Industry Use Cases / WORKED EXAMPLE
Which inspection threshold can your team use?
Test quality and review capacity together before accepting a model score.
For: quality engineers, operations leads and AI evaluators. You will produce: a threshold decision with explicit acceptance limits.
Illustrative data created by Industry AI Decision. These are teaching scenarios, not customer results or product benchmarks. Version 1.0 · 25 September 2026.
01 / WORKING DATA
Use the same inspection batch for both thresholds.
This synthetic batch contains 10,000 units: 100 defective and 9,900 good. “Positive” means sent for review. Every positive takes 2 minutes; review capacity is 360 minutes per batch. The example acceptance target is at most 10 missed defects. These figures are assumptions for the exercise.
| Threshold | TP | FP | FN | TN | Reviews |
|---|---|---|---|---|---|
| A | 90 | 198 | 10 | 9,702 | 288 |
| B | 80 | 99 | 20 | 9,801 | 179 |
TP: defect correctly flagged. FP: good unit flagged. FN: defect missed. TN: good unit not flagged.
02 / YOUR TURN
Can either threshold meet both limits?
Calculate recall, precision and review time. Then check the missed-defect limit and the review-capacity limit separately.
Show the calculation and answer
A: recall 90/100 = 90%; precision 90/288 = 31.25%; review time 288 × 2 = 576 minutes. It meets the missed-defect limit but exceeds review capacity by 216 minutes.
B: recall 80/100 = 80%; precision 80/179 = 44.69%; review time 179 × 2 = 358 minutes. It fits review capacity but misses 20 defects, exceeding the limit of 10.
Neither meets both gates. Higher precision alone does not make B acceptable. A capacity change, better classifier, better review workflow or another candidate threshold needs evaluation.
03 / COST SENSITIVITY
Check what your cost comparison includes.
Assume a false positive costs $5 and a missed defect costs $100. These are illustrative incremental error costs; do not add the same review cost twice.
| Threshold | False-positive cost | Missed-defect cost | Error-cost subtotal |
|---|---|---|---|
| A | 198 × $5 = $990 | 10 × $100 = $1,000 | $1,990 |
| B | 99 × $5 = $495 | 20 × $100 = $2,000 | $2,495 |
These subtotals exclude implementation, routine review of true positives and other operating costs. The financial comparison does not override the quality or capacity gates.
04 / COMPLETED EXAMPLE
Decision: hold production release; resolve the failed gate.
| Decision field | Example entry |
|---|---|
| Preferred experiment | Test how to reduce A’s 576-minute review requirement while retaining its detection performance. |
| Current release decision | Hold: neither candidate meets both agreed limits. |
| Owner | Quality lead owns missed-defect acceptance; operations lead owns review capacity. |
| Next evidence | A new held-out batch, performance by product/line, review-time observations and an escalation rule. |
| Change control | Record the model, threshold, data period and reviewer before changing the production policy. |
Copy your own decision fields: batch and label definition; TP/FP/FN/TN; minutes per review; available minutes; missed-defect limit; cost assumptions; accepted threshold or hold reason; owner; recheck date.
05 / USE THE TOOL
Reproduce both scenarios, then replace the assumptions.
Enter the four counts from row A, choose USD, and set false-positive cost to 5 and false-negative cost to 100. Calculate, then repeat with row B. Set Input evidence to Estimated and identify these as synthetic examples. The tool calculates detection and error-cost metrics; review capacity is the separate calculation above.
Free membership provides tools and saved results. Create an account or sign in. For the concepts, read metric selection and thresholds as an operational policy.
Metric definitions: scikit-learn classification metrics. The batch, costs and acceptance limits are original illustrative assumptions.
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