USE CASES

From investigation to action, faster.

Orbexus helps teams investigate performance changes, review investigation candidates, and prioritize the next best action using connected operational data.

Try Product Request Demo

Demo note: Orbexus demo uses synthetic manufacturing data. Outputs require human review before operational use.

Investigation WorkspaceSignals reviewed before action

FPY dropMissed recordsRework hoursCycle-time spread

Top candidateStation 90 - Hipot Test

Quality loss + rework burden + flow impact

Validate leakage currentCompare upstream assembly conditionReview control-plan evidence

Use Cases page: This page explains which manufacturing problems Orbexus can support and what evidence is used.

MANUFACTURING PROBLEMS

Use cases at a glance.

Each use case keeps the same operating context, then makes the review path clearer: what changed, what evidence is available, what Orbexus can surface, and where human review remains required.

Use Case	Signal Reviewed	Evidence Used	Expected Output	Human Review Boundary
FPY / RTY Drop	Yield decline, failed records, station performance	Quality records, station trends, rework, PFMEA/control-plan context	Investigation candidates and review path	Quality team validates cause before approving action
Rework Burden	Rework hours, repeat issues, rework category growth	Rework logs, station context, product mix, defect history	Ranked rework patterns and focus areas	Team validates process, supplier, training, or design cause
OEE Loss	Availability, performance, quality loss	Downtime, cycle time, quality records, station context	OEE contributor summary and next review path	Operations validates classification and actions
Cycle-Time Bottleneck	Cycle-time spread, queue pressure, throughput gaps	Station records, route context, product mix, shift/calendar data	Bottleneck candidates and evidence patterns	Operations validates constraints and countermeasures
Governed Report Generation	Weekly trends, actions, risks, evidence, caveats	Dashboards, investigation records, reference definitions	Review-ready report	Reviewer approves before sharing or decision use

FPY / RTY Drop Investigation

Problem: First-pass yield or rolled throughput yield drops and teams need to understand where to focus.
Signals Orbexus checks: FPY trend, RTY trend, failed records, missed records, station performance, model mix, route changes.
Evidence used: Quality records, station history, PFMEA/control-plan context, rework patterns, and approved definitions.
Recommended review path: Rank candidate stations, review evidence patterns, validate caveats, and assign the next checks.
Human-review boundary: Orbexus surfaces candidates. Quality teams validate cause before approving actions.
Expected output: Investigation summary with evidence, caveats, and recommended next checks.

Rework Burden Review

Problem: Rework increases and teams need to know which issue deserves attention first.
Signals Orbexus checks: Rework hours, rework categories, repeat issues, station trends, product mix, and process variation.
Evidence used: Rework logs, scrap/rework categories, station context, route data, and historical comparison.
Recommended review path: Group rework signals, estimate burden, review recurring patterns, and prioritize follow-up.
Human-review boundary: Orbexus does not approve containment or corrective action. Teams review and decide.
Expected output: Prioritized rework review with top contributors and evidence-backed next actions.

Cycle-Time Bottleneck Review

Problem: Production slows because station constraints or route variation are hard to see across scattered data.
Signals Orbexus checks: Cycle-time spread, station trends, route/model variation, takt gaps, queue signals, and throughput changes.
Evidence used: Cycle-time records, station definitions, route context, product mix, and operating calendar.
Recommended review path: Identify bottleneck candidates, compare variation, check upstream/downstream context, and assign validation steps.
Human-review boundary: Operations teams validate constraints before changing staffing, routing, or process rules.
Expected output: Bottleneck review with candidate stations, supporting signals, and recommended checks.

OEE Loss Investigation

Problem: OEE declines and teams need to separate availability, performance, and quality signals.
Signals Orbexus checks: Availability, performance, quality, station OEE, downtime reasons, cycle time, and quality loss.
Evidence used: OEE/downtime data, quality records, station context, route data, and approved definitions.
Recommended review path: Separate loss categories, rank impact areas, review evidence, and document caveats.
Human-review boundary: Orbexus supports prioritization. Leaders and owners approve operational changes.
Expected output: OEE loss summary with top contributors and review-ready evidence.

Governed Report Generation

Problem: Weekly reporting is manual, inconsistent, and difficult to tie back to evidence.
Signals Orbexus checks: Trend changes, risks, actions, improvement candidates, open reviews, and missing evidence.
Evidence used: Governed metrics, dashboard snapshots, investigation summaries, caveats, and review history.
Recommended review path: Collect approved evidence, draft a report, highlight caveats, and route for human review.
Human-review boundary: Reports require human review before business or quality-system use.
Expected output: Human-reviewed weekly performance or investigation report.

AI-Guided Investigation

Problem: Teams spend too much time manually connecting quality, rework, station, and reference signals.
Signals Orbexus checks: Quality changes, rework burden, throughput shifts, station patterns, reference context, and missing evidence.
Evidence used: Dashboard metrics, standardized data, PFMEA/control-plan context, approved definitions, and investigation history.
Recommended review path: Surface candidates, explain supporting evidence, document caveats, and route the summary for review.
Human-review boundary: Orbexus guides the investigation. People validate findings, approve actions, and own decisions.
Expected output: Human-review-ready investigation summary with evidence and next checks.

Data Standardization & Preflight

Problem: Teams need confidence that uploaded data is usable before insights or reports are trusted.
Signals Orbexus checks: Files ingested, columns present, required fields, row counts, freshness, quality checks, and warnings.
Evidence used: Client files, mapping rules, reference definitions, validation checks, and issue logs.
Recommended review path: Map fields, run preflight checks, expose warnings, and clarify what evidence is missing.
Human-review boundary: Data owners review mappings and exceptions before outputs are trusted.
Expected output: Data readiness summary with gaps, warnings, and recommended cleanup steps.