## 3 Manufacturing System Bottleneck Research
### Bottleneck Identifications
#### 3.1.3 Bottleneck Identification Based on Data-Driven Approaches  
Researchers announced that the data-driven approaches are more effective in addressing the dynamic changes within systems. By leveraging real-time data feedback from online monitoring systems, dynamic bottlenecks within the system can be promptly identified [26; 77]. [PERSON] and [PERSON] proposed a data-driven method for bottleneck identification and diagnoses based on priority ranking and active cycle methods [78]. [PERSON] proposed a data-driven, simulation-free method for identifying both short-term and long-term bottlenecks by collecting data such as buffer records and blocking records [79]. [PERSON] proposed a dynamic bottleneck identification method based on the total runtime duration, which applies to unstable discrete manufacturing systems [80; 81]. By monitoring real-time data from the system, this method can identify both static and transient bottlenecks. [PERSON], [PERSON], and [PERSON] developed an indirect system bottleneck identification method based on historical data [82]. Furthermore, experts developed breakpoint methods and heuristic algorithms for dynamic bottleneck identification by collecting external characteristics of production lines such as buffer capacity, starvation time, and equipment utilization [67; 83]. However, these identification methods are only applicable to systems with buffers and are susceptible to the capacity of the buffers, which may lead to misjudgment of bottlenecks.  
This data-driven identification method can not only identify dynamic bottlenecks in real-time but also assist enterprises in dynamically adjusting production plans and decisions. However, it cannot provide a quantitative calculation method for dynamic bottlenecks or clarify the specific impact of dynamic bottlenecks on manufacturing system performance. Therefore, there are also limitations to data-driven bottleneck identification methods.  
To make a more intuitive comparison of the above bottleneck identification methods, we drew up a table to list their strengths and limitations, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Identification Methods** & **Strengths** & **Limitations** \\ \hline \multirow{4}{*}{Based on static models} & Easier to understand; it can be built and & \multirow{4}{*}{Applies only to long-term stable systems and lacks flexibility.} \\  & analyzed more quickly. & & \\ \cline{1-1}  & Does not involve a time dimension, & \\ \cline{1-1}  & which means that less computing and & \\ \cline{1-1}  & resources are required. & \\ \hline \multirow{4}{*}{Based on simulations} & More flexible and has higher accuracy & \multirow{4}{*}{Accuracy relies on software performance and alignment with the actual system.} \\  & than the static model-based method. & \\ \cline{1-1}  & By simulating different scenarios and & \\ \cline{1-1}  & conditions, you can identify and take & \\ \cline{1-1}  & preventive measures in advance. & \\ \hline \multirow{4}{*}{Based on data-driven approaches} & Can identify real-time dynamic & \multirow{4}{*}{Quantitative calculation of bottlenecks is not available.} \\  & bottlenecks in the system. & \\ \cline{1-1}  & Helps dynamically adjust predictions and & \\ \cline{1-1}  & decisions in response to system changes. & \\ \cline{1-1} \cline{1-1}  & & \\ \cline{1-1} \cline{1-1}  & & \\ \cline{1-1} \cline{1-1}  & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Bottleneck identification methods and their strengths and limitations.