| name | pra-sensitivity-contingency |
| description | Size a project contingency reserve and rank the tasks that drive total uncertainty, using the PRA package. Use after (or alongside) a Monte Carlo simulation when a user asks "how much reserve do I need?" or "which tasks should I focus mitigation on?"
|
Sensitivity analysis
sensitivity_tool(task_dists_json, cor_mat_json) measures each task's
contribution to total project variance. It takes the same input format as
mcs_tool (a JSON array of task distributions plus an optional correlation
matrix). Returns one value per task; higher values are the primary drivers of
overall uncertainty. Results are typically shown as a tornado chart. Direct
mitigation resources at the highest-sensitivity tasks first.
Contingency reserve
contingency_tool(phigh, pbase) returns the buffer between a high-confidence
percentile and the base estimate:
contingency = P_high - P_base
phigh (default 0.95): high-confidence percentile — there is a phigh
probability the actual value will not exceed it.pbase (default 0.50): the median ("most likely") total.
Important: call mcs_tool first — contingency_tool operates on the most
recent Monte Carlo results.
Common confidence levels
- P80 contingency: moderate confidence, common in commercial projects.
- P95 contingency: high confidence, common in government/defense projects.
- Management reserve: an additional buffer for unknown-unknowns, typically
5–10% of BAC, held above contingency.
Workflow
mcs_tool(num_sims, task_dists_json, cor_mat_json)contingency_tool(phigh = 0.95, pbase = 0.50)sensitivity_tool(task_dists_json, cor_mat_json)- Reduce uncertainty in high-sensitivity tasks to shrink the needed reserve.
References
- GAO Cost Estimating and Assessment Guide (GAO-20-195G).
- GAO Schedule Assessment Guide (GAO-16-89G).
