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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 by Industrial IQ
See which duplicate MRO records create working-capital exposure before cleanup begins.
Spare-parts catalog deduplication diagnostic for asset-intensive industrial operations. No ERP integration. No long implementation. A governed, evidence-based finding delivered as five executive report artifacts.
✓ Scoped delivery plan✓ Commercial scope confirmed after review✓ No ERP integration required✓ Sample and workbench access available✓ Source file purged after report
⚠ SAP ECC 6.0 end-of-support: 2027. Every month without a pre-migration catalog diagnostic multiplies remediation cost.
What a disordered MRO catalog costs your organisation — and why it stays hidden
Standard ERP reports show inventory value. They do not show which records are duplicates, which items are double-stocked under different descriptions, or how much capital is tied up in phantom inventory. PartsCleanse AI makes the invisible visible.
Planning assumption — duplicate risk
Duplicate-rate assumptions can indicate whether a catalog diagnostic is worth running, but they are not customer outcome evidence. Uploaded catalog, inventory, procurement, and finance evidence is required before any operational or financial decision.
Item Number
Description
UOM
Plant
On-hand Qty
Value
Status
1000432
GATE VALVE 2 INCH 150LB RF SS316
EA
P001
4
$8,400
✓ Master record
1000897
VALVE GATE 2" 150# STAINLESS
EA
P001
3
$6,300
⚠ Duplicate — T1
1003241
2IN GATE VLV SS 150LB RAISED FACE
EA
P002
5
$10,500
⚠ Duplicate — T1
1001105
BEARING 6205-2RS SKF
EA
P001
12
$960
✓ Master record
1002877
BALL BEARING 6205 ZZ SEALED
EA
P001
8
$640
⚠ Duplicate — T1
1004532
6205 2RS DEEP GROOVE BEARING
EA
P003
10
$800
⚠ Duplicate — T1
+ 2,409 more duplicate families in your catalog...
Illustrative exposure in the example above: $17,800 in this sample excerpt. Treat it as a planning example until uploaded data and owner review produce customer-specific evidence.
Who uses PartsCleanse AI
Role value map — what each decision-maker gets
PartsCleanse AI is purchased by operations, procurement, finance, and master data leaders. Each persona has a distinct pain, a distinct outcome, and a distinct metric they will use to justify the engagement internally.
VO
VP Operations
Their pain
OEE loss from false stockouts driven by duplicate SKUs that inflate reorder triggers and create phantom inventory
PartsCleanse AI solves
Identifies every duplicate SKU contributing to false stockout signals and quantifies its OEE and downtime cost contribution
Metric they track
OEE %, emergency buy frequency, unplanned downtime cost per event
HP
Head of Procurement
Their pain
Duplicate supplier orders, tail-spend from blind catalog, preferred-supplier logic broken by alias and shadow records
PartsCleanse AI solves
Cleans the item master so preferred supplier routing, spend consolidation, and price compliance work correctly
Capital locked in duplicate and obsolete MRO inventory that never surfaces in standard inventory or financial reviews
PartsCleanse AI solves
Quantifies capital exposure with an investor-grade number tied to real SKU count, unit values, and duplicate confidence tiers
Metric they track
Working capital freed ($), inventory write-down avoided, annual carrying cost reduction
MM
Materials Management Director
Their pain
SAP S/4HANA migration requires a clean, consistent material master — duplicate ECC records multiply into migration validation failures
PartsCleanse AI solves
Pre-migration deduplication eliminates records that block S/4HANA consistency validation and material master import rules before go-live
Metric they track
Migration risk items resolved before go-live, material master defect rate post-cutover
IT
Master Data / IT Lead
Their pain
MRO data quality governance requires a repeatable, auditable cleansing process without ERP integration overhead or data extraction risk
PartsCleanse AI solves
CSV-only input, zero ERP integration, governed output with full audit trail — fits MDM governance programs without IT escalation
Metric they track
Clean records %, data quality score post-remediation, governance cycle time reduction
Industry coverage
Six industries. One diagnostic engine. Industry-specific discriminator logic.
Select your industry to highlight the relevant challenge and PartsCleanse AI response. All six industries are served by the same engine — industrial discriminator classes are applied according to the equipment taxonomy of your sector.
Oil & Gas
Industry challenge
Multi-decade SAP, Maximo, and Oracle catalogs can accumulate duplicate-risk signals across upstream, midstream, and downstream assets. Emergency-procurement assumptions from blind catalog conditions must be replaced by uploaded-data evidence before financial claims are made. S/4HANA migrations expose years of deferred catalog governance.
PartsCleanse AI response
Processes exported MRO item-master fields — including UNSPSC, manufacturer, plant code, and unit-of-measure where available — against 7 industrial discriminator classes. Rotating and static equipment parts remain subject to confidence tiers and engineering review before consolidation. Duplicate families are delivered as review-ready findings.
Manufacturing
Industry challenge
OEE programs stall when duplicate SKUs create false stockout signals that may trigger unplanned downtime and emergency-procurement candidates. The catalog problem is invisible until it is diagnosed with source-backed evidence.
PartsCleanse AI response
Identifies duplicate-record candidates that may generate false-stockout signals and estimates their OEE relevance for review. The remediation CSV supports SAP or Oracle item-master planning after owner validation.
Mining & Metals
Industry challenge
Multi-site operations accumulate MRO catalog debt during equipment expansions and acquisitions. Large mining catalogs can carry material duplicated-inventory exposure when planning assumptions are replaced by uploaded-data evidence. Cross-site part aliases are often a high-value and hard-to-see duplicate class.
PartsCleanse AI response
Handles multi-plant catalog exports and identifies cross-site duplicate-family candidates. Discriminator logic helps route similar-specification but non-interchangeable mechanical components to review before consolidation.
Pharmaceutical / Life Sciences
Industry challenge
Regulated maintenance environments require traceable, consistent parts records. Duplicate catalog entries can create documentation inconsistencies that should be reviewed before inspections or change-control decisions.
PartsCleanse AI response
Diagnostic output is a governed, confidence-tiered finding with source records, scoring rationale, discriminator penalties, and an evidence chain for owner review.
Food & Beverage
Industry challenge
Food and beverage operators running lean manufacturing and traceability programs can find that duplicate MRO records inflate working capital assumptions and obscure true inventory positions during record reviews.
PartsCleanse AI response
Identifies duplicate-SKU candidates that may lock working capital in excess MRO stock and produces a capital exposure estimate for finance review. The clean catalog CSV supports CMMS remediation planning without ERP write-back.
Utilities & Energy
Industry challenge
Aging transmission, generation, and distribution asset bases accumulate catalog disorder across decades of ERP migrations and equipment additions. Large utility catalogs can carry material duplicate-exposure assumptions, but customer-specific values require uploaded inventory, cost, and item-master evidence.
PartsCleanse AI response
Engine logic is designed for large exported catalogs and uses 7-class discriminator controls to route similar-description but non-interchangeable electrical and mechanical components to review. Critical-spare findings require discriminator and owner-review context before action.
How it works
Six steps from CSV export to five governed report artifacts
No ERP integration. No consultant on-site. No months-long implementation. Delivery timing is confirmed during scope review and depends on source-file readiness, field quality, and review depth.
1
Export your MRO item master
⏱ ~15 minutes
Export your active MRO catalog from SAP (transaction SE16 on table MARA/MARC), Maximo (Item Master report), Oracle (Item Master extract), or any CMMS/ERP as a UTF-8 encoded CSV file. Required columns: Item Number and Description. Recommended: UOM, Manufacturer Name, UNSPSC Code, Plant/Site Code, Material Group.
ai2coe.com/portal/partscleanse
CSV format — required and recommended columns
Column
Required?
Purpose
Item Number
Required
Unique ERP identifier per row
Description
Required
Item master description text — short or long
UOM
Recommended
Unit discriminator — EA vs BOX vs MTR
Manufacturer
Recommended
Prevents OEM vs generic false consolidation
UNSPSC
Optional
Improves category-level blocking accuracy
2
Create your AI2COE account
⏱ ~5 minutes
Register at ai2coe.com. Provide your name, work email, and organisation. Email verification is required before report generation. Portal access supports sample review and scoped diagnostic preparation; production-scale usage requires commercial review.
ai2coe.com/portal/partscleanse
Registration — free account setup
Santhana Krishnan
procurement@company.com
PETRONAS Carigali
Create account → Prepare diagnostic
No ERP integration. Runtime depends on source-file size, field mapping, and run settings.
3
Upload and validate your CSV
⏱ File-dependent
Log in to the PartsCleanse AI workbench at /portal/partscleanse. Drag and drop your exported CSV or click to browse. The format validator checks column names, encoding, row count, and null Item Numbers before the engine runs. Any format issues are flagged before diagnostic processing starts.
ai2coe.com/portal/partscleanse
Upload validator — format check results
📁
mro_catalog_q2_2025.csv
2.4 MB · 48,312 rows detected
✓ Item Number column detected
✓ Description column detected
✓ UTF-8 encoding verified
✓ No null Item Numbers found
✓ Manufacturer column detected
⚠ UOM column not found — discriminator accuracy may be reduced
Proceed to analysis →
4
Run the diagnostic engine
⏱ Runtime varies by file
Click 'Run Diagnostic'. The PartsCleanse AI engine applies TF-IDF weighted blocking to group similar descriptions, fuzzy scoring to compute pair similarity, and 7-class industrial discriminator penalties to prevent unsafe consolidation of non-interchangeable parts. Runtime depends on file size, field quality, infrastructure, and run settings. Real-time progress is displayed at each stage.
Discriminators active: size class · pressure class · material family · model number · functional subtype · commercial unit · part category
5
Review your duplicate families
⏱ 30–60 minutes
The browser-based Open Findings report groups duplicate records into three confidence tiers: Tier 1 (safe to consolidate — no discriminator conflicts), Tier 2 (consolidation recommended with review), and Tier 3 (flag for manual engineering validation). Each family shows item numbers, descriptions, similarity score, discriminator penalties applied, and estimated capital exposure.
Click 'Download Reports' to receive all five governed report artifacts. The browser Open Findings report remains accessible to your account. The Excel, Word, PDF, and clean CSV downloads are generated for your session. The remediation CSV is formatted for direct re-import to SAP, Oracle, or Maximo — no manual reformatting required.
ai2coe.com/portal/partscleanse
Report artifacts — all 5 ready for download
🌐Open Findings ReportBrowser · Persistent
View →
📊Duplicate Analysis WorkbookExcel
Download →
📝Executive SummaryWord
Download →
📄Board Summary ReportPDF
Download →
🗂Clean Deduplicated CatalogCSV · ERP-ready
Download →
What you receive
Five governed report artifacts prepared for scoped review
Every PartsCleanse AI engagement produces five distinct artifacts serving different audiences: the board, the operations team, the procurement team, and the ERP remediation team.
🌐
Open Findings Browser Report
Persistent · Authenticated access
Interactive duplicate family browser sorted by capital exposure. Filterable by Tier, plant, and category. Accessible to your account after session ends. Share with operations and procurement reviewers without sending a file.
📊
Excel Workbook
Excel .xlsx · Full duplicate family data
Complete duplicate family register with similarity scores, discriminator penalties, item numbers, descriptions, capital exposure per family, and confidence tier. Sortable, filterable, and ready for pivot analysis by your materials team.
📝
Word Executive Summary
Word .docx · Board-ready narrative
Two-page executive narrative covering: catalog profile, duplicate-rate assumption context, top duplicate-family candidates by estimated exposure, recommended review approach, and working-capital assumptions. Formatted for internal distribution and leadership review.
📄
PDF Board Report
PDF · Print-ready
Single-document PDF combining the executive summary, duplicate family summary table, planning comparison, and remediation recommendation. Formatted for printing and board-level distribution without requiring Excel or Word.
🗂
Clean Deduplicated Catalog CSV
CSV · ERP re-import ready
Your original catalog with duplicate records flagged for consolidation, merged, or removed according to Tier recommendations. Formatted for direct re-import to SAP material master, Maximo Item Master, or Oracle Item tables — no manual reformatting required.
Planning assumptions
The numbers — useful only when the evidence class is visible
These example ranges help frame whether a diagnostic is worth running. They are not published benchmark outputs, customer outcome evidence, or promised outcomes. Uploaded data replaces assumptions with source-backed evidence.
Assumption
Duplicate-risk range for early planning
Requires uploaded-data confirmation
Planning band
Illustrative exposure on 50K-SKU scenarios
Requires uploaded-data confirmation
Sample
Controlled sample-mode runtime evidence
Observed in controlled sample mode
15 days
SLA delivery commitment
Business days from kickoff
Planning assumption — annual carrying cost
Carrying-cost drag should be treated as an estimated planning signal until matched to uploaded inventory value, aging, stock status, and finance-approved carrying-cost assumptions.
Where duplicate records obscure preferred supplier logic, emergency-procurement premium should be treated as a hypothesis until purchase history, supplier context, and owner review confirm the pattern.
Estimate your exposure
Quick exposure-planning estimate — three inputs
Enter catalog parameters below for an indicative planning estimate. For a fuller assumption-led model including carrying cost and procurement leakage, use the exposure planning estimator.
MRO Duplicate Exposure Estimator
Indicative estimate — based on buyer-entered assumptions
6,500
Estimated duplicate SKUs
$27.6M
Capital at risk
$4.1M
Annual carrying cost
This is an indicative estimate using user-entered assumptions. Actual exposure varies by catalog age, ERP history, site count, cost fields, and owner-reviewed evidence. Run full model →
How we compare
PartsCleanse AI vs. Verdantis vs. Verusen
Three fundamentally different service models. The right choice depends on whether you need a governed diagnostic finding or an ongoing catalog hosting platform.
Dimension
PartsCleanse AI
Verdantis
Verusen
ERP integration required
✓ No — CSV only
✕ Yes — ERP API required
✕ Yes — SAP/Oracle API
Implementation timeline
✓ Scoped diagnostic timeline
Months (typical)
Months (typical)
Engagement model
✓ Fixed-fee diagnostic
Enterprise platform
AI supply chain SaaS
Commercial scope
✓ Confirmed after source and review scoping
Enterprise pricing
Enterprise pricing
Data retention model
✓ Source purged after report
Catalog hosted on platform
Catalog hosted on platform
Industrial discriminator logic
✓ 7 classes — no unsafe consolidation
Enrichment-based matching
AI supply chain matching
Mid-market accessible
✓ Scoped diagnostic available without an IT integration project
Typically enterprise-only
Typically enterprise-only
Competitive differentiator — service model
Catalog enrichment and supply chain optimization platforms may store and host catalog records as part of their operating model. PartsCleanse AI is a diagnostic engine: the source file is processed, the governed finding is delivered, and the source file is purged after report generation. No platform lock-in. No ongoing subscription required.
Trust and compliance
Enterprise procurement documentation — all published
All documentation required for enterprise vendor qualification is publicly available — no NDA required to review, no sales call required to download.
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.