# IIoT System for Intelligent Detection of Bottleneck in Manufacturing Lines
## 2 Literature Review
### The Bottleneck in Manufacturing Lines  
In the reviewed literature [1; 9; 12], terms related to the bottleneck in production lines included congestion, capacity below demand, process limiting throughput, and production blockage. Essentially, a bottleneck signifies an obstruction to the regular flow of production. These situations frequently occur on manufacturing lines due to various factors, such as improper synchronization between workstations, poorly formed products hindering proper flow, or inadequate maintenance of a machine, leading to operation below its normal performance level.  
In production lines, instances often arise where machines or workstations exhibit asymmetry in the product processing speed. To address this, buffers or accumulators are installed to equalize the overall capacity of the manufacturing line. However, miscalculations in buffer size, in response to production demand, can result in bottlenecks by creating product accumulations that disrupt workstation performance. Additionally, situations involving product dependencies may occur, such as the absence of an additive hindering proper mixing in paint manufacturing or the lack of flavor aroma affecting the filling process of yogurt.  
Additional causes include inefficient maintenance of machines, frequent stoppages, failure to adapt to the demands of the rest of the line, slowdowns in the manufacturing line supply chain, and inadequately trained production personnel.  
Once the bottleneck arises, a thorough investigation and identification of its origin become necessary. This involves a comprehensive examination of the production line, reviewing production indicators and external dependencies. Monitoring critical sensors and actuators, including examples such as counting detectors, vibration sensors, or motor noise detectors, becomes essential to detect faults in machine components. Although they may not be within the control range of the machine or workstation, keeping sensor and actuator values within acceptable limits is crucial to avoiding product failures or problems with machine components. Therefore, analyzing these sensors and actuators will be vital to detect future bottlenecks through predictive analysis. Current technologies based on IIoT and Industry 4.0 standards facilitate real-time capture of these components and sensors, enabling efficient data generation and training for bottleneck prediction.  
Technological advancements and enhanced production data analysis have had a significant impact on manufacturing operations, enabling improved predictions of maintenance needs and failure forecasting [6; 7; 8]. Initiatives, as explored in studies such as [9], leverage machine vision and simulation to enhance the identification of bottlenecks in the manufacturing process.  
The authors uniformly underscored three crucial elements in their analysis: data quality, consistency in production information, and data from sensors and actuators. This work puts forth a framework focused on these aspects to capture and integrate key data, with the objective of improving bottleneck identification. For an in-depth exploration of the proposal components, please refer to Section 3.