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Organizational change management in the implementation of Cloud PLM software

The implementation of a Cloud PLM system is far more than a technical upgrade. It is a strategic transformation that significantly shapes your organizational culture. As our Fast Forward approach and customer success stories demonstrate, structured onboarding ensures rapid technical proficiency and efficient system usage.

However, the true lever for sustainable success does not lie in the software alone. Without organizational change management, even the most powerful system risks falling short of its full potential. The goal is not merely to provide a tool, but to actively shape processes and bring people along on the journey.

Integrating Cloud PLM: three key focus areas for change management

A Cloud PLM system centralizes data, standardizes processes, and enables cross-functional collaboration. This is an enormous opportunity, but also a challenge that is far beyond learning new software features.

A robust organizational change management strategy must address the following core aspects:

1. Rethinking and optimizing processes

Every organization has established ways of working. Many of these processes have evolved historically – often optimized within individual departments, but rarely considered holistically across the entire product lifecycle. The introduction of Cloud PLM software offers the ideal opportunity to rethink existing processes.

Identifying inefficiencies: Where do media breaks, duplicate data entry, or manual steps exist that the system could automate?
Standardization: Cloud PLM systems are typically based on best practices. Instead of merely mapping these technically, organizations should critically assess where internal processes can be aligned with system logic to fully benefit from these standards.
Cross-departmental alignment: PLM breaks down silos. This requires redefining responsibilities and interfaces, often involving stakeholders from engineering, procurement, manufacturing, sales, and service.

2. Establishing new ways of working

Adapting to the system involves much more than learning where to click. It requires a fundamental shift in how people work:

Data-centric thinking: Employees must understand that the Cloud PLM system is the single source of truth for product data. This means entering data consistently and responsibly rather than relying on local or informal solutions.
Transparency and collaboration: Cloud PLM software enables transparent workflows across the entire product lifecycle. This requires employees to take a holistic view of their work and be open to collaborating across departments.
Responsibility and ownership: Centralized data brings new responsibilities for data maintenance and quality. Change management helps define and assign these new roles clearly.

3. Cultural change, acceptance, and employee enablement

Every software implementation has a cultural dimension. Fear of the unknown, resistance to change, or skepticism about benefits can jeopardize system adoption. Effective organizational change management must address these factors:

Transparent communication: Why is the system being introduced? What benefits does it bring to individuals and the organization as a whole? These messages must be clear and communicated continuously from the start.
Active involvement: Employees – especially key users – should be actively involved early in the process. This turns them into ambassadors and multipliers for the new system.
Targeted enablement: Beyond technical training, employees need support in navigating the new process landscape and recognizing personal benefits. Creating early success experiences and providing ongoing support are critical.
Leadership as a driver: Management must lead by example, communicate the vision, and provide the necessary resources. Without leadership commitment, sustainable change is unlikely.

This blog post highlights the consequences of insufficient change management and outlines the phases employees typically go through during transformation processes.

Conclusion

The true foundation for organizational adaptation to Cloud PLM – rethinking legacy processes and aligning the organization culturally with the system – must be designed and actively driven by the company itself.

Successful Cloud PLM implementation combines technology with the necessary human adaptability. While our Customer Success Management team supports you throughout onboarding and the long-term operation of your Cloud PLM system, the key to lasting success lies in a well-designed and actively lived organizational change management approach.

Only organizations willing to transform their ways of working and their culture can unlock the full potential of cloud-based PLM.

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.”

Find out in our webcast when SaaS PLM systems are a viable alternative to PDM systems.

Digital transformations & organizational change management

What do climate change, pandemic developments, and statistical risks have in common with the impact of digital transformation on an organization’s people? Short answer: There’s a well-documented human tendency to ignore or underestimate complex or abstract risks. And what does that have to do with organizational change management? I’ll explain it all in this blog post.

A classic example of statistical risks involves the impact of a lack of exercise, nicotine, or alcohol on the increasing risk of cardiovascular diseases. When organizations embark on a digital transformation, they face a staggering 70% risk of failure. So, where’s the connection?

In both scenarios, there are recognized and effective solutions to minimize these statistical risks. When it comes to digital transformation, that solution is the consistent application of organizational change management. It’s not enough to be perfectly set up technically and methodologically. The human factor – the people who are the target of the change – must also be a central focus.

What is organizational change management?

Organizational change management is a systematic approach. It actively manages the human side of change in an organization. This includes a range of processes, technologies, or strategies to support people through change and thus achieve the desired outcome.

“Managers assess that 69% of transformations fail due to insufficient change management.”

– Change Management Compass 2023, Porsche Consulting

Sounds familiar?

In my consulting practice, I often find that anecdotal or psycho-educational examples help people tap into their own experiences, sparking memories of similar situations. I often hear reactions like, “That’s exactly how it is for us too,” or “I can totally relate to a project like that.” That’s the exciting moment when things suddenly click, and connections are seen in a whole new light. Let me give you two examples:

1. Unclear direction

In one instance, the stated vision for a change program was simply: An 8% EBITA increase. However, employees in their day-to-day work couldn’t grasp how their efforts, directly or indirectly, contributed to this vision, which, let’s be honest, was more of a metric target.

They had no idea when or how this would be measured, what would happen next, or what it meant for them personally. They also didn’t know the consequences of either falling short or exceeding the target. Ultimately, this vision was a pretty ineffective way to provide direction.

It’s rarely just one missing piece. More often, it’s a combination of several underestimated or misunderstood situations and actions that, together, negatively impact the outcome far more significantly than any single one would appear to do.

2. Lack of Sponsorship

Understanding is fundamental to the willingness to support a change project. Employees must recognize that this change is important and urgent and that the consequences of ignoring this fact really must be avoided.

Part of this understanding is that the situation naturally commands adequate management attention. Someone needs to be at the helm, actively and with clear commitment, steering the ship around these critical shoals.

Here are the three most common misunderstandings about sponsorship I’ve encountered in projects:

Top management views its sponsor role as something done exclusively behind closed doors, in steering committee meetings. Result: No one sees anyone actually steering.
Responsibility for the change is delegated to a management level that lacks the necessary decision-making authority. Result: Someone is handed a tiny paddle and told to steer a supertanker.
The entire topic of sponsorship simply hasn’t been formally clarified. Result: The steering wheel just spins wildly on its own.

Unclear visions and a lack of sponsorship are just two of the top drivers that can derail change initiatives.

Where skepticism and resistance brew

The human brain automatically fills in missing pieces. When intentions are opaque, information is scarce, and one’s own role in the changing landscape remains unclear, people instinctively fill those gaps with their own interpretations and explanations. These ideas spread like wildfire through the organization’s fastest communication channel – the rumor mill – where they’re vetted for plausibility. This includes things like ideas for better solutions, anxiety about no longer being seen as an expert, fear of losing the security that comes with familiar processes and tools, or the firm belief that “we can just keep doing things the way we always have.” Prominent themes are also appreciation, future prospects, and the fear of job loss.

To cut to the chase: we humans generally don’t like change. In fact, we often go to great lengths to preserve the status quo, or even to restore it if it’s been disrupted.

What role plays organizational change management?

At its core, organizational change management has three main goals:

Accelerating adoption by boosting user acceptance and reducing resistance to new solutions or ways of working.
Maximizing utilization by ensuring people consistently and effectively use new tools, processes, or systems, rather than reverting to old habits.
Optimizing efficiency by empowering users to work productively with new solutions and unlock the full potential of the transformation.

Organizational change management: your path to success

Humans are a critical component of any digital transformation. Organizational Change Management is a critical success factor for any change project, ensuring that technological investments actually deliver their intended value.

And as I’m sure you’ll agree, there are indeed parallels between climate change, pandemic developments, and statistical risks, and the impact of digital transformation on an organization’s people: The more abstract and complex a danger or risk is, the more likely people are to underestimate or even ignore it. To minimize the significant risks posed by human factors in digital transformation, the consistent integration of organizational change management isn’t just helpful – it’s absolutely essential.