Next Generation PLM
Next Generation PLM: Next Generation Product Lifecycle Management (PLM) systems are designed to handle the increasing complexity of modern products and processes. They are mindfully built with an eye on asset extension to better enable the implementation of MBSE, MBD, SPDM, LIMS, at a much lower cost of ownership.
They are configured with precedence to assure the MBx extensions will plug-in and integrate easily and data transformations with each extension will be very limited or not required at all.
The end-to-end Digital Engineering Master Data Model is King and is singularly the most critical enabler of the end-to-end Digital Thread.
Attributes of a Next Generation PLM Environment
A Next Generation Product Lifecycle Management (PLM) environment is characterized by several key attributes that enhance its functionality, scalability, and adaptability. Here are some of the main attributes:
Cloud-Based Solutions: Modern PLM systems leverage cloud technology to offer scalability, accessibility, and reduced IT overhead. This allows for easier updates and maintenance, as well as better collaboration across global teams.
Integration with Digital Twins and Digital Threads: These systems incorporate digital twins (virtual replicas of physical products) and digital threads (integrated data flows) to provide comprehensive insights throughout the product lifecycle.
Real-Time Intelligence: Next-gen PLM platforms deliver real-time data in context, enabling better decision-making and faster response times.
Interoperability and Open Architecture: They are built on open, flexible, and scalable architectures that support interoperability with other systems and tools, ensuring seamless data exchange and integration.
Model-Based Definitions (MBD): These environments use model-based definitions to manage product data, which includes not just geometric information but also behavior and context, enhancing the fidelity and usability of the data.
Model-based Systems Engineering (MBSE): These Environments use a Model-based approach to align Stakeholder Needs, and Technical Requirements to Physical, Logical, and Functional design of the Product and its verification and validation.
Simulation and Product Data Management: These Environments use a Model-based approach to system validation and verification which includes not just geometric information but also behavior and context, enhancing the fidelity and usability of the data.
Advanced Analytics and High-Performance Computing: Incorporating advanced analytics and high-performance computing capabilities allows for more sophisticated data analysis and simulation, driving innovation and efficiency.
SaaS Implementation: Software as a Service (SaaS) models are increasingly common, providing flexible, subscription-based access to PLM tools and reducing the need for extensive on-premises infrastructure.
Enhanced Collaboration Tools: Modern PLM systems offer robust collaboration features, enabling better communication and coordination among different stakeholders throughout the product lifecycle.
These attributes collectively help organizations manage complex product lifecycles more effectively, from initial design through to manufacturing, maintenance, and end-of-life
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Constructing your Next Generation PLM Environment
It’s all about the Data, a thread first perspective. Whether you are extending your current core Product Data Management System, replacing an existing system or implementing PLM for the first time, it is critical to focus on the end-to-end Digital Engineering Master Data Model and its relationships with the longer-term MBx Environments that will be deployed. Organizations that implement PLM in siloed stages absent regard for downstream asset extensions will be continually challenged by significant costs for reconfiguration and manual data transformation. With each extension of the asset e.g., MBD, MBSE, SPDM a substantial reengineering of the Master Data Model will be required. This will result in, what are far too often, manual data transformations. To take advantage of the process and application automation opportunities that come with these asset extensions, substantial reconfiguration of the core PDM capability will be required including but not limited to; Part Lifecycle Management, CAD Data Management, BoM Lifecycle Management, and Change Management. Let us show you how to optimize your Total Cost of Ownership for PLM. Contact US
As you thoughtfully and methodically build out your Master Data Model, you will identify a significant number of process automation opportunities that will substantially increase the value proposition for PLM. To learn more about these automation opportunities, Contact Us.
It’s all about the process, a process first perspective. Conversion to a Lean/Pull Digitally Threaded and highly optimized process and application environment requires a more substantial focus on process than most PLM Teams have executed to date. After all, Executives traditionally want their Big Mac Fast! They often fail to fund and staff the creation of a Data Driven Digital Engineering Strategy. They almost always fail to fund and staff the volume of process analysis & design work required to drive in a Real Measurable ROI. They want to build it fast, debug it even faster and adapt / extend it as they go regardless of the excessive investments required for reconfiguration and data transformation that come with each MBx extension of the asset.
Configure PLM Core with precedence. Unlike most of our competitors, DSA is highly skilled across the entire Digital Engineering landscape including PDM, CAD, MBD, MBSE, and SPDM. Our work across the entire Digitally Engineering landscape enables us to describe, in detail, how to configure core capability to ensure the entire Digital Engineering Environment can be easily integrated, digitally threaded, optimized for a maximum return on investment and implemented in stages at the lowest possible long term cost of ownership. To learn more about precedent configurations, Download our What Paper