# Trade Study Analysis > Use when you must run a trade study or alternative selection for an aerospace system or subsystem: set decision criteria with weights that sum to 1.0, score each candidate, build a Pugh matrix with plus/zero/minus marks against a baseline concept, compute weighted scores, judge the selection margin between the best and runner-up alternative, perturb the weights to test sensitivity, and confirm every candidate traces to requirement ids. Produces the ranked alternative list, the Pugh verdict, the sensitivity ranking, and the selection decision with margin and tie handling. Trigger: trade study, trade-off, Pugh matrix, decision criteria, weighted scoring, sensitivity analysis, alternative selection, selection margin. - Skill: `ashfordeou/trade-study-analysis` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add ashfordeou/trade-study-analysis` - Raw SKILL.md: https://api.skillmd.com/api/skills/ashfordeou/trade-study-analysis/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - License: Apache-2.0 - Author: ashfordeOU (https://skillmd.com/u/ashfordeou) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/ashfordeou/trade-study-analysis --- # Trade Study Analysis (systems-engineering-safety/mbse/trade-study-analysis) Use when the task is a trade study or alternative selection for an aerospace system or subsystem: weighted scoring of candidate concepts, Pugh matrix comparison against a baseline, sensitivity of the ranking to the criterion weights, and the selection decision with margin and traceability. ## Domain quick reference - Weighted scoring: score(A) = sum over criteria i of w_i * s_i(A), with the weights summing to 1.0. Example: criteria mass, cost, reliability with weights 0.5, 0.3, 0.2 and scores 8, 6, 9 give 8*0.5 + 6*0.3 + 9*0.2 = 7.6. - Weight validation: weights must sum to 1.0 within a small tolerance (about 1e-9). A set that does not sum to 1.0 raises ValueError instead of silently skewing the ranking. - Pugh matrix: rows are criteria, columns are alternatives, and each cell is +1 (better than the baseline), 0 (same as the baseline), or -1 (worse than the baseline). The baseline column scores 0 on every row by definition. Net score of an alternative = sum over rows of (cell - baseline cell); rank the alternatives by net score descending. - Selection margin: margin = best_score - runner_up_score. A margin at or below the numerical tolerance is a tie; a margin below the configured threshold (default 0.05 on the weighted score scale) flags a weak decision that needs more discrimination between the candidates. - Sensitivity analysis: perturb each weight upward by a fixed amount and renormalize the remaining weights so the set still sums to 1.0, then re-rank. If the winner changes under a single weight perturbation, the decision is sensitive to that criterion and the weight needs justification. - Traceability: every alternative's rationale should cite requirement ids. The check flags alternatives that cite no requirements and requirements that no alternative covers. - ARP4754A sets the development-planning context: alternative concepts are evaluated and the chosen concept is justified as part of the development plan, and the selection rationale traces back to the requirements. The scoring relations above are common decision-analysis methodology. ## Workflow 1. Define the decision criteria and the weights; verify the weights sum to 1.0 (the module raises ValueError otherwise). 2. Score each candidate on each criterion on one common scale (normalize any mixed scales first). 3. Compute the weighted score of each alternative with weighted_score(weights, scores). 4. Build the Pugh matrix with entries in {-1, 0, +1} and get the ranking with pugh_matrix_verdict(pugh_matrix, baseline_index). 5. Test the stability of the ranking with sensitivity_ranking(weights, scores, perturbation); rework the weights if a single perturbation flips the winner. 6. Apply selection_verdict(best_score, runner_up_score) and read the margin and the tie handling before declaring a winner. 7. Close the trade study with traceability_check(alternatives, requirement_ids) so every candidate and requirement is covered. ## Pitfalls - Weights that do not sum to 1.0: the ranking is skewed toward the criteria with the larger raw weights; the module rejects the set with ValueError instead. - Scoring on mixed scales: a 0 to 10 scale for one criterion and a 0 to 100 scale for another makes the wide scale dominate the weighted sum; normalize every criterion to one scale first. - Forgetting the baseline column: the Pugh matrix is meaningless without the reference concept that every cell is judged against. - Using raw sums instead of relative-to-baseline differences: a candidate that beats the baseline on every row must rank above the baseline, which only holds when each cell is compared with the baseline cell. - Treating a tie as a win: selection_verdict returns a tie verdict when the margin is at or below the tolerance; add a discriminating criterion or revisit the weights instead of picking arbitrarily. - Skipping the sensitivity check: a decision that flips under one weight perturbation rests on a single judgment and is not robust to review. - Closing without traceability: an alternative with no requirement ids cannot be justified in the certification context; run traceability_check before the selection is recorded. - Treating a small margin as a confident win: a margin below the threshold flags a weak decision even when the best score is the largest. ## Behavior contract (gate 3) The weighted scoring, Pugh matrix verdict, sensitivity ranking, selection verdict, and traceability check relations are exercised by the gate 3 contract test: scripts/test_trade_study_analysis.py against scripts/trade_study_analysis.py (stdlib unittest, offline). Run: python3 scripts/test_trade_study_analysis.py ## Compliance - Standards referenced, not reproduced: ARP4754A is a commercial SAE standard (purchase required); the trade study relations (weighted scoring, Pugh matrix, sensitivity analysis) are common decision-analysis methodology, summary-only per standards-map.yaml. - compliance: STANDARDS-REF, gated: false.