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PartsCleanse AI Documentation

Product-wise engine guide

PartsCleanse AI documentation for problem ownership, trigger events, required data, column mapping, diagnostic logic, evidence, score interpretation, confidence tiers, ROI levers, reports, actions, trust controls, and recurring value.

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Engine-to-Value Map

PartsCleanse AI turns source data into reportable value.

InputDescription + Material Id, Manufacturer, Mpn, Supplier, Uom
DiagnosticInterchangeability classification: exact duplicate, probable duplicate, same OEM reference, substitute candidate, and unsafe match.
OutputCatalog health score, evidence table, confidence tiers, report, actions, and score history.
ValueTaxonomy/specification extraction and missing-attribute backlog by material family. Reference enrichment readiness for OEM catalog, supplier catalog, UNSPSC, eCl@ss, and multilingual descriptions.
Engine operating brief

How this diagnostic works for an ICP.

Business problemMRO catalog deduplication, field quality, UOM consistency, and duplicate capital exposure.
Buyer ownerCFO, CIO, Procurement, Maintenance, and Materials leaders
Trigger eventDuplicate SKU complaints, SAP or Maximo migration prep, MRO catalog cleanup, supplier alias confusion, or unreliable material descriptions.
Input data requiredDescription
Optional fieldsMaterial Id, Manufacturer, Mpn, Supplier, Uom, Quantity, Unit Cost, Site, Currency
Upload workflowUpload -> Validate -> Analyze -> Evidence -> Score -> Report -> Action -> Repeat
Column mappingIndustrial IQ profiles source-fit, suggests field matches, asks the user to normalize unmapped required fields, then recalculates mapping readiness before the engine runs.
Evidence generatedSource-backed evidence rows, reason codes, confidence tiers, review status, assumptions, limitations, and action-owner context.
Score outputCatalog health score
Report outputPartsCleanse AI Catalog Diagnostic Report
Confidence tiersHigh-confidence findings can move to review; weak or unsafe matches stay in human-review queues.
Recurring use caseMonthly or quarterly re-uploads create score history, action progress, benchmark comparison, and renewal value reporting.
Required fields

Minimum viable upload and best upload.

EngineRequired Data FileRequired FieldsOptional FieldsOutput GeneratedMinimum Viable UploadBest Upload
PartsCleanse AI Material or item master CSV Description Material Id, Manufacturer, Mpn, Supplier, Uom, Quantity Catalog health score, evidence, report, actions, score history Description Required fields plus site, value, date, owner, asset, supplier, and criticality context where available.
Diagnostic logic

What this engine analyzes.

  • Interchangeability classification: exact duplicate, probable duplicate, same OEM reference, substitute candidate, and unsafe match.
  • Golden-record candidate generation with completeness, OEM/MPN, specification, value, and UOM reasons.
  • Supplier alias, canonical UOM, OEM/part-number, taxonomy, and duplicate-prevention evidence.
Recurring value

How the engine matures after the first run.

  • Taxonomy/specification extraction and missing-attribute backlog by material family.
  • Reviewer queue by value, confidence, unsafe-match risk, and duplicate-family size.
  • Cross-site catalog health trend and duplicate-prevention readiness score.
  • Reference enrichment readiness for OEM catalog, supplier catalog, UNSPSC, eCl@ss, and multilingual descriptions.
  • Catalog knowledge graph connecting material, OEM, supplier, UOM, site, specification, and review status.
  • Continuous duplicate-prevention gate for future item creation governance.
Report interpretation

How to read the output.

PartsCleanse AI Catalog Diagnostic Report includes Catalog health score, evidence records, confidence tiers, assumptions, limitations, action tracker items, score history, and no-write-back governance language.

Catalog IntelligenceValue lever
Uploaded dataCustomer-specific diagnostic required
PartsCleanse AIRelated diagnostic engine
EstimateAssumption-labeled until reviewed
Benchmark assumptionUploaded evidenceConfidence tierHuman reviewReportable value
Industrial IQ platform bridge

How this connects to AI2COE Industrial IQ

Catalog Intelligence is not treated as an isolated content topic. Industrial IQ connects it to uploaded data, engine evidence, confidence tiers, executive reports, actions, score history, and governance review.

Run Industrial IQ Snapshot Explore 8 Diagnostic Engines
PartsCleanse AIcreates catalog evidence and duplicate-family findings.
InventoryMind AIextends catalog signals into inventory risk, dead stock, excess stock, and stockout exposure.
ProcureMind AIconnects supplier and purchase signals to emergency buying, repeat purchases, and leakage.
FinanceMind AItranslates operating findings into working-capital exposure, carrying cost, and ROI scenarios.
AssetMind AIconnects parts to asset relevance, equipment coverage, and plant-register context.
ReliabilityMind AIconnects spare availability to maintenance readiness, false-stockout risk, and shutdown planning.
ReadyMind AIevaluates ERP, data, governance, and AI readiness gaps before transformation spend.
GovernanceMind AImanages confidence, evidence traceability, human review, and auditability.
Force Team validation

PartsCleanse AI Documentation buyer enablement checklist.

Problem solvedTurns an operational data question into source-backed diagnostic evidence.
Who caresCFO, COO, CIO, procurement, maintenance, reliability, ERP, governance, and board stakeholders where relevant.
Data neededCSV exports with stable IDs, descriptions, quantities, values, dates, site, asset, supplier, and owner context where available.
Output deliveredScores, evidence table, confidence tiers, executive report, action tracker items, score history, and governance status.
Value quantifiedWorking capital, carrying cost, emergency premium, dead stock, stockout risk, readiness gaps, or governance risk depending on diagnostic intent.
Decision supportedWhether to act, review, normalize data, escalate findings, fund remediation, or repeat the diagnostic cadence.
Report shared internallyExecutive, CFO, procurement, inventory, readiness, governance, or renewal value report.
Recurring valueBaseline score -> re-upload -> score movement -> action closure -> benchmark comparison -> renewal value report.
Assumptions and limitationsBenchmarks and estimates remain planning context until customer-specific uploaded data is analyzed and reviewed.
Trust controlsNo ERP write-back, evidence traceability, confidence tiers, human review, false-positive control, audit trail, and data-retention boundaries.
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