# IIoT System for Intelligent Detection of Bottleneck in Manufacturing Lines
## 7 Conclusions  
The convergence of IIoT and the Industry 4.0 paradigm facilitates the development of a new generation of components, sensors, and actuators, gradually integrating them into manufacturing lines. This ongoing integration of communication protocols and smart systems into operational technology addresses the historical gap that existed with IT technologies. These advancements enhance accessibility to data, enabling seamless analysis and monitoring in both real time and deferred time.  
The study of bottlenecks in manufacturing lines has been extensive, often relying on analysis and simulation. This proposal aligns more closely with predictive models and references from Industry 4.0, drawing parallels with disciplines such as predictive maintenance.  
This manuscript presents a framework for advanced bottleneck detection in manufacturing lines. The proposed solution involves designing an IIoT architecture with four integrated layers, incorporating a Machine Learning system as a predictive engine to support data analytics for Industry 4.0 systems. Technological advancements in services, communications, and real-time event transmission have made smart sensors essential for providing quality information. These sensors can be integrated into data frames to analyze complex issues, as discussed in this article. The abundance of data, whether from sensors or indicators, poses challenges related to effective bottleneck detection.  
The framework, built on the IIoT architecture and Industry 4.0 principles, offers an integrated solution to the problem at hand. It encompasses phases for capturing information from sensors and actuators, integrating datasets that provide bottleneck information, and facilitating real-time data distribution for predictive modeling with Big Data. Additionally, it enables monitoring and stores historical information for continuous data training. In the experiment, the four layers of the architecture were operational, executing various phases of the designed process. The selected sensors were categorized and prepared for integration, forming the data frame. Considering their real-time acquisition, the platform for event transmission persistently provided information along with timestamps for distribution.  
The architecture provided us with a set of classified data in real time, persisted in its efforts, generated historical data, and predicted the bottleneck risk through a trained Machine Learning model provided by Big Data. In addition, it monitored and analyzed its traceability to detect latencies. As a result, we have obtained an improved detection of bottlenecks in production lines and a predictive indicator to help make decisions in the business context and in manufacturing management.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14010323/s1](https://www.mdpi.com/article/10.3390/app14010323/s1)]([https://www.mdpi.com/article/10.3390/app14010323/s1](https://www.mdpi.com/article/10.3390/app14010323/s1)).  
Conceptualization, M.J.R.A.; methodology, M.J.R.A.; validation, M.J.R.A. and I.A.C.; proposal, M.J.R.A.; writing--original draft preparation, M.J.R.A.; writing--review and editing, M.J.R.A.; conceived and designed the experiments, M.J.R.A.; reviewing, I.A.C.; supervision, I.A.C. and J.A.C.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Spanish Ministry of Science, Innovation, and Universities, under Project PID2022-142043 NB-I00.  
Not applicable.  
The data presented in this study are available in Supplementary Materials  
The authors declare no conflicts of interest.