Thoughts about Implementing PLM in 2025

I often talk with engineering teams and manufacturing companies about PLM implementation. A common approach I hear about is called “Big Future PLM Planning.”

While it is very exciting to think about “big future”, the problem with this approach is that many companies get stuck between their big plans for a perfect PLM system and the reality of using outdated tools like Excel and old databases.

This gap slows companies down – they have a perfect plan and, at the same time, live in the misserable reality. Here are some of my thoughts about how to make it different. In my view, the new way of thinking is needed. Two aspects of these approach – flexibility and putting data in the center.

The Problem with Traditional PLM Planning

Traditional PLM plans often aim to replace entire systems in one big project. It is ok on the paper, but usually fails in the real life. Modern products are more complex than ever, requiring close collaboration between engineering, manufacturing, supply chain, and support teams. Every company has their “PLM” even if they didn’t implement any software. This status quo of tools, emails, spreadsheets, and process is the reality that is hard to change overnight (and this is wrong)

Therefore, these “big” plans are high-risk. Their “all-or-nothing” nature often leads to stalled projects, wasted money, and unmet expectations. The sad results – companies stuck with inefficient processes and outdated tools, while competitors move ahead with faster, more flexible solutions.

A Better Way: Agile and Data-Focused PLM

I think companies need to rethink PLM implementation. The solution is step-by-step approach that focuses on data. This method builds systems that can adapt and grow with the company’s needs.

A successful PLM system starts with good data planning. A central data system makes it simple to add new tools without disrupting workflows. This ensures everyone works with reliable, consistent data, which improves accuracy across the board. Clean, organized data helps teams make better decisions, leading to more innovation and improved efficiency.

Build in Phases – Minimum Viable Lifecycle Stages

I recommend to break the PLM project into smaller steps reduces risks and ensures steady progress. Which obviously brings the questions – how to identify these “steps”. My recommendation is to think about lifecycle stages as a foundation of these steps. For example – “design phase”, “engineering BOM”, etc. If those lifecycle steps feels too big, make it even smaller and focus on a specific discipline or process (eg. design to manufacturing process).

Focus on high-impact area will allow to maximize the output for the business to achieve significant value early in the process.

Finally, my recommendation always to pick the project, department, or division to start with..

Use Flexible and Cloud-Based Solutions

You organize need systems that can grow and adapt. The time of bulky tools that requires 6 months installation and configuration is over. There is really no any good reason why any step of the implementation will be longer than 2 months.

Modern, flexible, SaaS platforms allow companies to create systems with parts that can be configured and customized for specific needs.

What is important is to think about separation of three layers in your implementations strategy:

• data
• user experience
• analytics

It will help you to easier update or modify the system as requirements evolve.

Will it work?

A great question to start with :). Here are three reasons why it makes sense to me and why it helped to companies I’ve been working with:

• Flexible data-centric thinking helps you to focus on the data that describes your product and lifecycle in a more precise way
• Smaller, manageable steps reduce the risks of failure, you. have a chance to fix your plans
• Solid data foundation helps to use additional tools such as AI and analytics with an ease that impossible with monolithic PLM platforms.
• Focusing on modern data-centric tools will help to combine different services together around the data foundation.

What is my conclusion?

The time is over for monolithic all-in-one systems. We need to re-think the PLM implementations and how tools can be used around the data. The future of PLM implementations isn’t about creating one perfect system all at once. It’s about starting small, focusing on good data, and building a system that can grow and adapt over time.

Instead of aiming for a “perfect” PLM system from day one, focus on creating something flexible and scalable. This way, you can stay competitive, handle challenges, and prepare your company for the future. The key element of this process is to stop focusing being an application centric and, instead, to become a data-centric.

Focus on what data need to be managed first can help to build a more resilient data management and collaborative environment.

Just my thoughts…

Best, Oleg

Disclaimer: I’m the co-founder and CEO of OpenBOM, a digital-thread platform providing cloud-native PDM, PLM, and ERP capabilities. With extensive experience in federated CAD-PDM and PLM architecture, I’m advocates for agile, open product models and cloud technologies in manufacturing. My opinion can be unintentionally biased.