Roles of Big Data and AI in Manufacturing

In the past companies in the manufacturing industry reacted to the poor quality of their products once produced. The defective products would then be analyzed and compared to alternatives to determine how the product was not of a high standard. This process was both time-consuming and expansive. There was no immediate feedback to the worker and a poor understanding of how to fix the defects. Since the integration and advancement of AI and Big Data, it is now possible to get immediate feedback and not only understand the defects and implications of a product but to also predict the expected quality of the product with specific settings. High-speed, low-latency systems allow workers to see the impact of their work and to act as simply or as detailed as they need to understand a problem. Provide tools for regression analysis and process monitoring using data from sensors and/or Manufacturing Execution Systems (MES) for understanding complex relationships between causes and effects and to predict the response of a change in process/input before testing or actual implementation. The main purpose of this research is to contribute and add value to the literature for the utilization of Artificial Intelligence and Big Data Engineering in different manufacturing applications and areas. This research paper shall study and assess AI and Big Data Roles in manufacturing using qualitative analysis and how they can be used to simulate the effect of these changes and predict potential outcomes. This saves time and money too since it’s a lot cheaper to do things virtually rather than physically change the production line. Data mining is also very effective for understanding the correlation between the cause and effect of patterns to make informed decisions for the future.