Factory Yield Tracking: 2% Waste Per Batch Across 100 Batches = 20% Annual Loss
A factory producing 10,000 units monthly at $10 COGS per unit accepts 2% scrap = 200 units wasted monthly = $2,000 cost. Over a year: $24,000. But if scrap is from a fixable process issue (machine calibration, operator error), it's preventable waste. AskBiz tracks yield by batch to spot patterns.
- The Normalized Scrap Problem
- Why Yield Tracking Is Hard
- AskBiz: Yield Tracking by Batch, Material, Operator, Machine
- Continuous Improvement Cycle
- Real Example: Automotive Supplier
The Normalized Scrap Problem#
Most factories normalize scrap: "2-3% is normal for this process." They accept it. But accepted waste is still cost. A manufacturer producing circuit boards might have 3% defect rate because: (a) 1% from unavoidable environmental factors (dust, temperature). (b) 2% from preventable issues (operator error, machine drift). They treat all 3% as "normal" and price accordingly. But if they could eliminate the 2% preventable scrap, they'd improve margin by 2% of COGS, which is huge.
Why Yield Tracking Is Hard#
Factories track final yield (units produced / units expected). If 100 units are expected, 97 are produced, yield is 97%. But they don't track why: Was it material batch A, operator X, machine Y, setup Z? Without granular data, patterns are invisible. Worker A might have 5% scrap, Worker B might have 1%, but management doesn't know because they don't track by operator.
AskBiz: Yield Tracking by Batch, Material, Operator, Machine#
AskBiz logs: (1) Batch ID and expected yield. (2) Actual units produced. (3) Units scrapped and reason (material defect, machine error, operator error, environment). (4) Operator name, machine, material lot. Weekly report shows: (1) Overall yield (95.8%). (2) Yield by operator (A: 96%, B: 98%, C: 92%). (3) Yield by machine (Machine 1: 97%, Machine 2: 94%). (4) Yield by material lot (Lot-2024-0510: 98%, Lot-2024-0511: 93%). (5) Scrap reasons (40% operator error, 30% machine drift, 20% material defect, 10% environment). From this, the factory identifies: "Machine 2 has 94% yield (3% below target). Likely misalignment or worn tooling. Schedule maintenance." Or: "Operator C has 92% yield. Provide coaching on quality control." Or: "Material Lot-2024-0511 had 7% scrap. Check supplier QC."
Continuous Improvement Cycle#
Track → Identify issue → Fix → Measure improvement. AskBiz enables this. Week 1: Identify Machine 2 issue. Week 2: Maintenance done. Week 3: Machine 2 yield improves to 97%. Improvement tracked and quantified.
Real Example: Automotive Supplier#
An automotive parts supplier (producing brackets for car seats) had 4% scrap rate across all production. They assumed it was normal and priced accordingly. After implementing AskBiz yield tracking: (1) Discovered that scrap varied: Operator A (3%), Operator B (5%), Operator C (2%). (2) Operator B was damaging units during finishing (over-sanding). Training reduced his scrap from 5% to 3%. (3) Material lot from Supplier X had 6% scrap (vs. 2% for Supplier Y). Negotiated quality improvements with Supplier X. (4) Machine #3 had 5% scrap (vs. 3% average). Maintenance revealed worn bearings. Replacement reduced scrap to 3%. (5) Overall scrap improved from 4% to 2.5%. On $5M annual production, that's $75K in recovered margin.
Setting Up Yield Tracking: What to Log at Each Stage#
Step 1: Assign a batch ID to every production run and log the material lot, operator, and machine at batch start — this is the minimum data set needed to slice yield by any dimension later. Step 2: At the QC checkpoint, log not just pass/fail counts but the specific defect reason using a short standardized list (material defect, machine tolerance, operator handling, environmental) rather than free-text notes that are hard to aggregate later. Step 3: Log scrap and rework separately — a unit that's reworked and sold still cost labor time even though it didn't become a total loss, and conflating the two understates the true cost of quality issues. Step 4: Review the weekly yield-by-dimension report (operator, machine, material lot) looking specifically for outliers more than one standard deviation from the group average — these are the highest-value places to investigate first, since they represent the biggest gap between current performance and what's already being achieved elsewhere in the same operation. Step 5: Close the loop by logging the fix applied and re-checking yield 1-2 weeks later, building a track record of what interventions actually worked, which compounds in value as the factory accumulates more of these before-and-after data points.
People also ask
What's an acceptable scrap rate?
Depends on process complexity. Stamping: 1-3%. Machining: 2-5%. Electronics: 0.5-2%. High-precision: <0.5%.
How do I know if scrap is preventable?
Compare across operators, machines, material lots. If one operator has 2% scrap and another has 5%, the difference is preventable.
Should I stop a production run if yield drops?
If yield drops below process capability, investigate. If it's a machine issue, stop and fix. If it's a one-time batch, monitor.
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Recover $50K-150K Annually From Preventable Scrap
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