What are some of the best practices for conducting system-level trade studies and sensitivity analysis?

1 Define the problem scope and objectives

Before you start any trade study or sensitivity analysis, you need to clearly define the problem scope and objectives. What are the system functions, requirements, and constraints? What are the performance measures and criteria for evaluating the design alternatives? What are the uncertainties and risks involved? How will you communicate and document the results? By answering these questions, you can establish the scope and objectives of your analysis, and avoid unnecessary complexity and ambiguity.

2 Develop a system model and architecture

The next step is to develop a system model and architecture that captures the structure, behavior, and interactions of the system and its components. You can use various methods and tools for this, such as SysML, MATLAB, Simulink, or Excel. The system model and architecture should be consistent, traceable, and verifiable, and should support the analysis of different design alternatives. You should also document the assumptions, limitations, and validation of your model and architecture.

3 Generate and evaluate design alternatives

The core of any trade study or sensitivity analysis is to generate and evaluate design alternatives. You can use various techniques for this, such as design of experiments (DOE), optimization algorithms, or heuristic methods. The goal is to explore the design space and find feasible and optimal solutions that satisfy the objectives and constraints. You should also consider the trade-offs and interactions among the performance measures and criteria, and use appropriate metrics and visualizations to compare and rank the design alternatives.

4 Perform sensitivity analysis and uncertainty propagation

Sensitivity analysis and uncertainty propagation are important steps to assess the robustness and reliability of your design alternatives. Sensitivity analysis helps you identify the most influential parameters and objectives on the system performance, and how they vary across the design space. Uncertainty propagation helps you quantify the effects of uncertainties and variations on the system performance, and how they propagate through the system model and architecture. You can use various methods for this, such as Monte Carlo simulation, interval analysis, or probabilistic methods.

5 Apply multi-criteria decision analysis

Multi-criteria decision analysis (MCDA) is a useful technique to support the selection of the best design alternative, based on multiple and conflicting criteria. MCDA helps you structure the decision problem, elicit and incorporate the preferences and values of the stakeholders, and apply rational and transparent methods to rank and choose the design alternative. You can use various methods for this, such as weighted sum, analytic hierarchy process (AHP), or TOPSIS.

6 Communicate and document the results

The final step is to communicate and document the results of your trade study or sensitivity analysis. You should use clear and concise language, graphs, tables, and diagrams to present the main findings, insights, and recommendations. You should also highlight the assumptions, limitations, and uncertainties of your analysis, and suggest possible improvements and future work. You should also follow the standards and best practices of your organization and domain for reporting and documenting the results.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?