In the evolving landscape of Product Lifecycle Management (PLM) and Product Data Management (PDM) implementations, Project Management and document management, the industry stands at the brink of a transformational shift. A typical PLM system from 1990s is not cool anymore. Modern PLM software should be different, but how?
In my previous articles I touched the question that triggered a good amount of healthy debates – What comes first: process definition or PLM implementation. I want to thank you all my readers and commenters online and offline for amazing discussions about importance of balanced definition of process for holistic product lifecycle when implementing PLM software. Product development process is complex and requires multiple steps to define. It is hard to do it upfront and it often requires continuous redefinition over time.
My previous discussions laid the groundwork by delving into the inherent challenges of PLM implementations, the often perplexing decision between defining processes versus initiating a PLM implementation, and the complex digital dance of business transformation. Check my earlier articles:
Redefining the approach to PLM implementation: a strategy beyond traditional PLM
Solving Process and System Adoption Challenges with SaaS PLM: A Deep Dive into OpenBOM
A recent exploration into redefining PLM implementation strategies further accentuates the need for a paradigm shift away from traditional methodologies. Today, I want to expand the conversation by discussing the transformative potential of integrating SaaS services, modern data management and data acquisition, seamless integration, pilot projects, and using AI in redefining PLM implementations. This blend of technologies and methodologies can bring a new era in PLM that is agile, data-driven, and AI-enhanced.
SaaS Services and Instant Data Acquisition
Traditional PLM implementations are heavy, requires complex system deployment and long implementation services. The main reason for that is complexity of deployment, configuration and customization of these product lifecycle management systems. This is how it was done for the last 25+ years – RFQ, implementation plan, deployment and finally implementation. The timelines of these implementations is 6-12 months in the best case scenario.
The cornerstone of this transformation is the deployment of modern SaaS services. The architecture of these services allows for immediate deployment, making them available within minutes. This instant availability empowers companies to rapidly experiment and construct their data environments with unprecedented speed.
The initially deployed system provides a core functions to focus on fixing deeply broken scenarios such as new product development, design suppliers collaboration, costing, supply chain risk assessment and others. The essence of this approach lies in its agility and the ability to bring an environment instantly, acquire data and provide a starting point for process improvement. But this is a starting point. Then system supposed to adapt to the evolving needs of businesses. All together, it will set new standards for PLM implementations.
Advanced Data Management and Integration
A traditional legacy data management approach demands definition of data model, mapping of existing data, importing legacy data and switching to the new environment. While nothing is wrong with this approach, it is deeply inefficient and linear. What can be changed in this approach? The main thing is data management combined with the capabilities of data integrations. Modern data management is flexible and can be adapted in a real time. By importing data from Excels and legacy databases, system will set themselves to perform an initial set of features instantly.
The advent of advanced and flexible data models marks a significant leap forward. These models facilitate out-of-the-box integrations with existing data sources, including CAD and other engineering data. This seamless integration capability underscores the shift towards a more cohesive and interconnected PLM ecosystem, where data silos are dismantled, and information flow is streamlined.
Pilot Projects: Agile Implementation Approach
While the industry was talking about agile PLM implementation for a long time, not much was done to support it. The deadlock between planning and implementation is the most visible problem in PLM industry. A combination of instant SaaS services and seamless integration lays the foundation for new type of PLM implementation – pilot projects + agile implementation.
Pilot projects represent the embodiment of agility in the context of PLM implementations. This approach focuses on capturing existing business processes to discern avenues for improvement. By favoring quick iterations and small, manageable steps, pilot projects enable organizations to adapt and refine their PLM strategies with minimal risk and maximum flexibility. This agile methodology underscores the transition from monolithic, cumbersome implementations to dynamic, results-oriented processes.
AI-Driven Process Optimization
Let’s take a look in the near future.The progress in development and advancing of modern AI methods – contextualizing LLM models, knowledge graph development capable to improve an overall business systems and PLM solutions. An initial formation of flexible data models, knowledge acquisition and contextualizing AI tools and LLM models specifically lay the foundation for the future of AI in PLM implementation.
The integration of knowledge graphs and Large Language Models (LLMs) is just a groundwork for AI-driven PLM implementations. By harnessing AI, companies can generate process definitions directly from existing data sources, such as CAD data, ECO forms, and Bill of Materials, along with legacy databases. This innovative approach not only streamlines the definition and improvement of processes but also heralds a new era of data-driven decision-making in PLM.
What Is My Conclusion?
The landscape of PLM is poised for a revolution. The methodologies and technologies of yesteryears are giving way to a new, agile, and AI-driven paradigm. This shift, characterized by instant data acquisition, advanced data management, pilot projects, and AI optimization, promises to redefine PLM implementations. By embracing this mixed approach, which combines bottom-up (AI-driven) and top-down (customer-guided) processes, organizations can navigate the complexities of PLM with greater efficacy and innovation.
As we look to the future, it is clear that the path to successful PLM implementations lies in our ability to adapt, integrate, and leverage the full potential of modern technologies. In this transformative journey, the goal is not just to manage product lifecycles but to reimagine them in a way that drives unprecedented value and efficiency.
Just my thoughts…
Best, OlegDisclaimer: 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.
