Test and Validation Record
The structured record of product testing activities and results — containing test plans, test procedures, pass/fail outcomes, measurement data, environmental conditions, traceability to requirements, and the engineering judgment on whether results support design release.
Why This Object Matters for AI
AI cannot optimize test plans, predict test outcomes, or automate design validation without structured test data linked to requirements and design parameters; without it, 'has this design been adequately tested' requires engineers to manually review test reports and map results to requirements.
Product Engineering & Development Capacity Profile
Typical CMC levels for product engineering & development in Manufacturing organizations.
CMC Dimension Scenarios
What each CMC level looks like specifically for Test and Validation Record. Baseline level is highlighted.
Testing is informal and undocumented. Engineers test prototypes by 'trying it out' and declaring it 'good enough' based on experience. When a customer asks 'how did you validate this design?' the answer is 'we built one and it worked.' There are no test plans, no recorded measurements, and no pass/fail criteria written anywhere.
AI cannot perform any test analysis, coverage assessment, or outcome prediction because no test documentation exists.
Create basic test records — even a lab notebook with test descriptions, conditions, and pass/fail results establishes a starting point for formal validation.
Test results exist in lab notebooks, personal spreadsheets, and PowerPoint summaries prepared for design reviews. Each engineer documents tests differently — some record every measurement, others summarize with 'test passed.' Finding the test results for a specific product means tracking down the engineer who ran the test and asking for their files. Test criteria are often implicit — 'it didn't break' rather than 'deflection under 2mm at 500N.'
AI could extract test information from scattered documents using NLP but cannot reliably assess test coverage or validate results because test criteria, procedures, and outcomes are documented inconsistently.
Standardize a test record template with defined fields — test ID, linked requirement, test procedure, acceptance criteria, environmental conditions, measurement data, and pass/fail determination.
A standard test record template is used consistently. Every test has an ID, linked requirement, procedure reference, acceptance criteria, and recorded results. Test records are stored in a shared location organized by product and test phase. Engineers can find all tests for a product. But traceability between test records and requirements is maintained in a separate cross-reference document. Test-to-requirement coverage analysis requires manual matrix building.
AI can generate test summaries, identify tests without clear acceptance criteria, and flag anomalous results. Cannot perform automated requirement coverage analysis because test-to-requirement traceability is in a separate manual document.
Implement a test management system that formally links test cases to requirements, captures structured measurement data, and automatically generates coverage matrices.
Test records are managed in a test management system with formal traceability to requirements. Each test links to the requirement(s) it verifies. Measurement data is structured — individual readings, statistical summaries, environmental conditions. Coverage matrices generate automatically. An engineer can query 'show me all requirements for Product X that have not yet been verified by test' and get a reliable answer. Test history across product revisions is complete.
AI can perform automated test coverage analysis, identify verification gaps, predict test outcomes based on historical patterns, and optimize test sequences. Can correlate test results with field performance to assess test effectiveness.
Implement schema-driven test records with machine-readable acceptance criteria and formal entity relationships to design parameters, manufacturing processes, and field performance data, all queryable via API.
Test records are schema-driven entities with machine-evaluable acceptance criteria. The system does not just record 'deflection was 1.8mm against a 2mm limit' — it evaluates pass/fail automatically from the raw measurement data against formally encoded criteria. Test records link to the specific design parameters they validate, the manufacturing process variables that affect outcomes, and the field failure modes they screen for. An AI agent can answer 'if I change this design parameter by 10%, which tests will be affected and what is the predicted outcome?' with quantified confidence.
AI can perform fully autonomous test planning, result evaluation, and coverage optimization. Predictive test models forecast outcomes before tests are run. Automated test-to-field correlation identifies which tests are most effective at screening real-world failure modes.
Implement real-time test data streaming where measurement results publish as structured events the moment they are captured, enabling live test monitoring and adaptive test execution.
Test records generate automatically from connected lab equipment and simulation environments in real-time. Every measurement, sensor reading, and simulation result is captured, structured, and evaluated against acceptance criteria as it happens. The system adapts test execution in real-time — if an early test reveals unexpected behavior, subsequent tests adjust automatically. The test record is a living validation stream, not a static report.
Fully autonomous product validation. AI manages the complete test lifecycle — planning, execution, evaluation, and adaptation — in real-time. The validation record is a continuous intelligence stream.
Ceiling of the CMC framework for this dimension.
Capabilities That Depend on Test and Validation Record
Other Objects in Product Engineering & Development
Related business objects in the same function area.
CAD Model and Design File
EntityThe digital product definition maintained in CAD systems — 3D models, 2D drawings, assemblies, geometric dimensions and tolerances (GD&T), revision history, and the parametric relationships that define how design features interact and constrain each other.
Engineering Bill of Materials (EBOM)
EntityThe engineering-owned product structure defining components, sub-assemblies, and materials from a design perspective — including part numbers, revision levels, material specifications, make-versus-buy designations, and the effectivity dates that track which configuration is current.
Design Requirement Specification
EntityThe structured set of functional, performance, regulatory, and customer requirements that the product design must satisfy — including requirement IDs, acceptance criteria, priority, verification method, traceability links to test cases, and compliance status maintained through the development lifecycle.
Engineering Change Order
EntityThe formal record documenting a proposed or approved change to a product design — containing the change description, affected parts, reason for change, impact assessment (cost, schedule, tooling, inventory), approval signatures, and implementation status across engineering, manufacturing, and supply chain.
Material Specification
EntityThe engineering-approved definition of materials used in the product — containing material grades, mechanical properties, chemical composition limits, environmental compliance status (RoHS, REACH), approved suppliers, and the test data supporting material qualification for each application.
Field Performance Feedback Record
EntityThe structured collection of product performance data from the field — warranty claims, failure analysis reports, customer usage patterns, reliability metrics (MTBF, failure rates), and environmental exposure data fed back to engineering to inform design improvements and validate reliability models.
Design Release Decision
DecisionThe stage-gate judgment point where engineering leadership evaluates whether a design is ready to release to manufacturing — assessing requirements coverage, test completion status, DFM compliance, risk items, and the evidence package required to authorize the transition from development to production.
Engineering Change Approval Decision
DecisionThe recurring judgment point where a change review board evaluates whether to approve, defer, or reject an engineering change — weighing technical merit, cost impact, schedule impact, inventory disposition, customer notification requirements, and regulatory re-certification needs against the benefit of the change.
Design Standard and Constraint Rule
RuleThe codified engineering standards, design rules, and constraints that product designs must satisfy — including company design standards, industry standards (ASME, ISO), regulatory requirements, manufacturability constraints, and the prohibited-materials lists that bound the design space.
Engineering Change Process
ProcessThe end-to-end workflow governing how product changes are proposed, evaluated, approved, and implemented — defining change request submission, impact analysis steps, review board composition, approval routing, implementation coordination across engineering-manufacturing-supply chain, and effectivity cutover procedures.
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