Product Lifecycle Management (PLM) and product data management software has come a long way over the last few years. As we approach the end of 2023, I’d like to take a step back and assess how the industry has evolved, what key trends are shaping the future of product lifecycle management, and the challenges and opportunities it faces. In this blog, I will explore the recent developments in the PLM space and offer insights into what the near future holds.
How Has the Industry Changed in the Last Couple of Years?
The most significant shift in the PLM industry has been the widespread adoption of cloud-based solutions. Starting from the early 2010s, large companies turned their faces to the “cloud”. In recent years, companies of all sizes and across various industries have migrated software to the cloud/SaaS.
Computer aided design, mechanical engineering, multiple engineering disciplines, PLM and manufacturing was one of the last verticals looking how to adopt the cloud. For the last 10 years, all PLM vendors in one way or another embarked to the “cloud journey”. But, in fact, the industry move is still ahead. The widespread adoption of SaaS/Cloud PLM didn’t happen yet. I can see acceptance, but not adoption. So, cloud is now not only the vision, but the reality of implementation. It is one big “WIP” project.
The next big challenge for product development process and PLM is to move beyond its traditional engineering-centric approach. Designing and building products, engineering tasks, supply chain management, electronic design, electrical engineering, civil engineering and many other disciplines using document management, product data management and PLM systems to manage business processes are evolving to support a more connected and holistic approach to product development and management. This involves integrating various processes, teams, and decision-making into a unified platform.
Anticipating Market Disruptions and Customer Demands
What are main disruptive trends and changes. PLM should have to move beyond mechanical engineering tasks and engineering release. In the near future, I can expect several disruptions and customer demands in the Product Lifecycle Management (PLM) and supply chain management space:
- Digital Transformation: As businesses continue their digital transformation journeys, they will demand PLM systems that seamlessly integrate with other digital tools and processes. This includes IoT devices, artificial intelligence, and machine learning systems.
- Decision Making: Companies will increasingly rely on PLM software not just for data management but also for intelligent decision support. PLM will play an important role in helping organizations make data-driven decisions throughout the product lifecycle.
- Shift from Documents to Data: PLM will move away from being primarily document-centric. Instead, it will focus on managing and leveraging data throughout the product lifecycle. This shift is essential for unlocking the full potential of data analytics and AI.
These disruptions and demands will vary in impact over the short, medium, and long terms, with more immediate changes driven by digital transformation initiatives and longer-term shifts driven by evolving customer expectations.
Key Trends Shaping the Market for the Next 5-7 Years
To predict the future is hard task. But I will try to look in my crystal ball and will try to tell what I can see there. Here are several key trends that in my view, will shape the PLM market over the next 5-7 years:
- Digital Twin/ Thread: This trend is set to continue as companies increasingly rely on digital tools and platforms to optimize their product development processes.
- AI and decision support across multiple lifecycle stage: PLM systems will evolve to provide intelligent insights and support decision-making at every stage of the product lifecycle.
- Put Data in the middle: PLM software will transition from managing documents to data, enabling real-time analytics and predictive modeling. In the past, documents managed by PLM were the “outcome” of design. Today, data must become a driving factor of PLM solutions.
On the technological front, several factors will drive these trends:
- Modern Data Management: PLM systems will adopt advanced data management techniques to handle the vast amounts of data generated during product development. Graph Databases will be in the middle of this transformation.
- Multi-Tenant Data: Multi-tenant architectures will enable efficient data sharing and collaboration among different stakeholders in the PLM ecosystem.
- AI and Analytics: Artificial intelligence and analytics will play a central role in extracting valuable insights from PLM data.
These trends will impact various regions and industries differently, with industries like aerospace, automotive, and electronics leading the way in adopting advanced PLM solutions.
Major Factors Driving the Market
The primary driving force in the PLM market remains digital transformation. As companies continue to embrace digital technologies, the need for advanced PLM solutions that can seamlessly integrate into their digital ecosystems becomes top priority. This will have a significant impact on PLM implementations in the short and medium terms.
Major Factors Restraining Growth
One of the major challenges inhibiting the growth of the product life cycle tools and PLM market is adoption. Technological advancements are important, but not enough. While technology is advancing rapidly, organizations often struggle to effectively implement any PLM solution. Therefore many PLM solutions stuck in the development and engineering release processes. Downstream, deployment and implementations of existing PLM solutions is questionable. Multiple problems starting from user experience to complex integrations and data locking are preventing using data from PLM systems in production process. The vision of vertically integrated PLM platforms is heavily incompatible with the realities of manufacturing companies and engineering practice. Combining existing PLM best practices with modern technologies can be a complex and time-consuming task. This challenge is likely to persist in the short, medium, and long terms unless addressed proactively.
Major Challenges in the Market and Responses
The biggest challenge in the PLM market is the dominance of a few major players who have held their positions for the past decade or more. Dassault Systemes, PTC, Siemens, partially Aras, Oracle and SAP. This is a short list of large players. It was almost the same 10-15 years ago. These companies often absorb smaller competitors through mergers and acquisitions, leading to limited diversity and innovation in the market.
SaaS and cloud is going to challenge this model. Manufacturing companies are going to choose SaaS and use these services since they provide a lower risk factor comparing to large vertical PLM platforms.
The industry needs to encourage new entrants and startups to bring innovative PLM solutions to the market. These solutions should challenge the status quo and offer fresh perspectives on how PLM can be implemented and utilized.
What is My Conclusion?
The PLM market is at an exciting juncture as we approach the end of 2023. After years of incremental changes, the industry is ready for a significant transformation. The key to unlocking the full potential of PLM lies in harnessing the power of digital transformation, data management, and intelligent decision-making.
The next generation of PLM tools is on the horizon, and they will be built on new platform foundations that prioritize data and innovation. As organizations continue to evolve and adapt to the changing landscape, PLM software will play an increasingly vital role in shaping the products of the future. It’s an exciting time for the PLM industry, and the potential for innovation is boundless.
Just my thoughts…
Disclaimer: I’m co-founder and CEO of OpenBOM developing a digital-thread platform with cloud-native PDM & PLM capabilities to manage product data lifecycle and connect manufacturers, construction companies, and their supply chain networks. My opinion can be unintentionally biased.