MRO catalog deduplication, field quality, UOM consistency, and duplicate capital exposure.
PartsCleanse AI Platform Engine: This engine path connects the operating problem, exported source data, diagnostic logic, evidence output, review boundary, and next action for Industrial IQ buyers. See how PartsCleanse AI fits the Industrial IQ engine family for catalog quality, duplicate families, field completeness, and material-master review.
Run This EngineDuplicate-family evidence, material description quality, and cleanup priority queue.
PartsCleanse AI follows the same Industrial IQ trace: exported files, field fit, diagnostic lens, confidence marker, human review, read-only boundary, and audit-ready action.
Exported ERP, EAM, CMMS, inventory, procurement, finance, asset, or work-order records.
Mapped fields, source rows, reason codes, and continuity from file to finding.
Catalog Intelligence evaluates the operating question with controlled engine logic.
High, moderate, review-required, or insufficient evidence states use labels and shapes, not color alone.
Accountable owners review exceptions, limitations, and next actions before remediation.
One-way export path. No autonomous ERP, EAM, CMMS, procurement, or inventory write-back.
Owner, action status, evidence source, report output, and audit metadata stay reviewable.
The selected diagnostic is shown inside the broader Industrial IQ operating model: exported operational data, source-backed evidence, confidence tiers, reports, action tracking, and no ERP write-back.
Catalog health score
CFO, COO, Inventory, Materials, and Supply Chain leadersInventory health score
CPO, Procurement Director, CFO, and Supply Chain leadersProcurement leakage score
CFO, Finance Head, Procurement, and Board advisorsWorking capital score
Asset Integrity, Maintenance, Reliability, and Operations leadersAsset intelligence score
Maintenance Director, Reliability Manager, COO, and Plant leadersMaintenance readiness score
CIO, CTO, COO, Data Governance, and AI Transformation leadersAI readiness score
CISO, CIO, Audit, Governance, and Transformation leadersGovernance readiness score
PartsCleanse AI should not force buyers to guess the right engine. The selector translates role and pain into the recommended Industrial IQ pilot, minimum data file, expected report, and next action.
| Buyer pain | Recommended engine | Readiness | Minimum evidence file |
|---|---|---|---|
| Duplicate or inconsistent item records | PartsCleanse AI | Commercial Pilot Ready | Material master / item master / supplier and UOM fields |
| Dead stock, excess inventory, stockout risk | InventoryMind AI | Commercial Pilot Ready | Inventory balance, movement, criticality, min/max |
| Emergency buys, repeat purchases, supplier leakage | ProcureMind AI | Enterprise Pilot Available | Purchase orders, supplier, price, emergency flags, stock on hand |
| Board-level exposure and carrying cost | FinanceMind AI | Enterprise Pilot Available | Inventory value, cost assumptions, duplicate or leakage evidence |
| Asset-to-part gaps and critical spare coverage | AssetMind AI | Sample Diagnostic Available | Asset register, material master, BOM/work-order references |
| Work-order readiness and false stockout risk | ReliabilityMind AI | Sample Diagnostic Available | Work orders, asset IDs, parts required, stock on hand |
| ERP, data, and AI readiness | ReadyMind AI | Sample Diagnostic Available | ERP export sample, ownership, approval status, governance context |
| Owner review, audit trail, responsible AI | GovernanceMind AI | Enterprise Pilot Available | Findings, confidence, review status, source records, owners |
Before cleansing the material master, diagnose duplicate families, weak descriptions, UOM inconsistency, field completeness, and duplicate-capital exposure.
The product flow keeps the buyer's review sequence visible: source data, diagnostic logic, evidence, report, governance boundary, and next action.
Catalog disorder hides duplicate families, UOM inconsistency, supplier aliases, obsolete records, and capital exposure. The product standard is not a feature list; it is a governed decision path from input data to reportable action.
| P0 pilot quality | 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. |
| P1 enterprise quality | 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. |
| P2 expansion quality | 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. |
Catalog disorder hides duplicate families, UOM inconsistency, supplier aliases, obsolete records, and capital exposure.
| Buyer intent | Primary owner | Evidence required | Report output | Next action |
|---|---|---|---|---|
| Prove duplicate SKU exposure | CFO | duplicate family, matching reason, discriminator conflict | PartsCleanse AI Catalog Diagnostic Report | Run Free Industrial IQ Snapshot |
| Prepare SAP or Maximo migration | CIO | duplicate family, matching reason, discriminator conflict | PartsCleanse AI Catalog Diagnostic Report | Run Free Industrial IQ Snapshot |
| Prioritize catalog cleanup | Procurement | duplicate family, matching reason, discriminator conflict | PartsCleanse AI Catalog Diagnostic Report | Run Free Industrial IQ Snapshot |
| Quantify duplicate capital | Maintenance | duplicate family, matching reason, discriminator conflict | PartsCleanse AI Catalog Diagnostic Report | Run Free Industrial IQ Snapshot |
| Create a review queue without ERP write-back | CFO | duplicate family, matching reason, discriminator conflict | PartsCleanse AI Catalog Diagnostic Report | Run Free Industrial IQ Snapshot |
Every report separates sample or benchmark assumptions from uploaded-data evidence. It is designed for executive reading, analyst inspection, and owner-assigned review without automatic ERP change.
Role-based packs make each engine commercially understandable for the buying committee while preserving the one-platform story.
| Buyer pack | Decision supported | Companion engines |
|---|---|---|
| CFO Pack | Fund value realization only after capital exposure, carrying cost, procurement leakage, and review confidence are visible. | Working Capital Intelligence, Inventory Risk Intelligence, Procurement Leakage Intelligence |
| CIO / ERP Pack | Prove ERP, data, governance, and AI readiness before integration, automation, or migration decisions. | AI Readiness Intelligence, Evidence Governance Intelligence, Asset-to-Part Intelligence |
| Procurement Pack | Convert supplier, PO, duplicate stock, and price-variance evidence into sourcing action without unsupported savings claims. | Procurement Leakage Intelligence, Inventory Risk Intelligence, Working Capital Intelligence |
| Maintenance / Reliability Pack | Prove work-order readiness, asset-to-part coverage, critical-spare availability, and false-stockout risk before maintenance or reliability programs scale. | Maintenance Readiness Intelligence, Asset-to-Part Intelligence, Inventory Risk Intelligence |
Enterprise buyers may eventually need data networks, inventory optimization platforms, MDM suites, source-to-pay workflows, EAM/APM systems, AI governance platforms, or services. AI2COE should run first when the buyer still needs bounded diagnostic proof, role-specific evidence, no ERP write-back, and a report the buying committee can inspect before larger spend.
The comparison lens is intentionally fair: some buyers need a full MDM suite, EAM/APM platform, source-to-pay workflow, AI governance platform, or advisory program. AI2COE should run first when the buyer needs exported-data proof, review levels, report output, and no ERP write-back before committing broader spend.
Enterprise buyers do not evaluate Industrial IQ as one person. Finance, operations, procurement, maintenance, ERP, security, and board sponsors each need a different proof path. This console gives every ICP a fast route to the right engine, data requirement, output, and trust control.
This is the product-quality contract for industry fit: each sector gets a buyer question, required evidence, report output, and next action. Lead and supporting fit are based on the Industrial IQ industry engine sequence; contextual checks keep the full platform visible without pretending every product is the first engine for every buyer.
| Industry | Fit | Diagnostic question | Evidence to expect | Buyer decision |
|---|---|---|---|---|
| Oil & Gas | Lead diagnostic | For Oil & Gas, can exported records covering shutdown spares, upstream/downstream equipment, refinery stores, SAP/Maximo exports, and emergency buy pressure prove the first catalog intelligence decision before spend? | duplicate-family evidence, missing discriminator fields, UOM conflicts, cleanup priority, and reviewer queue tied to shutdown spares, upstream/downstream equipment, refinery stores, SAP/Maximo exports, and emergency buy pressure. | decide what to cleanse first before MDM, ERP migration, or supplier enrichment work starts for Oil & Gas owners, using confidence tiers and source rows before action. |
| Mining | Supporting diagnostic | Where Mining already reviews remote mobile equipment, long replenishment cycles, shutdown windows, depot stores, and high-value inactive spares, does Catalog Intelligence add evidence for catalog quality, duplicate material families, description consistency, UOM conflicts, and material master cleanup priority? | duplicate-family evidence, missing discriminator fields, UOM conflicts, cleanup priority, and reviewer queue tied to remote mobile equipment, long replenishment cycles, shutdown windows, depot stores, and high-value inactive spares. | decide what to cleanse first before MDM, ERP migration, or supplier enrichment work starts for Mining owners, using confidence tiers and source rows before action. |
| Manufacturing | Lead diagnostic | For Manufacturing, can exported records covering production-line uptime, plant stores, MRO item masters, OEE pressure, and site-level buying variance prove the first catalog intelligence decision before spend? | duplicate-family evidence, missing discriminator fields, UOM conflicts, cleanup priority, and reviewer queue tied to production-line uptime, plant stores, MRO item masters, OEE pressure, and site-level buying variance. | decide what to cleanse first before MDM, ERP migration, or supplier enrichment work starts for Manufacturing owners, using confidence tiers and source rows before action. |
| Food & Beverage | Supporting diagnostic | Where Food & Beverage already reviews packaging lines, refrigeration, sanitation windows, plant maintenance, and spare availability during production runs, does Catalog Intelligence add evidence for catalog quality, duplicate material families, description consistency, UOM conflicts, and material master cleanup priority? | duplicate-family evidence, missing discriminator fields, UOM conflicts, cleanup priority, and reviewer queue tied to packaging lines, refrigeration, sanitation windows, plant maintenance, and spare availability during production runs. | decide what to cleanse first before MDM, ERP migration, or supplier enrichment work starts for Food & Beverage owners, using confidence tiers and source rows before action. |
| Pharmaceutical | Lead diagnostic | For Pharmaceutical, can exported records covering validated equipment, controlled maintenance, spare readiness, ERP governance, and quality-sensitive operations prove the first catalog intelligence decision before spend? | duplicate-family evidence, missing discriminator fields, UOM conflicts, cleanup priority, and reviewer queue tied to validated equipment, controlled maintenance, spare readiness, ERP governance, and quality-sensitive operations. | decide what to cleanse first before MDM, ERP migration, or supplier enrichment work starts for Pharmaceutical owners, using confidence tiers and source rows before action. |
| Utilities | Contextual check | If the Utilities review expands, can Catalog Intelligence test the bounded evidence around catalog quality, duplicate material families, description consistency, UOM conflicts, and material master cleanup priority without pretending to be the lead engine? | duplicate-family evidence, missing discriminator fields, UOM conflicts, cleanup priority, and reviewer queue tied to outage readiness, critical network assets, substations, regulated service continuity, and multi-site stores. | decide what to cleanse first before MDM, ERP migration, or supplier enrichment work starts for Utilities owners, using confidence tiers and source rows before action. |
| Data Centers | Contextual check | If the Data Centers review expands, can Catalog Intelligence test the bounded evidence around catalog quality, duplicate material families, description consistency, UOM conflicts, and material master cleanup priority without pretending to be the lead engine? | duplicate-family evidence, missing discriminator fields, UOM conflicts, cleanup priority, and reviewer queue tied to generators, UPS, cooling, electrical components, SLA-sensitive uptime, and site redundancy. | decide what to cleanse first before MDM, ERP migration, or supplier enrichment work starts for Data Centers owners, using confidence tiers and source rows before action. |
| Aviation MRO / Airlines | Supporting diagnostic | Where Aviation MRO / Airlines already reviews AOG readiness, compliance-sensitive parts, maintenance planning, supplier traceability, and schedule reliability, does Catalog Intelligence add evidence for catalog quality, duplicate material families, description consistency, UOM conflicts, and material master cleanup priority? | duplicate-family evidence, missing discriminator fields, UOM conflicts, cleanup priority, and reviewer queue tied to AOG readiness, compliance-sensitive parts, maintenance planning, supplier traceability, and schedule reliability. | decide what to cleanse first before MDM, ERP migration, or supplier enrichment work starts for Aviation MRO / Airlines owners, using confidence tiers and source rows before action. |
| Healthcare Systems | Contextual check | If the Healthcare Systems review expands, can Catalog Intelligence test the bounded evidence around catalog quality, duplicate material families, description consistency, UOM conflicts, and material master cleanup priority without pretending to be the lead engine? | duplicate-family evidence, missing discriminator fields, UOM conflicts, cleanup priority, and reviewer queue tied to facility uptime, clinical support infrastructure, critical services, biomedical-adjacent stores, and procurement controls. | decide what to cleanse first before MDM, ERP migration, or supplier enrichment work starts for Healthcare Systems owners, using confidence tiers and source rows before action. |
| Rail, Metro & Transit | Contextual check | If the Rail, Metro & Transit review expands, can Catalog Intelligence test the bounded evidence around catalog quality, duplicate material families, description consistency, UOM conflicts, and material master cleanup priority without pretending to be the lead engine? | duplicate-family evidence, missing discriminator fields, UOM conflicts, cleanup priority, and reviewer queue tied to rolling stock, depot spares, safety-critical parts, maintenance windows, and asset-to-part mapping. | decide what to cleanse first before MDM, ERP migration, or supplier enrichment work starts for Rail, Metro & Transit owners, using confidence tiers and source rows before action. |
| Telecom Network Operators | Contextual check | If the Telecom Network Operators review expands, can Catalog Intelligence test the bounded evidence around catalog quality, duplicate material families, description consistency, UOM conflicts, and material master cleanup priority without pretending to be the lead engine? | duplicate-family evidence, missing discriminator fields, UOM conflicts, cleanup priority, and reviewer queue tied to field maintenance, network spares, regional depots, tower/power equipment, and restoration readiness. | decide what to cleanse first before MDM, ERP migration, or supplier enrichment work starts for Telecom Network Operators owners, using confidence tiers and source rows before action. |
| Ports, Marine Terminals & Shipping | Contextual check | If the Ports, Marine Terminals & Shipping review expands, can Catalog Intelligence test the bounded evidence around catalog quality, duplicate material families, description consistency, UOM conflicts, and material master cleanup priority without pretending to be the lead engine? | duplicate-family evidence, missing discriminator fields, UOM conflicts, cleanup priority, and reviewer queue tied to Ports and marine terminals operate cranes, conveyors, berth equipment, mooring systems, pumps, motors, hydraulics, vessel support assets, and logistics infrastructure. Duplicate item records can reduce equipment readiness, increase emergency buying, and hide capital across maintenance stores. PartsCleanse AI creates evidence for terminal engineering, maintenance, procurement, and finance teams.. | decide what to cleanse first before MDM, ERP migration, or supplier enrichment work starts for Ports, Marine Terminals & Shipping owners, using confidence tiers and source rows before action. |
| Aerospace & Defense Maintenance Depots | Contextual check | If the Aerospace & Defense Maintenance Depots review expands, can Catalog Intelligence test the bounded evidence around catalog quality, duplicate material families, description consistency, UOM conflicts, and material master cleanup priority without pretending to be the lead engine? | duplicate-family evidence, missing discriminator fields, UOM conflicts, cleanup priority, and reviewer queue tied to Aerospace and defense depots manage controlled spares, mission-support inventory, repair kits, rotables, test equipment, hydraulic parts, seals, fasteners, and maintenance documentation. Duplicate records can inflate inventory and slow readiness decisions, but consolidation must remain controlled and auditable. PartsCleanse AI creates an evidence layer for accountable review rather than uncontrolled deletion.. | decide what to cleanse first before MDM, ERP migration, or supplier enrichment work starts for Aerospace & Defense Maintenance Depots owners, using confidence tiers and source rows before action. |
| Warehousing, Distribution Centers & 3PL | Contextual check | If the Warehousing, Distribution Centers & 3PL review expands, can Catalog Intelligence test the bounded evidence around catalog quality, duplicate material families, description consistency, UOM conflicts, and material master cleanup priority without pretending to be the lead engine? | duplicate-family evidence, missing discriminator fields, UOM conflicts, cleanup priority, and reviewer queue tied to Warehouses, distribution centers, and 3PL networks depend on conveyors, sorters, rollers, belts, sensors, motors, controls, forklifts, scanners, and facility systems. Duplicate records increase spare-parts carrying cost and reduce confidence during fulfillment disruptions. PartsCleanse AI provides a fast diagnostic for automation, facilities, operations, procurement, and finance teams.. | decide what to cleanse first before MDM, ERP migration, or supplier enrichment work starts for Warehousing, Distribution Centers & 3PL owners, using confidence tiers and source rows before action. |
| Commercial Fleet, Trucking & Logistics | Contextual check | If the Commercial Fleet, Trucking & Logistics review expands, can Catalog Intelligence test the bounded evidence around catalog quality, duplicate material families, description consistency, UOM conflicts, and material master cleanup priority without pretending to be the lead engine? | duplicate-family evidence, missing discriminator fields, UOM conflicts, cleanup priority, and reviewer queue tied to Fleet and logistics operators manage distributed vehicles, maintenance depots, tires, brakes, filters, batteries, sensors, engine parts, belts, hydraulics, and local purchasing practices. Duplicate item records increase carrying cost, technician search time, and procurement fragmentation. PartsCleanse AI helps fleet leaders quantify the catalog side of availability and maintenance cost control.. | decide what to cleanse first before MDM, ERP migration, or supplier enrichment work starts for Commercial Fleet, Trucking & Logistics owners, using confidence tiers and source rows before action. |
| Construction & Heavy Equipment Fleets | Contextual check | If the Construction & Heavy Equipment Fleets review expands, can Catalog Intelligence test the bounded evidence around catalog quality, duplicate material families, description consistency, UOM conflicts, and material master cleanup priority without pretending to be the lead engine? | duplicate-family evidence, missing discriminator fields, UOM conflicts, cleanup priority, and reviewer queue tied to Construction and heavy equipment operators manage project-based stores, mobile fleets, hydraulic hoses, filters, undercarriage parts, pumps, bearings, engine spares, attachments, fluids, and field maintenance records. Duplicate catalogs hide stock, increase emergency procurement, and weaken equipment utilization. PartsCleanse AI provides a governed diagnostic across depots, projects, and equipment classes.. | decide what to cleanse first before MDM, ERP migration, or supplier enrichment work starts for Construction & Heavy Equipment Fleets owners, using confidence tiers and source rows before action. |
| Higher Education & Multi-Campus Facilities | Contextual check | If the Higher Education & Multi-Campus Facilities review expands, can Catalog Intelligence test the bounded evidence around catalog quality, duplicate material families, description consistency, UOM conflicts, and material master cleanup priority without pretending to be the lead engine? | duplicate-family evidence, missing discriminator fields, UOM conflicts, cleanup priority, and reviewer queue tied to Universities and multi-campus institutions maintain HVAC, electrical systems, generators, pumps, filters, elevators, labs, utilities, plumbing, and campus facilities across decentralized stores and maintenance teams. Duplicate records make budget stewardship and technician response harder. PartsCleanse AI gives facilities, procurement, finance, and operations leaders a practical evidence pack without requiring system integration.. | decide what to cleanse first before MDM, ERP migration, or supplier enrichment work starts for Higher Education & Multi-Campus Facilities owners, using confidence tiers and source rows before action. |
| Hospitality, Resorts & Gaming | Contextual check | If the Hospitality, Resorts & Gaming review expands, can Catalog Intelligence test the bounded evidence around catalog quality, duplicate material families, description consistency, UOM conflicts, and material master cleanup priority without pretending to be the lead engine? | duplicate-family evidence, missing discriminator fields, UOM conflicts, cleanup priority, and reviewer queue tied to Hospitality, resort, and gaming operators maintain guest-facing facilities, HVAC, kitchens, laundry, elevators, refrigeration, pumps, lighting, security systems, and multi-property maintenance stores. Duplicate item records increase cost and slow response when equipment affects guest experience or revenue operations. PartsCleanse AI converts maintenance catalog disorder into a property-level and portfolio-level diagnostic.. | decide what to cleanse first before MDM, ERP migration, or supplier enrichment work starts for Hospitality, Resorts & Gaming owners, using confidence tiers and source rows before action. |
| Engine | Decision domain | Primary ICP | Minimum upload | Action |
|---|---|---|---|---|
| PartsCleanse AI | Catalog Intelligence | CFO, CIO, Procurement, Maintenance, and Materials leaders | Description | Run |
| InventoryMind AI | Inventory Risk Intelligence | CFO, COO, Inventory, Materials, and Supply Chain leaders | Material Id, Quantity | Run |
| ProcureMind AI | Procurement Leakage Intelligence | CPO, Procurement Director, CFO, and Supply Chain leaders | Po Number, Description | Run |
| FinanceMind AI | Working Capital Intelligence | CFO, Finance Head, Procurement, and Board advisors | Material Id, Stock Value | Run |
| AssetMind AI | Asset-to-Part Intelligence | Asset Integrity, Maintenance, Reliability, and Operations leaders | Asset Id, Description | Run |
| ReliabilityMind AI | Maintenance Readiness Intelligence | Maintenance Director, Reliability Manager, COO, and Plant leaders | Work Order, Description | Run |
| ReadyMind AI | AI Readiness Intelligence | CIO, CTO, COO, Data Governance, and AI Transformation leaders | Process Name, Data Source | Run |
| GovernanceMind AI | Evidence Governance Intelligence | CISO, CIO, Audit, Governance, and Transformation leaders | Finding Id, Finding Type | Run |
These cards show the decision frames, inputs, outputs, and proof status a buyer committee should expect. Benchmark and sample frames are labeled; uploaded data replaces assumptions with customer-specific evidence.
Oil & Gas upstream · SAP / Maximo pre-migration item master
"Use the diagnostic to replace catalog-risk assumptions with duplicate-family evidence, confidence tiers, and capital-exposure interpretation."
Finance, maintenance, procurement, and ERP owners
Run PartsCleanse AIManufacturing multi-plant · Material master, supplier aliases, UOM variants, and OEM references
"The report shows where duplicate item families can drive repeated buying, supplier leakage, and standardization work."
Procurement and master-data governance
Run PartsCleanse AIMining remote operations · High-value critical spares across site catalogs
"Remote teams can inspect which candidate families are safe, which need engineering review, and which should not be merged."
Operations, reliability, and CIO / ERP teams
Run PartsCleanse AIClaims discipline: public examples are benchmark/sample frames unless marked as approved customer evidence. Savings or remediation outcomes require uploaded-data diagnostics and human review.
Before a buyer shares private data, this proof pack shows the source export, field-mapping gate, source-fit gate, diagnostic signal, evidence output, and repeat path for Catalog Intelligence.
Start with description, manufacturer, MPN, supplier, UOM, site, value.
Confirm Description. Add Material Id, Manufacturer, Mpn, Supplier where available.
Weak coverage is labeled as an assumption or limitation before scoring.
Findings show source rows, match reasons, discriminator conflicts, review level, assumptions, and owner status before cleanup action.
Review duplicate family, matching reason, discriminator conflict, confidence tiers, assumptions, limitations, and owner actions.
Rerun after owner review to compare score movement and open findings.
View sample report Download sample CSV Mapping template Compare alternatives
Control boundary: read-only diagnostic, no ERP write-back, source-file purge after report generation, confidence-tiered evidence, and human review before action.
The output UX is designed for executive reading and data-owner inspection: score, evidence table, confidence, report pack, action tracker, score history, export artifacts, and review ownership stay connected.
Score is a diagnostic interpretation, not a certified rating.
Rows show source context, reason codes, confidence, assumptions, and limitations.
Findings stay separated by source quality before owner action.
Report sections include Catalog health score, duplicate-family evidence, unsafe-match controls, CFO exposure view.
Output becomes governed work only after buyer review.
Recurring runs show what changed after owner decisions.
Data owners and executives can inspect the same report package.
The accountable owner reviews evidence before remediation or system change.
HTML sample PDF report Sample CSV Data dictionary Run Snapshot
Output boundary: sample outputs demonstrate structure only. Uploaded-data diagnostics are source-backed, confidence-tiered, no-write-back, and human-reviewed before action.
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.
PartsCleanse AI validates uploaded data, maps source fields, runs deterministic analyzers, creates evidence records, assigns confidence, estimates impact, and produces an executive report.
Sample mode uses deterministic AI2COE data and is clearly labeled. Uploaded-data mode follows the same mapping, validation, evidence, scoring, PDF export, email, action-tracker, and score-history path.
| Input | Need | Common aliases | Meaning |
|---|---|---|---|
| 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. |
| Recommended file | Fields that improve score confidence |
|---|---|
| Material or item master CSV | description, manufacturer, MPN, supplier, UOM, site, value |
| Inventory balance export | quantity, stock value, site, currency |
| Supplier / OEM reference file | supplier aliases, manufacturer names, OEM part numbers |
Duplicate-family value, confidence-adjusted exposure, recoverable working-capital range.
False stockout signals, duplicate item creation, maintenance uncertainty, ERP migration blockers.
Duplicate families, review levels, cleanup priority, clean CSV baseline, executive report.
| Priority | Capability depth |
|---|---|
| P0 | Interchangeability classification: exact duplicate, probable duplicate, same OEM reference, substitute candidate, and unsafe match. |
| P0 | Golden-record candidate generation with completeness, OEM/MPN, specification, value, and UOM reasons. |
| P0 | Supplier alias, canonical UOM, OEM/part-number, taxonomy, and duplicate-prevention evidence. |
| P1 | Taxonomy/specification extraction and missing-attribute backlog by material family. |
| P1 | Reviewer queue by value, confidence, unsafe-match risk, and duplicate-family size. |
| P1 | Cross-site catalog health trend and duplicate-prevention readiness score. |
| P2 | Reference enrichment readiness for OEM catalog, supplier catalog, UNSPSC, eCl@ss, and multilingual descriptions. |
| P2 | Catalog knowledge graph connecting material, OEM, supplier, UOM, site, specification, and review status. |
| P2 | Continuous duplicate-prevention gate for future item creation governance. |
| Competitive moat | Competes with SPARETECH, Verdantis, Prometheus, SAP MDG, and MDM suites by staying diagnostic-first, export-first, no-write-back, and board-reportable. |
| Buyer | Decision question | Evidence source |
|---|---|---|
| CFO | duplicate capital exposure: fund cleanup sequencing before capital targets are committed | CFO exposure view |
| COO | false stockouts and planner searchability: protect operational availability while cleanup is planned | catalog risk report |
| Procurement | duplicate item creation and supplier confusion: standardize buying evidence before supplier action | procurement review queue |
| Maintenance | spare identification risk: improve searchability without unsafe consolidation | maintenance evidence table |
| ERP / data governance | cleanup priority and migration readiness: sequence SAP, Maximo, or MDM remediation safely | master-data backlog |
| Output layer | Example | Why it matters |
|---|---|---|
| Score | Catalog health score | 0-100 signal with risk level and trend-ready snapshot. |
| Score formula | Deterministic calculation | The report exposes the scoring formula and component inputs; random scores are not used. |
| Duplicate family | Part family evidence | Description, manufacturer, MPN, UOM, size/material discriminator, and exposure. |
| Finding | PartsCleanse AI Catalog Diagnostic Report | Issue title, severity, source engine, and owner-facing action. |
| Evidence | Mapped source records | Source-row references, relevant fields, analyzer reason codes, and review level. |
| Evidence graph | Source -> finding -> evidence -> action | The result carries an evidence graph for review, report, action, and score-history continuity. |
| Confidence | High / Medium / Needs Review | Coverage, completeness, source-field quality, and analyzer agreement. |
| Action | Owner review item | Owner action, priority, due window, and review status. |
| Renewal value | Recurring management view | The report shows exposure identified, review queue size, actions created, and next review cadence. |
| Step | Layer | Governed behavior |
|---|---|---|
| 1 | Upload | CSV export enters the parser. Source file retention rules are disclosed. |
| 2 | Map | ERP/CMMS aliases are inferred, then corrected or confirmed by the user. |
| 3 | Validate | Required fields, completeness, missing values, and confidence reducers are shown before run. |
| 4 | Analyze | Engine-specific analyzers generate findings, evidence, and impact estimates. |
| 5 | Govern | Findings receive review levels and owner-decision status before any action. |
| 6 | Report | Executive report, evidence table, action tracker, and score snapshot are produced. |
This runbook makes the engine functional for buyers before a pilot: what to upload, what must pass, what the engine analyzes, what evidence is produced, and what owner decision is required.
| Step | Gate | Engine artifact | Buyer decision |
|---|---|---|---|
| 1 | Minimum source | Material or item master CSV | Start with Description. Best first run adds description, manufacturer, MPN, supplier, UOM, site, value. |
| 2 | Source-fit gate | Confirm required fields, aliases, completeness, and weak mappings. | Context fields such as Material Id, Manufacturer, Mpn, Supplier, Uom improve confidence and reduce assumptions. |
| 3 | Operational analysis path | PartsCleanse AI | 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. |
| 4 | Evidence output | PartsCleanse AI Catalog Diagnostic Report | Score, findings, evidence rows, confidence tiers, assumptions, limitations, action queue, and score-history snapshot. |
| 5 | Acceptance gate | Human-reviewed diagnostic | Owner accepts, rejects, defers, or requests more data before remediation, optimization, or system change. |
| 6 | Repeat path | Recurring intelligence | Rerun after review actions to compare score movement, open findings, and unresolved evidence. |
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.
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.
It diagnoses duplicate MRO records, weak descriptions, UOM conflicts, missing manufacturer fields, and cleanup priority so buyers can decide what to review before MDM, ERP cleanup, or catalog remediation.
Start with a material or item master export. Manufacturer, MPN, supplier, UOM, plant, stock value, usage, and PO references improve confidence and business interpretation.
No. The first diagnostic starts from exported CSV or workbook files and does not require SAP, Maximo, Oracle, IFS, EAM, or CMMS integration.
No. It produces source-backed findings and review queues only. ERP changes, record merges, and item retirement stay under buyer governance.
A catalog health score, duplicate-family register, confidence-tiered evidence table, cleanup priority queue, executive report, and action tracker items.
ERP/data governance, materials management, procurement, maintenance, reliability, and finance should review together because interchangeability, value, and buying behavior must all be validated.
MDM and services may execute stewardship and remediation. PartsCleanse AI runs first to prove which records matter, where confidence is strong, and what should be reviewed safely.
Run a bounded catalog Snapshot with sample or exported item-master data, inspect the evidence table, then decide whether a paid diagnostic or remediation scope is justified.
PartsCleanse AI should help a buying committee answer one practical question: what can your exported data prove, which findings are confidence-tiered, who must review them, and what action is safe after the report. Choose the next step based on buyer readiness, not a generic demo sequence.
Choose the next action based on where the buying committee is now: proof review, data readiness, pilot scoping, or security validation.
Inspect evidence rows, confidence tiers, limitations, scores, and owner actions before sharing private data.
View Sample Reports Stage: Have an export ready Run an Industrial IQ SnapshotStart with exported operational data or sample data and route the issue to the right diagnostic engine.
Run Snapshot Stage: Need committee alignment Download the buyer evaluation guideUse the finance, operations, technology, procurement, maintenance, and security checklist for internal review.
Download Buyer Guide Stage: Active initiative Request a founder-led pilotUse this path when ERP migration, inventory action, procurement leakage, or AI readiness needs a scoped diagnostic.
Request Pilot Stage: Security review Review the security briefValidate no ERP write-back, source-file purge, human review, access controls, DPA/SLA path, and retention boundaries.
Review Security BriefTrust boundary: No ERP write-back. Source files purged after report generation. Human review before action. Sample reports use demonstration data until replaced by uploaded-data diagnostics.
The broader MRO data cleansing diagnostic remains the parent path; this route gives buyers the narrower system, problem, or data-quality context needed before choosing PartsCleanse AI or a sample report.
PartsCleanse AI remains the product engine for catalog, item-master, duplicate-family, manufacturer, UOM, and MRO data-quality evidence.
Useful source exports include item or material master records, descriptions, manufacturer and part-number fields, UOM, plant/site, inventory balance, supplier context, purchase history, and optional asset or work-order context.
Outputs include source-backed findings, duplicate candidates, weak-field indicators, confidence tiers, review actions, and executive report evidence.
Support pages should reinforce the same boundary: read-only diagnostics, no ERP write-back, source-file purge after report generation, and human review before action.
The parent diagnostic page carries the broad commercial intent. This route keeps the narrower SAP, Maximo, duplicate-part, software, or item-master context while giving buyers a clean path to the full diagnostic.
No. The diagnostic path uses exported data and does not write corrected records back to SAP, Maximo, Oracle, EAM, CMMS, or other source systems.
Industrial IQ is designed for evidence-first buyers. Review sample proof, run a bounded Snapshot, align the buyer committee, or request a founder-led diagnostic pilot when the operating problem is ready for review.
See what the report looks like before sharing internal data.
Have a data export Run Catalog IntelligenceStart with an export-first diagnostic path and no ERP write-back.
Need committee alignment Download Buyer Evaluation GuideGive finance, operations, procurement, ERP, security, and maintenance the same evaluation frame.
Ready for review Request Founder-Led PilotAsk for a founder-led pilot review when the problem has an owner and source data is available.
Grounded in approved AI2COE content only. No unsupported claims.