Maintenance readiness score
Use the score as a diagnostic signal that needs evidence review.
Industrial IQ | AI2COE | ReliabilityMind AI
ReliabilityMind AI Maintenance Readiness Report
Report takeaway
What this sample report answers
ReliabilityMind AI Sample Report shows how AI2COE Industrial IQ turns sample operational data into evidence rows, confidence tiers, score output, actions, report value, and governance controls. Sample results prove workflow and output structure; uploaded customer data is required for customer-specific findings.
Sample boundary: private source files are not required to view this sample. In live diagnostics, uploaded source files are used to generate report packs and then purged.
Review owner
Enterprise buyers inspecting report quality before uploading private operational data.
Input files
Engine-specific operational CSV exports or deterministic AI2COE sample data.
Diagnostic evidence
Evidence, scores, assumptions, limitations, actions, and governance status.
Next action
Sample report output map
How to evaluate the Maintenance Readiness Intelligence output bundle
This visual map helps buyers trace the sample report as a product output: source file, field mapping, evidence rows, confidence context, report pack, and governed owner action stay connected before any private upload.
work order, asset, required spare
Inspect source context, confidence, assumptions, and limits.
ReliabilityMind AI Maintenance Readiness Report
Use HTML and PDF views for buyer committee review.
Owner, status, next review
Keep remediation buyer-controlled after the sample is replaced with uploaded data.
Baseline and rerun movement
Use recurring reviews to see what changed after accepted actions.
CSV, mapping, data dictionary
Give data owners the field expectations before private upload.
Output boundary: this sample proves output structure and workflow only. Uploaded operational data is required before customer-specific findings can be reviewed.
Evaluation kit
Download the supporting assets for this Maintenance Readiness Intelligence sample.
Use the HTML sample to inspect report structure, then use the supporting files to understand what data owners would prepare before replacing sample assumptions with uploaded-data evidence.
PDF
Offline review artifact for buyer committees.
Sample CSVDeterministic sample input used for testing the workflow.
MappingColumn mapping guide for source-fit review.
DictionaryField definitions and expected diagnostic meaning.
Product pageReview the Maintenance Readiness Intelligence engine page.
Trust boundary: sample assets are deterministic demonstration files. Live diagnostics remain read-only, require owner review before action, and do not write back to ERP/EAM/CMMS systems.
Executive Summary
33.3Maintenance readiness score
Board-Level RiskRisk level
Medium ConfidenceConfidence
23Evidence records
Source mode: Sample dataset result. Sample results demonstrate workflow and report structure; uploaded-data results replace assumptions with customer source evidence.
Score interpretation: Lower health or readiness scores indicate higher unresolved exposure, weaker readiness, or stronger review need. Scores are deterministic and derived from mapped source fields, findings, evidence, and component inputs.
Executive interpretation: Focus on asset integrity, turnaround readiness, working capital exposure, and audit-safe ERP preparation.
Report delivery and governance controls
Email
Report email path
Authenticated Industrial IQ runs attempt branded report email delivery and retain delivery status in the platform report inventory.
Review
Human review required
Low-confidence or high-impact findings should be accepted, rejected, assigned, deferred, or marked needs-more-data before remediation.
ERP safety
No ERP write-back
Industrial IQ produces evidence and recommendations only. It does not autonomously change SAP, Maximo, Oracle, EAM, CMMS, procurement, inventory, or asset records.
Data handling: Uploaded source files are processed to generate the diagnostic report pack and then purged. Summary metrics and audit metadata may be retained for governance.
CFO command view
306250.0Capital exposure signal
36750.0-85750.0Recoverable range
55125.0Annual leakage signal
Board-Level RiskBoard attention band
Diagnostic components
3AVAILABILITY GAPS
3FALSE STOCKOUT SIGNALS
1REPEAT DEMAND GROUPS
1SHUTDOWN GAPS
active_deterministic_evidence_engineDIAGNOSTIC DEPTH
2REQUIRED FIELDS MAPPED
9OPTIONAL FIELDS MAPPED
24SOURCE ROWS PROFILED
Score formula: 100 - readiness_risk/source_rows*100 with shutdown and critical availability gaps weighted triple Random score used: False
| Score input | Value |
|---|---|
| availability gaps | 3 |
| false stockout signals | 3 |
| repeat demand groups | 1 |
| shutdown gaps | 1 |
| required fields mapped | 2 |
| optional fields mapped | 9 |
| source rows profiled | 24 |
| estimated row value total | 132000.0 |
| missing priority rows | 0 |
| repeat failure rows | 8 |
| stale critical work rows | 7 |
| validation issue count | 0 |
| validation blocker count | 0 |
| prompt injection signal count | 0 |
Product maturity and competitive depth
Competitive position: Complements EAM/APM platforms by finding hidden data and spare-readiness risk before maintenance teams act in system workflows.
| Priority | Implemented product capability |
|---|---|
| P0 | Work-order spare availability, false-stockout risk, repeat demand, shutdown readiness, and stale critical work.; Duplicate-family-aware false-stockout detector using catalog signatures and stock evidence.; Shutdown readiness checklist for planned outage or turnaround rows. |
| P1 | Repeat failure pattern evidence, planner action queue, maintenance priority quality, and work-order aging risk.; Maintenance readiness report by site, priority, failure code, and spare availability.; Reliability manager view that links demand recurrence to corrective action opportunities. |
| P2 | Turnaround package readiness scoring and outage-freeze exception list.; Monthly maintenance readiness trend by site and work-order class.; Service-risk scenario model for critical spare coverage and false-stockout reduction. |
ICP packaging
| Package | Engines | Decision supported |
|---|---|---|
| COO Pack | ReliabilityMind AI, AssetMind AI, InventoryMind AI | Prioritize site readiness, asset coverage, false stockout risk, and operational action queues. |
| Maintenance / Reliability Pack | ReliabilityMind AI, AssetMind AI, InventoryMind AI, PartsCleanse AI | Prove work-order readiness, asset-to-part coverage, critical-spare availability, and false-stockout risk before maintenance or reliability programs scale. |
Advanced product insights
| Product output | Diagnostic value |
|---|---|
| shutdown readiness checklist | [{"check": "Critical work orders have required parts", "status": "fail"}, {"check": "Shutdown rows have stock coverage", "status": "fail"}, {"check": "Priority field is mapped", "status": "pass"}, {"check": "Repeat failure demand is reviewed", "status": "fail"}, {"check": "False-stockout candidates routed to PartsCleanse", "status": "review"}] |
| turnaround package score | {"interpretation": "Lower score means more outage or turnaround readiness exceptions need review.", "score": 0} |
| repeat failure patterns | [{"failure_code": "PM", "relationship": "failure code -> repeated spare demand -> reliability action", "rows": 18}, {"failure_code": "SEAL LEAK", "relationship": "failure code -> repeated spare demand -> reliability action", "rows": 6}] |
| planner action queue | {"critical_work_items": 7, "owner": "Maintenance planner / reliability engineer", "priority_data_quality_items": 0, "repeat_failure_items": 8} |
Buyer committee views
CFO
Can quantified exposure justify a diagnostic or remediation budget?
ReliabilityMind AI shows 306250.0 as the current capital or leakage signal before owner review.
Next question: Which findings have enough confidence and value to enter the financial business case?
COO
Which findings threaten operational continuity, site readiness, or uptime?
4 high-attention findings require operational owner review.
Next question: Which findings must be resolved before the next outage, shutdown, or planning cycle?
CIO
Is the data ready for governed AI without ERP write-back risk?
Industrial IQ produced evidence from exports only and did not change ERP, EAM, CMMS, or procurement systems.
Next question: Which missing fields or governance gaps should be fixed in the next export?
Procurement
Where do supplier, purchase, or stocked-but-purchased signals need review?
Procurement actions should be evidence-led and routed through human review before supplier action.
Next question: Which supplier or purchase findings are defensible enough for category review?
Maintenance
Will spare availability and catalog quality support maintenance execution?
Maintenance should use the evidence queue to protect planned work and critical assets.
Next question: Which findings block planned work, shutdown readiness, or critical equipment coverage?
Board
Is this risk material enough to fund recurring diagnostic intelligence?
The result is diagnostic evidence, not an autonomous system change or unsupported ROI claim.
Next question: Should leadership fund the next diagnostic cycle, review queue, or remediation scope?
Evidence graph
Model: Source Record -> Finding -> Evidence -> Confidence -> Business Impact -> Recommended Action -> Review Status -> Report -> Score History
31 nodes | 30 evidence relationships. This graph links uploaded source rows to findings, confidence, business impact, recommended actions, report output, and score history.
Renewal value view
306250.0EXPOSURE IDENTIFIED
6REVIEW QUEUE SIZE
6ACTIONS CREATED
0ACTIONS REVIEWED
24500.0CONSERVATIVE VALUE REALIZATION
49000.0BASE VALUE REALIZATION
monthly for high-risk sites; quarterly for controlled sitesNEXT REVIEW CADENCE
Recurring value interpretation: Compare this run against the next upload to show exposure reviewed, actions completed, score movement, and remaining risk.
Key Findings
Trust control: Each finding must be interpreted with its confidence, evidence count, mapped fields, and source records. Similar-looking industrial records may still require owner review before action.
| Analyzer | Finding | Severity | Confidence | Evidence | Action |
|---|---|---|---|---|---|
| Work-Order Parts Availability Analyzer | 3 rows show work-order spare availability gaps | CRITICAL | 84% | 3 | Resolve high-priority work orders with low or zero spare availability. |
| False Stockout Risk Analyzer | 3 rows show potential false stockout cases | MEDIUM | 72% | 3 | Run PartsCleanse AI to detect alternate duplicate records before emergency buying. |
| Repeat Demand Analyzer | 1 rows show repeat demand patterns | MEDIUM | 72% | 1 | Review repeat spare demand for recurring failure or preventive maintenance adjustment. |
| Shutdown Readiness Analyzer | 1 rows show shutdown readiness gaps | CRITICAL | 84% | 1 | Close outage-critical spare gaps before execution freeze. |
| Failure Pattern Analyzer | 8 rows show repeated failure-code demand | HIGH | 82% | 8 | Review repeated failure patterns with reliability engineering and link parts to corrective actions. |
| Stale Critical Work Analyzer | 7 critical work-order rows are aging without clear readiness | CRITICAL | 86% | 7 | Escalate stale critical work orders into shutdown readiness review. |
Evidence Records
| ID | Confidence tier | Severity | Description | Value | Source | Reason codes |
|---|---|---|---|---|---|---|
| E-0518a3a5 | Medium Confidence | CRITICAL | 3 rows show work-order spare availability gaps | 7000.0 | row:5:MAT-005-005 | material-id-present, description-signature, site-context, spec-token-match |
| E-50801a69 | Medium Confidence | CRITICAL | 3 rows show work-order spare availability gaps | 4000.0 | row:10:MAT-004-010 | material-id-present, description-signature, site-context, spec-token-match |
| E-e7e68afd | Medium Confidence | CRITICAL | 3 rows show work-order spare availability gaps | 9000.0 | row:15:MAT-003-015 | material-id-present, description-signature, site-context, spec-token-match |
| E-25d9cb29 | Medium Confidence | MEDIUM | 3 rows show potential false stockout cases | 9000.0 | row:7:MAT-001-007 | material-id-present, description-signature, site-context, spec-token-match |
| E-cd75590a | Medium Confidence | MEDIUM | 3 rows show potential false stockout cases | 8000.0 | row:14:MAT-002-014 | material-id-present, description-signature, site-context, spec-token-match |
| E-00d90b1c | Medium Confidence | MEDIUM | 3 rows show potential false stockout cases | 7000.0 | row:21:MAT-003-021 | material-id-present, description-signature, site-context, spec-token-match |
| E-8dffb90a | Medium Confidence | MEDIUM | 1 rows show repeat demand patterns | 132000.0 | row:1:MAT-001-001 | material-id-present, description-signature, site-context, spec-token-match |
| E-6312216d | Medium Confidence | CRITICAL | 1 rows show shutdown readiness gaps | 2000.0 | row:8:MAT-002-008 | material-id-present, description-signature, site-context, spec-token-match |
| E-9adf7512 | Medium Confidence | HIGH | 8 rows show repeated failure-code demand | 3000.0 | row:1:MAT-001-001 | material-id-present, description-signature, site-context, spec-token-match |
| E-4146a2b0 | Medium Confidence | HIGH | 8 rows show repeated failure-code demand | 4000.0 | row:2:MAT-002-002 | material-id-present, description-signature, site-context, spec-token-match |
| E-36b0a92e | Medium Confidence | HIGH | 8 rows show repeated failure-code demand | 5000.0 | row:3:MAT-003-003 | material-id-present, description-signature, site-context, spec-token-match |
| E-3ca6f4c7 | Medium Confidence | HIGH | 8 rows show repeated failure-code demand | 7000.0 | row:5:MAT-005-005 | material-id-present, description-signature, site-context, spec-token-match |
| E-ed318cb5 | Medium Confidence | HIGH | 8 rows show repeated failure-code demand | 6000.0 | row:4:MAT-004-004 | material-id-present, description-signature, site-context, spec-token-match |
| E-10c396c0 | Medium Confidence | HIGH | 8 rows show repeated failure-code demand | 2000.0 | row:8:MAT-002-008 | material-id-present, description-signature, site-context, spec-token-match |
| E-0a71ee92 | Medium Confidence | HIGH | 8 rows show repeated failure-code demand | 6000.0 | row:12:MAT-000-012 | material-id-present, description-signature, site-context, spec-token-match |
| E-7766b756 | Medium Confidence | HIGH | 8 rows show repeated failure-code demand | 2000.0 | row:16:MAT-004-016 | material-id-present, description-signature, site-context, spec-token-match |
| E-4cb456e0 | High Confidence | CRITICAL | 7 critical work-order rows are aging without clear readiness | 7000.0 | row:5:MAT-005-005 | material-id-present, description-signature, site-context, spec-token-match |
| E-8b17f240 | High Confidence | CRITICAL | 7 critical work-order rows are aging without clear readiness | 2000.0 | row:8:MAT-002-008 | material-id-present, description-signature, site-context, spec-token-match |
| E-16c4807a | High Confidence | CRITICAL | 7 critical work-order rows are aging without clear readiness | 4000.0 | row:10:MAT-004-010 | material-id-present, description-signature, site-context, spec-token-match |
| E-9355c18c | High Confidence | CRITICAL | 7 critical work-order rows are aging without clear readiness | 9000.0 | row:15:MAT-003-015 | material-id-present, description-signature, site-context, spec-token-match |
| E-cadbc38d | High Confidence | CRITICAL | 7 critical work-order rows are aging without clear readiness | 2000.0 | row:16:MAT-004-016 | material-id-present, description-signature, site-context, spec-token-match |
| E-d1751dd8 | High Confidence | CRITICAL | 7 critical work-order rows are aging without clear readiness | 6000.0 | row:20:MAT-002-020 | material-id-present, description-signature, site-context, spec-token-match |
| E-c4d59ea9 | High Confidence | CRITICAL | 7 critical work-order rows are aging without clear readiness | 2000.0 | row:24:MAT-000-024 | material-id-present, description-signature, site-context, spec-token-match |
Recommended Actions
P1
Review repeat spare demand for recurring failure or preventive maintenance adjustment.
Owner: Maintenance Director | Due: 60 days
P0
Review repeated failure patterns with reliability engineering and link parts to corrective actions.
Owner: Maintenance Director | Due: 30 days
P0
Escalate stale critical work orders into shutdown readiness review.
Owner: Maintenance Director | Due: 30 days
P1
Run PartsCleanse AI to detect alternate duplicate records before emergency buying.
Owner: Maintenance Director | Due: 60 days
P0
Resolve high-priority work orders with low or zero spare availability.
Owner: Maintenance Director | Due: 30 days
P0
Close outage-critical spare gaps before execution freeze.
Owner: Maintenance Director | Due: 30 days
Mapping and validation
| Input | Source column | Completeness | Confidence | Reason |
|---|---|---|---|---|
| work_order | work_order | 100.0% | 100.0% | Exact ERP alias |
| description | description | 100.0% | 100.0% | Exact ERP alias |
| material_id | material_id | 100.0% | 100.0% | Exact ERP alias |
| asset_id | asset_id | 100.0% | 100.0% | Exact ERP alias |
| quantity | quantity | 100.0% | 100.0% | Exact ERP alias |
| stock_on_hand | stock_on_hand | 100.0% | 100.0% | Exact ERP alias |
| priority | priority | 100.0% | 100.0% | Exact ERP alias |
| planned_shutdown | planned_shutdown | 100.0% | 100.0% | Exact ERP alias |
| failure_code | failure_code | 100.0% | 100.0% | Exact ERP alias |
| site | site | 100.0% | 100.0% | Exact ERP alias |
| order_date | order_date | 100.0% | 100.0% | Exact ERP alias |
Source fit, AI match, and normalization
100%Source fit score
100.0%AI match score
100%Mapping readiness score
94.3%Diagnostic confidence score
Workbench interpretation: Source fit measures whether the uploaded file contains recognizable inputs. AI match measures column-mapping confidence. Diagnostic readiness measures whether the normalized mapped data can support trustworthy engine output.
| Quality signal | Value |
|---|---|
| source fit score | 100 |
| ai match score | 100.0 |
| diagnostic readiness score | 100 |
| required mapped | 2 |
| required total | 2 |
| optional mapped | 9 |
| optional total | 9 |
| required completeness | 100.0 |
| row count | 24 |
| column count | 32 |
| blockers | 0 |
| warnings | 0 |
| source fit band | Strong |
| ai match band | Strong |
| readiness band | Strong |
| diagnostic confidence score | 94.3 |
| diagnostic confidence band | Strong |
Normalization plan
| Engine field | Source column | Original sample | Normalized preview | Rule |
|---|---|---|---|---|
| Work Order | work_order | WO-202601 | WO-202601 | Normalize work-order, maintenance order, notification, and shutdown package identifiers. |
| Description | description | Oil & Gas pump bearing seal kit model 1 stainless 4 inch | OIL & GAS PUMP BEARING SEAL KIT MODEL 1 STAINLESS 4 INCH | Normalize case, abbreviations, punctuation, industrial units, specification tokens, and obvious spacing noise. |
| Material Id | material_id | MAT-001-001 | MAT-001-001 | Trim whitespace, preserve leading zeroes, normalize item/material identifiers, and keep original source reference. |
| Asset Id | asset_id | AST-002 | AST-002 | Normalize asset, equipment, functional location, and tag identifiers. |
| Quantity | quantity | 3 | 3 | Parse numeric quantity, keep negatives for audit context, and separate blank/zero from missing. |
| Stock On Hand | stock_on_hand | 1 | 1 | Parse on-hand stock quantity and preserve site-level balance context. |
| Priority | priority | Routine | Routine | Normalize maintenance priority, shutdown, safety, urgent, routine, and critical work signals. |
| Planned Shutdown | planned_shutdown | N | N | Normalize outage, turnaround, planned shutdown, and campaign markers. |
| Failure Code | failure_code | PM | PM | Normalize failure, cause, repair, and problem code values. |
| Site | site | Plant-2 | Plant-2 | Normalize plant, site, storeroom, facility, depot, or operating-unit labels. |
| Order Date | order_date | 2026-05-02 | 2026-05-02 | Parse purchase/work-order date into recency and aging bands. |
Assumptions and limitations
Assumptions
- Uploaded data is treated as the source of truth for this diagnostic run.
- No ERP write-back is performed. Outputs are recommendations and evidence records only.
- Financial estimates use uploaded values where available and conservative assumptions otherwise.
- Industry language is adjusted for Oil & Gas: plants, wells, refineries, shutdowns, turnarounds, and asset integrity.
- Workbench scores were calculated before and after engine execution: source fit 100%, AI match 100.0%, mapping readiness 100%, diagnostic confidence 94.3%.
- Public sample report: deterministic AI2COE sample data was used. Replace with uploaded customer data for customer-specific findings.
Limitations
- Results are diagnostic signals, not final accounting entries.
- Low-confidence findings require human review before remediation.
- Missing source fields reduce confidence and may suppress some analyzers.
- Benchmarks are labelled assumptions unless validated by uploaded data.