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We all know the goal of the game: "Develop increasingly complex systems with smaller performance margins that meet the user's requirements in the shortest time, with high reliability, open and adaptable, and at the lowest cost". The environment we are working in continues to push Systems Engineering challenges to the next level. As Systems Engineers, we all want to win the game i.e. beat our competitors, satisfy our customers, develop unprecedented systems to address political and economic challenges. We have a proven process and a common set of tools, which we use and refine. Yet as we begin to participate in the game, we find that not only do we have huge challenges, but the rules keep changing. The customer/user requirements change, the conceptual design needs modification, the analysis reveals design challenges, budgets are reallocated. So how do we win this game? This paper presents a case for the increased application of a suite of enablers, processes, and practices referred to as Design for Six Sigma at appropriate integration points during the Systems Engineering process lifecycle.

It is becoming evident that in order to effectively compete in an increasingly challenging global marketplace, organizations are going to need to both extend and leverage their established Systems Engineering principles and practices across the entire enterprise. Early returns from an emerging Systems Engineering best practice, Critical Parameter Management, has proven itself highly effective in this regard. Critical Parameter Management is a set of tools, enablers, and best practices for managing, analyzing, and reporting technical product performance by unifying and integrating systems, engineering design, and manufacturing activities. Specifically, the use of a Critical Parameter Management has enabled Enterprise Systems intelligence and decision‐making through: real‐time, sensitivity analyses at the system, subsystem and component levels; efficiently shared technical product documentation and analyses; performance design margins that are statistically managed over the System product lifecycle for their customer and business benefit; and the capturing and leveraging of invested intellectual capital for future business reuse.