Process Skids, Modular Equipment & Packaged Plants worked example
Delivery Risk with schedule-slip severity of 3 score: a worked example in process skids, modular equipment & packaged plants
Here is what the math looks like when conditions slip. We hold every other input steady and drop schedule-slip severity to 3 score, then walk the calculation through step by step. Delivery Risk rolls three judgments about a skid package's schedule into a single weighted score so project teams can rank which jobs threaten on-time delivery.
The inputs for this scenario
- Schedule-slip severity: 3 score (the input this scenario stresses; the baseline uses 6)
- Slip likelihood: 4 score (held at the documented default)
- Slip detectability: 3 score (held at the documented default)
Working through the calculation
- The calculation starts from the formula this tool documents: Delivery Risk risk score = severity × 0.40 + occurrence × 0.35 + detection × 0.25.
- Risk score works out to 3.35 score at these inputs, and this is the headline figure for the scenario.
- Severity works out to 3 score at these inputs.
- Occurrence works out to 4 score at these inputs.
- Detection works out to 3 score at these inputs.
How this compares with the baseline
- Against the tool's baseline example, where schedule-slip severity sits at 6 score and the headline result is 4.55 score, this scenario comes in 26.37% below the baseline at 3.35 score.
- The practical read: the gap between this scenario and the baseline is entirely attributable to schedule-slip severity, so recovering it is worth quantifying in dollars before considering equipment or staffing changes. The score is only as good as the 1-to-10 judgments behind it; inconsistent scoring across estimators makes cross-project comparison unreliable.
Results at a glance
- Risk score: 3.35 score (headline result)
- Severity: 3 score
- Occurrence: 4 score
- Detection: 3 score
Run it with your numbers
- To rerun this with your own numbers, open the live Delivery Risk calculator, set schedule-slip severity to your actual value, and adjust the remaining inputs to match your operation.
Last reviewed 2026-05-12.