzing the distribution of blockages and starvation across the production line.  
[PERSON] and his research group used fuzzy neural networks to study the bottleneck identification and bottleneck shifting prediction of automated production lines, and they proposed a new definition of a workshop bottleneck for precision forging blades [106]. By establishing a bottleneck recognition index system, the bottleneck recognition problem is transformed into a multi-attribute decision problem, and a fuzzy neural network can be used to train the bottleneck recognition and prediction.  
The semiconductor manufacturing (SM) process involves both continuous dynamic changes and discrete event dynamics, making it one of the most complex models to analyze. Bottlenecks in this process can significantly impact system throughput, delivery times, and product quality. [PERSON] and his research team proposed an improved Adaptive Neuro-Fuzzy Inference System (ANFIS) and the research team applied this predictive method to the SM system [92]. The SM product line has categorical inputs in addition to numerical inputs, but the standard ANFIS can only accept numerical inputs. However, the research team believes that this improved method adaptively integrates fuzzy inference systems with neural networks, capable of reflecting the contribution of categorical inputs to firing strength through a transformation matrix method. The improved ANFIS structure is shown in Figure 3; it consists of six layers of calculations. The first layer is the adaptive node function, which is the membership of the fuzzy set associated with the input; it belongs to the Gaussian function. The second layer is the firing strength of fuzzy rule calculating. The third layer of functions is responsible for normalizing all firing strength of the rule. The fourth layer is the calculation of the firing strength of the categorical input. The fifth layer of the linear function is responsible for the calculation of fuzzy rule output. The sixth layer's function is used to compute the overall output of the ANFIS. For numerical input, such as processing time, utilization rate, buffer length, mean time between failures (MTBF), mean time to repair (MTTR), and Work In Progress (WIP), we just need to follow the calculation sequence from layers one to six. For categorical input calculation, the research team used 1-out-of-n to encode categorical inputs S\({}_{1}\) (the job types) and S\({}_{2}\) (the releasing strategy), then input to the transformation matrix instead of following the first three layers of calculation. After that, the matrix turns those inputs into a five-dimensional vector, which will be used for layer 4-6 calculation. Finally, experimental results show that this method can accurately predict the bottleneck of the SM system.