Artificial Intelligence-Based Hyperautomation for Smart Factory Process Automation

To modernize production or manufacturing processes, businesses must research innovative business automation technology. Automating industrial operations that demand a lot of human labor or bring little value to the organization allows workers to focus on more valuable duties. Hyperautomation will shape business efficiency and competitiveness in the future. Data-driven hyperautomation uses cutting-edge methods, which consist of robotic process automation (RPA), machine learning (ML), deep learning (DL), natural language programming (NLP), predictive analytics technologies, and artificial intelligence (AI). Hyperautomation improves security, governance, IT-business alignment, and automation costs. It improves AI and ML in business processes. Artificial intelligence, which uses computational approaches to build systems over time, is sometimes used interchangeably with ML and DL. Companies utilize trained and unsupervised algorithms to spot data trends. Hyperautomation requires the strategic use of AI, ML, and DL. The smart factory emphasizes hyper customization, responsive and distributed supply chains, interactive products, returning labor to factories, and providing a positive customer experience through flexible and adaptive production processes in challenging production environments, which is Industry 5.0‘s ultimate goal. To maximize labor and production asset utilization, automation and optimization are needed. Smart factory’s manufacturing process uses hyperautomation based on AI to introduce novel goods or improve old ones. This chapter emphasizes the need for AI-based hyperautomation for smart industrial process automation and the drawbacks of present and emerging solutions. It also points out the remaining problems.