Data migration to Cloud PLM systems

Challenges and best practices for successful data migration 

More and more companies are adopting cloud-based PLM systems to streamline their product development processes. Whether they are already using an on-premises PLM system and want to switch to a cloud solution or implementing a Cloud PLM system for the first time, one of the biggest challenges is the smooth and secure data migration.
How can this data be reliably transferred to the new system? In this blog post, we examine the challenges and best practices for successful data migration to Cloud PLM systems and offer tips on ensuring a smooth transition without data loss.

What challenges arise during data migration to Cloud PLM systems?

Migrating data to Cloud PLM systems, obstacles can present hurdles that complicate and delay the entire process:

  1. Data quality and consistency
    Legacy data is often incomplete or inconsistent. Missing attributes, invalid values, or duplicate records can hinder the migration process. Particularly with CAD models, missing files or broken references may prevent models from being imported completely
  2. Data scope and complexity
    Depending on the scope and complexity of the data being transferred, the migration process can be very time-consuming. Large datasets, such as entire version histories of CAD data or multi-level BOMs, require significant computing resources and can slow down the migration.
  3. Structural differences between systems
    Data structures in the new Cloud PLM system may differ from those in your legacy system. Attributes, data fields, or relationships between records may be organized differently, requiring data transformation or restructuring before import.
  4. Technical challenges
    Migrating data to a Cloud system brings specific technical issues. For example, along with ensuring file format compatibility, sufficient network bandwidth and data transfer rates must be guaranteed.
  5. Security and compliance requirements
    Strict security and compliance regulations must be followed when transferring sensitive data to the Cloud. Data must be encrypted during transport and storage, and data protection laws such as GDPR must be adhered to.

What key questions should you address before data migration?

Data migration is often underestimated, although it is one of the most critical tasks before a new PLM system goes live. You should address several key questions early to import your legacy data successfully.

First, determine which data objects will be transferred to the new system: Are you migrating CAD assemblies, parts and BOMs, office documents, or projects? It’s also essential to define the scope of the data: Do you want to migrate data from a specific project, a product, a specific company location, or the entire data archive?

You should also decide how much historical data you want to migrate. Do you want to transfer only the latest version or all versions, including the complete audit trail and engineering changes? These aspects are crucial as they influence the scope and complexity of the migration.

You should also carefully examine the content of the data itself. Consider whether all attribute values and CAD parameters are needed or if it’s sufficient to import only some of them. This is important to define which data should be stored in which objects and attributes in the target PLM system.

What makes data transfer with CIM Database Cloud so simple?

  1. User-friendly import tools
    The cloud-based PLM system CIM Database Cloud offers powerful, easy-to-use import tools specifically designed to simplify the migration process. They allow you a quick and efficient import of configuration data such as field selection values (e.g., dropdown fields), as well as PLM data such as CAD documents, parts, BOMs, office documents, projects, and requirement specifications.
  2. Support for various file formats
    CIM Database Cloud supports a wide range of file formats and data sources, making it easy to  import different data objects. These include Excel files, CAD formats, and the ReqIF format for requirement specifications.
  3. Automated validation processes
    CIM Database Cloud includes built-in validation mechanisms that help identify and correct potential errors during the import process. These functions automatically check whether the data is complete and consistent during import, contributing to high data quality.
  4. Iterative Migration Approach
    The platform supports an iterative migration approach, allowing you to import and test data step by step. This helps identify and resolve potential issues early on, without affecting the migration process. This approach reduces the risk of errors and accelerates data migration.
  5. Comprehensive Documentation and Support
    Alongside the migration process, CIM Database Cloud offers extensive documentation and tutorials. These contain clear instructions and examples on how to import and configure different data types. Additionally, customer success managers are available to assist if needed.

Conclusion

Data migration to cloud-based PLM systems is often fraught with many challenges. Successful data migration, therefore, requires careful planning, considering aspects such as data quality, scope, structural differences, and security requirements.
CIM Database Cloud enables you to efficiently migrate your PLM data and make your product development processes future-proof . With user-friendly import tools, support for various data formats, automated validation processes, and comprehensive documentation, companies can ensure the seamless and secure integration of their existing data. An iterative migration approach, combined with extensive preparation, minimizes risks and guarantees a smooth transition to the new system.

Scope 3 emissions: A challenge for companies

Reducing greenhouse gas (GHG) emissions is crucial in the fight against climate change. Many companies face the challenge that indirect emissions in their value chain, so-called Scope 3 emissions, are often the largest contributors. Since these emissions fall outside the direct control of the company, they are usually the most difficult to determine (and optimize). How can companies address these central challenges within their value chains?

What are Scope 1, 2, and 3 emissions?

The Greenhouse Gas (GHG) Protocol classifies emissions into three categories: Scope 1 for direct emissions from company-owned sources, Scope 2 for indirect emissions from purchased energy, and Scope 3 for all other indirect emissions, including those from upstream and downstream processes within the value chain. Scope 3 is particularly important because it often accounts for the majority of GHG emissions. The GHG Protocol defines 15 categories of Scope 3 emissions that arise from both upstream and downstream activities. These include raw material extraction, production and transportation of purchased components, and the use of the manufactured products by end consumers. These emissions are difficult to capture as they are not directly under the company’s control.

Corporate Carbon Footprint (CCF) vs. Product Carbon Footprint (PCF)

There are two central approaches to calculating emissions: the Corporate Carbon Footprint (CCF), which encompasses all activities of a company, and the Product Carbon Footprint (PCF), which focuses on the lifecycle of a specific product. The PCF is particularly important when it comes to determining emissions along the value chain. Companies that aim to measure their Scope 3 emissions also need data from their suppliers regarding the PCF of the components they purchase.

Why is measuring Scope 3 emissions important?

Companies can directly influence and therefore more easily calculate Scope 1 and Scope 2 emissions. However, Scope 3 emissions should not be overlooked when aiming to assess the entire value chain. Since emissions from upstream and downstream processes often are the largest sources of GHGs, this is the only way to identify and reduce “hotspots” within the value chain.

For many SMEs, significant emissions lie in the upstream processes. However, this is also particularly relevant for industries that rely on complex and globally distributed supply chains. The automotive industry, for instance, depends heavily on purchased components and services, which significantly impact the GHG balance. According to the study “Climate-Friendly Production in the Automotive Industry” by the Öko-Institut e.V., an average of 74.8% of Scope 3 emissions occur during the usage phase, while in-house production (Scope 1 and 2 emissions) only accounts for about 1.9%, and 18.6% originate from the upstream value chain with purchased components. As the industry focuses more and more on e-mobility, the Scope 3 emissions of purchased components – and thus those from suppliers – come into sharper focus as a key lever.

Challenges in the supply chain

The pressure on suppliers to make their production more efficient and sustainable is growing, along with the need for transparency regarding the emissions of the supplied parts. Key challenges in the supply chain include data quality and availability. To tackle this and reduce greenhouse gas emissions, companies need to break new ground, ranging from material selection to production methods. A solid data foundation supports these necessary decisions, as well as the accurate documentation of emissions.
Capturing Scope 1 and Scope 2 emissions is already mandatory under the GHG Protocol Corporate Standard, while Scope 3 reporting is currently optional. However, the importance of Scope 3 reporting is increasing, as demonstrated by EU regulations like the Corporate Sustainability Reporting Directive (CSRD) and the associated European Standards (ESRS). These regulations emphasize the disclosure of emissions as a central aspect of climate action and sustainable business practices.

Three key steps to reduce Scope 3 emissions

  1. Optimize data management: Companies should collect comprehensive data on their products and their lifecycles to make design and portfolio decisions in favor of sustainability.
  2. Ensure data sovereignty and trust: Accurate calculation of Scope 3 emissions requires control over data, particularly in the context of the upstream and downstream value chains.
  3. Use open interfaces: Open data interfaces are essential for seamless integration and communication within the value chain. Approaches like the Asset Administration Shell (AAS) and concepts such as the Digital Product Passport (DPP) can provide valuable support.

Conclusion

Measuring and optimizing Scope 3 emissions is one of the greatest challenges for companies seeking to improve their GHG balance. By leveraging better data, optimizing collaboration within the supply chain, and ensuring transparent reporting, companies can meet regulatory requirements and make progress toward a more sustainable future.

Read a more detailed article on Scope 3 emissions on the CONTACT Research Blog.

Five project management issues that software must solve in the industry

What can software accomplish in engineering projects? What typical project management problems do digital tools have to solve in industry? Which functions are required for this? And how can small and medium-sized companies manage digitalization in this area with relatively few resources? 

Project management software becomes essential

The requirements for product development are continuously increasing. This is driven by the growing share of electronics and software components, the high number of product variants, as well as new laws and compliance regulations. All of this inevitably impacts the complexity of engineering projects. Therefore, project management issues, particularly those affecting small and medium-sized enterprises (SMEs), can no longer be resolved without digital support.

Which problems must software solve in engineering projects?

Digital tools for project management are abundant: from Excel spreadsheets and SharePoint to dedicated software solutions. However, SMEs in the industry often encounter limitations with these tools. This becomes apparent when examining five common problems that suppliers face in project management:

1. Lack of transparency

It is crucial for the success of industrial projects that everyone involved has access to up-to-date project and product data at any time. Such a data foundation requires software that can be connected to all data sources in the development organization (ERP, CAD, CAx, etc.) via interfaces. Project management tools without integrated data management cannot achieve this.

This leads to various problems. Project participants often work with isolated solutions that are used only in specific departments, locations, or companies. The resulting lack of interfaces forces team members to exchange information manually, for example, through emails or SharePoint.

This approach generates a lot of work. Data must be continually updated, which distracts employees from more important tasks. Additionally, they need to regularly exchange information about the current status of the data. It is also common for data to get lost among the multitude of emails so that it has to be requested manually.

All of this creates delays that negatively impact project progress, costs, productivity, and customer satisfaction.

2. Significant risk of manual errors

If project teams do not have access to a consistent data foundation, the risk of misunderstandings during data exchange increases. When product and project data have to be entered manually into documents or spreadsheets, it is only a matter of time before transfer errors occur.

In the industry, such errors can have serious consequences. For instance, a transposition error made by an engineer can affect ordering processes in procurement. Similarly, the design team might develop their designs based on incorrect or unclear product data.

Such mistakes are not just annoying. They jeopardize the success of the project, drive up project costs, and damage the company’s reputation with customers and partners.

3. Limited flexibility for spontaneous changes

In development projects, it is not uncommon for customers to spontaneously change the requirements for a product. This often creates chaos, especially in small and medium-sized companies. To implement these changes, close coordination with the customer’s interdisciplinary teams is essential. Furthermore, suppliers must optimally time their own supply chains and implement changes as quickly as possible.

Both skills require flexible processes and clean data handling. Project managers must be able to plan and implement necessary processes at short notice, even after the project has started. In addition, data statuses must be precisely documented and reconciled between the departments and companies involved.

Otherwise, changes become expensive and time-consuming. For example, today only a few suppliers still use prototype tools. Instead, due to tight project timelines, they immediately move to steel and iron. This approach is faster but creates significant difficulties in the event of changes, which could be avoided through greater flexibility.

4. Experience-based knowledge is not used systematically

Insights gained from a customer project are rarely incorporated into the planning and execution of subsequent orders. This often impacts costs. For example, factors that caused delays in working with customers or partners are not considered when preparing the next quote. As a result, expensive mistakes or misunderstandings frequently occur.

When companies do not systematically document insights and lessons learned – which is not possible with every project management tool – the dependency on individual employees increases. For instance, during the COVID-19 pandemic, many experts left their companies due to poor order volumes. This loss of knowledge is particularly difficult for smaller businesses to compensate for in times of skilled labor shortages. In many companies, it still has an impact today.

5. New requirements due to laws and regulations

New laws and regulations bring numerous additional requirements for traditional industries. Many of them are aimed primarily at large companies. These, however, have to pass on specific requirements to their suppliers and partners.

Processes that companies have successfully used for decades no longer work under these circumstances. At the same time, suppliers lack the know-how and resources to adapt to the new conditions. For instance, customers are increasingly demanding that suppliers provide the carbon footprint of the parts they produce. This requires suitable processes and IT systems that companies must integrate alongside their day-to-day operations.

Laws and compliance requirements will continue to change the industry in the coming years. Those who ignore this trend risk losing their customers and, in the worst case, even face legal consequences.

What if companies ignore common mistakes in project management?

Project management software that is designed to meet the requirements of the industry must be able to solve these problems. If it doesn’t, project planning, management, and controlling will suffer, as will collaboration between project teams and external stakeholders. The results are

  • financial losses,
  • waste of resources,
  • delays,
  • unsatisfied customers,
  • loss of reputation,
  • frustrated employees,
  • internal tensions within the project team, and (in worst-case scenarios)
  • legal consequences.

Outlook

Many of the problems described in this article will become more pressing in the next few years. This is due to new laws and compliance requirements, but also the increasing use of new technologies. To address these issues, effective communication, efficient data exchange, integration of project and product data, and a high degree of standardization are essential. For this, you need software that not only offers project management functions but also includes PLM (Product Lifecycle Management) capabilities.

Our software CIM Database Cloud is such a solution. It is a powerful PLM tool that helps companies with product development as well as project management. The project management functions enable you to link schedules, tasks, and documents and support project managers in completing projects on time and budget. The cloud solution also makes it easier to connect globally distributed locations and implement laws and regulations.