Asset intelligence score
Use the score as a diagnostic signal that needs evidence review.
Industrial IQ | AI2COE | AssetMind AI
AssetMind AI Asset-to-Part Risk Report
Report takeaway
What this sample report answers
AssetMind 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 Asset-to-Part 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.
asset ID, part reference, criticality
Inspect source context, confidence, assumptions, and limits.
AssetMind AI Asset-to-Part Risk 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 Asset-to-Part 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 Asset-to-Part 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
87.5Asset intelligence score
ControlledRisk level
Medium ConfidenceConfidence
6Evidence 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
65375.0Capital exposure signal
7845.0-18305.0Recoverable range
11767.5Annual leakage signal
ControlledBoard attention band
Diagnostic components
0UNLINKED ROWS
0CRITICAL SPARE GAPS
3OBSOLETE SPARE ROWS
active_deterministic_evidence_engineDIAGNOSTIC DEPTH
2REQUIRED FIELDS MAPPED
8OPTIONAL FIELDS MAPPED
24SOURCE ROWS PROFILED
Score formula: 100 - asset_risk/source_rows*100 with critical spare gaps weighted triple Random score used: False
| Score input | Value |
|---|---|
| unlinked rows | 0 |
| critical spare gaps | 0 |
| obsolete spare rows | 3 |
| required fields mapped | 2 |
| optional fields mapped | 8 |
| source rows profiled | 24 |
| estimated row value total | 154200.0 |
| equipment class gap rows | 0 |
| retired asset stock rows | 3 |
| validation issue count | 0 |
| validation blocker count | 0 |
| prompt injection signal count | 0 |
Product maturity and competitive depth
Competitive position: Stays above EAM systems like Maximo, Oracle, IFS, and Prometheus by diagnosing exported data rather than replacing asset workflows.
| Priority | Implemented product capability |
|---|---|
| P0 | Asset-to-part linkage, plant-register relevance, critical asset spare coverage, obsolete asset spare exposure, and asset risk heatmap.; Inference from asset ID, equipment tag, description, manufacturer, model, work-order text, and BOM-like references.; Linked, weakly linked, and unlinked critical-spare classification. |
| P1 | Equipment hierarchy risk heatmap, retired-asset stock queue, equipment-class gaps, and criticality-weighted exposure.; Asset-part knowledge graph connecting asset, material, site, equipment class, status, and spare coverage.; COO and maintenance report views by plant, equipment class, and criticality. |
| P2 | Asset criticality matrix and spares coverage index by plant, line, equipment class, and location.; BOM readiness diagnostic before EAM/CMMS modernization.; Portfolio-level asset-spare coverage trend for recurring reviews. |
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. |
| CIO / ERP Pack | ReadyMind AI, GovernanceMind AI, PartsCleanse AI, AssetMind AI | Prove ERP, data, governance, and AI readiness before integration, automation, or migration decisions. |
| 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 |
|---|---|
| linkage classes | {"linked": 21, "unlinked_or_obsolete_review": 3, "weakly_linked": 0} |
| criticality matrix | {"critical_asset_gap_rows": 0, "critical_with_stock_rows": 4, "noncritical_review_rows": 21} |
| asset part knowledge graph | [{"asset": "AST-002", "equipment_class": "Pump", "material": "MAT-001-001", "relationship": "asset -> spare part -> site -> coverage status", "site": "Plant-2"}, {"asset": "AST-003", "equipment_class": "Pump", "material": "MAT-002-002", "relationship": "asset -> spare part -> site -> coverage status", "site": "Plant-3"}, {"asset": "AST-004", "equipment_class": "Compressor", "material": "MAT-003-003", "relationship": "asset -> spare part -> site -> coverage status", "site": "Plant-4"}, {"asset": "AST-005", "equipment_class": "Pump", "material": "MAT-004-004", "relationship": "asset -> spare part -> site -> coverage status", "site": "Plant-1"}, {"asset": "AST-006", "equipment_class": "Pump", "material": "MAT-005-005", "relationship": "asset -> spare part -> site -> coverage status", "site": "Plant-2"}, {"asset": "AST-007", "equipment_class": "Compressor", "material": "MAT-000-006", "relationship": "asset -> spare part -> site -> coverage status", "site": "Plant-3"}, {"asset": "AST-008", "equipment_class": "Pump", "material": "MAT-001-007", "relationship": "asset -> spare part -> site -> coverage status", "site": "Plant-4"}, {"asset": "AST-009", "equipment_class": "Pump", "material": "MAT-002-008", "relationship": "asset -> spare part -> site -> coverage status", "site": "Plant-1"}, {"asset": "AST-001", "equipment_class": "Compressor", "material": "MAT-003-009", "relationship": "asset -> spare part -> site -> coverage status", "site": "Plant-2"}, {"asset": "AST-002", "equipment_class": "Pump", "material": "MAT-004-010", "relationship": "asset -> spare part -> site -> coverage status", "site": "Plant-3"}, {"asset": "AST-003", "equipment_class": "Pump", "material": "MAT-005-011", "relationship": "asset -> spare part -> site -> coverage status", "site": "Plant-4"}, {"asset": "AST-004", "equipment_class": "Compressor", "material": "MAT-000-012", "relationship": "asset -> spare part -> site -> coverage status", "site": "Plant-1"}] |
| spares coverage index | {"coverage_percent": 87.5, "coverage_rule": "Rows with asset, material, equipment class, active status, and stock signal are treated as stronger coverage."} |
Buyer committee views
CFO
Can quantified exposure justify a diagnostic or remediation budget?
AssetMind AI shows 65375.0 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?
1 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
10 nodes | 9 evidence relationships. This graph links uploaded source rows to findings, confidence, business impact, recommended actions, report output, and score history.
Renewal value view
65375.0EXPOSURE IDENTIFIED
2REVIEW QUEUE SIZE
2ACTIONS CREATED
0ACTIONS REVIEWED
5230.0CONSERVATIVE VALUE REALIZATION
10460.0BASE 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 |
|---|---|---|---|---|---|
| Obsolete Asset Spare Analyzer | 3 rows show spares tied to inactive or retired assets | MEDIUM | 72% | 3 | Review obsolete asset spares for transfer, disposal, or engineering retention. |
| Retired Asset Stock Analyzer | 3 rows show stock tied to retired or inactive assets | HIGH | 80% | 3 | Review retired-asset stock for disposal, transfer, or engineering retention exceptions. |
Evidence Records
| ID | Confidence tier | Severity | Description | Value | Source | Reason codes |
|---|---|---|---|---|---|---|
| E-c9c68588 | Medium Confidence | MEDIUM | 3 rows show spares tied to inactive or retired assets | 8775.0 | row:7:MAT-001-007 | material-id-present, description-signature, site-context, value-bearing-row, spec-token-match |
| E-c9f44e33 | Medium Confidence | MEDIUM | 3 rows show spares tied to inactive or retired assets | 8800.0 | row:14:MAT-002-014 | material-id-present, description-signature, site-context, value-bearing-row, spec-token-match |
| E-fc362cef | Medium Confidence | MEDIUM | 3 rows show spares tied to inactive or retired assets | 8575.0 | row:21:MAT-003-021 | material-id-present, description-signature, site-context, value-bearing-row, spec-token-match |
| E-c1d909b1 | Medium Confidence | HIGH | 3 rows show stock tied to retired or inactive assets | 8775.0 | row:7:MAT-001-007 | material-id-present, description-signature, site-context, value-bearing-row, spec-token-match |
| E-4059dd1d | Medium Confidence | HIGH | 3 rows show stock tied to retired or inactive assets | 8800.0 | row:14:MAT-002-014 | material-id-present, description-signature, site-context, value-bearing-row, spec-token-match |
| E-6b8a3ac5 | Medium Confidence | HIGH | 3 rows show stock tied to retired or inactive assets | 8575.0 | row:21:MAT-003-021 | material-id-present, description-signature, site-context, value-bearing-row, spec-token-match |
Recommended Actions
P1
Review obsolete asset spares for transfer, disposal, or engineering retention.
Owner: Asset Integrity | Due: 60 days
P0
Review retired-asset stock for disposal, transfer, or engineering retention exceptions.
Owner: Asset Integrity | Due: 30 days
Mapping and validation
| Input | Source column | Completeness | Confidence | Reason |
|---|---|---|---|---|
| asset_id | asset_id | 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_status | asset_status | 100.0% | 100.0% | Exact ERP alias |
| criticality | criticality | 100.0% | 100.0% | Exact ERP alias |
| equipment_class | equipment_class | 100.0% | 100.0% | Exact ERP alias |
| site | site | 100.0% | 100.0% | Exact ERP alias |
| last_used_date | last_used_date | 100.0% | 100.0% | Exact ERP alias |
| quantity | quantity | 100.0% | 100.0% | Exact ERP alias |
| unit_cost | unit_cost | 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 | 8 |
| optional total | 8 |
| 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 |
|---|---|---|---|---|
| Asset Id | asset_id | AST-002 | AST-002 | Normalize asset, equipment, functional location, and tag 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 Status | asset_status | Active | ACTIVE | Normalize active, retired, inactive, obsolete, mothballed, and decommissioned states. |
| Criticality | criticality | Medium | MEDIUM | Normalize High/Medium/Low, shutdown, safety, AOG, and critical-spare signals. |
| Equipment Class | equipment_class | Pump | Pump | Normalize equipment class, system, line, and functional hierarchy labels. |
| Site | site | Plant-2 | Plant-2 | Normalize plant, site, storeroom, facility, depot, or operating-unit labels. |
| Last Used Date | last_used_date | 2026-03-02 | 2026-03-02 | Parse date-like values into age and activity bands. |
| Quantity | quantity | 3 | 3 | Parse numeric quantity, keep negatives for audit context, and separate blank/zero from missing. |
| Unit Cost | unit_cost | 975 | 975 | Parse unit cost, retain source currency, and separate uploaded value from benchmark assumption. |
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.