Although long hailed as a potent technology for transforming industrial processes, as authors of a recent paper note, “AI cannot be rushed.” Significant issues related to AI’s practical use in manufacturing remain, including finding applications that deliver value in a complicated sector, that satisfy the market, and that meet capital demands. 

To provide a concise roadmap for methodical AI development and integration into industrial operations, such challenges and their solutions must be carefully considered.

In “Artificial Intelligence as a Driver of Industrial Competitiveness,” published in the 2022 International Conference on Smart and Sustainable Technologies in Energy and Power Sectors (SSTEPS), the authors examine these AI-related challenges and opportunities.

AI and Industrial Applications
The authors offer an overview of industrial AI and its key enabling technologies. They also discuss available near-term applications that can help create further opportunities for AI in the industrial sector as the technology evolves. 

AI can be applied in various use cases throughout manufacturing’s production cycle, from generative product design to inventory management. To apply AI successfully, however, individual industrial firms need skilled individuals, the right data, and a foundation of AI engines and frameworks, onsite hardware, and data architectures.

Smart Manufacturing Challenges
Various sectors are successfully implementing computer vision, language processing, robotics, and other technologies in AI applications. However, as the authors point out, big data from manufacturing equipment has a very different structure than that in business and other office-focused sectors. As a result, AI has had mixed success in industrial applications.

Near-Term Opportunities
Among the emerging opportunities for AI use in industrial processes, the authors highlight language recognition for simple tasks, deploying cameras to catalog the environment, and using virtual personal assistants for logistics.

As in other fields, using AI in manufacturing’s digital spaces offer immediate opportunities for improving analysis and optimizing efficiencies. Here, the authors highlight three key opportunities for AI use in industry:

  • Monitoring, including for quality control and supply-chain risk management
  • Optimization, including for fleet management and process planning
  • Control, including for factory automation and smart grids

The authors also highlight other promising AI-solutions for industrial processes, including predictive maintenance, fault detection and isolation, and inventory monitoring. They further note that while many factories have begun using rudimentary robotics, autonomous vehicles, and other forms of factory automation, possibilities for AI to expand the capabilities and roles of these technologies on the factory floor are only beginning to be explored.

Learn More
As the authors point out, not every AI model is intricate. Simple models can be used to harvest low-hanging fruit in various manufacturing areas. The key is to ensure that essential elements—including resiliency, backup systems, and safety protocols—are in place.

To find out more, download “Artificial Intelligence as a Driver of Industrial Competitiveness,” by Parikshit Vasisht, Sudhakar Ranjan, Rahul J. Jadhav, Prasanna R. Rasal, and Nripesh Kumar Nrip, from the IEEE Computer Society’s Digital Library.

AI: A Tool to Drive Industrial Competitiveness
Sign up for IEEE SustainTech Expo Alerts
Privacy Policy