# IIoT System for Intelligent Detection of Bottleneck in Manufacturing Lines
## 1 Introduction  
A bottleneck is a process, machine, or productive stage of the system that impedes throughput from working effectively [1]. The reason for this situation is inadequate work, or having a low level of productivity. Consequently, it causes a delay within manufacturing operations, in addition to impacting the rest of the stages of the manufacturing line. There are different causes of the bottleneck; for example, inefficient maintenance on the machines, constant stops, non-adaptation to the demands of the rest of the line, slowdown of the supply chain of the manufacturing line, and untrained production and maintenance personnel not knowing how to correctly interpret information and incorrectly managing workstations. Furthermore, the matter is not always visible. Hence, a crucial aspect lies in the smart handling of data within the manufacturing context. Thanks to technological advances in industrial operational technologies (OT) in Industry 4.0 and the Industrial Internet of Things (IIoT) [2], processes for acquiring information from sensors and actuators have been improved. Advances in communications and wireless technologies, such as 5G, can capture information quickly. Aspects such as Big Data and data analytics have also contributed to classifying product line problems to compare and prevent different situations. After a literature review on these aspects, we found proposals [3] such as data analysis throughout diverse types of uncertainty, such as market variability, execution of indistinct services, or the user. Other authors [4] proposed the cognitive enhancement aspects or Machine Learning to improve safety and industrial maintenance activities. Regarding the bottleneck in assembly lines, the authors of [5] directly dealt with its resolution through the technical application of line balance using the lean manufacturing methodology combined with the IoT. Moreover, studies such as [6; 7; 8] delve into the application of advanced systems in industrial maintenance. In these studies, the authors investigated, in a general way, crucial factors, such as the captured data quality, and logical factors, such as the existence of a uniform information system between stations and human conditions, maintenance, and the performance of machines. Augmented reality is another technological advancement. The authors in [9] specified an approach to addressing the bottleneck on production lines based on cycle time calculations collected using product-specific RFID tags to identify bottlenecks. In conclusion, concerning the studies investigated, most of them agree on three fundamental points to consider related to optimization and maintenance aspects: the data quality, the information consistency in the manufacturing process, and the data acquired from sensors and actuators. As we have seen, there are numerous studies about the bottleneck issue, mainly focused on analysis and simulation. The reason for this study and the gap we observed with respect to previous studies is the need to adjust this analysis to a current smart factory paradigm based on current Industry 4.0 references. The objective is to establish an advanced method to detect bottlenecks in manufacturing lines. Specifically, to create a framework to predict bottlenecks from data analysis. Therefore, this work complements the previous analysis of this issue with a proposed architecture based on current IIoT models and communication standards to create a system for detecting bottlenecks in manufacturing lines. The system relies on three core components: acquiring and analyzing key data [10], primarily from production line sensors and actuators, processing and integrating data using an Industry 4.0 reference architecture [11], and training these data through a Big Data system for bottleneck prediction. The rest of the article organizes the content as follows: Section 2 is a chapter dedicated to the literature review.