Playbook

First Pass Yield (FPY) Improvement Experiment Pack

A short, practical playbook with guided experiments, root-cause checklists, poka-yoke prompts, a digital inspection template, and a pilot evaluation scorecard you can run and save.

Interactive Tool

FPY Improvement Experiment Pack (Pilotable)

Welcome

This pack helps teams run focused experiments to raise first-pass yield (FPY) by catching defects earlier, simplifying steps, and testing error-proofing ideas. Use the guided experiments to scope a small pilot, capture observations, and evaluate impact using a simple scorecard — so you learn quickly and avoid treating inspection as the primary quality strategy.

How to use this playbook: Select one or two experiments, define a pilot scope (units, stations, days), run the changes, record results here, then review the pilot evaluation to decide next steps. Aim for measurable improvement (FPY, rework hours, throughput) and clear learnings for scaling.

Note: This tool records pilot data so you can compare before/after and build organizational memory.

Choose the experiments you'll run during this pilot. Run 1-2 at a time for clear learning.
How many units will you include in this pilot? Choose a size that gives meaningful measurement but remains manageable.
How many days will you run the pilot?
Check all that apply for this defect family. Use these as prompts during root-cause analysis.
Pick points to sample or inspect during the pilot.
Describe the physical or process change to prevent the defect (fixtures, guides, sensors, templates, simple alarms).
Estimate feasibility for the pilot and short-term deployment.
Select items to include in your digital inspection form for the pilot.
Enter the numeric sample size or frequency for inspections. Use 1 for every unit.
How often will this inspection run during the pilot?
Enter the measured FPY percent for the area/process before the pilot.
Enter the FPY percent observed during or immediately after the pilot.
Rate the magnitude of FPY improvement from this pilot (0 = no change, 10 = major improvement).
Estimate the reduction in rework hours per shift or per pilot unit set.
Rate net throughput impact (positive values = throughput improved).
Rate added operator burden (0 = heavy extra work, 10 = no additional burden). Higher is better.
What did you learn about root causes, data quality, or unintended effects?
E.g., scale, adjust, design robust poka-yoke, integrate with MES, or stop and choose a different experiment.
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