Scrap reasons are unknown and yield loss isn't actionable
Scrap is accepted as 'normal' because nobody can tie it to specific causes, machines, or conditions.
The problem
Who feels it most
Operations managers, QA teams, and finance/controlling who see material costs but can't explain variances.
How common is this?
High. Scrap and rework are standard benchmarking metrics across every manufacturing sector, indicating broad, persistent relevance.
Typical workaround today
End-of-shift scrap tallies, 'scrap buckets' with no attribution, and Excel reconciliation that happens days later.
Why ERP / WMS doesn't solve it
ERP can record scrap quantities but rarely captures real-time reason codes linked to machine events, shift context, and operator actions without heavy customisation.
Business impact
Scrap percentage by SKU stays invisible without structured capture
Rework hours and material loss accumulate silently
Internal failure costs are often substantial but unattributed
Structured scrap capture with production context and correlation analysis
Quick scrap event capture: operator selects a reason code and enters quantity — or scrap counter integration captures it automatically.
Every scrap event is tied to the current batch, order, machine, timestamp, and operator — building a rich dataset for analysis.
Top loss tree dashboard shows scrap by reason, product, line, and shift — revealing patterns like 'scrap spikes after changeover' or 'higher scrap on night shift'.
Quality loss feeds directly into OEE-lite calculations, making yield loss visible alongside availability and speed loss.
Scrap reason taxonomy templates (customisable per industry) ensure consistent categorisation from day one.
Ready to solve this?
Book a demo and we'll show you exactly how Frontlink addresses this problem in your environment.