7 PLM Trends to Watch in 2024

Q4 is hear and the run towards the end of 2024 begins. I think, it is a remarkable year for product lifecycle management. There are some many topics that are changing PLM implementations, adoption, business and technology. In my article today, I want to share my perspective on the important trends in PLM software, product lifecycle, and product data management. Product Lifecycle Management (PLM) is undergoing significant transformations, driven by the convergence of new technologies and evolving business needs.

The desire of all businesses to work with up to date information is transformation PLM systems (Note, I use PLM system as a term that unified both PLM software and Business Strategy). The landscape of PLM systems is shifting from traditional methods of document management and controlling product data to more flexible, interconnected, and data-driven approaches. It comes from both side – adotping new technologies in modern PLM software and changing business models and adoption by companies.

Businesses that will follow the trends and adopt new methods in product development process will not only enhance their operational efficiency but also drive innovation and adaptability. It will help to include customer feedback, optimize production process and improve data integration.

Below are the seven most impactful trends reshaping PLM in 2024:

Switch from Documents to Data

The shift from a document-centric to a data-centric approach in PLM is no longer just a trend—it’s becoming a necessity. Historically, PLM systems were focused on managing documents such as CAD files, drawings, and engineering change orders. While companies feel that as an easy adoption of technologies to mimic existing processes with automation and brining electronic forms to the same processes, on the grand schema of things led to data silos, redundancies, disconnected processes, and inefficiencies in decision-making. Data hidden in documents is invisible, document exchange is limited, the intelligence of modern technologies cannot be applied when everything looks like folders and files (even shared across a company).

Today, the focus is on managing data at a granular level. By treating every piece of product information as discrete data, organizations can access, analyze, and act on that data in real time. The transformation is capture data, semantically connect it to other pieces of data and to allow to software to operate and build logic on this level of granularity.

This transformation enables cross-functional teams—from design to manufacturing to supply chain—to collaborate more effectively. Additionally, it allows businesses to automate workflows, integrate advanced analytics, and leverage AI for smarter, faster decisions. Moving away from documents to a unified, data-driven approach creates a more agile environment, reducing bottlenecks and empowering teams to innovate.

Breaking Monolithic Systems with Composable Architectures

The era of inflexible, monolithic PLM systems is giving way to composable architectures, and this trend is accelerating in 2024. Traditionally, companies had to rely on large, all-encompassing PLM systems that were difficult to modify, integrate, or scale. These monolithic solutions often led to vendor lock-in, making it hard for organizations to evolve their systems as their needs changed.

In contrast, composable architectures offer modular building blocks that allow companies to tailor their PLM solutions to their specific needs. With composability, businesses can assemble, disassemble, and reassemble systems using interchangeable components, which enhances adaptability and reduces costs. For instance, companies can integrate best-in-class tools and components for CAD, PLM, MES, ERP, and supply chain management without being constrained by a single platform. This is a switch from the last decade of “integrated platforms” demonstrated their inefficiency and lack of robustness. This modular approach promotes innovation, allowing organizations to experiment with new technologies while maintaining a flexible, future-proof infrastructure. Combined with openness (I will talk about it later), it set a new perspective for modern PLM software adoption.

Enabling the Digital Thread Across the Extended Enterprise

The concept of the Digital Thread—connecting data across every stage of the product lifecycle—is expanding beyond the four walls of individual organizations. In 2024, the focus is on extending this digital thread to the entire ecosystem, including suppliers, partners, and even customers. The foundation of digital thread begins with data (not document), the trend I just talked above. Systems focused on how pieces of information can be connected together across multiple systems.

By weaving a seamless flow of information across the extended enterprise, organizations can create a holistic view of their product lifecycle, from initial design to post-sale support. This interconnectedness improves traceability, reduces lead times, and minimizes errors. For example, when a design change occurs, the impact can be immediately assessed across manufacturing and the supply chain, enabling faster responses and fewer disruptions. Digital thread is a big enabler for holistic impact analysis allowing to engineers to get insight earlier in the process about future supply chain challenges and existing customer issues. As global supply chains become more complex, ensuring that all stakeholders can access real-time, accurate data is crucial to maintaining operational efficiency and reducing risks.

AI-Powered Data Insights in PLM

Artificial Intelligence (AI) is transforming PLM by turning product data into actionable insights. In 2024, AI is being leveraged to not only analyze historical data but also to predict future trends, optimize processes, and identify potential risks. AI-powered predictive analytics, for instance, can help businesses foresee supply chain disruptions, machine failures, or design flaws before they occur.

Moreover, AI is enabling the automation of routine tasks such as data classification, part reuse, and change management, freeing up human talent to focus on innovation and high-value activities. By extracting deeper insights from vast amounts of data, AI is helping organizations make more informed decisions, streamline operations, and accelerate time to market.

I think, we start to see the beginning of AI applications. It is still very early in the process, but the way I see AI adoption is by injecting specific elements of AI tech in existing systems. Two main factors that will help AI to make progress are (1) data availability and (2) demand for decision support.

SaaS and Multi-Tenant PLM Becoming the Standard

In 2024, SaaS-based PLM solutions are separated into two major groups – (1) mainstream PLM platform that “SaaSify themself” by hosting using cloud platforms such as AWS, Azure, GCP and others and (2) new SaaS PLM development. For the second group, multi-tenant architecture is becoming the industry norm. SaaS offers businesses numerous advantages, including lower subscription business model, faster deployment, and seamless updates. Not all multi-tenant architectures are the same either. Therefore learning about more specific technological elements of a solution is important. Share a common infrastructure while maintaining data security and customization improve standardization and adoption either.

This trend is enabling organizations to scale their PLM capabilities rapidly without worrying about the complexities of maintaining on-premise systems. SaaS solutions often come with open APIs following modern technological pattern making it easier for companies to create a cohesive digital ecosystem. For businesses looking to remain agile in an increasingly competitive market, SaaS and multi-tenant architectures provide the flexibility and efficiency needed to respond to change.

Leveraging Graph-Based Data Models

The adoption of graph-based data models in PLM is on the rise, as they offer a more natural way to manage the complex relationships inherent in product data. Graphs are adopted because of two main reasons – (1) expressiveness of graph data models for complex product data and (2) robustness of graph databases to support queries and analytics that is not achievable using traditional SQL RDBMS. Traditional relational databases struggle with the intricacies of product structures, dependencies, and configurations, often leading to data fragmentation. Graph databases excel at handling interconnected data, allowing for more efficient tracking of dependencies, assemblies, and variants.

In 2024, companies are learning how to use graph-based models to manage bills of materials, product configurations, and lifecycle stages in real time. This approach not only improves data visibility but also enhances the ability to query and visualize complex relationships, making it easier for organizations to manage variants, ensure compliance, and optimize designs.

Reusing Standard Components and Services, Open Systems, and Low Code/No Code Customization

Reusability, openness, and customization are at the forefront of PLM innovation in 2024. Companies are recognizing the value of reusing standard components and services to accelerate development cycles, maintain consistency across product lines, and reduce costs. The ability to reuse proven components also leads to higher-quality products and reduces the risk of errors.

Moreover, the trend toward open systems is breaking down barriers between tools and platforms, enabling greater interoperability. Open systems foster collaboration across the supply chain, allowing organizations to integrate third-party tools and services without being restricted by proprietary solutions. This openness is crucial for driving innovation and staying competitive in a rapidly evolving market.

Additionally, the rise of low code/no code platforms is democratizing PLM customization, empowering non-technical users to modify workflows, create dashboards, and develop applications. This increased flexibility not only accelerates time to value but also allows companies to respond more quickly to changing business requirements.

Business Focus

One of the most debated topic I noticed in 2024 is PLM systems and business. A shocking numbers from CIMdata market research shows that only 17% of manufacturing companies participated in their survey cannot operate without PLM. Other 37% think PLM is an engineering software and 27% said, that PLM is too expensive. There is a big value gap and opportunity for everyone in the industry to bring modern solutions to solve business problems.

What is my conclusion?

In my view, the PLM landscape is undergoing profound changes in 2024. From AI-driven insights to the adoption of graph-based models, composable architectures, and open systems, these trends are positioning organizations to innovate faster and collaborate more effectively. By embracing these trends, businesses will be better equipped to navigate the complexities of modern product development, drive operational efficiency, and remain competitive in a dynamic global market.

Therefore, my main conclusion and recommendation to all PLM developers, manufacturing companies developing PLM strategies and adopting PLM technologies is to look for business problem first and how to make the difference. 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.