TABLE OF CONTENTS
It’s never been a better time to be an engineer, as PLM tools have evolved more in the past few years than in the past few decades. Product lifecycle management isn’t new, but today’s PLM tools are designed for speed, automation, and modern hardware development.
There’s a generational shift in who builds hardware and how. Engineers are now well-versed in agile software workflows. AI is expected in enterprise tools, not as an add-on. Time-to-productivity is now measured in days, not months. And the world’s best software companies—Google, SpaceX, Amazon, etc., are also among some of the world’s most advanced hardware companies.
Expectations for PLM software have changed. So we put together a list of the PLM tools every engineer should be using today.
What is PLM?
PLM (product life cycle management) has long been utilized in industries such as aerospace and automotive, but has evolved into the digital backbone of today’s hardware companies.
Product lifecycle management centralizes part data, tracks revisions, connects engineering with procurement and manufacturing, and keeps every bill of materials accurate.
In many industries, PLM software now serves as a complete digital thread — aligning product development, sourcing decisions, and time-to-market from start to finish.
How is AI Used in PLM?
AI is a natural fit for PLM because hardware products generate massive amounts of data — CAD models, bill of materials, supplier quotes, compliance reports — often in different formats across different systems.
AI-native PLM can process this information in its original form, validate data in real-time, predict the impact of design changes, and flag supply, cost, or compliance risks before they delay production.
The software continuously learns from product history, understanding past design decisions, supplier issues, and change outcomes, making smarter recommendations over time and reducing rework across future projects.
PLM Tools Engineers Should Be Using
Natural Language Search
What if engineers could search the way they talk and actually get results that help them work faster?
Natural language search in PLM makes that possible. No SQL. No memorized field names. No complex search interfaces. Just clear answers to questions like:
- “Where is part 100-00004 used?”
- “Show me parts added in the last 30 days over $500.”
- “Which assemblies use a discontinued component?”
Powered by AI and tuned for engineering context, natural language search saves time, improves adoption, and gives engineers real answers when they need them most.
Intelligent Part Selection and Sourcing
Engineers can use sourcing intelligence in PLM to choose parts based on cost, availability, lifecycle status, and supply risk — not just function or fit. Instead of searching across multiple distributor websites, real-time supplier data, such as Octopart, is available in a single view.
AI-native PLM improves part selection and sourcing by:
- Providing cost, lead time, and lifecycle status directly in the design environment.
- Normalizing BOM management by detecting duplicate or mismatched parts.
- Standardizing supplier names and sourcing data formats.
- Reducing redesigns and helping teams catch supply risks early in development.
Embedded 3D Viewers
Embedded 3D viewers allow engineers, suppliers, and manufacturers to inspect assemblies and components directly within the PLM platform. Teams can rotate models, analyze fit, and leave feedback — without screenshots or file exports. This significantly speeds up design reviews and improves communication across disciplines and suppliers.
AI-Powered Automation
AI in PLM is not limited to search alone. It actively predicts the impact of changes, flags missing data, and validates key fields before designs move forward. Part numbering errors, missing suppliers, outdated revisions, and approval requirements are caught automatically. Engineers move faster with fewer mistakes, and change orders get approved instantly because they’re correct the first time. No need for time-consuming redos.
Low-Code / YAML Configuration
PLM systems should let engineers configure workflows, fields, and validation rules without waiting on IT or expensive consultants. With low-code tools or YAML-based editors, teams can adjust change approval steps, lifecycle states, and required data fields. AI-generated validation logic lets engineers write rules in plain language —“Block release if supplier field is empty”— while the system converts it into functional configuration.
GitHub-Inspired Change Management
The best PLM tools manage hardware changes with the same traceability that software teams expect from GitHub. They allow teams to:
- Track every revision to parts, drawings, and BOMs.
- See who changed what and why for full accountability.
- Route engineering change orders through built-in approval workflows.
- Notify reviewers automatically in Slack, JIRA, or email.
- Get change orders approved the first time, instead of being rejected for missing data.
These Improved workflows create accountability, keep design releases moving, and turn your bill of materials into the foundation of a working digital thread.
Integrations with CAD, MES, ERP, and RM
A PLM is only as powerful as the systems it connects to. Your PLM should integrate naturally with:
- CAD tools like Altium, SolidWorks, Siemens NX, and Onshape — allowing engineers to release parts and BOMs into PLM without leaving their design environment.
- MES (Manufacturing Execution Systems), such as First Resonance, Boltline, and Tulip, manage shop-floor production and ensure that manufacturing always builds from the latest approved revision.
- ERP (Enterprise Resource Planning) platforms like NetSuite — where procurement, inventory, and finance rely on accurate part, cost, and supplier data synced from PLM.
- RM (Requirements Management) software like Requirements Portal (prev. Valispace) and Flow — ensuring hardware designs stay aligned with engineering requirements and compliance standards.
These integrations eliminate manual uploads, prevent version mismatches, and maintain a continuous digital thread from design to production and procurement.
PDM (Product Data Management)
PDM software is often confused with PLM or treated as a separate system, but it should be an integral part of your PLM strategy. With PDM embedded organically in a PLM, engineers can manage revisions, protect CAD files, and collaborate on designs without leaving their preferred tools.
Product data management (PDM software) keeps design data connected to sourcing, bill of materials, and change approvals while protecting intellectual property through secure or ITAR-compliant cloud storage.
Key PDM features engineers should look for:
- Version control with revision tracking and file protection.
- Check-in / check-out control to prevent overwrites and show who is editing a file.
- Access controls to manage who can view, edit, or release files.
- Searchable storage for quick retrieval of drawings and specifications.
- Audit trails tracking every file change and approval.
- Secure cloud or ITAR-compliant storage to protect IP and meet export-controlled requirements.
What is the Best PLM for Engineers?
There are many PLM systems on the market, but very few are built for how engineers actually work today. The best PLM software should:
- Be actually built for speed, not retrofitted with AI later.
- Include PDM as part of the platform, not as a separate system.
- Integrate directly with leading CAD, ERP, MES, and RM tools.
- Support AI-powered search, sourcing, and automation.
- Offer GitHub-style change control with fast, trackable approvals.
- Allow engineers to configure workflows using YAML or low/no-code tools.
- Maintain an actual working digital thread from design to manufacturing.
Engineers don’t need another system to manage; they need a PLM to design faster, reduce rework, make better sourcing decisions, and get change orders approved the first time. That is the new standard for product lifecycle management.
Duro is the first AI-native PLM built this way, combining integrated PDM, CAD connectivity, sourcing intelligence, and a true digital thread in a single platform.
Here’s why we rebuilt the platform for modern engineering teams:
