Example

Floor 4 is slipping. What should the team do?

This is the running example used across this site, told in full: a multifamily project where the same electrical rough-in package repeats on every floor, and Floor 4 has fallen behind. Walk through how the picture—and the recommendation—changes as evidence arrives.

This example uses simulated construction data to demonstrate the product concept. It is not a customer result or an industry benchmark.

Five-stage overview of the scenario: material constraint identified, kits delivered, second crew added with supervision, production checks confirmed, forecast updated with a narrowed range.
The full scenario at a glance: constraint identified → material expedited → crew added → production recovers → forecast updates. Illustrative concept, simulated data.

The current project state

Electrical rough-in has run at plan on Floors 1–3. On Floor 4, production has dropped to 62% of the planned rate. The milestone that matters is turnover of Floor 4 to drywall in nine working days.

The platform's current estimate, with evidence still incomplete:

Floor 4 — Electrical rough-inBehind plan
  • Progress 48% complete (plan: 60%)
  • Material readiness 5 of 8 unit kits complete Uncertain
  • Crew Crew B, 6 electricians
  • Turnover probability 54% (likely range 41–66%)

Uncertainty reflects incomplete field information, not a system fault.

Six-floor project map of repeated units. Floor 4 is highlighted: most units flagged with incomplete material, one unit blocked, one crew active. Floors below are largely complete; floors above not yet started.
The six-floor project map: complete floors below, future floors above, and Floor 4 highlighted with material flags, one blocked unit, and the active crew. Illustrative concept, simulated data.

Evidence arrives from the field

Over the next day, three field updates arrive. Each one changes the estimated state of the work—and the value of the available actions.

Evidence timeline
  • Mon 07:20 Daily report: two electricians pulled to punch-list work on Floor 2.
  • Mon 09:45 Warehouse: 3 of 8 material kits missing device boxes and fittings.
  • Tue 11:10 Superintendent: a second crew becomes available Thursday.

After these updates, turnover probability under the current plan drops to 38% (range 27–51%).

The decision point

A second crew is available Thursday. The instinctive response is to add it to Floor 4 immediately. The platform compares the practical options from the same view of current conditions:

Comparison of four actions with expected turnover probability and main consideration
ActionTurnover probabilityMain consideration
Continue current plan38% (27–51%)No added cost; delay likely continues
Add crew Thursday47% (35–58%)Incomplete kits limit useful work; congestion risk
Expedite kits only61% (50–71%)Procurement cost; crew stays productive

Illustrative results based on a simulated project scenario.

The historical trap

Looking only at history, adding crews appears to make things worse: across past projects, work that received additional labor finished later than work that did not.

The history is misleading. Crews were added to areas that were already in trouble. The poor outcomes reflect the conditions that triggered the decision—not the effect of the added labor.

Cornerstone accounts for the conditions surrounding past decisions before estimating whether an action is likely to help now.

Two-panel comparison. Top: the raw historical view, where actions carry wide, unfavorable outcome ranges, flagged in amber. Middle: the sequence of conditions that triggered the action. Bottom: the context-adjusted view in blue, where the same actions show favorable ranges once conditions are accounted for.
Raw history (amber) vs. the context-adjusted estimate (blue). The association is driven by which work received the action, not by the action alone. Illustrative concept.

The context-adjusted conclusion

Under current conditions—incomplete kits, a partially staffed crew, congestion risk in occupied units—adding labor immediately is estimated to produce limited benefit. Added labor is likely to help only after readiness constraints are addressed.

The recommendation: expedite the missing kits now, then add the second crew Thursday, with the key assumption that kits arrive within two working days. If procurement cannot confirm that, the alternative is to resequence the second crew to Floor 5, where kits are complete.

The result — and what was learned

The team expedites the kits; they arrive Wednesday afternoon. The second crew starts Thursday on a ready workface. By the following Monday, production is back to 94% of plan and turnover probability has risen to 81% (range 72–88%).

The observed outcome—conditions, decision, result—feeds back into the platform, improving early estimates for the same repeated work on Floors 5 through 12.

After action — Floor 4Recovering
  • Production vs. plan 94%
  • Turnover probability 81% (range 72–88%)
  • Learning Kit readiness threshold updated for Floors 5–12

Illustrative product concept — simulated scenario.

Four-stage sequence: the constrained area identified on the floor plan, the prioritized constraint list, the chosen expedite-then-add-crew option, and the forecast fan narrowing after the action.
How the action played out: constraint identified → options ranked → expedite-then-add-crew applied → forecast narrows. Illustrative concept, simulated data.

What this example deliberately leaves out

This public walkthrough shows the decision logic: conditions, actions, expected consequences, confidence, and learning. It does not expose model parameters, adjustment sets, equations, or update mechanics. A more detailed demonstration is available privately under appropriate confidentiality.

Have a Floor 4 of your own?