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ProductionPlanningOne current plan for the office and the floor.Production and operatorsWork instructions, scans and shift handover on the tablet.Quality and batch recordChecks, deviations and release without paper.
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Contract manufacturing and private labelYour customers are brands with high expectations.Co-packing and fillingFood and supplementsLot numbers, shelf life and labels per order.FoodSupplementsBakery and confectioneryBeveragesAnimal feed and pet foodChemicals and cosmeticsMixing and filling to order, documents per batch.Cosmetics and personal careCleaning productsPaints, coatings and adhesivesFlavours and fragrancesMake-to-order manufacturingMetal, plastics and assembly.Printing, packaging and labelsPlastics processingMetalworkingMachine and equipment buildingFurniture and interior fittings
Industry not listed?Do you produce to order?

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How Frontlink connects, with an API for your own tools.

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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.

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Today, and with Frontlink.

Today

End-of-shift scrap tallies, 'scrap buckets' with no attribution, and Excel reconciliation that happens days later.

With FrontlinkStructured scrap capture with production context and correlation analysis
  1. Quick scrap event capture: operator selects a reason code and enters quantity, or scrap counter integration captures it automatically.
  2. Every scrap event is tied to the current batch, order, machine, timestamp, and operator, building a rich dataset for analysis.
  3. 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'.
  4. Quality loss feeds directly into OEE-lite calculations, making yield loss visible alongside availability and speed loss.
  5. Scrap reason taxonomy templates (customisable per industry) ensure consistent categorisation from day one.
Time to value
Days to 1 week
Complexity
Low
Works with machine counters
Yes

The problem

  1. Who feels it most

    Operations managers, QA teams, and finance/controlling who see material costs but can't explain variances.

  2. 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.

  3. How common is this?

    High. Scrap and rework are standard benchmarking metrics across every manufacturing sector, indicating broad, persistent relevance.

Business impact

  1. Scrap percentage by SKU stays invisible without structured capture
  2. Rework hours and material loss accumulate silently
  3. Internal failure costs are often substantial but unattributed

Solved by

  1. PerformanceFrontlink measures rate, downtime and scrap per line, per order and per shift. So you see where things go wrong while it still matters, and what an order really cost.
  2. Quality and batch recordQuality checks appear as a checklist on the tablet, with the norm next to each one. The batch record builds itself from what was actually made, and release and certificate follow without searching.

Frequently asked questions.

Who typically feels this problem?

Operations managers, QA teams, and finance/controlling who see material costs but can't explain variances.

Why doesn't an ERP or WMS system solve this?

ERP can record scrap quantities but rarely captures real-time reason codes linked to machine events, shift context, and operator actions without heavy customisation.

How does Frontlink solve this?

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.

How quickly does it deliver value?

Typical time to value: Days to 1 week. Implementation complexity: Low.

Which process takes you the most time?

Book a 30-minute call. We look at your own examples together.

Book a call