QualityLine · Automotive electronics
Automotive manufacturing analytics.
Built for electronics.
Connect assembly, inspection and test records to investigate quality issues in electronic modules. QualityLine helps your engineers compare production conditions, follow unit history and decide which suspected causes to validate.
From PCBA to electronic module
Put each failure back into its production context.
A test result tells you what failed. Product revision, component lot, assembly records and test conditions help explain what to investigate next. Keep those details together when reviewing an ECU, sensor assembly or other electronic module.
Review upstream evidence
Compare available SPI, AOI, placement and reflow records with downstream test results. Check that board and component identifiers map correctly across the sources.
Separate product and test variation
Investigate failed measurements by product revision, test program, station and fixture. Keep first attempts separate from retests when comparing yield.
Follow the affected units
Bring recorded inspections, tests and repairs into one history. Identify missing records before drawing conclusions about a unit or production group.
Illustrative investigation · No customer data
Why is an electronic module failing one functional test?
A quality engineer sees more failures on a current-consumption test. The next task is to establish whether the change follows the product, a component lot, the assembly process or the measurement setup.
- Define a comparable group. Select the same module revision and test sequence. Review the number of units behind the failure rate.
- Connect the records. Match the module to its board, inspection results, available component-lot references and original test measurements.
- Check alternative explanations. Compare stations and fixtures, review program changes, and preserve retest history. A component lot associated with failures is a lead to examine.
- Validate and follow up. Inspect representative units, test the suspected mechanism and measure the result after an approved corrective action.
Suppliers, plants and product changes
Agree the data boundaries before comparing performance.
Start with a module family, a production line and one quality question. Expand to other suppliers or plants once identifiers, access and reporting definitions are consistent.
Supplier and material context
Use component-lot or incoming-inspection records where your suppliers can provide them. Confirm access, granularity and the mapping to production units; analytics cannot reconstruct an unrecorded lot assignment.
Comparable plants and lines
Align product revisions, test coverage, limits and counting rules. A different product mix or fixture can explain a yield difference before any process defect is involved.
Returns and warranty investigations
Connect a returned unit to its production history when identifiers and records are available. Use the evidence within your established investigation, containment and customer-response process.
Implementation and ownership
Review sample files or interfaces, refresh timing, hosting, permissions and engineering responsibilities. Agree the success metric and review period before starting.
Traceability coverage depends on the records supplied. Vehicle-level or VIN links need an explicit mapping from the module to the vehicle; they are not assumed. Quality and engineering teams retain responsibility for disposition and corrective action.
Published customer evidence
Molex: production visibility in electronics manufacturing.
Molex customer account
22%Reported production-capacity increase within the first year
A customer-specific result; starting capacity and unit volumes are not stated in the source.
A rollout starting with one production chain
The published case describes Molex as a Tier 1 electronics supplier to automotive customers. Its interview with Marcel Bondar, Director of Quality, Telematics & Wireless M2M, discusses production visibility, connected manufacturing data and analytics.
The initial go-live covered one production chain. The reported result belongs to that customer account and is not a forecast for another plant or product.
Source: company context on page 2, implementation on page 5 and reported results on page 6.
Common questions
Evaluating analytics for automotive electronics
What does automotive manufacturing analytics cover here?
This page focuses on electronic modules and their assembly, inspection, test and repair records. The purpose is to investigate manufacturing quality and yield. The scope of any additional mechanical, vehicle or field data is agreed during the technical review.
Can QualityLine connect supplier data with test results?
Yes, where agreed data sources provide usable identifiers and access. Component lots or incoming-inspection results must map to the relevant boards or modules. Missing supplier records and unmatched identifiers limit the conclusions that can be drawn.
Can we compare different lines or plants?
Connected records can support comparisons across agreed sites. First align product revisions, test programs, limits and first-attempt counting rules. Compare equivalent production groups so differences in product mix or test setup do not distort the result.
Does traceability automatically extend to a vehicle VIN?
No. A link from a board or module to a vehicle requires an explicit, reliable mapping in the available data. Define the required traceability scope and verify its coverage during integration.
Can analytics help with returned products?
When a returned unit can be identified, its production, test and repair history can support the investigation. The value depends on data coverage and the quality of the return findings. Analytics does not guarantee that a defect or recall will be prevented.
What should we bring to a demo?
Choose a module family and a recurring quality question. Bring a description of the assembly and test flow, example data fields, relevant identifiers and the metric you want to improve. Agree a separate, secure process before sharing production records.
Start with a module and a quality question
Bring the failure your team is trying to explain.
Review the available assembly and test data, the records that can be matched, and how you will judge a useful investigation.
