Module 11 · Lesson 4 of 13

Measure submission pack

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Learning objectives

Assemble a Measure submission pack that proves data trustworthiness.
Fix the measurement system before quantifying performance.
Report baseline stability and capability with honest limitations.

Required evidence

Detailed process map
Operational definitions (point, method, condition, unit, tolerance)
Data-collection plan and check sheet
Sampling and stratification plan
MSA (Gage R&R or attribute agreement)
Baseline time plots, Pareto and descriptive statistics
DPU/DPMO/yield where valid
Stability and capability with assumption checks
Revised target and refreshed benefit

Copy-friendly Measure pack checklist

Operational definitions signed
MSA passes minimum thresholds
Sampling is representative
Baseline is stable before capability
Raw flat data and codebook retained
Exclusions documented with reason

Garment-factory example

A measurement project defines a chest-measure point, tape method and tolerance; runs a 3-appraiser × 10-part gauge study; then samples across size, style and shift for two stable weeks before capability is calculated.

Method

Pilot the data-collection sheet before scaling.
Repair the measurement system before analysis.
Store raw flat data plus codebook, not only summaries.

Common mistakes

Capability calculated on unstable data.
Convenience sampling.
Redefining defects mid-collection to improve the number.

Knowledge check

Pick one answer per question. Explanations appear after you submit.

1. Baseline capability is credible only when:
- Sample size exceeds 30
- The process is stable and the measurement system passes MSA
- The sponsor approves
- The target is met
2. Operational definitions must specify:
- Only the unit
- Point, method, condition, unit and tolerance
- The inspector's name
- The buyer's opinion

Author: Sanjeewa Dehiwalage · Last reviewed: 2026-07-21