# IIoT System for Intelligent Detection of Bottleneck in Manufacturing Lines
## 6 Challenges, Open Issues, and Future Work  
In delving into the analysis, several gaps, challenges, and unresolved aspects have become apparent, directly impacting the proposed architecture in the realm of Industry 4.0:  
* The challenge lies in outdated Programmable Logic Controllers (PLCs) with non-adaptable communication protocols, hindering Industry 4.0 principles of flexibility, scalability, and seamless integration. This limitation not only affects compatibility but also introduces communication latency with modern systems. Adapting to today's integrable systems becomes a significant challenge for companies, impacting their competitiveness and the smooth transition to Industry 4.0 models.
* Another notable challenge is the computational complexity associated with data processing in a local industrial environment. This challenge affects the feasibility of leveraging available data, particularly when considering access. As highlighted in [10],many Machine Learning techniques face scalability and generation capacity issues due to the complexity introduced by incorporating Big Data in the cloud. This limitation poses an open issue, necessitating a thorough investigation into the analysis and classification of key quality data to mitigate the processing volume. It emphasizes the importance of focusing on high-quality data relevant to the specific process or problem under analysis.
* In our research focused on Machine Learning algorithms for manufacturing, particularly in predictive maintenance, SVM and KNN classification algorithms were explored for bottleneck analysis in manufacturing lines. In addition to the considerations, the authors of [27] presented a table illustrating the successful application of these algorithms in predicting failures and errors, including experimentation in IIoT sensor networks. However, an open challenge, as mentioned in [28], is the need for improved accuracy in these algorithms, requiring support from suitable feature selection methods. This underscores the difficulty in many cases to identify the key data essential for the required analysis.  
In Appendix C, detailed information about the development environments employed for experimentation has been provided.  
Here, we acknowledge the limitations and outline future work. We have highlighted the need for a more in-depth study of a specific manufacturing line to thoroughly evaluate the proposed solution. Future efforts involve advancing experimentation by implementing the study on a real manufacturing line, transitioning from a proof-of-concept approach. The focus will shift toward practical application, testing, and measurements within the scope of a specific production line. The ultimate goal is to initiate a project that implements all layers of the proposed IIoT architecture.  
Secondly, the research established that the SVM and KNN algorithms proved to be the most efficient for analyzing the bottleneck problem in manufacturing lines, drawing on analogous studies from existing literature in predictive maintenance. To validate their effectiveness, there is a need for measurement in a real manufacturing system. Additionally, ongoing training of information and optimization of the algorithms are recommended to ensure accurate predictions through continual contrast with real manufacturing data.  
This research sought to optimize the proposed IIoT architecture by studying existing models in Industrial Big Data and merging data from diverse sources, such as similar manufacturing lines. Furthermore, there are plans to expand experimentation with additional protocols, such as OPC-UA, and explore applications beyond, including testing the quality of the manufacturing process. Additionally, further studies will investigate future considerations introduced by Industry 5.0 with the 5.0 society concept [72], assessing their impact on 6G, which is anticipated to fully integrate artificial intelligence [73].