The plan says 96 days. What date can you actually commit to?
A plan adds durations along its longest path and returns one date. Every one of those durations is an estimate, and along a chain of dependencies the tasks that come in early do not pay for the ones that run late, because nothing starts until the last of its predecessors is done. Schedule risk analysis simulates the same network thousands of times and turns that single date into a confidence ladder.
Method guide for Google Sheets Schedule Risk (Monte Carlo CPM) Pro engine
What schedule risk analysis is
You give every activity an optimistic, likely and pessimistic duration and list what it waits on. Each trial draws a duration for every activity, runs the forward pass to get a finish, then runs the backward pass to see which activities had no float on that particular trial. Ten thousand trials give you a distribution of finish dates, and for every activity the share of trials in which it sat on the driving path.
That second number, the criticality index, is the one a deterministic plan cannot give you. A plan has one critical path. A real project has several candidates, and which one drives the finish depends on how the durations land.
The questions it answers
- How likely is the date already in the plan?
- What date can I hold eight times out of ten?
- How much schedule contingency is that, in days?
- Which activities really drive the finish, and which am I chasing for no reason?
When you do not need to simulate
Skip it when the work is short, mostly sequential and well understood. Three tasks you have run twenty times do not need ten thousand trials. Skip it when nothing hinges on the date, and skip it when your durations are not estimates at all but fixed contractual windows.
Be careful what you conclude from it, too. The tool samples durations on the network you gave it, so it does not level resources or reroute logic. If one team is committed to two parallel paths, the model will happily run them at the same time, and spotting that is still your job.
A worked example
Fourteen activities of an enterprise system cutover, in working days. Two workstreams run in parallel after the requirements freeze and converge on integration test, then acceptance, rehearsal and go-live.
| Activities | 14, with predecessors |
| Estimates | optimistic, likely and pessimistic days on each |
| Longest chain of likely durations | 96 working days |
| Sampling | PERT |
| Simulation | 10,000 trials, seed 7 |
The answer
Ten thousand runs of the same network:
- Plan on paper
- 96 working days
- P50 finish
- 104 days, the coin-flip date
- P80 finish
- 109 days, the date to commit
- Data migration drives it
- 75% of runs
The 96-day plan is beaten in fewer than one run in ten, so it was never a commitment, it was a best case. The date you can hold eight times out of ten is 109 working days, and the 13-day gap between the two is schedule contingency, better held openly than hidden a day at a time inside individual tasks. Criticality then says where attention pays: data migration drives the finish in 75% of runs, the interface build in 25%, and business readiness in none of them. That is one workstream to protect and one to stop chasing in the status meeting.
The same seed makes a before-and-after comparison mean something. In the extension of time template, one disputed activity is on the driving path in 96% of trials, so it cannot be waved away as having float. But rerunning with that one activity back at its planned duration, and nothing else changed, moves the P80 finish by about 10 working days against a claim of eight weeks, because a competing path takes over as the driver. Both runs share a seed, so the difference is the change rather than the sampling.
Figures from the shipped What Finish Date Can I Commit To? template, reproduced by running the same code the add-on runs, at 10,000 trials with PERT sampling and seed 7.
What you get back in the sheet
- A task table on the sheet: id, name, predecessors, optimistic, likely, pessimistic.
- PERT or triangular sampling from the three-point columns, whichever you prefer to defend, or any of the 36 distribution families when a task deserves its own shape.
- Completion percentiles at 10, 25, 50, 75, 80, 90 and 95 percent, with mean, standard deviation, minimum and maximum.
- A criticality index for every activity: the share of trials in which it sat on the driving path.
- Schedule drivers: how strongly each task's sampled duration moved the finish, so you know which estimates are worth tightening first.
- A path-convergence note, in the run's own numbers: wherever parallel paths meet, the latest one sets everything after the join, so an overrun on any path pushes the finish out while an underrun on one is absorbed by the others. A plan built on one number per task cannot see that.
- A seed, so two runs differ only by the thing you changed. That is what turns a comparison into evidence.
- A SIPmath 3.0 export of the finish trials, so another SIPmath tool can carry this schedule's uncertainty into its own model.
- A written reading of the result. Every run ends with a card titled “What this means”: the probability that matters, the biggest driver, and what to tighten first. On the free tier the tool writes it from its own figures. On Pro you also get an AI reading of the same figures, written by a language model, and See what was sent shows the whole payload: “Ratios, shares, counts and cell references only. No cell values, no labels, no names, no formulas.”
Free or Pro
This is a Pro engine, one of five. Every free install includes five full-quality runs of any Pro engine: the same engine with nothing switched off, at any model size, on your own numbers. The five are one allowance shared across all five Pro engines, not five for each. A run counts only once it has produced a report, so a cancelled or failed run costs you nothing.
After that, this engine asks you to upgrade and nothing else does. Every statistics, forecasting and machine-learning tool stays free on every plan, and optimization and what-if stay free with limits set by the method rather than the plan: 2,000 decision cells on Simplex LP, 32 changing cells in a scenario. Nothing you have built stops working. Pro also removes the “Made with Sortia” footer from generated report tabs.
The other four Pro engines are Monte Carlo risk simulation, Decision trees, Critical Chain and Optimization under uncertainty. Pro is $199/year, and a Day Pass covers seven days for $9 if you have a single decision to make.
Templates that use it
Each one loads into your sheet with the numbers already in place, and every figure on its page was computed by the tool itself.
- The worked example above, in fullWhat Finish Date Can I Commit To?
- Whether a delay actually pushed the finish, read off criticality and a second runDid That Delay Actually Push the Finish Date?
- The same method on a kitchen timetable, if you want the idea in ten minutesFinish Before the Party?
The other 31 models in the library that run schedule risk analysis:
- Which tasks actually set your launch date?Launch Plan: Find the Critical Path
- Which risks actually deserve the money?Project Risk Register, Priced
- Whose calendar is the real critical path?Is Anyone Double-Booked?
- Which task should you actually chase?Office Move: What Sets the Date?
- Which part of the conference actually decides the date?Conference Plan: What Sets the Date?
- Does the plan need one person in two places?Agency Load: Who Is Overbooked?
- Which event risks are worth paying to prevent?Event Risk Register
- You are 43% done. When does the project actually finish?Will the Project Finish on Time?
- What will this project actually cost at completion?Project Health Check (EVM)
- Whose cost to complete should you believe?Is the Cost to Complete Claim Credible?
- Should you certify this payment application?Should I Certify This Payment Application?
- Which finish date can you put in the contract?The P80 Construction Schedule
- When does the punch list actually finish?Is Closeout on Track?
- Why is the textbook schedule too optimistic?The Classic Project Network, Simulated
- A product launch risk register pricing five ways a launch goes wrong against mitigation cost, from a calendar invite to production fixes. Free template.What Could Go Wrong With This Launch?
- Is the overrun general or is it one bad line?Is the Event Build On Track?
- A legal matter risk register pricing five case risks against mitigation cost, showing which of the five mitigations lose money in expectation. Free template.What Could Go Wrong in This Matter?
- Is the rollout slowing, or does it just feel slow?Is the Rollout Where It Should Be by Now?
- A construction site risk register pricing five risks where every mitigation pays for itself, plus the exposure left for the contingency line. Free template.What Could Go Wrong on Site?
- Which stream will move the delivery date?When Does This Engagement Actually Finish?
- Is the cheapest fix on your risk list the best one?What Could Go Wrong This Term?
- A consulting engagement risk register ranking five risks by exposure and by mitigation value, since the two rankings are not the same list. Free template.What Could Go Wrong on This Engagement?
- Is your busiest person really the problem?Is Anyone Booked on Two Clients at Once?
- How late will the release really be?Can We Ship on That Date?
- How often does this plan beat the deadline?Will We Be Ready for Peak?
- Which date can you defend when the client pushes?Can I Promise That Date to the Client?
- Which of your peak-season fixes actually pay?What Could Go Wrong at Peak?
- Would three extra days move your critical path?What Sets the Go-Live Date?
- Two tasks both have slack. Can you spend it twice?When Will the Course Be Ready?
- Which fund risks are worth paying to cover?What Could Go Wrong in the Fund?
- How much of the opening date is just waiting?What Sets the Opening Date?
Try it in your own sheet
- Open Sortia in Google Sheets and choose Start from a template.
- Pick one of the models above, and it loads with the inputs filled in.
- Change the assumptions to fit your situation and press Run.
Never used Google Sheets? Start here goes the whole way, in seven steps, and assumes nothing.
Other methods: Monte Carlo Decision trees Critical chain Optimization under uncertainty Statistics Machine learning Forecasting Optimization What-if analysis