Artificial Intelligence– Human AI Collaboration
The COVID-19 outbreak created uncertainties and changes in every aspect of daily life and work. It’s also pushing business leaders and plant owners to act more quickly than ever before, to survive and outperform others. This provided more possibilities for AI technologies and has speeded AI deployment in manufacturing.
From a practical perspective, we will see more AI-based applications specifically targeted toward various aspects of decision making, such as improving production/operation efficiency, reducing downtime, providing predictive maintenance, optimizing supply chain, and reducing energy consumption.
Human and AI capabilities complement each other. Humans are creative, can see above the current task, and can apply knowledge from other experiences in a current task. However, humans are less perfect in repetitive tasks. AI is high-performance and diligent, but less creative. There is great potential for human and AI collaboration, and humans must interact collaboratively with AI as they continue to fill the roles of project owners, system trainers, final users and will interact with AI throughout the lifecycle of a project.
AI is being applied to a wide range of applications and industries. In process control, AI and machine learning are being applied to advanced process control (APC) applications and autonomous plant operations. In discrete manufacturing, AI is being applied to robotics. All processes could theoretically be controlled by some form of artificial intelligence in the long-term. Whatever the application, there are some basic steps identified by ARC to ensure success in your AI deployment:
Based on presentations from ARC’s 2022 European Forum, this strategy report provides the latest AI use cases and comprehensive comparison of strengths and challenges of humans vs. AI. This report presents how humans and AI can collaborate effectively and which key dimensions and applications to consider. Companies that supported the event include Microsoft Project Bonsai, Dow Chemical, NNaisense, ABB, and Throughput AI.
The following industrial use cases for AI technologies were all shared at the 2022 ARC European Forum.
Microsoft Project Bonsai shared their views on autonomous systems, and how the Bonsai platform can optimize equipment and processes by sensing and responding in real time. According to Microsoft, autonomous transformation is an evolutionary process consisting of four stages:
From the second stage, enterprises may use supervised and unsupervised learning to realize things like better predictive maintenance and demand prediction. The Bonsai platform combines simulation, deeper enforcement learning, and machine learning. Bonsai can help users build AI models with their own experience and industry know-how and speed up the development of the latter three stages. Below are some Bonsai case studies:
Dow Chemical shared how cloud enabled predictive maintenance via optimizing manufacturing data and analytics. Unplanned downtime accounts for vast amounts of lost revenue for the process industries. Leveraging predictive maintenance to act before failure happens is one of the leading applications for AI in manufacturing.
In Dow Chemical’s own project, three distinct persons work together as a team.
