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 →
Industrial IQ Industry Diagnostic

Industrial Decision Intelligence for Mining Operations.

Mining operators carry high-value spares across mobile fleets, fixed plant, process equipment, remote warehouses, and contractor-managed maintenance records. Duplicate item masters hide stock, increase emergency buys, and weaken maintenance planning when a site cannot confidently identify what it already owns. PartsCleanse AI gives mining leadership an evidence-first view of duplicate families, capital exposure, commodity concentration, and site-level cleanup priorities.

Run Free Industrial IQ Snapshot
MiningIndustry operating context
5Relevant engines
Read-onlyNo ERP write-back
Mining and heavy asset diagnostic visual showing mobile equipment, fixed plant, stores, asset registers, maintenance signals, and spare-parts readiness.
Mining, construction equipment, and heavy-asset pages use this family when fleet uptime, asset-to-part linkage, and critical-spare evidence are central.
Industry operating environment

Catalog intelligence for mobile fleets, fixed plant, conveyors, crushers, and remote spares.

Mining operators carry high-value spares across mobile fleets, fixed plant, process equipment, remote warehouses, and contractor-managed maintenance records. Duplicate item masters hide stock, increase emergency buys, and weaken maintenance planning when a site cannot confidently identify what it already owns. PartsCleanse AI gives mining leadership an evidence-first view of duplicate families, capital exposure, commodity concentration, and site-level cleanup priorities.

Asset contextmobile equipment, fixed plant, conveyors, crushers, mills, haulage, and remote-site maintenance
Operating pressureequipment availability, remote stock risk, critical-spare coverage, and production continuity
Source landscapeSAP, Maximo, Oracle, fleet maintenance systems, EAM, CMMS, warehouse spreadsheets
Mining Industrial AI CoE blueprint

Build the Mining AI operating model around evidence, not disconnected pilots.

For Mining, an Industrial AI Centre of Excellence should begin with source exports from SAP, Maximo, Oracle, fleet maintenance systems, EAM, CMMS, warehouse spreadsheets, classify evidence quality, assign owner review, and route each operating question to the right Industrial IQ engine before AI or Agentic AI scale decisions.

Open the Industrial AI CoE Framework

Inventory Risk Intelligencedecide which inventory actions are safe to review before optimization or reduction targets are approved
Asset-to-Part Intelligencedecide which equipment, BOM, and spare relationships need review before EAM, CMMS, or reliability action
Maintenance Readiness Intelligencedecide which readiness gaps need maintenance owner review before planned work, outage, or shutdown windows
Trust boundaryRead-only diagnostics, no ERP write-back, source-file purge after report generation, and human review before action.
Sector problems

The first operating questions to diagnose.

Operating problem 1

Duplicate spares across mine sites, mobile fleets, fixed plant, and warehouse catalogs.

Operating problem 2

Capital-at-risk analysis for high-value components, bearings, belts, pumps, filters, and electrical spares.

Operating problem 3

Site and commodity slicing for reliability, maintenance planning, and procurement teams.

Operating problem 4

Controlled review backlog before SAP, Maximo, or CMMS material governance programs.

Relevant exports

Mining source files should be reviewed as evidence objects, not as a single text upload.

Start with exported CSV or workbook files from the systems the buying committee already trusts. Industrial IQ uses them for read-only diagnostic evidence; it does not write back to ERP, EAM, CMMS, procurement, inventory, or finance systems.

Export objects: each source file is treated as bounded diagnostic evidence with assumptions, limitations, confidence tiers, and owner review.

Source objectPrimary fieldsOptional contextDiagnostic questionEngine ownership
Material masterMaterial master export Material ID, description, manufacturer, MPN, UOM, supplier Plant/site, item type, criticality, preferred supplier, lifecycle status Checks whether part identity can be trusted before catalog cleanup, ERP migration, inventory action, or AI readiness work. PartsCleanse AISupporting: ReadyMind AI
Inventory balanceInventory balance export Material ID, plant/site, storage location, quantity, value, status Min/max policy, last movement, reservation, condition, valuation class Separates inventory exposure, false-stockout signals, stock status, and working-capital context from catalog identity findings. InventoryMind AISupporting: FinanceMind AI
Purchase ordersPurchase orders export PO, supplier, material, date, quantity, price, currency Buyer group, contract reference, plant/site, emergency flag, expedite flag Routes supplier overlap, emergency-buy patterns, price variance, and procurement-leakage candidates to procurement review. ProcureMind AISupporting: FinanceMind AI
Asset registerAsset register export Asset ID, functional location, equipment class, criticality Site, parent asset, manufacturer, install date, lifecycle state Anchors asset-readiness review in equipment context, criticality, hierarchy quality, and EAM/CMMS source fit. AssetMind AISupporting: ReliabilityMind AI
Work ordersWork orders export Work order, asset, task, status, priority, materials used Failure code, completion date, planner group, downtime, labor hours Routes maintenance-delay, material-availability, repeated work, and reliability-risk signals to owner review. ReliabilityMind AISupporting: InventoryMind AI
AI readiness recordAI readiness record Process, source, owner, quality, dependency, use case Business owner, system owner, target decision, limitation, remediation dependency Evaluates use-case readiness, source fit, ownership, and first-action constraints before AI adoption. ReadyMind AISupporting: GovernanceMind AI
Source-object rule: material master, inventory balance, purchase order, financial valuation, asset register, BOM, work-order, readiness, and governance records are separate evidence contracts. Do not combine them into one generic upload schema.
Review engine-specific required fieldsView sample reports
Worked evidence scenario

InventoryMind AI first-check question for Mining.

Can exported mining data separate true stock shortage from duplicate inventory, remote-site imbalance, and weak equipment-to-part linkage?

Next action

Upload a small export pack, review the confidence tiers, and route candidate findings to the accountable owner before remediation, optimization, or AI adoption work starts.

Start with exported data
Source-file semantic integrity Canonical source-to-engine map for Mining. Open the governed source-object map used by industry pages.

Industrial IQ routes each exported data object to the engine that can interpret it safely. This prevents material masters, inventory balances, purchase orders, financial valuation records, asset registers, BOMs, work orders, readiness inputs, and governance records from being treated as interchangeable evidence.

Source objectPrimary fieldsOptional contextEngine ownershipDiagnostic useTrust boundary
Material master Material ID, description, manufacturer, MPN, UOM, supplier Plant/site, item type, criticality, preferred supplier, lifecycle status PartsCleanse AISupporting: ReadyMind AI Part identity trust, duplicate candidates, naming quality, manufacturer ambiguity, UOM consistency, and catalog readiness. Catalog findings are review candidates; no ERP write-back or blind merge.
Inventory balance Material ID, plant/site, storage location, quantity, value, status Min/max policy, last movement, reservation, condition, valuation class InventoryMind AISupporting: FinanceMind AI Excess, obsolete, slow-moving, false-stockout, and working-capital exposure signals. Inventory findings are exposure signals until owner review confirms action.
Purchase orders PO, supplier, material, date, quantity, price, currency Buyer group, contract reference, plant/site, emergency flag, expedite flag ProcureMind AISupporting: FinanceMind AI Supplier overlap, emergency-buy patterns, stocked-but-purchased signals, and procurement leakage candidates. Procurement signals require buyer review before supplier, contract, or sourcing action.
Financial valuation Material, unit cost, inventory value, reserve, carrying assumptions Currency, valuation class, site, reserve policy, planning assumption owner FinanceMind AISupporting: InventoryMind AI Valuation exposure, carrying-cost assumptions, reserve context, and finance-ready planning bands. Financial outputs are planning estimates unless validated with approved finance records.
Asset register Asset ID, functional location, equipment class, criticality Site, parent asset, manufacturer, install date, lifecycle state AssetMind AISupporting: ReliabilityMind AI Asset-to-part linkage, critical-spare coverage, hierarchy gaps, and equipment context signals. Asset findings guide review; they do not alter EAM or CMMS records.
BOM Asset/equipment ID, material ID, quantity, position BOM level, criticality, site, equipment class, manufacturer reference AssetMind AISupporting: PartsCleanse AI BOM coverage, asset-to-part linkage, critical-spare context, and part identity review. BOM findings guide owner review; they do not change EAM, CMMS, or ERP records.
Work orders Work order, asset, task, status, priority, materials used Failure code, completion date, planner group, downtime, labor hours ReliabilityMind AISupporting: InventoryMind AI Maintenance-delay signals, repeated work-order patterns, spare availability context, and shutdown-readiness risk. Reliability signals require maintenance or reliability owner review before operational action.
AI readiness record Process, source, owner, quality, dependency, use case Business owner, system owner, target decision, limitation, remediation dependency ReadyMind AISupporting: GovernanceMind AI Industrial AI Readiness, ERP/data readiness, owner coverage, source-fit gaps, and first-use-case constraints. Readiness output is an assessment path, not an implementation certification.
Governance record Finding, source, confidence, reviewer, status, approval, audit event Exception reason, retention state, action owner, review notes GovernanceMind AISupporting: ReadyMind AI Evidence traceability, confidence-tier governance, human-review status, audit trail, and action ownership. Governance records document review control; they do not replace accountable approval.
Relevant engines only

Route each Mining question to the evidence owner.

Relevant diagnostic

InventoryMind AI

excess stock, obsolete inventory, false stockouts, critical-spare gaps, and carrying-cost exposure

Output: InventoryMind AI Inventory Risk Report
Relevant diagnostic

AssetMind AI

asset-to-part linkage, BOM coverage, equipment hierarchy quality, critical-spare coverage, and EAM readiness

Output: AssetMind AI Asset-to-Part Risk Report
Relevant diagnostic

ReliabilityMind AI

maintenance readiness, work-order spare availability, repeat demand, false-stockout risk, and shutdown readiness

Output: ReliabilityMind AI Maintenance Readiness Report
Relevant diagnostic

PartsCleanse AI

catalog quality, duplicate material families, description consistency, UOM conflicts, and material master cleanup priority

Output: PartsCleanse AI Catalog Diagnostic Report
Relevant diagnostic

FinanceMind AI

working-capital exposure, carrying-cost assumptions, duplicate inventory cost, and board-readable value boundaries

Output: FinanceMind AI Working Capital Report

Sector-specific AI vocabulary

Mining AI terminology mapped to source evidence and engine ownership.

ConceptOperational meaningSource-data contextPrimary engine
Mining AI Readiness AI readiness in Mining means checking whether fleet, EAM, CMMS, inventory, supplier, maintenance, and site-context exports are fit to support governed AI use cases before implementation. fleet, EAM, CMMS, inventory, supplier, maintenance, and site-context exports ReadyMind AI
Mining Artificial Intelligence Use Case Readiness Artificial intelligence use case readiness in Mining evaluates whether a proposed use case has source data, owner accountability, and review controls. fleet, EAM, CMMS, inventory, supplier, maintenance, and site-context exports ReadyMind AI
Mining Industrial AI Governance Industrial AI governance in Mining defines how AI-supported decisions are reviewed, explained, limited, and approved before action. fleet, EAM, CMMS, inventory, supplier, maintenance, and site-context exports GovernanceMind AI
Mining AI Risk Management AI risk management in Mining connects trustworthy AI practices to equipment availability, remote stock risk, critical-spare coverage, and production continuity without treating unreviewed model output as operational proof. fleet, EAM, CMMS, inventory, supplier, maintenance, and site-context exports GovernanceMind AI
Mining Human-in-the-Loop Review Human-in-the-loop review in Mining keeps accountable owners between diagnostic findings and operational action. fleet, EAM, CMMS, inventory, supplier, maintenance, and site-context exports GovernanceMind AI
Mining Explainable AI Explainable AI in Mining means AI-supported findings can be traced to evidence, assumptions, limits, and owner review. fleet, EAM, CMMS, inventory, supplier, maintenance, and site-context exports GovernanceMind AI
Mining AI Evidence Classification AI evidence classification in Mining separates observed, derived, estimated, and hypothesis evidence before decision use. fleet, EAM, CMMS, inventory, supplier, maintenance, and site-context exports GovernanceMind AI
Mining Confidence Tiering Confidence tiering in Mining labels diagnostic findings by evidence strength so review owners do not treat every signal equally. fleet, EAM, CMMS, inventory, supplier, maintenance, and site-context exports GovernanceMind AI

Open the Mining AI vocabulary index.

Country and regional authority paths

Mining buyers can choose a country path without relying on generic market copy.

Each country path maps local operating context, source-system expectations, trust boundaries, and diagnostic next steps. AI2COE does not claim country offices, named customers, partners, certifications, or promised outcomes unless separately verified.

Country authority path

Argentina

Argentina path for Mining: Oil & Gas, Mining and Lithium, Renewable Energy, Agribusiness and Food. Use this route when buyers need country operating context, source-system expectations, trust boundaries, and a diagnostic next step. Argentine oil, gas, lithium, mining, renewable-energy, agribusiness, utility, manufacturing, port, and industrial-service teams need readiness evidence before operational data is used for inventory or AI decisions.

 Country authority path

Kazakhstan

Kazakhstan path for Mining: Oil & Gas, Mining, Critical Minerals, Utilities. Use this route when buyers need country operating context, source-system expectations, trust boundaries, and a diagnostic next step. Kazakhstani oil, gas, mining, critical-minerals, utility, rail, logistics, industrial-equipment, and energy-service teams need source-backed diagnostics before MRO, inventory, procurement, or AI readiness action.

 Country authority path

Democratic Republic of the Congo

Democratic Republic of the Congo path for Mining: Mining, Power Generation, Utilities, Construction and Heavy Equipment. Use this route when buyers need country operating context, source-system expectations, trust boundaries, and a diagnostic next step. Democratic Republic of the Congo mining, power, utility, logistics, telecom, and infrastructure teams need country-specific operating evidence before ERP cleanup, MRO data remediation, inventory action, procurement change, or industrial AI adoption.

 Country authority path

Brazil

Brazil path for Mining: Oil & Gas, Mining, Pulp and paper, Utilities. Use this route when buyers need country operating context, source-system expectations, trust boundaries, and a diagnostic next step. Remote mines, plants, ports, utilities, refineries, and infrastructure networks can carry the same spare under different descriptions, suppliers, units, and storerooms.

Diagnostic scenario

Can exported mining data separate true stock shortage from duplicate inventory, remote-site imbalance, and weak equipment-to-part linkage?

Begin with the available exported evidence, record missing context as a limitation, and keep the result in owner review before operational action.

Lead engineInventoryMind AI
ReportInventoryMind AI Inventory Risk Report
Decisiondecide which inventory actions are safe to review before optimization or reduction targets are approved
Example evidence

What a review team can inspect.

Evidence remains tied to source fields, assumptions, limitations, confidence tiers, and named review owners.

Evidence 1Duplicate-risk planning lens
Evidence 2Remote-site inventory lens
Evidence 3Mobile and fixed-plant MRO coverage
Buyer-role interpretation

One evidence pack, role-specific decisions.

Finance, operations, technology, procurement, maintenance, and governance owners interpret the same diagnostic through different decision responsibilities.

CFO, COO, Inventory, Materials, and Supply Chain leadersdecide which inventory actions are safe to review before optimization or reduction targets are approved
Asset Integrity, Maintenance, Reliability, and Operations leadersdecide which equipment, BOM, and spare relationships need review before EAM, CMMS, or reliability action
Maintenance Director, Reliability Manager, COO, and Plant leadersdecide which readiness gaps need maintenance owner review before planned work, outage, or shutdown windows
CFO, CIO, Procurement, Maintenance, and Materials leadersdecide what to cleanse first before MDM, ERP migration, or supplier enrichment work starts
Product-scope boundary

Diagnostic evidence before system or operating change.

Industrial IQ analyzes approved exports and produces evidence, confidence tiers, scores, reports, and review actions. It does not operate production equipment, replace ERP, EAM, CMMS, procurement, inventory, maintenance, or control systems, or write findings back to those systems.

Trust boundary: Read-only diagnostics. No ERP write-back. Source files are purged after report generation. Human review is required before action.
Industry FAQ

Questions to resolve before the first diagnostic.

Industry FAQ 01

Why is duplicate MRO catalog data a board-level issue for mining operators?

Mining operations often carry material MRO inventory across remote sites. A duplicate or unfindable spare can trigger expedited freight, lost production risk, and excess safety stock simultaneously. The financial impact is not only an IT problem — it is a working-capital and operational-continuity issue that belongs on the balance-sheet review agenda before any ERP or CMMS governance program begins.

Industry FAQ 02

Does PartsCleanse AI support SAP S/4HANA migration preparation for mining?

Yes. Mining operators running SAP can use the transition window around SAP S/4HANA planning to assess material master consistency across plant codes, duplicate candidates, and source-fit gaps before cutover. PartsCleanse AI produces diagnostic evidence that program teams can review before deciding remediation scope.

Industry FAQ 03

Can the diagnostic separate exposure by site, mine, or depot?

Yes. If the export includes site, plant, store, warehouse, or depot fields, the report preserves those dimensions so leadership can see whether duplication is local to one mine, shared across a region, or enterprise-wide. Site-level slicing lets maintenance and procurement prioritize cleanup by operational impact and geographic remoteness.

Industry FAQ 04

Does PartsCleanse AI require integration with SAP, Maximo, or the CMMS?

No. The diagnostic starts from a CSV catalog export from any ERP or CMMS system. No API connection, no integration project, and no IT procurement cycle is required. Upload the file and receive confidence-tiered duplicate families, a capital-at-risk estimate, and five executive report artifacts for review.

AI2COE Copilot