With the continuing digitalization of engineering, the requirements for product lifecycle management (PLM) are also evolving. Cloud platforms and AI technologies enable more efficient management, analysis, and cross-location use of product data.

PLM in transition

For many years product lifecycle management (PLM) has been a key factor in the management and organization of product data throughout the entire product lifecycle – from initial concept to design and manufacturing to service and maintenance.

As a rule, PLM systems were predominantly operated as “on-premises” solutions. This means that the software was installed on the company’s own servers, operated by the internal IT department and were responsible for infrastructure, maintenance, updates, and system integration. However, this model is changing with rising digitalization: increasingly providers are turning to cloud-based platforms available on the Internet. One particularly common model is Software as a Service (SaaS).

In the SaaS model, the software is not installed locally but offered as a service using a cloud platform. Users access the system via a web browser or special clients, while the provider manages its operation, maintenance, and updates. This results in several fundamental changes for companies:

  • No need for their own infrastructure to operate the PLM system,
  • Regular updates and new features from the provider,
  • Worldwide access to product data,
  • Simplified collaboration between different locations.

The software can use this data to develop hundreds or even thousands of suggestions. These are sorted according to criteria such as weight, strength, or energy consumption. The design engineer then evaluates the versions and selects the one that best suits the specific project.

At the same time, data-driven methods and AI-based applications are becoming increasingly important in engineering. Modern platforms enable the analysis of large amounts of product and development data, helping to gain new insights. As a result, PLM is transforming from a classic data management system into an integrated platform for engineering data, collaboration, and data-based decision support.

Benefits of cloud PLM for engineering organizations
Cloud-based PLM platforms unleash many new opportunities for companies to organize development processes and utilize product data. The most frequently mentioned benefits include:

  • Improved collaboration between distributed development teams,
  • Scalable IT Infrastructures for large amounts of data,
  • Faster delivery of new features and updates,
  • Simplified integration with other digital systems.

For internationally active companies in particular, cloud platforms can notably facilitate collaboration between different development sites.

As this topic plays a central role in the introduction of modern PLM platforms, the opportunities and advantages of cloud PLM in engineering will be examined in more detail in a subsequent article.

Integration of AI into modern engineering platforms
In addition to the shift of PLM systems to the cloud, the integration of artificial intelligence (AI) is also becoming increasingly important. Engineering companies today have large amounts of product and development data at their disposal. This data is generated, for example, by:

  • CAD models and engineering data
  • Simulations and tests
  • Manufacturing information
  • Change and development documentation

AI technologies reveal new possibilities for systematically evaluating this data and making it usable for development processes. Potential areas of application include:

  • Automatic classification of components
  • Support for the reuse of components
  • Analysis of large engineering data sets
  • Support for generative design methods

In combination with cloud platforms, such functions can be more easily integrated and made available to different teams.

New requirements for engineering organizations
With the increasing use of cloud platforms and AI technologies, requirements for handling engineering data within companies are also adapting. PLM systems often contain particularly valuable company information, including detailed design data, manufacturing information, development strategies, simulation results, and much more. Handling this data requires appropriate organizational and technical frameworks, such as in the areas of data management, system architecture, and engineering data governance.

In addition, the use of AI systems in development processes raises new questions. These topics will be examined in more detail in a subsequent article.

Opportunities for the future
The evolution continues toward hybrid systems that combine simulation, machine learning and real-time data. This reveals new possibilities in diverse areas.

In the future, sensors in prototypes could provide data that is directly incorporated into new calculations, and sustainability metrics such as carbon footprint could be automatically considered, while AI systems could suggest design decisions based on previous projects. This integration will make generative design a key tool in digital product development and Industry 4.0.

Outlook
Cloud-based platforms and AI technologies are changing the way engineering data is managed and used. PLM is increasingly becoming a central digital platform for collaboration in engineering. Companies are faced with the challenge of combining new technological possibilities with existing development processes and creating suitable structures for handling growing amounts of data.

The further development of cloud PLM will therefore be shaped not only by technological innovations but also the ability of companies to meaningfully integrate these technologies into their engineering processes.

The following articles in this series will therefore take a closer look at the practical applications, advantages and challenges of modern PLM platforms.