InventoryMind AI

Answer-first product brief

InventoryMind AI turns uploaded operational data into decision evidence.

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.

Executive rule: this engine does not replace SAP, Maximo, Oracle, EAM, CMMS, procurement, inventory, or maintenance systems. It creates governed evidence before teams decide what to remediate.

Engine contract

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.

Input data required

Material Id
Quantity

Optional inputs

Description
Unit Cost
Stock Value
Last Movement Date
Movement Qty
Demand
Criticality
Min Stock
Max Stock
Site

Buyer relevance

Primary personaCFO, COO, Inventory, Materials, and Supply Chain leaders

Sample dataPublic sample CSV, mapping template, data dictionary, HTML report, and PDF report are available before private upload.

Diagnostic logicDeterministic analyzers read mapped source fields, generate findings, attach evidence, and expose assumptions and limitations.

MetricInventory health score

Score outputInventory health score: lower values mean higher dead-stock, excess, stockout, duplicated-stock, and carrying-cost risk.

GovernanceNo ERP write-back. Findings require owner review before remediation.

Active outputScore, findings, evidence, confidence, report, action tracker, and score history.

Report outputInventoryMind AI Inventory Risk Report with HTML report, CSV evidence, PDF export, action tracker entry, score history snapshot, and email delivery status.

Report emailCompleted authenticated runs attempt branded report email delivery and retain delivery status in the report inventory.

Try before upload

Run a public sample, then replace assumptions with uploaded data.

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.

Run Industrial IQ Snapshot Run sample diagnostic Download sample PDF Sample CSV Mapping template Data dictionary Product page

Accepted columns and aliases

What InventoryMind AI can map from SAP, Maximo, Oracle, Infor, Hexagon EAM, CMMS, and CSV exports.

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.

Multi-file diagnostic pack

Best customer results come from the right export pack.

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

Value model

What leadership can use from this engine.

Working capital

Working capital model

Dead stock, slow-moving inventory, excess stock, carrying-cost leakage.

Service risk

Service risk model

Low or zero-stock items with recent demand, criticality, or recurring usage.

Decision output

Decision output model

Inventory health score, stockout watchlist, overstock evidence, review actions.

Product depth

P0, P1, and P2 capabilities built into the Industrial IQ product model.

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 committee interpretation

How each executive reads the same diagnostic output.

Buyer	Decision question	Evidence source
CFO	Can the finding be tied to capital exposure, carrying cost, leakage, or payback discipline?	InventoryMind AI
COO	Does the evidence reduce operating risk, downtime exposure, site friction, or service disruption?	InventoryMind AI
CIO / ERP owner	Are source fields mapped, export quality visible, and ERP write-back avoided unless governed?	InventoryMind AI
Procurement	Does the diagnostic expose supplier, PO, duplicate spend, stocked-but-purchased, or price-variance risk?	InventoryMind AI
Maintenance / Reliability	Does the evidence affect work-order readiness, false stockout, shutdown coverage, or critical-spare confidence?	InventoryMind AI
Data governance	Can findings be reviewed, accepted, rejected, audited, and defended after the report is shared?	InventoryMind AI

Evidence and confidence model

What the engine produces after a governed run.

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 confidence tier.
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	Recommended 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.

Workflow

Upload to diagnostic to recurring intelligence.

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 confidence tiers and human-review status before any action.
6	Report	Executive report, evidence table, action tracker, and score snapshot are produced.

Industry fit

Configured for asset-intensive operating reality.

Oil & GasSAP S/4HANA migration, turnaround readiness

Miningremote stockouts, haul truck downtime

ManufacturingOEE improvement, plant consolidation

Utilitiesoutage readiness, regulatory audit

Power Generationplanned outages, turbine spare coverage

Chemicalsprocess safety, shutdown readiness

Food & Beverageline uptime, multi-plant standardization

Transportation & Logisticsfleet uptime, depot duplication

Ports & Marinecrane downtime, terminal uptime

Aviationaircraft-on-ground risk, MRO depot duplication

Construction & Heavy Equipmentequipment availability, site-level duplicate stock

Healthcare Facilitiesclinical uptime, biomed asset coverage

Higher Education Campusescampus maintenance visibility, storeroom consolidation

Government & Public Infrastructureauditability, public asset uptime

Data Centersuptime assurance, critical facilities spares

Renewable Energyremote-site availability, turbine spare coverage

Water & Wastewaterservice continuity, pump station spare coverage

Benchmark and claims discipline

Assumptions are separated from uploaded-data results.

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.

Source resultUploaded data, mapped fields, evidence records, score snapshot

AssumptionBenchmark, industry range, carrying-cost assumption, ROI scenario

GovernanceOwner review, confidence tier, audit log, no write-back

Benchmark methodology Run this engine

Knowledge graph

Problem -> ERP export -> industry context -> engine evidence -> action.

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.

Frequently asked questions

Questions buyers ask before running Inventory Risk Intelligence.

What data does InventoryMind AI need?

InventoryMind AI requires Material Id, Quantity. Optional fields such as Description, Unit Cost, Stock Value, Last Movement Date, Movement Qty, Demand improve confidence and business-impact precision.

What does InventoryMind AI produce?

It produces InventoryMind AI Inventory Risk Report, a 0-100 inventory health score, evidence records, confidence tiers, recommended actions, and a review-ready executive summary.

Does AI2COE write back to SAP, Maximo, Oracle, or any CMMS?

No. AI2COE diagnostics are decision-support outputs. They do not change ERP, EAM, CMMS, procurement, inventory, or asset records automatically.

How does confidence tiering work?

Findings are ranked by source-field coverage, data completeness, evidence quality, analyzer agreement, and whether a human owner should review the recommendation before action.

How should leadership use the report?

Use the report to decide whether the issue is measurable, material, governable, and worth funding before starting a larger ERP, inventory, procurement, maintenance, or AI transformation program.