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System Design

Overview
SynGenics builds on a number of well-known structured approaches to identify optimum solutions to complex issues. The SynGenics process helps assure that requirements are understood and the best forward-looking decisions are made from design to finished product or technology. The strengths of this mathematics-based method are its conceptual simplicity coupled with analytical rigor. At its simplest requirements are identified and alternatives that might satisfy those requirements are evaluated, compared, and rated. It is a robust process with multiple benefits. The methodology is recursive (step-by-step), flexible, and scalable. It provides a consistent framework for subject-matter experts and managers to capture alternatives, recognize the "grayness" in requirements, and realize the highest probability of success. It reveals sensitivities and quantifies risk. It is easily updated as new knowledge or requirements become available. Through statistical analysis, the process predicts the optimal point at which requirements (or criteria) are met, thus giving researchers and customers an objective function that accommodates all the criteria. This approach helps to assure that funds and efforts are channeled to the candidate technologies most likely to provide the overall best possible value—meeting the customer’s needs at a price that customer is willing to pay.

Team Development
Systems Design is a collaborative effort. Project teams work best when they contain subject matter experts appropriate to the problem—engineers, material scientists, regulators, logisticians, financial analysts, customers, and users.

Requirements Definition and Optimization
Identifying requirements—what the final product, technology, or system must do is crucial. The customer provides information such as: performance, reliability, portability, schedule, life-cycle cost, and maintainability. Criteria are documented with description, unit of measure, customer, priority, objective, threshold, desirability, and type. Requirements, also called desirements, are documented for each customer, and exit or solution criteria are determined.

Technology Alternatives, Worksheets & Scorecards
The team considers possible solutions to the requirements. Alternative solutions are measured against each criterion in a process called Value Analysis. Expert opinion, simulation, statistically designed experiments, or other Quantitative Technology Assessment (QTA) methods may be performed to predict the alternative performance with respect to a requirement. Worksheets and scorecards are compiled, desirability functions are applied, and resulting values are aggregated using a weighted geometric mean. Risk measures are calculated. Top-level results include expected value, desirability, and risk.

Optimizing Solutions
Once the team has focused on a particular technology alternative, the process refines that solution. Design variables are evaluated in different combinations to produce response-value predictions using statistically designed experiments and response surface methodology. Regression analysis or other QTA methods are performed to generate predictive functional relationships. Multi-attribute desirability optimization applies an optimization formula to establish the best-value, most robust solution.

A Best-value Solution
Applying this mathematical process early in a program permits the team to focus on the few alternatives most likely to lead to successful product development. The process can be efficiently revisited to refine or reevaluate alternatives if criteria change or as a result of refinements to the process.

Summary Information

Requirements are identified and alternatives that might satisfy those requirements are evaluated, compared, and rated. It is a robust process with multiple benefits.

Project teams work best when they contain subject matter experts.

Identifying requirements—what the final product, technology, or system must do is crucial.

The team considers possible solutions to the requirements. Alternative solutions are measured against each criterion in a process called Value Analysis.

Once the team has focused on a particular technology alternative, the process refines that solution.

Applying this mathematical process early in a program permits the team to focus on the few alternatives most likely to lead to successful product development.