Model readiness review
Examine the schedule or cost model before simulation. Identify logic, coding or scope issues that could distort the analysis and agree how they will be addressed.
05 / Schedule and cost risk analysis
Put a confidence level on the date and the number
We review the schedule, estimate and risk inputs before modelling uncertainty. Where the available information supports an integrated assessment, schedule and cost are linked through defined dependencies and shared risk drivers. Results include their assumptions, limitations and sensitivity to alternative scenarios.
Purpose & approach
A single forecast cannot describe every possible outcome.
Risk analysis makes uncertainty explicit. We work with the project team to define the model, examine the quality of its inputs and test the sensitivity of the results. Findings are presented with their assumptions so the analysis can inform decisions without implying certainty.
Scope of support
What we do.
The service is tailored to the assignment. We agree the scope, required inputs and acceptance criteria before delivery.
Risk and uncertainty workshops
Capture uncertainty ranges and discrete risk events with the people closest to the work. Record the rationale, ownership and evidence behind each input.
Monte Carlo analysis
Run schedule or cost simulations using agreed distributions and dependencies. Distinguish uncertainty in the estimate from separately modelled risk events to reduce double counting.
Confidence and sensitivity
Present completion-date and final-cost ranges at agreed confidence levels. Identify the activities, assumptions and risks with the strongest influence on the modelled outcome.
Mitigation and contingency scenarios
Compare selected responses, alternative sequences and contingency approaches. Show how each option changes the result and which assumptions it depends on.
Updates and decision records
Refresh the analysis when the underlying plan or risk picture changes. Preserve the model basis and record the decisions taken from each review.
Practical deliverables
Outputs the team can use.
- Input quality assessment
- Monte Carlo schedule and cost models
- P50 and P80 outcomes and other agreed confidence levels
- Integrated schedule and cost results
- Ranked risk drivers
- Scenario and mitigation comparisons
- Contingency and schedule allowance recommendations
- Stated assumptions and limitations
Read the result with its basis.
The base example uses 361 synthetic outcomes. Confidence levels describe this dataset, not project certainty.
This is a comparison of illustrative datasets, not a Monte Carlo simulation or a client forecast. The mitigation example assumes a two-week shift in all outcomes.
Assessment basis
Make the assumptions reviewable.
A risk result needs a documented basis, agreed inputs and a record of what changed between assessments.
Establish the starting point.
Record the schedule and estimate versions, data date, scope boundaries and identified data-quality issues.
Show how uncertainty was modelled.
Document ranges, risk events, dependencies and the rationale agreed with the people responsible for the work.
Connect the result to action.
Compare scenarios, identify the main drivers and record mitigation owners, costs and review dates.
Your questions
Define the right assignment.
What does a confidence level mean?
It is a probability within the model and its stated assumptions. It is not a guarantee that the project will finish by that date or within that cost.
Can schedule and cost risk be considered together?
Yes, where the available information supports the relationship. Time-dependent costs, shared risk drivers and dependencies need to be defined explicitly.
Start a conversation
Discuss your projectLet’s define the support you need.
The analysis gives decision-makers a clearer view of exposure, the drivers behind it and the options worth testing.