Why this page exists
This guide explains the thinking behind Orbexus.
Orbexus is designed for manufacturing environments where performance problems rarely come from one clean data source.
A first-pass yield drop may involve station history, missed records, rework patterns, product mix, process characteristics, PFMEA context, control-plan evidence, and human judgment. A dashboard can show that a number changed, but teams still need a disciplined way to understand what evidence should be reviewed next.
This page explains the manufacturing terms, problem patterns, platform capabilities, use cases, and governance boundaries behind Orbexus. It is meant to help quality teams, operations teams, plant leaders, executives, and analytics teams understand what the platform does, why it matters, and how recommendations should be reviewed.
Orbexus overview
What Orbexus is.
Orbexus is a governed manufacturing intelligence platform. It helps teams connect scattered quality, operations, rework, cycle-time, OEE, PFMEA, control-plan, and reference data into a standardized operating context.
The platform supports evidence-led dashboards, AI-guided investigations, role-based views, and human-reviewed reports. It helps teams move from disconnected exports and manual reporting toward a structured way to understand performance, prioritize issues, and review action paths.
Orbexus does not replace MES, QMS, ERP, PLM, historians, or reporting tools. It is positioned as an intelligence layer that helps teams make better use of manufacturing data and context that already exists across systems, files, exports, and workflows.
The core manufacturing problem
Manufacturing data is available, but not always usable.
Many plants have the data needed to understand performance, but the data is often scattered across files, systems, exports, and manual reports. Different teams may use different names, calculations, filters, and assumptions.
Scattered data
Quality records, rework logs, cycle-time files, OEE summaries, PFMEA, control plans, and business reports often live in different places.
Why it matters: Teams spend time collecting and reconciling information before they can begin the investigation.
Inconsistent definitions
The same station, product, defect, route, or process step may appear under different names across files.
Why it matters: Without shared definitions, dashboards can create debate instead of alignment.
Silent data quality issues
Missing columns, stale files, incomplete records, and inconsistent field values can make analysis look more certain than it really is.
Why it matters: Recommendations are only useful when teams know what evidence was present and what was missing.
Dashboards without investigation logic
Dashboards can show what changed, but they often do not explain what evidence should be reviewed next.
Why it matters: Teams still need a path from signal to investigation, validation, caveats, and action.
AI without governance
Generic AI can summarize data, but manufacturing teams need recommendations connected to approved definitions and controlled review.
Why it matters: Manufacturing decisions can affect quality, safety, delivery, cost, and compliance.
Manual reporting burden
Weekly reviews and investigation reports often depend on copy-paste work, tribal context, and manual interpretation.
Why it matters: Reporting time reduces the time available for review, validation, and improvement.
What Orbexus does
Orbexus turns scattered manufacturing data into governed decision support.
Orbexus is not only a dashboard. It is a structured manufacturing intelligence layer that helps teams move from raw data to standardized context, from standardized context to evidence-led investigation, and from investigation to human-reviewed reports and actions.
Files, exports, logs, records, and reference documents.
Products, routes, stations, defects, units, dates, and definitions.
Required fields, freshness, row counts, missing values, and warnings.
PFMEA, control plans, business rules, and investigation playbooks.
Ranked patterns, caveats, and next review paths.
Dashboards, reports, summaries, and action paths.
- Standardize inconsistent files and field names.
- Check whether data is complete, fresh, and usable.
- Connect quality, rework, cycle-time, OEE, PFMEA, and control-plan context.
- Surface investigation candidates using evidence patterns.
- Expose caveats and missing information.
- Create review-ready reports with evidence and human-review language.
Before insights
Why standardization matters.
Before teams can trust insights, they need shared definitions.
Manufacturing analysis often breaks down before the dashboard begins. A station may have multiple names. A product family may be grouped differently across files. A route may change over time. One report may use failed records while another uses failed units. These differences can create confusion, especially when teams are trying to investigate performance quickly.
How Orbexus helps: Orbexus standardizes key manufacturing concepts such as products, models, routes, stations, work centers, defects, process characteristics, dates, shifts, units, and metric definitions. This creates a shared operating context so quality, operations, leadership, and analytics teams are reviewing the same evidence.
Human-review boundary: Orbexus can suggest or apply mappings based on configured rules, but client owners should approve mappings and definitions before they are used for production decisions.
Before trust
Why data preflight matters.
Bad data should be visible before it drives decisions.
Manufacturing teams need to know whether the data is ready before using it for dashboards, AI guidance, reports, or decisions. A missing route column, stale quality file, incomplete station mapping, or low row count can change how much confidence teams should place in the output.
How Orbexus helps: Orbexus preflight checks whether required files exist, required columns are present, row counts are reasonable, freshness is acceptable, key fields are populated, and data quality warnings are visible.
Human-review boundary: Orbexus identifies readiness issues and warnings. Data owners decide whether the data is acceptable, needs correction, or should be excluded from a formal review.
Before action
Why evidence lineage matters.
Every recommendation should be traceable.
In manufacturing, recommendations are only useful when reviewers can understand what supports them. Teams need to know which records, definitions, time windows, filters, rules, and caveats were used.
How Orbexus helps: Orbexus is designed to connect recommendations back to source evidence, dashboard calculations, reference context, governed knowledge, missing evidence, and human-review notes.
Human-review boundary: Evidence lineage supports review. It does not replace engineering judgment, quality-system approval, or process ownership.
Manufacturing signals
Manufacturing signals Orbexus helps teams understand.
Orbexus uses manufacturing signals to help teams understand what changed, where to focus, and what evidence should be reviewed. Each signal has a business meaning, an operational meaning, and a review boundary.
FPY - First-Pass Yield
- Plain definition
- FPY measures the percentage of units that pass a process step or inspection the first time without rework, repair, or repeat processing.
- Why it matters
- FPY shows where quality is being lost before rework hides the issue. A plant may still ship product, but low FPY can signal hidden cost, capacity loss, inspection burden, or process instability.
- How Orbexus helps
- Orbexus helps review FPY changes by connecting failed records, station history, product or model mix, route context, defect categories, rework patterns, PFMEA knowledge, and control-plan context.
- Example
- A weekly review shows FPY dropped from 94 percent to 88 percent on one assembly route. Orbexus compares the change by station, model, shift, defect category, and rework behavior. It may surface a test station as a top investigation candidate because failures increased there during the same time window.
- Human-review boundary
- Orbexus does not confirm the final cause of the FPY drop. It helps rank evidence patterns and guide the review path.
RTY - Rolled Throughput Yield
- Plain definition
- RTY estimates the probability that a unit passes through multiple process steps without failure or rework.
- Why it matters
- A single station may look acceptable, but small losses across several steps can combine into a significant route-level yield problem.
- How Orbexus helps
- Orbexus connects step-level performance, route definitions, station behavior, model mix, and defect history to show where cumulative yield loss is building.
- Example
- Several stations show FPY between 96 percent and 98 percent. Individually, each step looks reasonable. Viewed together, RTY shows the route is losing too many units before final inspection.
- Human-review boundary
- Orbexus can show where RTY is being lost and which evidence supports the pattern. It does not decide which process change should be approved.
OEE - Overall Equipment Effectiveness
- Plain definition
- OEE combines availability, performance, and quality to show how effectively equipment or a process is being used.
- Why it matters
- OEE can decline for downtime, slow cycle time, quality loss, setup time, minor stops, or product mix. A single OEE percentage is not enough unless teams can separate the contributors.
- How Orbexus helps
- Orbexus helps separate OEE loss into availability, performance, and quality signals. It connects downtime reasons, station performance, cycle-time records, quality records, route context, and approved definitions.
- Example
- OEE drops from 82 percent to 74 percent in a weekly plant review. Orbexus compares downtime, cycle-time spread, quality loss, and station-level OEE to show which contributor should be reviewed first.
- Human-review boundary
- Orbexus supports OEE investigation and evidence review. It does not automatically approve downtime classifications or operational changes.
Rework Burden
- Plain definition
- Rework burden describes the time, labor, cost, capacity, and operational impact caused by fixing units that did not pass correctly the first time.
- Why it matters
- Rework can hide process problems. A team may maintain shipments by fixing units, but rework still consumes capacity, increases queue time, and adds quality risk.
- How Orbexus helps
- Orbexus reviews rework logs, rework categories, repeat issues, station context, product mix, defect history, and historical comparison to rank which rework pattern deserves review first.
- Example
- Rework hours increase by 35 percent over two weeks. Orbexus compares rework categories, stations, models, and defect trends. It may surface that one rework category is growing faster than others and appears after a specific test station.
- Human-review boundary
- Orbexus ranks the evidence pattern. Manufacturing and quality teams validate whether the issue is process, training, supplier, inspection, equipment, or design related.
Scrap
- Plain definition
- Scrap refers to material, components, or units that cannot be recovered or economically reworked and must be discarded.
- Why it matters
- Scrap directly affects cost and can point to quality, process, supplier, handling, or design issues.
- How Orbexus helps
- Orbexus can connect scrap categories, station context, defect history, product mix, shift patterns, process routes, and related rework behavior.
- Example
- Scrap cost rises on one product family. Orbexus compares defect categories, station history, route changes, and prior rework attempts to show whether the pattern is isolated or broader.
- Human-review boundary
- Orbexus does not determine financial disposition or approve scrap policy. It supports evidence review and prioritization.
Cycle Time
- Plain definition
- Cycle time is the time needed to complete a process step, station operation, route segment, or full production flow.
- Why it matters
- Cycle-time variation can create bottlenecks, queues, missed takt, delayed shipments, and poor flow. Average cycle time alone may hide spread, outliers, or station-specific constraints.
- How Orbexus helps
- Orbexus reviews cycle-time records, station definitions, route context, model mix, operating calendars, queue signals, and throughput changes to identify bottleneck candidates.
- Example
- A line meets daily output only with overtime. Orbexus reviews cycle-time spread by station and model and may show that one station has wider variation than expected and creates downstream waiting.
- Human-review boundary
- Orbexus identifies bottleneck candidates and evidence patterns. Operations teams validate the actual constraint and decide on staffing, maintenance, layout, sequencing, or process changes.
Station Performance
- Plain definition
- Station performance describes how a work center, station, machine, cell, or process point performs against quality, time, throughput, availability, and rework expectations.
- Why it matters
- Many manufacturing issues become clearer when viewed by station. A plant-level number may hide a local problem driving yield, rework, or flow loss.
- How Orbexus helps
- Orbexus standardizes station names, connects station-level records across files, and compares station behavior across defects, cycle time, rework, OEE, and route context.
- Example
- ST-120, Station 120, Test 120, and Final Test may refer to the same station. Orbexus can align those references so evidence is reviewed as one station.
- Human-review boundary
- Orbexus helps standardize and analyze station evidence. It does not assign operational accountability without human review.
PFMEA
- Plain definition
- PFMEA stands for Process Failure Mode and Effects Analysis. It documents potential process failure modes, effects, causes, controls, and risk considerations.
- Why it matters
- PFMEA knowledge helps teams connect current operating issues to known process risks and expected controls.
- How Orbexus helps
- Orbexus uses PFMEA knowledge as governed reference context during investigation. When a defect, station, or process issue appears, PFMEA context can help reviewers understand known failure modes and existing controls.
- Example
- A recurring defect appears at a press-fit operation. Orbexus can surface related PFMEA knowledge so reviewers can check whether the failure mode is already documented and what controls exist.
- Human-review boundary
- Orbexus does not rewrite or approve PFMEA updates automatically. It helps connect investigation evidence to governed process knowledge.
DFMEA
- Plain definition
- DFMEA stands for Design Failure Mode and Effects Analysis. It documents potential design-related failure modes, effects, causes, and controls.
- Why it matters
- Some manufacturing symptoms may relate to design sensitivity, product variation, tolerance stack-up, or use conditions.
- How Orbexus helps
- Orbexus can treat DFMEA knowledge as reference context when product, quality, and process evidence suggests a design-related review path may be relevant.
- Example
- A defect appears only for one product variant. Orbexus can help connect the manufacturing signal with product and design context so engineering reviewers can decide whether DFMEA knowledge should be reviewed.
- Human-review boundary
- Orbexus does not approve design changes. It supports evidence organization and review routing.
Control Plan
- Plain definition
- A control plan describes how a manufacturing process is monitored and controlled. It may include process steps, characteristics, inspection methods, control methods, reaction plans, and responsibility.
- Why it matters
- When a performance issue appears, teams need to know what controls were expected and what evidence should exist.
- How Orbexus helps
- Orbexus connects control-plan knowledge to quality and operations evidence so reviewers can check whether expected controls were present and whether a reaction plan should be reviewed.
- Example
- An FPY drop appears at a test station. Orbexus connects the issue to relevant control-plan checks and guides reviewers to inspect required evidence before deciding what action is appropriate.
- Human-review boundary
- Orbexus does not approve control-plan changes. It supports review of evidence and context.
Platform capabilities
Orbexus platform capabilities.
Orbexus combines data preparation, manufacturing context, governed knowledge, evidence logic, and reporting into one structured workflow.
Data Sources
Client data may come from Excel files, CSV files, MES exports, QMS records, ERP extracts, PFMEA files, DFMEA files, control plans, rework logs, scrap logs, OEE data, downtime data, or future integrations.
Orbexus treats these sources as the input layer. Incoming data is standardized and checked before it is trusted for dashboards or AI-guided review.
Data Standardization
Data standardization converts inconsistent manufacturing files and terms into shared definitions and structures.
Orbexus standardizes column names, data types, values, stations, lines, units, routes, products, dates, and required fields.
Data Preflight
Data preflight checks whether incoming data is ready for trusted analysis.
Orbexus checks files ingested, columns present, required fields, row counts, freshness, missing values, and data quality warnings.
Reference Data Center
The Reference Data Center is the governed source of manufacturing context. It stores approved definitions for products, processes, routes, stations, work centers, defects, units, calendars, and business rules.
AI Knowledge Center
The AI Knowledge Center organizes governed manufacturing knowledge that supports investigation, including PFMEA knowledge, control-plan knowledge, failure modes, defect knowledge, quality rules, heuristics, patterns, and playbooks.
Governed Manufacturing Intelligence Layer
The central Orbexus layer where standardized data, reference context, AI guidance, evidence, governance, and human review come together.
Evidence Lineage
Evidence lineage connects recommendations back to the data, definitions, records, and context that support them.
Human Review
Human review keeps AI-guided recommendations inside a responsible manufacturing decision process before actions, approvals, or changes are made.
Command Center
Command Center summarizes quality, rework, cycle time, OEE, and top investigation candidates for leadership review.
AI Quality Insights
AI Quality Insights rank investigation candidates using evidence patterns, context, caveats, and governed knowledge. They do not confirm final root cause.
Investigation Playbooks
Guided workflows for reviewing FPY drop, rework burden, OEE loss, and cycle-time bottlenecks using required evidence, validation steps, caveats, and human review.
AI Report Builder
AI Report Builder helps generate human-reviewed weekly performance and investigation reports from governed evidence.
Integration Readiness
Integration readiness describes the future-ready path for MES, QMS, ERP, PLM, databases, warehouses, and reporting workflows through data mapping, security review, governance planning, and deployment design.
Problems Orbexus can help solve
The problems Orbexus is built to support.
We know yield dropped, but not where to look first.
Orbexus helps review FPY and RTY drops by comparing station behavior, failed records, product mix, route context, rework patterns, and governed knowledge.
Rework is increasing, but the biggest driver is unclear.
Orbexus helps group rework by category, station, product, model, and time window to show which pattern is growing or recurring.
Cycle time is hurting flow, but the bottleneck is not obvious.
Orbexus compares cycle-time spread across stations, routes, models, and time windows to surface bottleneck candidates.
OEE is down, but teams are debating why.
Orbexus separates OEE into availability, performance, and quality contributors and connects them to downtime, cycle-time, quality, and station context.
Reports take too long and depend on manual copy-paste.
Orbexus helps assemble review-ready reports from governed evidence, including trends, investigation candidates, caveats, and human-review notes.
Client data is not ready for reliable dashboards.
Orbexus standardizes incoming files, checks required fields, identifies missing data, and shows readiness warnings before analysis is trusted.
Use case deep dives
Detailed manufacturing use cases.
FPY / RTY Drop Investigation
- Client problem
- The team sees a drop in first-pass yield or rolled throughput yield, but the issue is spread across stations, models, shifts, and defect records.
- Signals checked
- FPY trend, RTY trend, failed records, missed records, station performance, model mix, route changes, defect categories, rework patterns, PFMEA context, and control-plan context.
- Evidence used
- Quality records, station history, route definitions, product or model mix, rework logs, approved definitions, PFMEA knowledge, and control-plan knowledge.
- How Orbexus helps
- Orbexus standardizes records, checks readiness, compares the yield change across stations and product context, and ranks investigation candidates.
- Expected output
- FPY / RTY trend summary, top station candidates, evidence patterns, missing evidence or caveats, suggested review path, and human-reviewed investigation summary.
- Human-review boundary
- Orbexus supports investigation. It does not confirm root cause or approve process changes.
Rework Burden Review
- Client problem
- Rework is increasing, but teams are not sure which rework issue deserves attention first.
- Signals checked
- Rework hours, rework categories, repeat issues, station trends, product or model mix, process variation, scrap relationship, and historical comparison.
- Evidence used
- Rework logs, scrap or rework categories, station context, route data, product context, quality records, and historical comparison.
- How Orbexus helps
- Orbexus groups and compares rework patterns across stations, products, time windows, and categories to show whether the issue is isolated, recurring, growing, or connected to another signal.
- Expected output
- Rework burden summary, top rework categories, station and product context, evidence-backed prioritization, review path, caveats, and weekly leadership summary.
- Human-review boundary
- Orbexus does not assign final cause. It helps prioritize the pattern for review.
Cycle-Time Bottleneck Review
- Client problem
- Production flow is slow or inconsistent, but bottlenecks are hidden across stations, product mix, routes, and time windows.
- Signals checked
- Cycle-time spread, station trends, route or model variation, takt gaps, queue signals, throughput changes, operating calendar, and shift patterns.
- Evidence used
- Cycle-time records, station definitions, route context, product mix, calendar and shift data, and throughput records.
- How Orbexus helps
- Orbexus compares cycle-time behavior across stations and product context. It helps identify bottleneck candidates and shows whether the signal is consistent, temporary, product-specific, or tied to missing data.
- Expected output
- Cycle-time distribution summary, bottleneck candidates, station and model context, evidence patterns, caveats, missing data, and review-ready action path.
- Human-review boundary
- Orbexus supports bottleneck review. Operations teams validate the constraint and approve changes.
OEE Loss Investigation
- Client problem
- OEE declines, but the team needs to separate availability, performance, and quality contributors.
- Signals checked
- Availability, performance, quality, station OEE, downtime reasons, cycle time, quality loss, route context, and station context.
- Evidence used
- OEE summaries, downtime data, quality records, station context, cycle-time records, and approved definitions.
- How Orbexus helps
- Orbexus separates the OEE signal into contributors and connects those contributors to evidence. It helps teams see whether the loss is driven by downtime, slow performance, quality loss, or a combination.
- Expected output
- OEE contributor summary, availability/performance/quality breakdown, downtime and station context, top evidence patterns, and human-reviewed next steps.
- Human-review boundary
- Orbexus supports investigation and prioritization. It does not approve downtime classifications or operational changes automatically.
Governed Report Generation
- Client problem
- Weekly performance and investigation reports take too long to build and often depend on manual copy-paste work.
- Signals checked
- Dashboard metrics, standardized data, investigation history, FPY / RTY, OEE, rework burden, cycle-time trends, PFMEA context, control-plan context, approved definitions, evidence, and caveats.
- Evidence used
- Current performance data, historical comparison, investigation notes, reference definitions, evidence lineage, and human-review notes.
- How Orbexus helps
- Orbexus assembles a structured report that summarizes performance trends, top issues, evidence, caveats, and recommended review paths.
- Expected output
- Weekly review summary, investigation deck, evidence summary, caveats, human-review notes, and action follow-up list.
- Human-review boundary
- Orbexus does not replace report approval. It creates a review-ready draft from governed evidence.
Data Standardization & Preflight
- Client problem
- The team has manufacturing data, but files are inconsistent, missing required fields, or hard to compare.
- Signals checked
- Files ingested, columns present, required fields, row counts, freshness, missing values, data type issues, station naming, product naming, unit consistency, and route alignment.
- Evidence used
- Incoming files, column mapping, reference data, approved definitions, preflight warnings, and data quality checks.
- How Orbexus helps
- Orbexus maps messy files into standard manufacturing concepts and flags readiness issues before downstream analysis.
- Expected output
- Data readiness summary, column mapping view, required-field checks, freshness and row-count status, warnings and issues, and mapping actions for review.
- Human-review boundary
- Orbexus flags issues and proposes mapping paths. Client owners approve definitions, mappings, and readiness decisions.
Role-based value
One evidence layer. Different views for every team.
Quality Engineering
Quality teams need to understand FPY, RTY, defects, escapes, rework patterns, PFMEA context, control-plan evidence, and the evidence behind recommendations.
Orbexus helps review first-pass loss drivers, surface process clues, connect PFMEA and control-plan context, and support evidence-led investigation.
Manufacturing Operations
Operations teams need to protect flow, reduce downtime, understand bottlenecks, and prioritize constraints.
Orbexus helps review cycle-time spread, station performance, OEE loss, throughput disruption, and downtime context.
Plant Leadership
Plant leaders need a clear view of operating health and weekly priorities.
Orbexus summarizes plant-level health, compares lines or areas, ranks focus areas, and supports evidence-backed weekly review outputs.
Executive Leadership
Executives need to connect manufacturing performance to business impact without getting lost in raw operational detail.
Orbexus can summarize cost exposure, improvement potential, governed reports, and operating risks in a leadership-ready format.
Continuous Improvement
Continuous improvement teams need to find recurring patterns, evaluate opportunities, prioritize impact, and track follow-through.
Orbexus helps identify improvement candidates using evidence patterns and operational signals.
Operations Analytics
Analytics teams need reliable data, consistent definitions, and visibility into data gaps.
Orbexus helps monitor data availability, field coverage, mapping issues, preflight warnings, and reliability of analysis outputs.
Engagement path
Start with understanding. Move toward controlled deployment.
Demo
The demo is for exploration using synthetic manufacturing data. It helps visitors understand the Orbexus workflow without requiring client data.
- Dashboard experience
- Investigation candidates
- Evidence and caveats
- Report review workflow
Pilot
The pilot validates Orbexus with client sample files, field mapping, preflight, and reviewed outputs.
- Sample data readiness
- Missing or inconsistent fields
- Priority use cases
- Governance and review process
Production Path
The production path covers deployment planning, data connections, security review, governance model, user roles, review workflows, and integration readiness.
- Data source planning
- Reference governance
- Human-review gates
- Reporting and integration planning
Trust and review
Designed for review, not uncontrolled automation.
Orbexus is designed to support responsible manufacturing decision-making. The platform can organize evidence, rank investigation candidates, highlight caveats, and generate review-ready reports. It does not bypass accountable quality, operations, engineering, or leadership review.
- Orbexus does not automatically confirm root cause.
- Orbexus does not approve process changes.
- Orbexus does not replace quality-system approval.
- Orbexus does not approve PFMEA changes.
- Orbexus does not approve control-plan changes.
- Orbexus does not guarantee savings.
- Orbexus does not replace responsible human review.
- Orbexus does not imply live integrations unless a production connection is implemented and validated.
Future popup registry
Short definitions for future Learn More buttons.
Later, we can add hover, focus, and tap popups across the main pages. These short summaries should link back to the full anchors on this guide.
FPY
FPY measures how many units pass the first time without rework. Orbexus helps review FPY drops using station, defect, route, and evidence patterns.
RTY
RTY shows cumulative yield across multiple process steps. Orbexus helps reveal where small losses combine into larger route-level performance loss.
OEE
OEE combines availability, performance, and quality. Orbexus helps separate which signal is driving the loss and what evidence should be reviewed.
Rework Burden
Rework burden shows how much time and capacity are spent fixing units. Orbexus helps rank the rework patterns that deserve review first.
Data Preflight
Data preflight checks whether files are complete, fresh, and usable before they drive dashboards or AI-guided review.
Evidence Lineage
Evidence lineage connects a recommendation back to the data, definitions, records, and context that support it.
Human Review
Human review keeps AI-guided recommendations inside a responsible manufacturing decision process.
Pilot
A pilot validates Orbexus with sample client files, field mapping, preflight results, and human-reviewed outputs.
Control Plan
A control plan defines expected process controls. Orbexus helps connect performance issues to the controls and evidence that should be reviewed.
FAQ
Common buyer questions.
What makes Orbexus different from a dashboard?
A dashboard shows metrics. Orbexus is designed to connect metrics to standardized context, evidence patterns, governed knowledge, investigation workflows, caveats, and human-reviewed reports.
Does Orbexus confirm root cause?
No. Orbexus surfaces investigation candidates and evidence patterns. Responsible teams validate cause and approve actions through their established quality and operations processes.
Does Orbexus replace MES, QMS, ERP, or PLM?
No. Orbexus is an intelligence layer that helps teams use data and context from existing systems, files, exports, and future integrations.
Can Orbexus work with files before system integrations?
Yes. The demo and pilot path can start with files or exports. Production connections can be planned after data readiness, security review, and governance requirements are understood.
Why is human review required?
Manufacturing decisions can affect quality, safety, cost, delivery, and compliance. Human review ensures recommendations are validated before process, quality-system, release, or control changes are made.
What data does Orbexus need?
The required data depends on the use case. FPY review may need quality records, station history, route context, defect categories, and rework patterns. OEE review may need availability, performance, quality, downtime, and cycle-time data. Data preflight helps identify what is present and what is missing.
Next step
Understand the problem. Then try the workflow.
Explore the Orbexus demo or request a guided walkthrough focused on your quality, operations, or leadership workflow.