The traditional systems engineering process can be likened to the "telephone game," which you probably played as a kid.
Participants line up, and the first one whispers a phrase in the ear of the next person and so on. When the last participant yells out the heard phrase, everyone finds out how distorted the phrase had become. Everyone inevitably snickers and giggles when they find out "My mom mopped the motel" turned into "Mimes stopped caring that they smell."
While the telephone game is fun, complex systems engineering is a serious and multidimensional challenge.
Let's now change the rules of the game a little bit. The game instructor asks each participant a different question about the message at the end of the game. Each participant could only communicate what it thinks is important in answering the question. Finally, each one can only relay its message to those within reach.
The engineering of complex systems is like the telephone game's challenge in 2020, requiring all participants to know the original message. This is especially important, because the interests of each participant are different, operating in different disciplines while collaborating on the system. If there isn't a central anchor, it is easy to imagine that over time, multiple and conflicting messages will be circling around.
A digital engineering strategy combats this by moving the participants into an ecosystem that allows them to more openly share systems engineering information — the message — and, more important, curate from that information to fit within the context of their individual needs.
How digital engineering optimizes decision modeling
When we talk about connecting and exchanging information from one party to another, we recognize that our real purpose is to drive sound decision-making. Decision modeling provides a means to understand how each decision impacts program requirements, risks, and opportunities that may then lead to another decision and so on.
Decision modeling forms the basis for a trade analysis where you could examine a gain in one benefit that might result in a loss of another. If you replace X, how does it affect Y? Or, how does the totality of factors X, Y, and Z contribute to mission effectiveness?
Most decisions I deal with are not simple and can have millions or even billions of outcomes. My job involves hundreds of complex “what if?” questions that I can’t efficiently answer without leveraging digital engineering strategies.
Digital engineering maps out the hand-offs between parties and reduces the errors in the process. When I think about how replacing a legacy system architecture will impact an entire organization’s capabilities, I use digital engineering-aided decision modeling to rapidly weigh the pros and cons.
Decision modeling provides a means to understand how each move impacts downstream program requirements, risks, and opportunities that may impact the next move and so on.