Works from CSV or workbook exports produced from SAP, IBM Maximo, Oracle ERP, Hexagon EAM, Infor, and other ERP, EAM, or CMMS systems. No direct integration or write-back is required — Review data requirements →
PartsCleanse AI

PartsCleanse AI MRO Catalog Duplicate Evidence Pattern

Representative product case-study pattern showing how PartsCleanse AI turns exported item-master data into duplicate-family, weak-description, UOM, manufacturer, and owner-review evidence.

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RepresentativeAnonymized pattern
Diagnostic-firstEvidence before remediation
Source-file purgeAfter report generation
Evidence-led diagnostic report showing score history, maturity bands, confidence levels, and prioritized enterprise AI recommendations.
Sample reports show the structure of evidence rows, confidence tiers, assumptions, limitations, actions, and executive outputs.
Operating context

What the buyer is trying to decide.

A maintenance and master-data team needs to understand whether duplicate spare-parts records, weak descriptions, manufacturer ambiguity, UOM conflicts, and obsolete candidates are serious enough to justify cleanup work before ERP migration, MDM investment, or inventory action.

CIO / ERPMRO LeadMaintenanceProcurementCFOData Governance
Control evidence
  • Representative PartsCleanse AI product pattern
  • Candidate findings require owner review
  • No ERP write-back
  • Source files purged after report generation
Visual evidence map

How exported operational data becomes buyer-ready evidence.

Use this map to understand the diagnostic flow before reading the detailed posture change. It shows source context, evidence conversion, owner review, and safe action boundaries.

Source evidence

Files and context the diagnostic would inspect.

Material master export
Manufacturer and MPN fields
UOM and plant context
Inventory and supplier context
Step 1 Source exports Read-only uploaded files
Step 2 Map and normalize Field fit, aliases, context
Step 3 Evidence table Reason codes and confidence
Step 4 Owner review Human decision before action
Evidence signal 1 Evidence output

Duplicate families, weak descriptions, obsolete candidates, UOM conflicts, and manufacturer ambiguity are grouped by confidence tier.

Evidence signal 2 Decision output

Cleanup candidates are routed to accountable owners with reason codes, limitations, and review state.

Evidence signal 3 Trust posture

No automatic merge, no ERP write-back, and source-backed evidence before remediation.

Review roles

Who reviews the evidence.

CIO / ERPMRO LeadMaintenanceProcurementCFO
Stage: No ERP write-back
Stage: Source files purged after report generation
Stage: Confidence-tiered evidence
Stage: Human review before action
Before / after diagnostic posture

The change AI2COE is meant to create inside the buying committee.

Before diagnostic
Catalog visibilityDuplicate candidates are reviewed manually across item descriptions, manufacturer names, MPNs, UOMs, and plant codes.
Business pressureMaintenance, procurement, and finance disagree on whether the catalog problem is operational or only cosmetic.
Governance boundaryThe team needs evidence before approving any merge, deletion, or remediation action.
After diagnostic
Evidence outputDuplicate families, weak descriptions, obsolete candidates, UOM conflicts, and manufacturer ambiguity are grouped by confidence tier.
Decision outputCleanup candidates are routed to accountable owners with reason codes, limitations, and review state.
Trust postureNo automatic merge, no ERP write-back, and source-backed evidence before remediation.
Enterprise trust posture

Proof controls buyers expect before they upload operational data.

Source purge Uploaded source files are purged after report generation; summary metrics and Open Findings remain.
No ERP write-back The diagnostic creates evidence for review. It never changes, deletes, merges, or overwrites ERP records.
Local currency Reports display money in the user's selected or country-derived currency, while USD remains the base audit calculation.
Audit trail Report ownership, access, quota, and feedback events are retained for governed review.
Session downloads Excel, Word, PDF, and CSV downloads are available only in the active generation session.
Open Findings Browser findings remain available without retaining the original source catalog rows.
Buyer interpretation

This is the level of evidence a first paid pilot should produce.

The purpose of an Industrial IQ pilot is not to claim instant remediation. It is to create a defensible management fact base: source-backed findings, confidence distribution, exposure assumptions, readiness issues, and a review sequence that executives can govern.

Executive review lens

Use this page to decide the next evidence step.

Industrial IQ pages are designed to move enterprise buyers from operating pain to source-backed diagnostic evidence, not to force an immediate platform commitment.

Audience

Finance, operations, procurement, maintenance, reliability, ERP/data, security, and executive sponsors.

Evidence to prepare

Relevant ERP, EAM, CMMS, inventory, procurement, asset, work-order, or readiness exports where available.

Output

A diagnostic route, sample proof format, report evidence, confidence tier, owner review path, or commercial next step.

Trust boundary

Read-only diagnostics, no ERP write-back, source-file purge after report generation, and human review before action.

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