AI automation helps optimize PLM systems with AI, improving product development, data use, automation, and decisions.
Product Lifecycle Management (PLM) systems organize product data, process data, bills of materials (BOMs), and compliance data for manufacturing and engineering companies. There are still roadblocks to optimum PLM system use for most organizations.
AI Integration & Application Enablement offer a flexible solution. Implementation of AI within existing PLM systems can improve product development and help simplify common tasks and build data usage to the desired optimum level within a given PLM system.
This article describes the advantages of AI-integrated PLM systems and how to get started.
What Is AI Integration in PLM?
PLM systems have historically focused on data storage and workflow management. They tend to be rule-based and require a lot of manual data entry.
AI with PLM improves systems with:
- Machine learning
- Predictive analytics
- Generative AI
- Real-time data processing
The integration of these features can allow PLM applications to provide real-time data analysis to teams and support decisions for the entire lifecycle of a product.
AI can extend the capabilities of systems like Siemens, PTC, Aras and Dassault, to name a few.
Why Integrate AI With PLM?
Using AI can provide organizations with a significant improvement in their PLM system investments. These benefits include:
- Increased speed of product development due to AI automation of manual tasks and generative design
- Increased product PLM predictions of quality and potential supply chain issues
- There is a reduction in costs to implement the new AI system as opposed to replacing the prevailing PLM system
- Increased cross-team engagement based on insights
- Reduction or elimination of product PLM system sustainability issues based on AI simulation and design
Engineering teams, along with Manufacturing and IT teams, must address data quality, legacy system integrations, and platform tailoring.
Make Your PLM Smarter.
When Should You Consider AI Integration?
AI integration can be useful when:
- You already have a mature PLM system and want to add intelligent capabilities.
- You want to support applications such as generative design, predictive quality, or smart BOMs.
- Several departments use PLM and need better access to product information.
- Your organization has useful product and operational data that it does not fully use.
- You want to improve your PLM system in stages rather than replace it.
How to Integrate AI Into a PLM System
Further, defining a roadmap can help mitigate risks and create an efficient project structure.
1. Assess Your PLM System
This phase includes evaluation of the flexibility of an existing PLM system as related to data architecture and integrations, as well as the level of customization.
2. Choose the Right Use Cases
Prioritize problems where the application of AI will be most valuable. This is a good opportunity to implement generative design practices, conduct predictive maintenance, manage changes, manage product requirements, incorporate smart BOMs, etc.
3. Select an Integration Approach
This phase is focused on the implementation of an interface. This can include native AI, APIs, middleware, or custom AI.
Also, implementing the integration at a module level will make for a good starting point for a pilot.
4. Start With a Pilot
Conduct a pilot to integrate AI across one PLM module or product line to understand the impact.
5. Connect AI With Existing Workflows
When a pilot is complete, integrate AI with PLM systems, applications, and user interfaces, along with the data pipelines.
6. Scale and Govern
Successful pilots of AI warrant its implementation in other areas. Set rules and parameters around AI while you monitor the performance of AI systems over time.
Common Challenges of AI Integration in PLM
AI can bring clear benefits, but organizations should plan for several challenges.
| Challenge | What It Involves |
|---|---|
| Legacy systems | Integrating AI with Older PLM Architectures and Data Models |
| Data quality | Creating Accurate and Usable Product Data |
| Team adoption | Managing Changes with Engineering, Manufacturing, and IT |
| Integration choices | Considering Built-in versus Custom AI |
Addressing these early along the AI integration path will result in fewer problems during deployment.
Tips for Better AI Integration
Keep the following points in mind:
- Start With AI Features That Are Available in Your PLM System
- AI-Based Product Data
- APIs, Integration Layers, and Other Core PLM Systems
- Use Engineering Oversight for Important Decisions
- Select AI Solutions That Can Support Your Requirements
- Save Engineering Time and Make Changes Quickly
- First Pass Yield
Conclusion
AI Application Enablement is a great way to improve a PLM system. Product development will get faster and better with better decisions leading to better collaboration and better use of the product data.
The best way to get there is with a focused assessment and clear use cases along with a small planned pilot. After the results are published, organizations can expand AI within their PLM systems while keeping oversight and governance.
