Dead stock, slow-moving stock, excess, stockout risk, and duplicated stock exposure.
InventoryMind 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 InventoryMind AI fits the Industrial IQ engine family for dead stock, excess stock, stockout risk, carrying-cost review, and site comparison.
Run This EngineDead stock, excess, obsolete, critical-spare, and false-stockout review signals.
InventoryMind 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.
Inventory Risk 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
InventoryMind 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 changing stocking rules, separate dead, slow-moving, excess, duplicated, critical, and stockout-risk evidence.
The product flow keeps the buyer's review sequence visible: source data, diagnostic logic, evidence, report, governance boundary, and next action.
Inventory teams cannot defend why capital is trapped in dead stock while critical spares still trigger stockout events. The product standard is not a feature list; it is a governed decision path from input data to reportable action.
| P0 pilot quality | Dead stock, slow-moving stock, excess inventory, stockout risk, critical spare coverage, and duplicate stock exposure. Min/max exception detection and transfer-before-purchase evidence. Carrying-cost estimate with sample-vs-uploaded-data labeling. |
| P1 enterprise quality | ABC/XYZ segmentation, site transfer candidates, inventory policy exceptions, and critical understock queue. Inventory health trend by site, value band, criticality, and duplicate-family exposure. Executive interpretation for CFO, COO, inventory, procurement, and maintenance. |
| P2 expansion quality | Service-level scenario simulator with stock-reduction, stockout-risk, and critical-spare protection assumptions. Monthly inventory optimization review with prior-run comparison and renewal value report. Benchmark comparison by industry, site type, and critical spare class. |
Inventory teams cannot defend why capital is trapped in dead stock while critical spares still trigger stockout events.
| Buyer intent | Primary owner | Evidence required | Report output | Next action |
|---|---|---|---|---|
| Find dead stock | CFO | stock position, movement age, criticality | InventoryMind AI Inventory Risk Report | Run Free Industrial IQ Snapshot |
| Reduce excess inventory | COO | stock position, movement age, criticality | InventoryMind AI Inventory Risk Report | Run Free Industrial IQ Snapshot |
| Expose slow-moving stock | Inventory Manager | stock position, movement age, criticality | InventoryMind AI Inventory Risk Report | Run Free Industrial IQ Snapshot |
| Validate stockout exposure | Maintenance | stock position, movement age, criticality | InventoryMind AI Inventory Risk Report | Run Free Industrial IQ Snapshot |
| Protect critical spare coverage | CFO | stock position, movement age, criticality | InventoryMind AI Inventory Risk 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, Catalog Intelligence, Procurement Leakage Intelligence |
| COO Pack | Prioritize site readiness, asset coverage, false stockout risk, and operational action queues. | Maintenance Readiness 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, Catalog 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, Catalog 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 inventory risk intelligence decision before spend? | stock exposure bands, slow-moving inventory, min/max exceptions, stockout risk, and carrying-cost review items tied to shutdown spares, upstream/downstream equipment, refinery stores, SAP/Maximo exports, and emergency buy pressure. | decide which inventory actions are safe to review before optimization or reduction targets are approved for Oil & Gas owners, using confidence tiers and source rows before action. |
| Mining | Lead diagnostic | For Mining, can exported records covering remote mobile equipment, long replenishment cycles, shutdown windows, depot stores, and high-value inactive spares prove the first inventory risk intelligence decision before spend? | stock exposure bands, slow-moving inventory, min/max exceptions, stockout risk, and carrying-cost review items tied to remote mobile equipment, long replenishment cycles, shutdown windows, depot stores, and high-value inactive spares. | decide which inventory actions are safe to review before optimization or reduction targets are approved 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 inventory risk intelligence decision before spend? | stock exposure bands, slow-moving inventory, min/max exceptions, stockout risk, and carrying-cost review items tied to production-line uptime, plant stores, MRO item masters, OEE pressure, and site-level buying variance. | decide which inventory actions are safe to review before optimization or reduction targets are approved for Manufacturing owners, using confidence tiers and source rows before action. |
| Food & Beverage | Lead diagnostic | For Food & Beverage, can exported records covering packaging lines, refrigeration, sanitation windows, plant maintenance, and spare availability during production runs prove the first inventory risk intelligence decision before spend? | stock exposure bands, slow-moving inventory, min/max exceptions, stockout risk, and carrying-cost review items tied to packaging lines, refrigeration, sanitation windows, plant maintenance, and spare availability during production runs. | decide which inventory actions are safe to review before optimization or reduction targets are approved for Food & Beverage owners, using confidence tiers and source rows before action. |
| Pharmaceutical | Contextual check | If the Pharmaceutical review expands, can Inventory Risk Intelligence test the bounded evidence around excess stock, obsolete inventory, false stockouts, critical-spare gaps, and carrying-cost exposure without pretending to be the lead engine? | stock exposure bands, slow-moving inventory, min/max exceptions, stockout risk, and carrying-cost review items tied to validated equipment, controlled maintenance, spare readiness, ERP governance, and quality-sensitive operations. | decide which inventory actions are safe to review before optimization or reduction targets are approved for Pharmaceutical owners, using confidence tiers and source rows before action. |
| Utilities | Supporting diagnostic | Where Utilities already reviews outage readiness, critical network assets, substations, regulated service continuity, and multi-site stores, does Inventory Risk Intelligence add evidence for excess stock, obsolete inventory, false stockouts, critical-spare gaps, and carrying-cost exposure? | stock exposure bands, slow-moving inventory, min/max exceptions, stockout risk, and carrying-cost review items tied to outage readiness, critical network assets, substations, regulated service continuity, and multi-site stores. | decide which inventory actions are safe to review before optimization or reduction targets are approved for Utilities owners, using confidence tiers and source rows before action. |
| Data Centers | Supporting diagnostic | Where Data Centers already reviews generators, UPS, cooling, electrical components, SLA-sensitive uptime, and site redundancy, does Inventory Risk Intelligence add evidence for excess stock, obsolete inventory, false stockouts, critical-spare gaps, and carrying-cost exposure? | stock exposure bands, slow-moving inventory, min/max exceptions, stockout risk, and carrying-cost review items tied to generators, UPS, cooling, electrical components, SLA-sensitive uptime, and site redundancy. | decide which inventory actions are safe to review before optimization or reduction targets are approved for Data Centers owners, using confidence tiers and source rows before action. |
| Aviation MRO / Airlines | Lead diagnostic | For Aviation MRO / Airlines, can exported records covering AOG readiness, compliance-sensitive parts, maintenance planning, supplier traceability, and schedule reliability prove the first inventory risk intelligence decision before spend? | stock exposure bands, slow-moving inventory, min/max exceptions, stockout risk, and carrying-cost review items tied to AOG readiness, compliance-sensitive parts, maintenance planning, supplier traceability, and schedule reliability. | decide which inventory actions are safe to review before optimization or reduction targets are approved for Aviation MRO / Airlines owners, using confidence tiers and source rows before action. |
| Healthcare Systems | Supporting diagnostic | Where Healthcare Systems already reviews facility uptime, clinical support infrastructure, critical services, biomedical-adjacent stores, and procurement controls, does Inventory Risk Intelligence add evidence for excess stock, obsolete inventory, false stockouts, critical-spare gaps, and carrying-cost exposure? | stock exposure bands, slow-moving inventory, min/max exceptions, stockout risk, and carrying-cost review items tied to facility uptime, clinical support infrastructure, critical services, biomedical-adjacent stores, and procurement controls. | decide which inventory actions are safe to review before optimization or reduction targets are approved for Healthcare Systems owners, using confidence tiers and source rows before action. |
| Rail, Metro & Transit | Supporting diagnostic | Where Rail, Metro & Transit already reviews rolling stock, depot spares, safety-critical parts, maintenance windows, and asset-to-part mapping, does Inventory Risk Intelligence add evidence for excess stock, obsolete inventory, false stockouts, critical-spare gaps, and carrying-cost exposure? | stock exposure bands, slow-moving inventory, min/max exceptions, stockout risk, and carrying-cost review items tied to rolling stock, depot spares, safety-critical parts, maintenance windows, and asset-to-part mapping. | decide which inventory actions are safe to review before optimization or reduction targets are approved for Rail, Metro & Transit owners, using confidence tiers and source rows before action. |
| Telecom Network Operators | Lead diagnostic | For Telecom Network Operators, can exported records covering field maintenance, network spares, regional depots, tower/power equipment, and restoration readiness prove the first inventory risk intelligence decision before spend? | stock exposure bands, slow-moving inventory, min/max exceptions, stockout risk, and carrying-cost review items tied to field maintenance, network spares, regional depots, tower/power equipment, and restoration readiness. | decide which inventory actions are safe to review before optimization or reduction targets are approved for Telecom Network Operators owners, using confidence tiers and source rows before action. |
| Ports, Marine Terminals & Shipping | Supporting diagnostic | Where Ports, Marine Terminals & Shipping already reviews 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., does Inventory Risk Intelligence add evidence for excess stock, obsolete inventory, false stockouts, critical-spare gaps, and carrying-cost exposure? | stock exposure bands, slow-moving inventory, min/max exceptions, stockout risk, and carrying-cost review items 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 which inventory actions are safe to review before optimization or reduction targets are approved for Ports, Marine Terminals & Shipping owners, using confidence tiers and source rows before action. |
| Aerospace & Defense Maintenance Depots | Supporting diagnostic | Where Aerospace & Defense Maintenance Depots already reviews 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., does Inventory Risk Intelligence add evidence for excess stock, obsolete inventory, false stockouts, critical-spare gaps, and carrying-cost exposure? | stock exposure bands, slow-moving inventory, min/max exceptions, stockout risk, and carrying-cost review items 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 which inventory actions are safe to review before optimization or reduction targets are approved for Aerospace & Defense Maintenance Depots owners, using confidence tiers and source rows before action. |
| Warehousing, Distribution Centers & 3PL | Lead diagnostic | For Warehousing, Distribution Centers & 3PL, can exported records covering 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. prove the first inventory risk intelligence decision before spend? | stock exposure bands, slow-moving inventory, min/max exceptions, stockout risk, and carrying-cost review items 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 which inventory actions are safe to review before optimization or reduction targets are approved for Warehousing, Distribution Centers & 3PL owners, using confidence tiers and source rows before action. |
| Commercial Fleet, Trucking & Logistics | Lead diagnostic | For Commercial Fleet, Trucking & Logistics, can exported records covering 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. prove the first inventory risk intelligence decision before spend? | stock exposure bands, slow-moving inventory, min/max exceptions, stockout risk, and carrying-cost review items 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 which inventory actions are safe to review before optimization or reduction targets are approved for Commercial Fleet, Trucking & Logistics owners, using confidence tiers and source rows before action. |
| Construction & Heavy Equipment Fleets | Supporting diagnostic | Where Construction & Heavy Equipment Fleets already reviews 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., does Inventory Risk Intelligence add evidence for excess stock, obsolete inventory, false stockouts, critical-spare gaps, and carrying-cost exposure? | stock exposure bands, slow-moving inventory, min/max exceptions, stockout risk, and carrying-cost review items 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 which inventory actions are safe to review before optimization or reduction targets are approved for Construction & Heavy Equipment Fleets owners, using confidence tiers and source rows before action. |
| Higher Education & Multi-Campus Facilities | Lead diagnostic | For Higher Education & Multi-Campus Facilities, can exported records covering 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. prove the first inventory risk intelligence decision before spend? | stock exposure bands, slow-moving inventory, min/max exceptions, stockout risk, and carrying-cost review items 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 which inventory actions are safe to review before optimization or reduction targets are approved for Higher Education & Multi-Campus Facilities owners, using confidence tiers and source rows before action. |
| Hospitality, Resorts & Gaming | Lead diagnostic | For Hospitality, Resorts & Gaming, can exported records covering 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. prove the first inventory risk intelligence decision before spend? | stock exposure bands, slow-moving inventory, min/max exceptions, stockout risk, and carrying-cost review items 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 which inventory actions are safe to review before optimization or reduction targets are approved 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.
Utilities and critical infrastructure · Inventory balance, movement history, criticality, and min/max exports
"InventoryMind separates dead, slow-moving, excess, and stockout-risk signals before policy changes are proposed."
CFO, inventory, and supply-chain leadership
Run InventoryMind AIData centers · Critical facilities spares, site inventory, and recent demand
"The cockpit shows which low-stock items matter operationally and which overstock records are capital discipline issues."
Facilities operations and inventory control
Run InventoryMind AIMining and heavy equipment · Remote depot stock, demand history, and transfer-before-buy candidates
"Site teams can challenge whether to transfer, buy, review, or protect inventory before the next shutdown window."
Maintenance, inventory, and procurement
Run InventoryMind 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 Inventory Risk Intelligence.
Start with material ID, quantity, stock value, site, min/max.
Confirm Material Id, Quantity. Add Description, Unit Cost, Stock Value, Last Movement Date where available.
Weak coverage is labeled as an assumption or limitation before scoring.
Findings show item, site, movement age, value, criticality, policy exception, review level, and action class before stocking change.
Review stock position, movement age, criticality, 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 Inventory health score, dead-stock register, stockout risk queue, excess inventory 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.
InventoryMind 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 InventoryMind AI Inventory Risk Report.
InventoryMind 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 |
|---|---|---|---|
| Material Id | Yes | 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. |
| Quantity | Yes | 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. |
| Description | Recommended | 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. |
| 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. |
| Stock Value | Recommended | stock_value; inventory_value; total_value; valuation; value; extended_value; stock_amount | Inventory value, extended value, book value, or working-capital value. |
| Last Movement Date | Recommended | last_movement_date; last_issue_date; last_used_date; movement_date; posting_date; last_trans_date; last_movement_days; age_days; days_since_last_movement | Date of last issue, receipt, movement, consumption, or transaction. |
| Movement Qty | Recommended | movement_qty; issue_qty; usage_qty; consumption; demand_qty; annual_usage; annual_usage_qty | Movement, issue, receipt, usage, or consumption quantity. |
| Demand | Recommended | demand; forecast; annual_demand; usage; consumption; work_order_demand; demand_qty; annual_usage_qty; wo_demand | Demand, forecast, usage, or consumption signal. |
| Criticality | Recommended | criticality; critical; abc; risk_class; equipment_criticality; asset_criticality | Criticality rating for part, asset, work order, or operating risk. |
| Min Stock | Recommended | min_stock; reorder_point; rop; safety_stock; minimum | Minimum stock, reorder point, safety stock, or minimum balance. |
| Max Stock | Recommended | max_stock; maximum; max_level; order_up_to | Maximum stock, policy max, target stock, or maximum balance. |
| 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 |
|---|---|
| Inventory balance CSV | material ID, quantity, stock value, site, min/max |
| Stock movement CSV | last movement, movement quantity, demand, usage history |
| Criticality file | criticality, site, equipment class, operating impact |
Dead stock, slow-moving inventory, excess stock, carrying-cost leakage.
Low or zero-stock items with recent demand, criticality, or recurring usage.
Inventory health score, stockout watchlist, overstock evidence, review actions.
| Priority | Capability depth |
|---|---|
| P0 | Dead stock, slow-moving stock, excess inventory, stockout risk, critical spare coverage, and duplicate stock exposure. |
| P0 | Min/max exception detection and transfer-before-purchase evidence. |
| P0 | Carrying-cost estimate with sample-vs-uploaded-data labeling. |
| P1 | ABC/XYZ segmentation, site transfer candidates, inventory policy exceptions, and critical understock queue. |
| P1 | Inventory health trend by site, value band, criticality, and duplicate-family exposure. |
| P1 | Executive interpretation for CFO, COO, inventory, procurement, and maintenance. |
| P2 | Service-level scenario simulator with stock-reduction, stockout-risk, and critical-spare protection assumptions. |
| P2 | Monthly inventory optimization review with prior-run comparison and renewal value report. |
| P2 | Benchmark comparison by industry, site type, and critical spare class. |
| Competitive moat | Competes with Verusen-style MRO optimization by making every recommendation source-backed, confidence-tiered, and reviewable before action. |
| Buyer | Decision question | Evidence source |
|---|---|---|
| CFO | working capital tied in inventory: separate reducible inventory from protected critical spares | inventory exposure report |
| COO | uptime and site stock imbalance: reduce stock without creating availability risk | site risk view |
| Procurement | buy-versus-transfer discipline: review transfers before buys | inventory action queue |
| Maintenance | critical-spare availability: protect spares required for planned work | critical-spare list |
| Reliability | false stockout exposure: resolve readiness gaps before failures recur | stockout-risk watchlist |
| Output layer | Example | Why it matters |
|---|---|---|
| Score | Inventory 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. |
| Finding | InventoryMind AI Inventory Risk 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 | Inventory balance CSV | Start with Material Id, Quantity. Best first run adds material ID, quantity, stock value, site, min/max. |
| 2 | Source-fit gate | Confirm required fields, aliases, completeness, and weak mappings. | Context fields such as Description, Unit Cost, Stock Value, Last Movement Date, Movement Qty improve confidence and reduce assumptions. |
| 3 | Operational analysis path | InventoryMind AI | Dead stock, slow-moving stock, excess inventory, stockout risk, critical spare coverage, and duplicate stock exposure. Min/max exception detection and transfer-before-purchase evidence. Carrying-cost estimate with sample-vs-uploaded-data labeling. |
| 4 | Evidence output | InventoryMind AI Inventory Risk 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.
InventoryMind 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 separates dead stock, slow-moving stock, excess inventory, duplicated stock, protected critical spares, and stockout-risk items before inventory reduction or optimization decisions.
Start with item master, inventory balance, stock value, movement history, usage, min/max, reorder point, lead time, criticality, and site or storeroom fields.
No. It diagnoses readiness and risk first so buyers know what data, exceptions, and policy questions should be reviewed before optimization.
No. It does not update min/max, reorder point, safety stock, or ERP inventory policy. It creates owner-reviewed evidence and action classes.
An inventory health score, exposure table, stockout-risk watchlist, transfer-before-buy candidates, carrying-cost review, report, and action tracker.
CFO, COO, inventory, materials, maintenance, reliability, procurement, and site owners should review because stock reduction and uptime protection are linked.
ERP reports show balances and movements. InventoryMind AI connects those exports into confidence-tiered risk, capital exposure, and owner-routed actions.
Run a Snapshot using item, stock, movement, value, and criticality exports before approving inventory reduction targets or policy automation.
InventoryMind 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 spare-parts inventory optimization page remains the parent commercial path; this route clarifies the narrower inventory signal, source exports, evidence output, and owner-review boundary.
InventoryMind AI remains the product engine for inventory risk, excess, obsolete and dead-stock candidates, critical-spare signals, stockout risk, and stock-value interpretation.
Useful source exports include inventory balances, item master, stock value, movement history, safety stock, reorder point, lead time, criticality, and optional asset/work-order context.
Outputs include inventory risk segments, excess or obsolete candidates, working-capital exposure bands, critical-spare signals, confidence tiers, and review actions.
Inventory outputs support review. They do not trigger automatic disposal, replenishment changes, policy changes, or ERP write-back.
The solution page carries the broad inventory optimization buyer journey. This route keeps the narrower obsolescence, finance, reliability, engine, or regional role clear.
No. They are diagnostic candidates and signals that require owner review before operational action.
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 Inventory Risk 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.