PLM – CONTACT Software Blog

Why AI engineering needs PLM

Artificial Intelligence (AI) is already an integral part of modern engineering. Especially when you‘re dealing with the development of complex machinery and its major challenges. For example, long product lifecycles, safety, compliance, and service traceability, decades of legacy product knowledge and multi-discipline engineering (mechanical, electrical, software, controls). In these areas, AI can help to reduce complexity, and thus, increase development speed and time-to-market.

Tools like ChatGPT and GitHub Copilot support many areas across a wide range of tasks. While these applications are easy to use, successful AI engineering is far more complex. This is due to intricate processes as well as a frequent lack of data quality, quantity, and infrastructure.

Here, PLM systems come into play.

PLM software not only provides a platform for managing product data. It’s an important interface for implementing AI-powered technologies across various engineering phases.

How to implement industrial AI in engineering?

Model training and data provisioning remain key hurdles for successful industrial AI.

Ready-to-use AI applications like ChatGPT and DALL-E are available for general purposes, such as text processing or image generation. However, there is a lack of instantly usable applications or models for complex industrial issues. Publicly available datasets rarely meet specific industrial requirements. As a result, you need to fine-tune AI models and build company-specific datasets and custom models.

In addition, deploying AI models in industrial environments presents further complexities. This includes

the integration with existing systems,
access security and authorization when managing the underlying data as well as
ensuring reliability and performance.

PLM systems provide the infrastructure to collect, structure, and process data. They facilitate AI model integration into existing processes and offer a central platform for managing AI solutions throughout the entire product lifecycle. This includes ensuring data quality, version control, and managing the generated outputs.

By connecting AI tools and PLM systems, your company ensures a consistently structured use of data. Thus, you built the foundation for successful AI applications.

A second major challenge lies in the complexity of AI implementation itself. This ranges from the selection of suitable models and algorithms to the adaptation of these models to the specific requirements of your company.

To make AI engineering work, IT, engineering, and data science must collaborate closely. PLM systems facilitate this by providing a unified platform that bridges these disciplines and drives teamwork.

PLM systems as enablers for AI

PLM systems with deep integration capabilities create the necessary conditions for incorporating AI into existing development processes. As central data hubs, they enable access to data from various sources in the product lifecycle.

By providing a consolidated data foundation, PLM systems clear the path for generative AI (GenAI) technologies. These advanced solutions independently generate fresh, creative content, from text to images, offering companies unprecedented opportunities, particularly within design and product development.

By facilitating the integration of AI-powered design tools, PLM systems ensure efficient management and versioning of AI-generated designs. For instance, AI-powered tools can automatically generate design variants of CAD models. They can also optimize these designs based on simulation results.

Connecting AI solutions to existing data structures is another critical aspect. By integrating AI into PLM systems, AI-powered design tools can be linked directly to your product data. This accelerates the entire development process. This improves component reuse, enhances collaboration between teams, and continuously optimizes product development.

Furthermore, PLM systems capture AI-generated insights and optimizations in a central repository, making them available for future use. As a result, you can significantly increase your capacity to innovate.

AI-based applications in engineering

The interaction between GenAI and PLM systems enables the integration of AI-driven design processes into existing workflows. This unlocks a wide range of applications:

Automated design variants

AI creates new design variations based on existing CAD data and optimizes them for parameters such as cost, material usage, or production options. This gives your engineers more time for creative design tasks.

Simulation-driven optimization

The synergy between simulation tools and GenAI algorithms enables a continuous feedback loop, allowing design concepts to be iteratively refined and optimized. This helps you to identify the best design choices based on comprehensive data analysis. It reduces the number of physical prototypes and saves both time and costs.

Optimized product development

AI dynamically adjusts designs to new requirements during the ongoing development process. In connection with the PLM system, your company can react faster to changes and adapt product development without delays. This offers significant benefits.

Use cases for AI in engineering.

Time-series forecasting

GenAI analyzes time-series data to predict future developments. By leveraging historical and operational data, PLM systems combined with AI help you to identify trends at an early stage and make informed decisions.

Data management and deployment

Integrating AI into industrial engineering is not only a technical challenge but also an organizational one.

Successful AI deployment requires suitable data management. PLM systems provide a platform to manage data in a structured manner – from collection and storage to provision for AI models. Data protection, security, and availability must be guaranteed.

In addition to data processing, AI deployment – the availability in the production environment – is also crucial. To integrate AI, your IT and engineering teams must work together. PLM systems make this process secure and efficient, while ensuring employees can easily use AI in their daily work.

Therefore, PLM systems must support the development and integration of AI models as well as ensure their operation and maintenance in day-to-day business.

PLM systems also serve as a platform to monitor, update, and improve AI models. By using operational data, these models can easily adapt to changes, ensuring consistently high product quality.

Trends and perspectives

PLM systems will increasingly serve as central platforms for managing and integrating AI solutions. AI-powered automation will become more prevalent, as the ability to learn from data and recognize complex relationships is constantly improving.

Another trend is the growing integration of AI in collaboration across different departments and companies. PLM systems will develop into platforms that support both internal processes and seamless cooperation across company boundaries. This leads to more efficient supply chains and accelerated innovation.

Additionally, explainable and transparent AI models are becoming increasingly important to strengthen trust in AI and increase acceptance in safety-critical areas of engineering.

An AI assistant integrated into the PLM solution CONTACT Elements.

An AI assistant integrated into our PLM solution CONTACT Elements.

Success factors for AI in engineering

Data availability and quality

High-quality data and its availability form the basis for the success of AI applications. PLM systems ensure that the required data is available in the appropriate quality through consistent data collection and management. This allows models to train on a solid database and deliver precise and reliable results.

Seamless process integration

To exploit the full added value of AI technologies, you must integrate them into existing workflows. PLM systems integrate AI-powered applications into existing processes and simplify their use. As a result, AI solutions can be implemented into existing systems without major adjustments and put into operation more quickly to deliver relevant results immediately.

Training and change management

Companies must prepare their employees specifically for the use of the new tools by providing technical training and conveying an understanding of their use in the respective work context. Well-planned change management promotes acceptance and actively involves all employees in the change process.

Management support

Support from management is crucial for the successful introduction of AI in engineering. Communicating clear goals and strategies for the AI transformation is just as important as providing the necessary resources. Meanwhile, management must foster a culture of change.

Conclusion

The integration of AI is revolutionizing engineering. PLM systems play a key role as the central hub for data. They create the necessary infrastructure to integrate AI applications into existing processes and use them efficiently. This enables you to optimize your product development processes and exploit the full potential of AI.

The path to success lies in the sensible use of data, the integration of AI models into the existing IT landscape, and the involvement of employees. Only those who tackle these challenges in a targeted manner will be able to shape the AI transformation in engineering sustainably.

A strategic approach is key. Companies should focus equally on technology and people. This is how they sustainably unlock the full potential of artificial intelligence in engineering.

Webcast: Accelerate complex machinery development

Ready for real results? If your team’s AI projects are still struggling with messy data or adding unwanted complexity to your product development, it’s time for a reality check. Join our upcoming Industry Talk to discover what it takes to make AI deliver reliable value for your engineering team.

What you will learn:

The Pitfalls: Why most AI initiatives fall short in complex product data environments.

The Foundation: The non-negotiable foundation needed to deliver truly reliable AI insights.

The Integration: Practical strategies to seamlessly embed AI within your existing engineering workflow.

When: June 25th, 11:00–11:45 am EST | 5:00–5:45 pm CEST

Cost comparison: Cloud PLM vs. On-Premises PLM

When deciding whether to upgrade your Product Lifecycle Management (PLM) system or implement one for the first time, the benefits are clear: more efficient processes, faster time-to-market, and improved collaboration. However, once you discuss features and use cases, another critical question arises: what does it actually cost, and which option is more cost-effective in the long run?

Choosing between Cloud PLM and traditional On-Premises PLM is often a balancing act between initial investment and ongoing operational costs. This article examines the costs of Cloud PLM vs. On-Premises PLM.

The traditional approach: On-Premises PLM

On-Premises PLM refers to purchasing and running the software on your own IT infrastructure. While it requires a high upfront investment, it gives you complete control over your system.

Upfront costs:

Software licenses: Typically, the highest cost. You buy perpetual licenses granting unlimited use. Prices vary depending on vendor, functionality, and number of users.
Hardware infrastructure: Powerful servers, storage, network components, and backup systems are necessary and must scale for future growth.
Facility costs: Server rooms with proper cooling, power supply, and security systems are essential.
Implementation and customization: Installation, configuration, data migration, and process customization often require external consultants and internal resources.
Training: Comprehensive training for administrators and end-users is essential to maximize system efficiency.

Ongoing Costs:

Maintenance and support contracts: Annual fees for updates, patches, and technical support typically amount to 15–25% of the original license cost.
IT staff: Dedicated IT personnel are needed for maintenance, troubleshooting, security, backups, and performance optimization.
Hardware maintenance and replacement: Servers and storage must be regularly maintained and replaced.
Energy costs: Running servers and cooling systems generates ongoing electricity costs.
Security measures: Antivirus, firewalls, and regular penetration testing are required for data protection.
Upgrades: Major version upgrades can be as complex as re-implementation, often involving adjustments, intensive testing, and retraining.

The flexible option: Cloud PLM

Cloud PLM (often offered as Software-as-a-Service, SaaS) provides the software and infrastructure via a third-party provider, usually requiring lower upfront costs.

Upfront costs:

Setup fees (optional): Some providers charge a one-time fee for account setup or initial configuration.
Implementation and customization: Configuration, data migration, and process-specific adjustments are necessary but often less extensive than On-Premises due to prebuilt infrastructure and standardized workflows.
Integrations: Connecting to existing On-Premises systems may require integration projects with associated costs.
Training: Training is still needed, but intuitive interfaces and online resources can reduce effort.

Ongoing costs:

Subscription fees: The primary cost. Monthly or yearly fees per user usually include software, infrastructure, updates, and basic support.
Scalability: Adding or removing users or storage is flexible and reflected in subscription fees.
Premium support/add-ons: Extra charges may apply for advanced support, additional features, or storage.
Customizations/integrations: Ongoing adjustments or new integrations may incur service fees.
Internet access: Reliable, high-speed internet is essential and should be factored in, even if already available.

Direct Cost Comparison

Evaluating total costs over 5–10 years is crucial for an informed decision.

Conclusion

The optimal solution depends on your company’s specific needs:

Small and medium-sized businesses (SMBs): Cloud PLM is often more cost-effective, offering low upfront costs, predictable monthly fees, and reduced IT workload. Tools like CONTACT Software’s Cloud PLM system provide scalable, flexible solutions and even free trials to experience cloud-based PLM firsthand.
Large enterprises or companies with complex requirements: On-Premises PLM may remain preferable, offering maximum control over data and systems if sufficient IT resources are available. High upfront costs can be justified over long-term usage.

Speeding up Product Carbon Footprint calculation with data ecosystems

Companies face the challenge of designing sustainable products and processes and reducing emissions across their entire lifecycle. The Product Carbon Footprint (PCF) captures all greenhouse gas emissions generated throughout a product’s lifecycle. While internal emissions data is often available, companies need to track and consolidate data across a product’s entire supply chain to determine the PCF. Requesting and collecting this information individually from every single supplier is hardly feasible. This is where sovereign data ecosystems like Catena-X and Manufacturing-X come into play. They enable easier, more controlled data exchange across company boundaries.

External data in PCF calculation

Product Lifecycle Management (PLM) systems already manage much of the data needed to determine the PCF. They contain information on products, variants, and bills of materials. However, many emissions originate earlier in the upstream value chain, for instance, during raw material extraction or through production and transportation processes. Requesting and maintaining this data is complex and is currently done using document-based templates, Excel spreadsheets, or specialized web portals. Data is shared on demand.

For suppliers, the approach with customer-specific portals and templates simply doesn’t scale. Requested data fields lack standardization, while input data, formats, and calculation methods often don’t align. This creates immense overhead for everyone involved: data is manually compiled, entered, and verified, increasing the risk of transfer errors.

Data ecosystems as an alternative

Data ecosystems such as Gaia-X and Catena-X counteract these data silos and simplify sharing across the entire supply chain. Instead of individually requesting necessary data and uploading it to various platforms for each customer, companies provide it in standardized data formats. If a participant in the ecosystem needs this data, they simply access it through defined protocols. Control remains with the data provider. Each participant decides for themselves which data they make available, with whom they share it, and for what purposes it can be used.

The foundation is a connector based on Eclipse Dataspace Components (EDC). Each participant uses their EDC connector to manage the data and conditions under which they wish to participate in the ecosystem. The connector compiles these into a searchable catalog. If another company wants to access the data, the two EDCs automatically negotiate the terms and conditions governing the data exchange. Only with such a legally binding agreement does the other participant gain access to the data. This way, every participant retains full control over their data.

PCF calculation within data ecosystems

PLM systems are the ideal starting point for PCF calculations. Bills of materials and work plans form the basis for capturing internal emissions. Data ecosystems now enable companies to integrate data from external partners and suppliers into their calculation. For purchased parts, not only suppliers but also their digital identities are managed within the data space. This makes it possible to search for and import PCF values for external items directly from the supplier’s EDC.

Once a product’s PCF calculation is complete, the results can be made available within the data ecosystem for further use along the value chain. Each company thus individually determines with whom and under what conditions it shares the data. The relevant data set is then available in its own EDC catalog, without the need for Excel spreadsheets and web portals.

Why PLM systems are the natural integration point

This entire workflow must take place where product data is already managed: PLM systems manage bills of materials, supplier relationships, and engineering workflows. They are the single source of truth for product information throughout the lifecycle.

PLM systems like CIM Database PLM are the ideal starting point for PCF calculations, as this is where products, bills of materials, and materials are managed.

CIM Database PLM manages all relevant data needed to exchange meaningful PCF values, including calculation methods and data quality statements.

Existing data is easily made available within the data space. The provider individually controls who can access the data and for what purposes.

Fully exploiting the potential of data ecosystems requires PLM systems with open standard interfaces. Engineering workflows now also govern how internal and external PCF data is integrated. The supplier database now includes identities within the data space, and audit trails capture external data exchanges in addition to internal changes.

Today, value creation largely stems from the ability to quickly and reliably exchange product data across the supply chain. Only deep integration between internal PLM systems and external data ecosystems can generate the necessary efficiency and build trust, both internally and externally.