How can we quantify the impact of a design change on scrap and yield?

You can quantify it, but only if you can separate the design change from everything else that changed around it.

The practical method is to compare scrap and yield at the part, operation, and revision level before and after the design change, then adjust for confounders such as supplier lot, material condition, tooling state, routing changes, inspection changes, machine changes, operator mix, and production volume. If you do not control for those factors, the number may be directionally useful but not defensible.

What to measure

At minimum, measure the same product family across a defined baseline period and post-change period using consistent definitions.

• First-pass yield by operation and by finished assembly

• Scrap rate by count, unit, weight, or cost, depending on how your plant records loss

• Rework and repair incidence, because some design changes reduce formal scrap but increase hidden recovery effort

• Nonconformance rate and defect codes tied to the changed characteristics

• Cost of poor quality, if finance and quality data can be linked reliably

• Cycle time impact, because yield improvements can come with longer processing or inspection time

Recommended approach

1. Define the exact design change window using the approved revision, effectivity, and disposition dates.

2. Identify the affected part numbers, configurations, routings, work centers, and suppliers.

3. Build a pre-change baseline and a post-change observation window with enough volume to be meaningful.

4. Segment results by operation, defect mode, machine family, supplier, and lot where possible.

5. Exclude or flag units built during transition conditions such as mixed inventory, temporary deviations, pilot builds, training periods, or parallel routings.

6. Compare the changed design against either its own historical baseline or a matched control population if other plant conditions were moving at the same time.

7. Validate whether the observed shift is statistically credible, not just operationally interesting.

How to calculate it

A simple starting point is:

• Scrap impact = post-change scrap rate minus pre-change scrap rate

• Yield impact = post-change first-pass yield minus pre-change first-pass yield

• Financial impact = change in scrap quantity or rework quantity multiplied by material, labor, outside processing, and delay cost where available

That is only the first layer. In most brownfield plants, a better estimate comes from stratified analysis or regression that controls for major drivers such as supplier, lot, machine, shift, and inspection method. If the design change altered tolerances, materials, joining method, or inspection characteristics, those factors need explicit treatment.

What usually makes the analysis fail

• No reliable link between engineering revision and actual as-built unit genealogy

• Scrap recorded only at the work order level, not by operation or defect mode

• Mixed old and new revision inventory consumed in the same period

• Simultaneous process changes, supplier changes, or tooling changes

• Inspection sensitivity changed, making defects appear to rise when detection simply improved

• Too little production volume after the change to distinguish signal from noise

• Rework moved off the books into concession, deviation, or informal recovery activity

In those cases, the honest answer is that you can estimate impact, but not attribute it cleanly.

Brownfield system reality

In many regulated environments, the needed data lives across PLM, ERP, MES, QMS, SPC, and sometimes spreadsheets. Quantification depends on whether those systems share consistent part, revision, lot, operation, and defect identifiers. If they do not, the work becomes a data reconciliation exercise before it becomes an engineering analysis.

This is why full replacement is often the wrong first move. Replacing PLM, MES, ERP, or QMS just to answer this question usually creates more qualification, validation, integration, and downtime risk than value in the near term. A narrower approach is usually safer: improve revision effectivity tracking, strengthen genealogy, standardize defect coding, and create a governed cross-system view of scrap, yield, and design revision.

What a credible answer looks like

A credible result usually includes:

• The exact design revision or effectivity studied

• The units, lots, and time period included and excluded

• The baseline and post-change sample sizes

• The scrap and yield deltas by operation and overall

• The main controlled variables and known uncontrolled variables

• Any transition effects, temporary work instructions, or training effects

• Confidence level or at least a clear statement of statistical and practical uncertainty

That level of traceability matters. Without it, the analysis may still help internal decision-making, but it will not stand up well to skeptical review.

So the short answer is: quantify the impact by linking approved design revision changes to as-built production records, then compare revision-level scrap and yield with controls for process and supply variation. If your data model, change control, or genealogy is weak, say so explicitly and treat the result as an estimate rather than a clean attribution.