## 9 Discussion  
Other models could have been used to simulate the same effect for the prediction, and the following discussion goes through some of these models exploiting their pros and cons in relationship to replacing the LSTM network. Although a random forest can handle nonlinear relationships in data and can handle mixed data types, it can also be slow for large datasets and may overfit without proper tuning. Although an SVM can handle nonlinear relationships in data and is effective for high-dimensional data, it is also slow for large datasets, sensitive to outliers, and requires a proper tuning of regularization parameters. Finally, a CNN is very effective for image and time-series data classification; however, in this particular case we do not seek a classification, but instead, we focus on obtaining a prediction. The choice of model ultimately depends on the specific problem and the characteristics of the data being analyzed, so we decided that for this particular case, the LSTM network was the most appropriate model. The market implications of using machine learning algorithms such as the LSTM network in industrial manufacturing systems can be significant. By detecting and preventing anomalies in the production line, companies can increase the efficiency and reliability of their operations, leading to improved product quality, reduced production time, and increased competitiveness in the market. Predictive maintenance and resource scalability can also help organizations optimize their resources and reduce costs, making them more financially sustainable and competitive in the market. From a practical perspective, the use of machine learning algorithms in industrial manufacturing systems can help organizations make better use of their data and improve decision-making. By automating the process of data analysis and anomaly detection, organizations can reduce the time and effort required to identify problems, allowing them to take prompt and effective corrective action. The social implications of using machine learning algorithms in industrial manufacturing systems are also important. By improving the reliability and efficiency of industrial operations, organizations can reduce waste, conserve resources, and improve working conditions for employees. In addition, by detecting and correcting anomalies in the production line, organizations can help to prevent accidents, reduce harm to the environment, and improve the overall quality of life in the community. Figure 16: Answer score graph. This approach may also be applied in larger contexts in several ways, for example, in supply chain management, to monitor and analyze data from various stages of the supply chain, from raw material procurement to product delivery, to detect anomalies and optimize the entire supply chain; for quality control, to analyze data from product testing and inspection, enabling organizations to detect defects and improve the quality of their products; for resource optimization, to optimize resource utilization, reduce waste, and improve operational efficiency. Another application could be in predictive demand forecasting to analyze historical sales data and predict future demand, enabling organizations to optimize production and distribution. The process of applying these types of models to industrial manufacturing systems can help organizations make decisions at various levels, such as the operational level, the maintenance level, and the management level. The monitoring and analyzing of real-time data from production lines, detecting anomalies and triggering alarms in real time, may help operators make quick and effective decisions to correct problems and minimize disruptions to the production process. The forecast of equipment failures enables organizations to schedule maintenance activities proactively, reducing downtime and improving equipment reliability, which may also provide managers with insights into production performance, resource utilization, and product quality.