Category: PLM

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:

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

Ongoing Costs:

  1. Maintenance and support contracts: Annual fees for updates, patches, and technical support typically amount to 15–25% of the original license cost.
  2. IT staff: Dedicated IT personnel are needed for maintenance, troubleshooting, security, backups, and performance optimization.
  3. Hardware maintenance and replacement: Servers and storage must be regularly maintained and replaced.
  4. Energy costs: Running servers and cooling systems generates ongoing electricity costs.
  5. Security measures: Antivirus, firewalls, and regular penetration testing are required for data protection.
  6. 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:

  1. Setup fees (optional): Some providers charge a one-time fee for account setup or initial configuration.
  2. 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.
  3. Integrations: Connecting to existing On-Premises systems may require integration projects with associated costs.
  4. Training: Training is still needed, but intuitive interfaces and online resources can reduce effort.

Ongoing costs:

  1. Subscription fees: The primary cost. Monthly or yearly fees per user usually include software, infrastructure, updates, and basic support.
  2. Scalability: Adding or removing users or storage is flexible and reflected in subscription fees.
  3. Premium support/add-ons: Extra charges may apply for advanced support, additional features, or storage.
  4. Customizations/integrations: Ongoing adjustments or new integrations may incur service fees.
  5. 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.

Standards, security, and AI: The role of PDM systems in the digital industry

A new order from the OEM comes in. But no one knows exactly which drawing in the PDM system is currently valid. Product development wants to introduce an AI application, but the required data is neither complete nor consistent. During the audit, proof of a change is missing, even though the measure was implemented weeks ago.

Situations like these are an everyday reality in many small and mid-sized enterprises. Often, these are merely symptoms whose cause lies deeper: in product data management (PDM). The importance of PDM systems for digital transformation is frequently underestimated, even though they form the foundation for many technologies and processes. This becomes clear when looking at three key goals of digitalization:

1. Competitive advantages through artificial intelligence

AI applications already support engineering today across all phases of the product lifecycle – whether in design, variant management, or manufacturing. Companies can automate processes across disciplines and departments and make decisions based on data-driven insights. However, implementing industrial AI solutions requires a database and structure on which AI algorithms can be trained.

For the use of AI, powerful and scalable PDM solutions are essential. They centralize and version large volumes of product data, such as CAD models, specifications, manufacturing information, and change documentation. This data is structured, prepared, and enriched with metadata in the PDM system. That creates the necessary quality of training data for AI models. Building on this, AI functions can be integrated as needed – for example, for design optimizations, predictive quality assurance, energy management, or variant configuration.

In the field of AI, the demands on PDM are particularly high. Without a powerful system, it is impossible to ensure the quality, consistency, and accessibility of data for AI. Problems such as inconsistencies, missing or unstructured metadata, and inadequate validation mechanisms result in a flawed and unreliable database for AI algorithms. Under such conditions, investments in AI applications fail to deliver results.

2. Meeting external standards

Transparency, consistency, and data integrity are three prerequisites for implementing quality and industry standards. To meet reporting obligations and process requirements, companies must manage all product-related information centrally, versioned, and traceable. This is done in the PDM system, which serves as a single source of truth and provides current, reliable data across organizational boundaries.

How important PDM software is for meeting external requirements is demonstrated by the example of Automotive SPICE (A-SPICE). This internationally recognized standard aims to ensure the quality and safety of electronics and software in vehicles. A-SPICE is designed to enable suppliers to develop safe, error-free software that can be integrated into other vehicle systems. At its core, it is about qualifying suppliers and avoiding risks during development.

The requirements of A-SPICE are particularly challenging for SMEs. Here again, effective product data management is crucial. PDM systems provide a framework that ensures the structure, control, and quality of work results required by A-SPICE throughout the entire development lifecycle. This is supported by functions for centralized data storage and availability, as well as version, change, and configuration management.

Currently, A-SPICE is not mandatory. Nevertheless, many automotive manufacturers use the framework to assess the process competence of their suppliers. Companies that fail to meet the standard risk losing customers.

It is foreseeable that A-SPICE will become a knockout criterion for OEMs. Companies that do not meet the standard will be excluded from the supply chain. This risk also exists with other regulations if product data management is inadequate. Therefore, companies must invest in their PDM.

3. Ensuring IT security

PDM is primarily seen as an administrative task. In product development, however, it is also a key focus of IT security. PDM systems are responsible for managing critical intellectual property – the product data itself. Protecting this sensitive information (CAD models, bills of materials, technical specifications, test results, customer information, etc.) is directly linked to the functions of the PDM system.

Unauthorized access, theft, manipulation, or data loss can be effectively prevented with PDM systems based on highly available architectures. Important modern features include:

  • Access control and authorization (roles and rights),
  • Robust encryption,
  • Multi-factor authentication,
  • Version control and change management,
  • Implementation of backup and recovery strategies, for example in the event of a cyberattack,
  • Audit trails and histories of data access and changes (for traceability in the event of security incidents), and
  • Risk management and compliance.

PDM systems should have no gaps in these areas. Otherwise, they become a security risk. A warning sign is when the software is based on outdated architectures or the vendor discontinues security updates and support. In such cases, companies are forced to isolate the tool in operation, which inevitably creates IT risks and inefficiencies.

Responsibility for data protection and cybersecurity is increasing in almost all industries. While some requirements primarily affect OEMs and tier-1 suppliers, these companies pass verification obligations and security requirements on to their suppliers and partners. As a result, smaller companies must also be able to collect, consolidate, and protect data using appropriate IT solutions.

More about PDM systems

Managing product data is a key focus point in digitalization. Whether standards are met, information is protected, and technologies such as AI have a chance depends on the performance of the PDM solution.

As of today, however, PDM software in many companies is a hidden cost driver. Older, slow, functionally limited systems are often in use, and they offer neither web nor cloud services. Such tools hinder coordination between departments and are a source of errors that can jeopardize entire projects.

Read how to solve this problem in our guide “When the PDM System Becomes a Risk.”