As previously noted, digital engineering practices are dramatically changing the way the Department of Defense develops its systems. This is because, when done well, digital engineering allows teams to better collaborate, simulate outcomes and make decisions about how to arrive at those outcomes prior to developing physical systems.

Underpinning an effective digital engineering strategy is product lifecycle management (PLM) data. This refers to all the data for the hundreds of thousands – if not millions – of parts that comprise complex systems. Think: fighter jets, aircraft carriers, drones or secure communications tools.

Every single part in those systems needs an associated record of its history. The DoD needs to know who made every part, and every part of a part, and it needs to understand the supply chain associated with each part’s development. The DoD needs to understand the risks that exist in that supply chain, and it needs to understand how to mitigate them. Additionally, the department needs to understand the connections in all that data and where there are areas of overlap, opportunity or risk – and how all that data is connected to the system’s requirements.

An Evolutionary Leap Forward in PLM Data Management Aligned to User Needs

It's hard to believe, but all this rich, dense data used to be kept in documents: spreadsheets, pdf’s, etc. The evolutionary leap forward in how we manage it today is akin to the relationship between the computers that sent the first space shuttle to the moon and the superiority of the ones we carry around in our pockets every day in the form of our smartphones. We are so far ahead of where we were when it comes to managing PLM data.

Today’s PLM data management tools help mission partners better connect product requirements – like weight or speed or performance – to the system components that can actually affect those things. And that’s part of the equation. But if requirements change, teams need traceability. They need to be able to quickly examine the components of a system and find where there are opportunities to make adjustments – like swapping out one part for another or asking a manufacturer to redesign a part – in order to bring about the desired result.

That’s why we developed a tool to do exactly that. It’s a reflection of the complexity of these systems and the inherent need to develop both requirements and system configurations with traceability and precision. We use it in collaboration with customers and partners to leverage standards-based visual modeling interfaces so teams can intuitively define and interact with digital versions of their systems.

Rich, Connected PLM Data Enables Simulation and Analysis that Inform Decision Making

By having PLM data in one place, connected with a digital thread, and the ability to navigate the relationships within the data, teams can operate more efficiently and develop better systems with better performance. They can develop realistic simulations, can model and test aspects of performance before going into production, and can understand the impact analysis of every component of a system.

This idea extends far beyond the DoD and far beyond physical systems. This same methodology applies to software development and information technology. It applies to other agency systems that are similarly complex. Think about the systems used by the Department of Homeland Security for passenger or baggage scanning at airports. Think about the variety of medical machines and devices that are distributed globally to healthcare centers that serve veterans. These are all massive, critically important systems, and they all need product lifecycle management data to run as efficiently and effectively as possible.

Key Considerations for Moving to a PLM Approach

Teams considering moving to a PLM approach should, first, look at how to migrate their authoritative data into PLM systems. From there, and in parallel, a sincere focus must be given to transitioning and training the workforce to use PLM systems for design. This is a major organizational change management consideration that should not be overlooked. With the right training and groundwork in place, teams are then better positioned to use PLM data to create models of architectures that support system design and that determine the data products that are required at each phase of a system’s lifecycle. This will enable teams to design digital threads to aggregate lifecycle data and support myriad data products that inform decision making and design.

With limited resources and constantly evolving technology, it’s easy to see why many agencies are turning to digital engineering, and PLM data alongside it, to help them make the most of their systems and to ensure they remain nimble and agile enough to meet their mission.