Automotive & tier 1 supply
Traceability at line rate, evidence at audit speed
Automotive quality is judged on two things: whether you can prove control of critical characteristics, and how fast you can contain an escape. Trust Quality Assurance links every measurement to the part, tool, operator and supplier lot that produced it, so containment is scoped in minutes instead of shifts.
- scrap cost vs. baseline
- -41%
- investigation time
- -62%
- typical Cpk on monitored CTQs
- 1.42
- plants in a typical rollout
- 9
Standards supported
- IATF 16949
- VDA 6.3
- ISO 9001
Typical monitored characteristics
- Weld strength
- Torque
- Seal width
- Surface finish
- Dimensional CTQs
- Leak rate
Where the cost sits
The problems we start from
These are the recurring findings from discovery workshops in this sector. If none of them describe your operation, a pilot is probably premature — and we will say so.
Operational problems
Escapes are found at end-of-line audit
By the time an audit finds a defect, several hours of production have shipped or entered finished goods, and containment has to cover everything in between.
Data lives in three systems
Process parameters in the historian, results in the gauge software, dispositions in a spreadsheet. Every investigation starts with a manual export and a VLOOKUP.
Customer complaints arrive without context
A field claim references a VIN or delivery note, not a subgroup. Reconstructing the genealogy takes days and rarely convinces the customer quality engineer.
Layered process audits consume engineering time
Evidence for LPA, PPAP and run-at-rate is assembled by hand for each review, pulling senior engineers off improvement work.
Engineering constraints
Cycle times leave no room for latency
Inspection decisions have to be made inside the takt time, which rules out any architecture that requires a cloud round trip per part.
Mixed-model production
The same line runs several part numbers per shift, each with its own control plan, limits and sampling requirements.
Tier-2 variability
Incoming material variation shows up as process drift, so supplier data has to be part of the same analysis rather than a separate report.
OEM-specific reporting formats
Each customer expects its own evidence structure, and manual reformatting is where errors and delays appear.
Platform capabilities
How the platform is configured for automotive
Same architecture, same modules — configured against the characteristics, sampling logic and evidence expectations of this sector.
Characteristic-level SPC
Control charts and capability per CTQ, part number, tool and cavity, with rule sets configured to your control plan rather than a generic template.
Full genealogy
Serial, lot, tool, operator, shift and supplier lot linked to every reading, so containment is scoped by the data rather than by assumption.
Vision on existing stations
Weld, surface and assembly defect classification on the cameras you already own, with confidence and reviewer verdict stored per part.
8D for customer escapes
Customer complaint to containment, root cause, corrective action and verified effectiveness in one record chain, exportable in the OEM's format.
Supplier quality loop
Incoming inspection, PPM scorecards and supplier 8D tracked against the same characteristics used internally.
- Siemens Opcenter
- SAP S/4HANA
- OPC UA
- Cognex
- Zeiss CMM
- Microsoft Entra ID
Business outcomes
What changed for operations like yours
Customer-reported figures measured against documented pre-deployment baselines. Ranges, not single numbers, because process maturity dominates the result.
- Scrap cost
- -41%
- Investigation time
- -62%
- Audit preparation
- -3 days
- First pass yield
- +2.1 pt
Scrap cost
Inline detection replaces end-of-line discovery
Investigation time
8 h to 3 h median for a contained defect
Audit preparation
Evidence packs generated from live records
First pass yield
96.4% to 98.5% on monitored lines
| KPI | Typical movement | Measurement note |
|---|---|---|
| First pass yield | +1.5 to +2.5 pt | Measured on monitored characteristics after 2 quarters |
| Scrap and rework cost | -25% to -45% | Against a documented 12-month baseline |
| Cpk on critical characteristics | 1.33 → 1.45+ | Sustained rather than sampled at PPAP |
| Containment scope | -70% parts affected | Genealogy narrows the suspect population |
| Customer escape rate | -30% to -50% | Fewer escapes reaching the OEM |
Expected ROI
A value case your controller can interrogate
Every line below is an assumption, not a promise. During a pilot each one is replaced with a measured figure from your own baseline, which is what makes the business case defensible in a capital review.
| Value driver | Assumption | Annual |
|---|---|---|
| Scrap avoidance | 0.9% scrap rate reduced by a third | €250k |
| Engineering time recovered | 2 engineers × 6 h/week of manual data work | €96k |
| Containment scope reduction | 3 events per year, 70% smaller suspect population | €180k |
| Audit and reporting effort | 12 audits and reviews per year, 3 days each recovered | €64k |
| Indicative total | Before platform and integration cost | €590k |
Payback
5–9 months
From first connector to cumulative break-even
How we validate it
- Baseline recorded before any change
- Success criteria written into the pilot scope
- Measured comparison in the pilot report
- Exit conditions agreed up front
Illustrative model using customer-reported ranges. Your pilot report replaces every line with measured figures from your own baseline.
Sealing defects previously found at end-of-line audit are now caught within the same run; scrap cost fell 41% and audit preparation dropped by three days per review.
Related sectors: Battery manufacturing, Semiconductor, Electronics & EMS
Start the evaluation
Find out where quality drift is hiding in your plant.
Bring one line, one defect family or one audit workflow. We will map the available data sources, quantify the cost of the current detection delay, and show the fastest route to measurable control.
45-minute technical walkthrough
With a solution architect who knows manufacturing data, not a scripted demo.
NDA before any data review
We can assess feasibility from sample exports without production access.
Written pilot scope
Baseline metrics, success criteria and exit conditions agreed up front.