Pocket PMOPERT analysis software for schedule estimates
Apply optimistic, most likely, and pessimistic durations to improve schedule estimates when uncertainty is high.
A connected workflow, not another isolated file
Pocket PMO keeps project management techniques close to the rest of the project record so decisions can be based on consistent data.
- Use three-point estimates instead of single-point guesses
- Make schedule uncertainty explicit for stakeholders
- Connect estimates to planning and reporting workflows
How teams use it
- Step 1
List activities or work packages from the project plan.
- Step 2
Enter optimistic, most likely, and pessimistic durations.
- Step 3
Review expected durations and use them in schedule planning.
Frequently asked questions
What is PERT analysis?
PERT analysis is a project estimation technique that combines optimistic, most likely, and pessimistic estimates to calculate an expected duration.
Who should use PERT analysis?
PERT is useful for project managers working with uncertain activities where a single duration estimate would hide risk.
How is a PERT expected duration calculated?
The expected duration weights the most likely estimate four times more heavily than the optimistic and pessimistic bounds, then divides by six. Pairing that expected value with the range between the bounds shows both the schedule you can commit to and the risk you are carrying.
Related planning pages
Work breakdown structure software for practical project planning
Create work breakdown structures that connect scope, estimates, resources, and reporting in one project planning workspace.
ROM estimation software for early project decisions
Build rough order of magnitude estimates for early project decisions and refine them as scope becomes clearer.
Critical path method software for schedule control
Identify critical activities, schedule dependencies, and float with CPM planning tools for project managers.
See it in Pocket PMO
Pocket PMO keeps project management techniques close to the rest of the project record so decisions can be based on consistent data.