The rapid evolution of technology and data management is reshaping industries. We can see how far data driven technologies are moving and scale of systems capable to operate global in real time with a amazing level of intelligence and sophistication. Yet many manufacturing companies are struggling to fully capitalize on the opportunities at hand.
In the recent LinkedIn post, Prof. Martin Eigner, highlighted that “the digitalization of products, systems, services, and engineering processes requires a shift towards integrative, interdisciplinary solutions, moving away from siloed approaches. Interdisciplinary system architectures are known but only partially implemented and resistance to technical and organizational innovations hinders progress”
Data In Manufacturing Organizations – The Reality and Promises
Here are some information I captured from reports about how data is used in manufacturing companies – Manufacturing in 2030: The Opportunity and Challenge of Manufacturing Data, Data As The New Capital In Manufacturing, EY: Is your digital strategy fit for the manufacturing future?
The use of data in manufacturing is both promising and concerning. Despite the rapid growth in data collection and potential, spreadsheets still dominate, with 70% of manufacturers manually entering data and 68% relying on them for analysis. While 44% of manufacturing leaders report that their data volume has doubled in two years and is expected to triple by 2030, only 25% feel confident in their data organization and traceability.
Most manufacturers recognize data’s potential for cost savings and business growth, but less than half understand its true financial value. Key challenges include the difficulty of managing data from diverse systems (53%) and a lack of analytical skills (28%). Despite these hurdles, 95% of manufacturers agree that data enables faster and higher-quality decision-making.
The digital transformation is a priority for manufacturing, with 68% of CEOs increasing digital investments. This involves reimagining business models, transforming to digital assets, and creating tailored KPIs across functions. However, companies face challenges in aligning digital initiatives, avoiding function overlaps (e.g., PLM, ERP, MES), and justifying ROI.
Looking ahead, the value of PLM in manufacturing remains uncertain, with only 17% of companies considering it indispensable, while many see it as costly or limited to engineering functions. Overall, the industry’s top goals are achieving faster, cheaper, and better processes, with data playing an increasingly central role in driving these improvements.
Data has emerged as a new form of capital in manufacturing, essential for process optimization but hindered by organizational and technical challenges. Despite the massive amounts of data being generated, manufacturing companies often fail to use it effectively, resulting in missed opportunities for optimization, innovation, and competitive advantage.
In the context of Product Lifecycle Management (PLM), understanding and adapting to emerging data trends and technologies is critical for building the next generation of PLM systems that support modern manufacturing needs.
Data as a Foundation: The Underutilization Challenge
Data is the foundation of every business process, especially in manufacturing, where precision and traceability is important to manage processes efficiently and support effectiveness of the decisions. However, most companies are not leveraging data to its full potential. Many rely on outdated systems like spreadsheets, legacy databases, or poorly integrated systems, leading to silos, inaccuracies, and inefficiencies. The fundamental issue lies in how data is organized, structured, and shared across the enterprise and its partners.
For product development process, this means that the most critical activity—managing product data throughout its product lifecycle—is not performed optimally. Despite the availability of cutting-edge technologies, data management systems in many companies are stuck with PLM software systems that are outdated and incapable of handling today’s data complexity. Without addressing the foundational product data management issue, the benefits of AI, automation, and advanced analyze data remain elusive.
The Technology Gap: SQL vs. Modern Data Management Solutions
Modern technologies like cloud computing, the semantic web, graph databases, and artificial intelligence have created a powerful foundation for robust and scalable data management to support entire product lifecycle. These tools can seamlessly process and connect massive datasets, enabling smarter and more efficient workflows. However, the leading PLM systems on the market have been slow to adopt these innovations. Instead, they are often built on outdated, monolithic architectures that rely on SQL databases, which struggle to handle the dynamic and interconnected nature of today’s data.
Graph databases, for example, offer a far superior way to represent relationships between data points—crucial for managing complex product structures, supply chains, and product histories. Semantic web technologies allow for more flexible, meaningful connections between different types of data. Yet, most PLM vendors continue to rely on rigid data models that are difficult to scale and integrate. This creates a significant barrier to innovation, as these systems are unable to leverage the true potential of AI, cloud scalability, or advanced data analytics. The technological gap leads to inability to cover an entire lifecycle of the information, to support machine learning, to include customer feedback, data about production process, service lifecycle management and many other data analysis that capable to improve the data management foundation of PLM software.
The Digital Thread: Moving Beyond Single-Tenant PLM
The concept of the Digital Thread gives a lot of promise for companies looking to integrate product data across multiple phases of the lifecycle and share it across different organizational boundaries. However, current major PLM platforms are built as isolated, single-tenant solutions that are focused solely on individual companies. Connecting product’s lifecycle using these architectures requires implementing complex data synchronizations and data extraction mechanisms to satisfy customer needs. In an increasingly connected world, this model is outdated and inefficient.
The Digital Thread requires the ability to connect multiple companies in a seamless, collaborative network—where data can flow securely and efficiently between different stakeholders, from design teams to suppliers to manufacturers. Yet, today’s leading PLM systems struggle to support such openness and connectivity. To fulfill the promise of the Digital Thread, PLM must move toward multi-tenant, collaborative models that allow for real-time data sharing across networks of companies.
AI Without Data: A Non-Starter
Artificial intelligence is the buzzword of the last two years. Introducing and massive adoption of ChatGPT demonstrated how generative AI can be used for content generation and summarization, but its potential remains untapped in many industries. Everyone wants to implement AI to improve decision-making, optimize processes, and drive innovation. However, without a solid data foundation, AI cannot deliver meaningful results. AI depends on large amounts of high-quality, well-structured data to train models and produce actionable insights. Unfortunately, the data in most of manufacturing environment is not designed to collect, manage, or organize data in a way that’s conducive to effective AI implementation. The gap is too big and injection of data about product life cycle including information about designing and building products is not available easy.
Building AI applications in manufacturing and PLM requires more than just technology—it requires rethinking how data is managed. Companies must focus on creating a solid, scalable, and flexible data infrastructure before jumping into AI projects. Only then can AI be effectively applied to areas like predictive maintenance, product optimization, and supply chain management.
Industry 4.0: PLM as the Data Backbone
As Industry 4.0 continues to drive the convergence of physical and digital worlds, PLM systems must evolve to become the central data backbone for the manufacturing industry. Industry 4.0 introduces concepts like smart factories, connected devices, and real-time data flows, all of which require PLM systems to manage massive amounts of information in real-time, across multiple platforms and technologies.
For PLM to meet the demands of Industry 4.0, it must shift from being a simple repository of product data to becoming a fully integrated system that connects design, manufacturing, and supply chain data. PLM must become a digital backbone that supports the continuous flow of information across the entire product lifecycle, providing a 360-degree view of product development and performance.
Composable Architecture: Openness Over Proprietary Platforms
The rise of composable architecture marks a clear break from the traditional vertically integrated, proprietary vendor platforms that have dominated the PLM space for decades. Composable architecture is all about flexibility and openness—it allows companies to build PLM ecosystems that are tailored to their specific needs, integrating the best tools and services from various vendors.
This shift demands that PLM vendors adopt open data standards and API-driven integrations, allowing for seamless connectivity between different systems. Proprietary, closed platforms that lock companies into a single vendor’s ecosystem are no longer viable. Instead, the future of PLM lies in systems that are modular, flexible, and interoperable—where companies can add or remove functionality as their business evolves.
What is my conclusion?
What is on the data management road ahead for PLM development? Data management technologies are transforming the PLM landscape, but PLM implementations and legacy systems are failing to keep up. Which cause situations where manufacturing companies have hard time to decide what to do. To follow a proposal for lift and shift of existing PLM systems to cloud platforms such as AWS, Azure, GCP? To develop PLM technologies by themselves using modern infrastructure? To adopt new and less proven solutions and platforms? The decision is not simple…
Most of manufacturing companies I talk to understand that they need to rethink how they manage product data. At the same time, transitioning away from spreadsheets and outdated systems and embracing modern technologies like cloud computing, graph databases, and AI feels dangerous and unproven. To thrive in the era of data driven processes and Industry 4.0, manufacturing companies need to find a modern PLM data backbone that is flexible, scalable, supports the Digital Thread, powers AI applications, and fosters openness and collaboration.
As we move forward, the companies that successfully adapt their PLM strategies to embrace these data foundation megatrends will be the ones that lead in innovation, efficiency, and competitiveness. The future of PLM lies not just in managing data, but in transforming it into actionable intelligence that drives every aspect of the product lifecycle. 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.
