Organizations are increasingly turning to physical AI to address pressing challenges such as labor shortages, rising operational costs, and the need for 24/7 availability in supply chains. Physical AI refers to the integration of artificial intelligence into physical systems that interact with the real world. This technology aims to create adaptive supply chains capable of responding to immediate disruptions and delivering efficiency.
As companies explore the feasibility of physical AI, they face questions regarding its practical applications and the return on investment for such deployments. EY analysts Gaurav Malhotra and Dr. Shawn Kimmel emphasized the importance of selecting pilot projects focused on key performance indicators and developing effective governance models and safety protocols.
The Case for Physical AI
Advancements in automation technology, driven by decreasing on-site computing costs and more sophisticated robotics, have made implementing physical AI increasingly viable. These advancements enable organizations to improve flexibility, enhance product quality, and create safer work environments, all while fostering resilient supply chains capable of withstanding disruptions.
Companies are not just integrating physical AI through specific machines; they are creating environments where systems can learn and coordinate both on the factory floor and throughout the enterprise.
While robots have been part of manufacturing for over 50 years, contemporary machines that combine human robotics and AI are relatively new. Manufacturers are using this technology for diverse tasks, including material handling and inventory inspection. The application of digital twins serves as a critical component, allowing organizations to test changes and simulate outputs before actual production.
When implemented responsibly, physical AI can significantly enhance productivity by automating repetitive tasks and improving quality control measures. Organizations experience improved throughput and reduced scrap rates as advanced inspection technologies help identify issues early in production. Increased synchronization of production and material movement also enhances working capital and inventory management.
However, the high costs of implementation and integration challenges with existing systems have limited widespread adoption. Some exemplary use cases of physical AI include:
- Make: Smarter Production
- Flexible assembly cells utilizing collaborative robots for mixed-model production.
- 2D and 3D vision systems for in-line part inspections.
- Autonomous changeovers guided by digital work orders and validated by digital twins.
- Move: Real-Time Logistics
- Adaptive intralogistics using autonomous mobile robots for material delivery.
- Smart knitting technologies that automate component assembly.
- Mobile manipulators enhancing inventory visibility through vision-guided sorting.
- Deliver: Autonomous Fulfillment
- Autonomous yard trucks and AI-driven dock scheduling for improved delivery times.
- Dynamic rerouting strategies to optimize logistics and customer service during disruptions.
To successfully build autonomous operations, supply chain leaders are advised to focus on three key strategies: prioritize high-impact opportunities linked to critical metrics, design systems for scalability, and invest in responsible AI practices, including employee training. By fostering a transparent approach to decision-making and enhancing workforce skills, organizations can leverage the transformative potential of physical AI while achieving sustainable success in an evolving manufacturing landscape.
For more insights, the full perspective on physical AI’s role in adaptive supply chains is available through EY.
