Case Studies

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How Much Test Coverage Is Enough Before a Product Goes to Field?

A risk-based method for OEM teams to decide whether PCBA production test coverage is sufficient before field release, without relying on an arbitrary coverage percentage.

Six-step engineering flow showing a late test-access need leading to a PCB or layout decision, released-data changes, fixture or probe-map changes, test-implementation changes and less directly comparable pilot evidence.

Why Late PCB Test-Point Decisions Slow Down Pilot Builds

Why late PCB test-access decisions create layout, fixture and test-program churn, with practical gates for access closure before pilot release.

Diagram showing Edge AI product changes entering through sensing and calibration, firmware and preprocessing, model and runtime, or compute and power, then propagating through input, execution, decision, physical operating and production evidence contracts before the requalification scope is selected.

Edge AI Requalification After Product Changes

Learn how to decide what must be revalidated when an Edge AI product changes—from sensors and firmware to models, memory, thermals and production test.

Comparison of hardware and inference operating envelopes for an Edge AI product, showing that hardware can remain functional under environmental stress even when task quality or timing has moved outside the application requirement.

Edge AI Environmental Validation Under Stress

Why Edge AI environmental validation must measure inference quality and timing—not just hardware survival—and how to build an environment-to-inference evidence chain.

Diagram of an Edge AI production-test architecture from controlled physical stimulus through sensor, acquisition, preprocessing, model input, inference, post-processing and product decision. Test points are classified as deterministic fixture checks, bounded engineering measurements, inference-path checks or application-decision checks, with a known digital replay path shown as an example diagnostic injection point.

How to Production-Test an Edge AI Product

How to production-test Edge AI hardware when model outputs are probabilistic: deterministic checks, sensor limits, replay testing, inference criteria and traceability.

Sensor Variation in Edge AI Production

How OEM teams can translate sensor and assembly variation into calibration, DFT, production test, traceability and requalification controls for Edge AI products.

Four nested evidence boundaries for Edge AI processor selection: peak compute capability, deployed-model performance, requirement-boundary application performance, and representative sustained product operation, with increasing decision confidence toward architecture freeze.

Edge AI Processor Selection: What to Measure Beyond TOPS

A practical evidence framework for Edge AI processor selection: model quality, application latency, concurrency, thermal behavior, and requirement margin before architecture freeze.

Why Edge AI Hardware Can Fail During Scale-Up

Why can a validated Edge AI prototype fail at scale? Learn how sensor, thermal, compute, configuration and manufacturing variation affect production readiness.

Comparison matrix showing how high-volume Tier-1 EMS and high-mix low-volume EMS models differ in production model, cost focus, engineering support, BOM management, test strategy, quality control, change management, communication, and buyer risk.

How OEMs Should Evaluate EMS Partners for High-Mix Low-Volume Electronics Manufacturing

How to evaluate an EMS partner for high-mix low-volume electronics — the operating discipline, test strategy, BOM control, and change management that decide a program, beyond unit price

Stage-gate diagram showing an OEM electronics product moving from design release through DFM review, BOM validation, test readiness, pilot build, issue closure and production ramp at an EMS partner.

EMS Transfer Readiness for High-Mix Industrial Electronics

Product transfer to an EMS partner fails when design completion is mistaken for manufacturing readiness. The eight things to finalize before pilot build.

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