Understanding how PLM has evolved enables us to strategize for the future. While quantifying progress is difficult, mapping out key evolutionary steps compactly tells the PLM story. This analysis offers perspective on PLM’s expanding scope, the accelerating pace of change, how evolution has enabled productivity gains, and strategies for companies to navigate PLM’s future.
PLM has undergone tremendous evolution as a process and system over the last 40 years, transforming product development and manufacturing. Let’s trace the major milestones that have shaped PLM’s growth and their implications.
Enablers of PLM evolution
Several factors have enabled the evolution of PLM:
Digitalization of manufacturing began with the advent of computer-aided design and manufacturing in the 1980s, paving the way for digital product definitions. The introduction of MRP/ERP systems in the 1990s played a crucial role in creating digital backbones, acting as catalysts for the evolution of PLM.
Hardware advances have been instrumental in shaping PLM’s trajectory. Exponential gains in processing power, storage, and networking capabilities have allowed for the development of ever-larger PLM systems. The use of on-premise servers in the 1990s facilitated the implementation of enterprise-wide PLM, while the transition to cloud infrastructure in the 2010s has been a driving force behind the rise of SaaS PLM solutions.
In the realm of software innovation, PLM has mirrored hardware advancements, expanding from basic Product Data Management (PDM) to integrated PLM platforms. The emergence of technologies such as the Internet of Things (IoT), analytics, and mobility has further expanded the capabilities of PLM.
The evolving landscape of product complexity has also played a pivotal role in shaping PLM. As products became more sophisticated, necessitating cross-functional collaboration for development, PLM responded by adapting to these emerging needs.
PLM Process Evolution
1980s – PDM emerges:
In the 1980s, product data management (PDM) emerged to manage CAD files, revisions and basic bill of materials (BOMs). PDM systems also helped automate engineering change processes and facilitated the shift towards digital product definitions over paper drawings.
1990s – Expand to manufacturing planning:
In the 1990s, PDM expanded to integrate with MRP and ERP manufacturing systems. This integration enabled creation of digital BOMs and routings for production, as well as the addition of quality processes like Advanced Product Quality Planning (APQP) and Production Part Approval Process (PPAP) to the PDM systems.
2000s – Support full product lifecycle:
In the 2000s, PDM evolved to support the full product lifecycle. Digital mockups were introduced for design visualization, fostering collaboration on requirements and project timelines. Supply chain connectivity for partners became an integral part of the system.
2010s – Add advanced integrated tools:
In the 2010s, additional advanced integrated tools were incorporated. Model-Based Systems Engineering (MBSE) integrated requirements and functional models, while closed-loop quality utilized Internet of Things (IoT) data. Application Lifecycle Management (ALM) seamlessly integrated software lifecycles into the broader PLM framework.
2020s – Model-centric approaches evolve:
In the 2020s so far, model-centric approaches have evolved. Model-based definitions (MBD) are replacing drawings, advanced automations generate design configurations from engineering options, and digital twins becomes a powerful tool enabling computational engineering simulations.
PLM System Evolution
1980s – On-premise enterprise software:
In the 1980s, the early PDM systems utilized a client-server architecture and provided limited integration.
1990s – Expand to enterprise document control:
In the 1990s, PDM systems expanded beyond engineering to enterprise-wide document control. Workflows were automated for product documentation. PDM systems were integrated with MRP/ERP, establishing PDM as a key system for the engineering domain.
2000s – First web-based PLM platforms:
The 2000s saw the emergence of the first web-based product lifecycle management (PLM) platforms. These enabled global collaboration in engineering. Modular PLM applications emerged for managing BOMs, CAD, and quality. The software-as-a-service (SaaS) model appeared but did not become mainstream yet.
2010s – Cloud PLM gains dominance:
In the 2010s, cloud-based PLM gained dominance. Cloud PLM used multi-tenant SaaS model and reduced hardware needs and costs. Mobility, analytics, and IoT integration became important PLM capabilities.
2020s – Smart, connected digital twins:
In the 2020s, PLM is transforming to support smart, connected digital twins. PLM integrates IoT and digital twins to capture live data from products and processes. PLM also uses artificial intelligence (AI) and machine learning (ML) for analytics, generative design, and optimization. PLM also provides tailored role-based experiences for different users and stakeholders.
Implications for PLM Buyers
As PLM buyers, you must factor in PLM’s rapid evolution over the past decades as you evaluate and strategize:
- Expect constant change: What worked yesterday may not work tomorrow. PLM is constantly evolving to keep up with new technologies and processes. You need to seek solutions and partners that are adaptable and flexible, and that can support your current and future requirements. You also need to be ready to embrace change and innovation in your own organization.
- Focus on capabilities over products: Given the swift evolution of PLM, you should not focus too much on specific products or features. Instead, you should look for platform capabilities that can meet your needs and goals. You should seek flexible, open platforms that integrate well over comprehensive fixed suites that may become obsolete or incompatible.
- Demand integration and expansion potential: PLM is not an isolated system. You need to ensure that your PLM solution can support integrated data and process flows across engineering, manufacturing, quality, and supply chain. You also need to seek platforms that can expand across domains and disciplines, such as software, electronics, and systems engineering.
- Prepare for new technologies: PLM is not static. It is influenced by new technologies and trends that reshape product development. You need to prepare for the impact of cloud, analytics, IoT, AI, and generative design on your PLM strategy. You need to ensure that your existing systems can embrace these technologies and stay vital. You also need to plan your budgets and teams accordingly to leverage these technologies effectively.
- Align investments to roadmaps: PLM is not a one-time purchase. It is a long-term investment that requires planning and alignment. You need to consider long-term roadmaps that are aligned to your product portfolio plans, not just immediate feature needs. You also need to demand realistic roadmap commitments from your solution providers and partners. You need to ensure that your PLM investments support your strategic vision and goals.
- Seek balanced standardization: PLM is not a one-size-fits-all solution. It needs to balance standardization and customization. You need to seek solutions that can standardize some core processes and data models for efficiency and consistency. But you also need to leave room for customization and differentiation as your needs evolve. You also need to ensure that your solutions have effective standards and best practices that are adopted by your users and stakeholders.
- Consider new business models: PLM is not a fixed cost. It is influenced by new business models and opportunities. You need to consider the tradeoffs of different models and how they affect your PLM strategy. You need to weigh the benefits of SaaS, which lowers barriers to emerging capabilities and reduces hardware needs, against the challenges of data security and control.
- Partner strategically: PLM is a complex and dynamic system that requires expertise and collaboration. You need to partner strategically with solution providers and other organizations that can help you navigate PLM’s ongoing complexity. You need to seek partners that have domain expertise and complementary strengths, and that can add value to your PLM strategy. You also need to take a long-term, win-win approach to partnerships, and build trust and mutual benefit.