AI: Transforming manufacturing in a post-COVID era
Over the last year, the pandemic created a huge financial impact on the global manufacturing production. Unfortunately, many companies were closed, some were facing demand and supply shortage, while also some had to hit the brakes on their daily operations.
While the promise of AI transforming the manufacturing industry is not considered something new, COVID-19 was a wakeup call for many manufacturers, increasing their interest on AI applications, in order to take accurate and data-driven decisions, facilitate automations, prevent failures before they take place, facilitate product development, and optimize the manufacturing process as a whole. In specific, a recent research by Google Cloud indicated that 76% manufacturing executives said they have embraced “digital enablers” such as AI, data analytics and cloud, while 66% who already use AI in their day-to-day operations, report that their reliance on AI is increasing, as it keeps augmenting their employees’ efforts.
Furthermore, the Google Cloud Manufacturing report highlights that AI is deployed in five top areas in day-to-day operations, quality inspection, supply chain management, risk management, product and/or production line quality checks, and inventory management.
To this end, here is where AI-PROFICIENT comes, aiming to integrate advanced AI technologies with manufacturing ones.
In specific, the project will combine human knowledge and AI capabilities to develop proactive control strategies in production efficiency, quality and maintenance. The manufacturing process will be transformed into a self-learning AI system, capable of incorporating the human feedback to reinforce suitable control strategies and decisions. For such an agile manufacturing process, AI- PROFICIENT will embed predictive and prescriptive AI analytics into the production systems, creating a computationally distributed AI environment.
The project intends to identify the effective means for human-machine interaction and increase the positive impact of AI technology on the manufacturing process as a whole, by integrating self-learning and self-prognostic AI services with production processes with the manufacturing systems and processes in an IIoT environment. In addition, the project aims to embed deep learning techniques and complex event processing capabilities in order to early detect process anomalies and provision of fault diagnostics, and to provide AI-based decision support for proactive maintenance at component and system level. Last but not least, AI-PROFICIENT will deliver a joint human-machine approach to improved production planning and execution.