Compatible with SAP  ·  IBM Maximo  ·  Oracle ERP  ·  Hexagon EAM  ·  Infor  ·  Any CMMS — Run an Industrial IQ diagnostic →
PartsCleanse AI

MRO Catalog Diagnostic

MRO catalog deduplication, field quality, UOM consistency, and duplicate capital exposure.

Run This EngineRun SampleAll Engines
Answer-first product brief

PartsCleanse AI turns uploaded operational data into decision evidence.

PartsCleanse AI is an active diagnostic engine: it parses source data, maps fields, validates quality, runs analyzers, scores risk, generates evidence records, assigns confidence tiers, creates review actions, and produces PartsCleanse AI Catalog Diagnostic Report.

Executive rule: this engine does not replace SAP, Maximo, Oracle, EAM, CMMS, procurement, inventory, or maintenance systems. It creates governed evidence before teams decide what to remediate.
Engine contract

PartsCleanse AI Catalog Diagnostic Report

PartsCleanse AI validates uploaded data, maps source fields, runs deterministic analyzers, creates evidence records, assigns confidence, estimates impact, and produces an executive report.

Input data required

  • Description

Optional inputs

  • Material Id
  • Manufacturer
  • Mpn
  • Supplier
  • Uom
  • Quantity
  • Unit Cost
  • Site
  • Currency
Buyer relevance
Primary personaCFO, CIO, Procurement, Maintenance, and Materials leaders
Sample dataPublic sample CSV, mapping template, data dictionary, HTML report, and PDF report are available before private upload.
Diagnostic logicDeterministic analyzers read mapped source fields, generate findings, attach evidence, and expose assumptions and limitations.
MetricCatalog health score
Score outputCatalog health score: lower values mean higher duplicate, field-quality, UOM, supplier-alias, and obsolete-record exposure.
GovernanceNo ERP write-back. Findings require owner review before remediation.
Active outputScore, findings, evidence, confidence, report, action tracker, and score history.
Report outputPartsCleanse AI Catalog Diagnostic Report with HTML report, CSV evidence, PDF export, action tracker entry, score history snapshot, and email delivery status.
Report emailCompleted authenticated runs attempt branded report email delivery and retain delivery status in the report inventory.
Accepted columns and aliases

What PartsCleanse AI can map from SAP, Maximo, Oracle, Infor, Hexagon EAM, CMMS, and CSV exports.

InputNeedCommon aliasesMeaning
Description Yes description; item_description; material_description; maktx; short_text; part_description; long_text; desc Item, part, asset, work-order, finding, or source-record description used by the engine.
Material Id Recommended material; material_id; material_number; matnr; item; item_number; item_id; sku; part; part_number; stock_code Unique material, SKU, item, or spare-part identifier from the source system.
Manufacturer Recommended manufacturer; mfr; maker; brand; oem; oem_name Manufacturer, OEM, brand, or maker name.
Mpn Recommended mpn; manufacturer_part_number; mfrpn; part_no; oem_part_number; vendor_part Manufacturer part number, OEM reference, supplier part number, or equivalent identifier.
Supplier Recommended supplier; vendor; vendor_name; lifnr; supplier_name Supplier, vendor, vendor alias, or purchasing source.
Uom Recommended uom; unit; unit_of_measure; meins; base_uom Unit of measure such as EA, Each, PCS, Meter, MM, Inch, Set, or Pack.
Quantity Recommended quantity; qty; stock_qty; on_hand; qty_on_hand; unrestricted; labst; stock_on_hand Quantity, balance, order quantity, stock quantity, or demand quantity depending on engine.
Unit Cost Recommended unit_cost; cost; price; moving_average_price; map; valuation_price; standard_price; unit_price Unit cost, average cost, standard price, last purchase price, or valuation rate.
Site Recommended site; plant; werks; location; storeroom; warehouse; depot; facility Plant, site, warehouse, storeroom, region, location, or operating unit.
Currency Recommended currency; currency_code; waers; iso_currency Currency code for cost, value, price, or exposure calculations.
Multi-file diagnostic pack

Best customer results come from the right export pack.

Recommended fileFields that improve score confidence
Material or item master CSVdescription, manufacturer, MPN, supplier, UOM, site, value
Inventory balance exportquantity, stock value, site, currency
Supplier / OEM reference filesupplier aliases, manufacturer names, OEM part numbers
Value model

What leadership can use from this engine.

Capital exposure

Capital exposure model

Duplicate-family value, confidence-adjusted exposure, recoverable working-capital range.

Operational risk

Operational risk model

False stockout signals, duplicate item creation, maintenance uncertainty, ERP migration blockers.

Decision output

Decision output model

Duplicate families, confidence tiers, review priority, clean CSV baseline, executive report.

Product depth

P0, P1, and P2 capabilities built into the Industrial IQ product model.

PriorityCapability depth
P0Interchangeability classification: exact duplicate, probable duplicate, same OEM reference, substitute candidate, and unsafe match.
P0Golden-record candidate generation with completeness, OEM/MPN, specification, value, and UOM reasons.
P0Supplier alias, canonical UOM, OEM/part-number, taxonomy, and duplicate-prevention evidence.
P1Taxonomy/specification extraction and missing-attribute backlog by material family.
P1Reviewer queue by value, confidence, unsafe-match risk, and duplicate-family size.
P1Cross-site catalog health trend and duplicate-prevention readiness score.
P2Reference enrichment readiness for OEM catalog, supplier catalog, UNSPSC, eCl@ss, and multilingual descriptions.
P2Catalog knowledge graph connecting material, OEM, supplier, UOM, site, specification, and review status.
P2Continuous duplicate-prevention gate for future item creation governance.
Competitive moatCompetes with SPARETECH, Verdantis, Prometheus, SAP MDG, and MDM suites by staying diagnostic-first, export-first, no-write-back, and board-reportable.
Buyer committee interpretation

How each executive reads the same diagnostic output.

BuyerDecision questionEvidence source
CFOCan the finding be tied to capital exposure, carrying cost, leakage, or payback discipline?PartsCleanse AI
COODoes the evidence reduce operating risk, downtime exposure, site friction, or service disruption?PartsCleanse AI
CIO / ERP ownerAre source fields mapped, export quality visible, and ERP write-back avoided unless governed?PartsCleanse AI
ProcurementDoes the diagnostic expose supplier, PO, duplicate spend, stocked-but-purchased, or price-variance risk?PartsCleanse AI
Maintenance / ReliabilityDoes the evidence affect work-order readiness, false stockout, shutdown coverage, or critical-spare confidence?PartsCleanse AI
Data governanceCan findings be reviewed, accepted, rejected, audited, and defended after the report is shared?PartsCleanse AI
Evidence and confidence model

What the engine produces after a governed run.

Output layerExampleWhy it matters
ScoreCatalog health score0-100 signal with risk level and trend-ready snapshot.
Score formulaDeterministic calculationThe report exposes the scoring formula and component inputs; random scores are not used.
Duplicate familyPart family evidenceDescription, manufacturer, MPN, UOM, size/material discriminator, and exposure.
FindingPartsCleanse AI Catalog Diagnostic ReportIssue title, severity, source engine, and owner-facing action.
EvidenceMapped source recordsSource-row references, relevant fields, analyzer reason codes, and confidence tier.
Evidence graphSource -> finding -> evidence -> actionThe result carries an evidence graph for review, report, action, and score-history continuity.
ConfidenceHigh / Medium / Needs ReviewCoverage, completeness, source-field quality, and analyzer agreement.
ActionOwner review itemRecommended action, priority, due window, and review status.
Renewal valueRecurring management viewThe report shows exposure identified, review queue size, actions created, and next review cadence.
Workflow

Upload to diagnostic to recurring intelligence.

StepLayerGoverned behavior
1UploadCSV export enters the parser. Source file retention rules are disclosed.
2MapERP/CMMS aliases are inferred, then corrected or confirmed by the user.
3ValidateRequired fields, completeness, missing values, and confidence reducers are shown before run.
4AnalyzeEngine-specific analyzers generate findings, evidence, and impact estimates.
5GovernFindings receive confidence tiers and human-review status before any action.
6ReportExecutive report, evidence table, action tracker, and score snapshot are produced.
Industry fit

Configured for asset-intensive operating reality.

Oil & GasSAP S/4HANA migration, turnaround readiness
Miningremote stockouts, haul truck downtime
ManufacturingOEE improvement, plant consolidation
Utilitiesoutage readiness, regulatory audit
Power Generationplanned outages, turbine spare coverage
Chemicalsprocess safety, shutdown readiness
Food & Beverageline uptime, multi-plant standardization
PharmaceuticalsGMP audit, validated maintenance
Transportation & Logisticsfleet uptime, depot duplication
Ports & Marinecrane downtime, terminal uptime
Aviationaircraft-on-ground risk, MRO depot duplication
Construction & Heavy Equipmentequipment availability, site-level duplicate stock
Healthcare Facilitiesclinical uptime, biomed asset coverage
Higher Education Campusescampus maintenance visibility, storeroom consolidation
Government & Public Infrastructureauditability, public asset uptime
Data Centersuptime assurance, critical facilities spares
Renewable Energyremote-site availability, turbine spare coverage
Water & Wastewaterservice continuity, pump station spare coverage
Benchmark and claims discipline

Assumptions are separated from uploaded-data results.

Public pages may use benchmark ranges to help leaders understand the problem. A diagnostic run replaces the benchmark with mapped source records, actual evidence, confidence tiers, and report ownership.

Low-confidence or high-risk findings are routed to human review. AI2COE does not make autonomous ERP updates or unsupported ROI claims.

Source resultUploaded data, mapped fields, evidence records, score snapshot
AssumptionBenchmark, industry range, carrying-cost assumption, ROI scenario
GovernanceOwner review, confidence tier, audit log, no write-back
Benchmark methodologyRun this engine
Knowledge graph

Problem -> ERP export -> industry context -> engine evidence -> action.

PartsCleanse AI connects the buyer problem to source-system evidence, industry risk language, report outputs, and governed action tracking. This makes the page readable to executives and buying committees without exposing private datasets or internal code.

Problem signalERP / CMMS exportField mappingCatalog IntelligenceEvidence tableHuman reviewExecutive report
Frequently asked questions

Questions buyers ask before running Catalog Intelligence.

What data does PartsCleanse AI need?

PartsCleanse AI requires Description. Optional fields such as Material Id, Manufacturer, Mpn, Supplier, Uom, Quantity improve confidence and business-impact precision.

What does PartsCleanse AI produce?

It produces PartsCleanse AI Catalog Diagnostic Report, a 0-100 catalog health score, evidence records, confidence tiers, recommended actions, and a review-ready executive summary.

Does AI2COE write back to SAP, Maximo, Oracle, or any CMMS?

No. AI2COE diagnostics are decision-support outputs. They do not change ERP, EAM, CMMS, procurement, inventory, or asset records automatically.

How does confidence tiering work?

Findings are ranked by source-field coverage, data completeness, evidence quality, analyzer agreement, and whether a human owner should review the recommendation before action.

How should leadership use the report?

Use the report to decide whether the issue is measurable, material, governable, and worth funding before starting a larger ERP, inventory, procurement, maintenance, or AI transformation program.

AI2COE Copilot