Manufacturers face significant challenges in today’s global market, including shifting customer demands, supply chain disruptions, and the rapid adoption of new technologies, including generative AI. To succeed, manufacturers must balance the need to increase revenue with the need to reduce costs across the value chain, spanning engineering, manufacturing, and supply chains.
Benefits of Generative AI in Product Engineering
Generative AI is transforming product engineering and R&D, enabling manufacturers to realize several benefits, including:
- Cost reduction: Optimizing product designs for cost, sustainability, and manufacturability can reduce product development and production costs.
- Better decision-making: Facilitated through data analysis and scenario simulation, generative AI provides valuable insights for informed decisions that can enhance product development, improve product quality, and better meet customer demands.
- Productivity and skills gap: Helps experienced designers automate tasks they do often, and inexperienced designers to get up to speed quickly and avoid errors with best practice guidance.
- Efficiency: Reduces the time taken by engineers to both search across, and interact with, product data from various sources across the product lifecycle.
- Faster time-to-market: Shorter product development cycles mean products can reach the market faster to capitalize on new opportunities more quickly.
- Innovation: Continuously analyzing product-related data from various sources, customer feedback, and learning from it with generative AI can suggest innovative solutions that might not be more readily apparent.
Establishing a Secure Engineering Data Foundation
Product engineering and R&D involve handling many types and modalities of data, including CAD files, technical specifications, product data and configurations, requirements, and process data. Manufacturers commonly use a range of systems, including PLM, ALM, and ERP systems, to manage this complex data. The following are examples where generative AI is helping to deliver value in a secure, engineering data foundation with AI on the Microsoft Cloud:
- Siemens has integrated Microsoft Teams, Microsoft Azure OpenAI Service, and Siemens’ Teamcenter PLM solution into an app to facilitate real-time communication and collaboration among frontline workers and engineers.
- Aras has introduced AI-assisted search and an intelligent copilot, using Azure OpenAI Service and Microsoft Copilot Studio on Azure, enhancing user interaction with PLM data, facilitating quicker access, analysis, and action on critical information through scalable search and conversational AI.
- PTC Codebeamer Copilot focuses on requirements authoring and analysis for the flagship Codebeamer Application Lifecycle Management (ALM) solution.
Accelerating Product Engineering and R&D
Engineers use a range of complex solutions in product engineering when producing product designs from CAD, CAM, and CAE applications. This also involves creating and using many different data types, from 3D CAD and CAM files, to CAE simulation datasets, documents, specifications, and various knowledge repositories. The following are examples where customers and generative AI-powered partner solutions are helping to deliver value in accelerating product engineering and R&D with AI on the Microsoft Cloud:
- HARTING reduced design time from weeks to minutes by introducing an AI-powered assistant fueled by Azure OpenAI Service and Microsoft Cloud for Manufacturing, interoperating with Siemens NX CAD for rapid design.
- Hexagon AI-powered automated CAM programming solution, ProPlanAI, reduces the time taken to program factory machine tools by 75%.
- Siemens copilot for NX X software uses an adapted industry AI model to help users ask natural language questions, access technical insights, and streamline design tasks for faster product development.
The Next Step: Unlock Innovation in Product Engineering with AI-Powered Digital Threads
The next stage in revolutionizing product engineering and R&D sees the addition of multi-agent AI systems that can orchestrate, collaborate, and scale across complex enterprise workloads, including product engineering solutions, supply chain, manufacturing execution systems, customer relationship management, field service, and enterprise resource planning. The following are several such examples of innovations that are fueling the emergence and promise of AI-powered digital threads:
- Aras InnovatorEdge is a new low-code API management framework for extending product digital thread ecosystems, which will also integrate with Microsoft Fabric, Microsoft 365 Copilot, and Microsoft Cloud for Manufacturing, enabling seamless connectivity for advanced analytics and AI-powered insights.
- Autodesk Fusion connects people, data, and process through the product development lifecycle.
- PTC is collaborating with Microsoft on an enterprise data framework and agentic model for PLM scenarios in PTC Windchill within Microsoft Fabric to accelerate manufacturers digital thread strategies and unlock insights and workflows across the value chain using AI-powered agents.
Conclusion
By using Microsoft Cloud for Manufacturing and AI-powered solutions from our partner ecosystem, manufacturers can securely unlock new levels of impact. The integration of AI-powered solutions and AI agents unlocks innovation, reduces costs and improves operational efficiencies, meaning manufacturers are better equipped to navigate challenges and seize opportunities.
FAQs
Q: What are the benefits of generative AI in product engineering?
A: Generative AI provides benefits such as cost reduction, better decision-making, productivity and skills gap, efficiency, faster time-to-market, and innovation.
Q: How is generative AI transforming product engineering and R&D?
A: Generative AI is transforming product engineering and R&D by enabling manufacturers to realize benefits such as cost reduction, better decision-making, productivity and skills gap, efficiency, faster time-to-market, and innovation.
Q: What are some examples of innovations that are fueling the emergence and promise of AI-powered digital threads?
A: Some examples include Aras InnovatorEdge, Autodesk Fusion, and PTC’s collaboration with Microsoft on an enterprise data framework and agentic model for PLM scenarios in PTC Windchill within Microsoft Fabric.
