Industrial IQ | AI2COE | ProcureMind AI

ProcureMind AI Procurement Leakage Report

Generated by: AI2COE sample user | Public demo | Sample operator
Industry: Oil & Gas | Rows analyzed: 24 | Generated: 2026-06-07T02:59:19
0.0Procurement leakage score
Board-Level RiskRisk level
Medium ConfidenceConfidence
13Evidence records
Source mode: Sample dataset result. Sample results demonstrate workflow and report structure; uploaded-data results replace assumptions with customer source evidence.
Score interpretation: Lower health or readiness scores indicate higher unresolved exposure, weaker readiness, or stronger review need. Scores are deterministic and derived from mapped source fields, findings, evidence, and component inputs.
Executive interpretation: Focus on asset integrity, turnaround readiness, working capital exposure, and audit-safe ERP preparation.

Report delivery and governance controls

Email

Report email path

Authenticated Industrial IQ runs attempt branded report email delivery and retain delivery status in the platform report inventory.

Review

Human review required

Low-confidence or high-impact findings should be accepted, rejected, assigned, deferred, or marked needs-more-data before remediation.

ERP safety

No ERP write-back

Industrial IQ produces evidence and recommendations only. It does not autonomously change SAP, Maximo, Oracle, EAM, CMMS, procurement, inventory, or asset records.

CFO command view

432075.0Capital exposure signal
51849.0-120981.0Recoverable range
77773.5Annual leakage signal
Board-Level RiskBoard attention band

Diagnostic components

0EMERGENCY ROWS
21STOCKED BUT PURCHASED ROWS
1REPEATED PURCHASE GROUPS
1PRICE VARIANCE GROUPS
1SUPPLIER ALIAS GROUPS
39042.5PROCUREMENT LEAKAGE ESTIMATE
active_deterministic_evidence_engineDIAGNOSTIC DEPTH
2REQUIRED FIELDS MAPPED
Score formula: 100 - procurement_risk/source_rows*100 with stocked-but-purchased and emergency rows weighted higher Random score used: False
Score inputValue
emergency rows0
stocked but purchased rows21
repeated purchase groups1
price variance groups1
supplier alias groups1
procurement leakage estimate39042.5
required fields mapped2
optional fields mapped10
source rows profiled24
estimated row value total169920.0
supplier overlap families1
missing commercial control rows0
contract leakage signals1

Product maturity and competitive depth

Competitive position: Competes against ERP-native PO reports by linking catalog duplication, stock availability, supplier aliases, and price variance into one governed diagnostic.
PriorityImplemented product capability
P0Emergency-buy, stocked-but-purchased, repeated-purchase, supplier-alias, price-variance, and vendor-overlap detection.; PO-to-stock evidence for buy vs transfer review.; Commercial control gaps where supplier or price fields are missing.
P1Supplier consolidation opportunities, buyer/category evidence view, contract-leakage signals, and emergency premium model.; Vendor overlap graph connecting item family, supplier alias, price variance, and purchase recurrence.; Procurement leakage score with action tracker items by buyer owner.
P2Buy vs transfer vs review recommendation engine with human approval controls.; Avoided-spend ledger and supplier consolidation QBR report.; Procurement benchmark pack by category, site, emergency rate, and supplier fragmentation.

ICP packaging

PackageEnginesDecision supported
CFO PackFinanceMind AI, PartsCleanse AI, InventoryMind AI, ProcureMind AIFund value realization only after capital exposure, carrying cost, procurement leakage, and review confidence are visible.
Procurement PackProcureMind AI, PartsCleanse AI, InventoryMind AI, FinanceMind AIConvert supplier, PO, duplicate stock, and price-variance evidence into sourcing action without unsupported savings claims.

Advanced product insights

Product outputDiagnostic value
buy transfer review{"buy_review": 0, "human_review": 3, "transfer_review": 21}
vendor overlap graph[{"family": "082b6edcf93a", "relationship": "item-family -> supplier aliases -> price/PO review", "supplier_count": 2}]
contract leakage signals1
emergency premium model{"base_premium_assumption": 0.0, "conservative_premium_assumption": 0.0, "emergency_value": 0, "limitation": "Premium is an estimate unless uploaded unit prices and normal-price references are present."}

Buyer committee views

CFO

Can quantified exposure justify a diagnostic or remediation budget?

ProcureMind AI shows 432075.0 as the current capital or leakage signal before owner review.

Next question: Which findings have enough confidence and value to enter the financial business case?

COO

Which findings threaten operational continuity, site readiness, or uptime?

1 high-attention findings require operational owner review.

Next question: Which findings must be resolved before the next outage, shutdown, or planning cycle?

CIO

Is the data ready for governed AI without ERP write-back risk?

Industrial IQ produced evidence from exports only and did not change ERP, EAM, CMMS, or procurement systems.

Next question: Which missing fields or governance gaps should be fixed in the next export?

Procurement

Where do supplier, purchase, or stocked-but-purchased signals need review?

Procurement actions should be evidence-led and routed through human review before supplier action.

Next question: Which supplier or purchase findings are defensible enough for category review?

Maintenance

Will spare availability and catalog quality support maintenance execution?

Maintenance should use the evidence queue to protect planned work and critical assets.

Next question: Which findings block planned work, shutdown readiness, or critical equipment coverage?

Board

Is this risk material enough to fund recurring diagnostic intelligence?

The result is diagnostic evidence, not an autonomous system change or unsupported ROI claim.

Next question: Should leadership fund the next diagnostic cycle, review queue, or remediation scope?

Evidence graph

Model: Source Record -> Finding -> Evidence -> Confidence -> Business Impact -> Recommended Action -> Review Status -> Report -> Score History

20 nodes | 19 evidence relationships. This graph links uploaded source rows to findings, confidence, business impact, recommended actions, report output, and score history.

Renewal value view

432075.0EXPOSURE IDENTIFIED
5REVIEW QUEUE SIZE
5ACTIONS CREATED
0ACTIONS REVIEWED
34566.0CONSERVATIVE VALUE REALIZATION
69132.0BASE VALUE REALIZATION
monthly for high-risk sites; quarterly for controlled sitesNEXT REVIEW CADENCE
Recurring value interpretation: Compare this run against the next upload to show exposure reviewed, actions completed, score movement, and remaining risk.

Findings

Trust control: Each finding must be interpreted with its confidence, evidence count, mapped fields, and source records. Similar-looking industrial records may still require owner review before action.
AnalyzerFindingSeverityConfidenceEvidenceAction
Stocked-But-Purchased Analyzer21 rows show stocked-but-purchased leakageHIGH82%21Compare PO lines with current stock and duplicate families before buying again.
Repeated Purchase Analyzer1 rows show repeated purchase patternsMEDIUM72%1Consolidate equivalent purchase lines and create preferred buying rules.
Price Variance Analyzer1 rows show price variance exposureMEDIUM72%1Normalize equivalent items and validate supplier price variance.
Supplier Alias Analyzer1 supplier alias clusters detectedMEDIUM70%1Normalize supplier aliases and route equivalent suppliers through a governed review.
Vendor Overlap Analyzer1 item families are bought through multiple supplier namesMEDIUM76%3Normalize supplier aliases and review preferred supplier rules for overlapping item families.

Evidence records

IDConfidence tierSeverityDescriptionValueSourceReason codes
E-de3526ceMedium ConfidenceHIGH21 rows show stocked-but-purchased leakage3135.0row:1:MAT-001-001material-id-present, description-signature, supplier-alias-signal, site-context, value-bearing-row, spec-token-match
E-10c31cffMedium ConfidenceHIGH21 rows show stocked-but-purchased leakage4760.0row:2:MAT-002-002material-id-present, description-signature, supplier-alias-signal, site-context, value-bearing-row, spec-token-match
E-60b3ee4bMedium ConfidenceHIGH21 rows show stocked-but-purchased leakage6675.0row:3:MAT-003-003material-id-present, description-signature, supplier-alias-signal, site-context, value-bearing-row, spec-token-match
E-6aad3bcdMedium ConfidenceHIGH21 rows show stocked-but-purchased leakage8880.0row:4:MAT-004-004material-id-present, description-signature, supplier-alias-signal, site-context, value-bearing-row, spec-token-match
E-00c9fc31Medium ConfidenceHIGH21 rows show stocked-but-purchased leakage11375.0row:5:MAT-005-005material-id-present, description-signature, supplier-alias-signal, site-context, value-bearing-row, spec-token-match
E-de62416bMedium ConfidenceHIGH21 rows show stocked-but-purchased leakage8160.0row:6:MAT-000-006material-id-present, description-signature, supplier-alias-signal, site-context, value-bearing-row, spec-token-match
E-3999988eMedium ConfidenceHIGH21 rows show stocked-but-purchased leakage2380.0row:8:MAT-002-008material-id-present, description-signature, supplier-alias-signal, site-context, value-bearing-row, spec-token-match
E-d57c51e3Medium ConfidenceHIGH21 rows show stocked-but-purchased leakage4005.0row:9:MAT-003-009material-id-present, description-signature, supplier-alias-signal, site-context, value-bearing-row, spec-token-match
E-bd530accMedium ConfidenceMEDIUM1 rows show repeated purchase patterns169920.0row:1:MAT-001-001material-id-present, description-signature, supplier-alias-signal, site-context, value-bearing-row, spec-token-match
E-dbe4fb48Medium ConfidenceMEDIUM1 rows show price variance exposure14520.0row:1:MAT-001-001material-id-present, description-signature, supplier-alias-signal, site-context, value-bearing-row, spec-token-match
E-77867bccMedium ConfidenceMEDIUM1 item families are bought through multiple supplier names3135.0row:1:MAT-001-001material-id-present, description-signature, supplier-alias-signal, site-context, value-bearing-row, spec-token-match
E-81b5a118Medium ConfidenceMEDIUM1 item families are bought through multiple supplier names4760.0row:2:MAT-002-002material-id-present, description-signature, supplier-alias-signal, site-context, value-bearing-row, spec-token-match
E-8018548cMedium ConfidenceMEDIUM1 item families are bought through multiple supplier names6675.0row:3:MAT-003-003material-id-present, description-signature, supplier-alias-signal, site-context, value-bearing-row, spec-token-match

Recommended actions

P1

Consolidate equivalent purchase lines and create preferred buying rules.

Owner: CPO | Due: 60 days

P0

Compare PO lines with current stock and duplicate families before buying again.

Owner: CPO | Due: 30 days

P1

Normalize supplier aliases and review preferred supplier rules for overlapping item families.

Owner: CPO | Due: 60 days

P1

Normalize equivalent items and validate supplier price variance.

Owner: CPO | Due: 60 days

P1

Normalize supplier aliases and route equivalent suppliers through a governed review.

Owner: CPO | Due: 60 days

Mapping and validation

InputSource columnCompletenessConfidenceReason
po_numberpo_number%%
descriptiondescription%%
material_idmaterial_id%%
suppliersupplier%%
unit_priceunit_price%%
quantityquantity%%
order_dateorder_date%%
order_typeorder_type%%
emergency_flagemergency_flag%%
stock_on_handstock_on_hand%%
sitesite%%
currencycurrency%%

Source fit, AI match, and normalization

100%Source fit score
100.0%AI match score
100%Mapping readiness score
92.5%Diagnostic confidence score
Workbench interpretation: Source fit measures whether the uploaded file contains recognizable inputs. AI match measures column-mapping confidence. Diagnostic readiness measures whether the normalized mapped data can support trustworthy engine output.
Quality signalValue
source fit score100
ai match score100.0
diagnostic readiness score100
required mapped2
required total2
optional mapped10
optional total10
required completeness100.0
row count24
column count30
blockers0
warnings0
source fit bandStrong
ai match bandStrong
readiness bandStrong
diagnostic confidence score92.5
diagnostic confidence bandStrong

Normalization plan

Engine fieldSource columnOriginal sampleNormalized previewRule
Po Numberpo_numberPO-2026001PO-2026001Normalize blanks, trim source values, preserve original evidence, and label any assumptions before engine execution.
DescriptiondescriptionOil & Gas pump bearing seal kit model 1 stainless 4 inchOIL & GAS PUMP BEARING SEAL KIT MODEL 1 STAINLESS 4 INCHNormalize case, abbreviations, punctuation, industrial units, specification tokens, and obvious spacing noise.
Material Idmaterial_idMAT-001-001MAT-001-001Trim whitespace, preserve leading zeroes, normalize item/material identifiers, and keep original source reference.
SuppliersupplierIndustrial Supply CoINDUSTRIAL SUPPLY CONormalize supplier aliases and vendor naming variants for leakage and overlap analysis.
Unit Priceunit_price10451045Parse PO unit price and compare with normal price signals when available.
Quantityquantity33Parse numeric quantity, keep negatives for audit context, and separate blank/zero from missing.
Order Dateorder_date2026-05-022026-05-02Parse purchase/work-order date into recency and aging bands.
Order Typeorder_typeStandardSTANDARDNormalize purchase order class, emergency type, stock/non-stock signal, and expedited context.
Emergency Flagemergency_flagNNNormalize Y/N, urgent, breakdown, rush, AOG, emergency, and expedited markers.
Stock On Handstock_on_hand11Parse on-hand stock quantity and preserve site-level balance context.
SitesitePlant-2Plant-2Normalize plant, site, storeroom, facility, depot, or operating-unit labels.
CurrencycurrencyUSDUSDNormalize ISO currency code or use country/system assumption with visible limitation.

Assumptions and limitations

Assumptions

  • Uploaded data is treated as the source of truth for this diagnostic run.
  • No ERP write-back is performed. Outputs are recommendations and evidence records only.
  • Financial estimates use uploaded values where available and conservative assumptions otherwise.
  • Industry language is adjusted for Oil & Gas: plants, wells, refineries, shutdowns, turnarounds, and asset integrity.
  • Workbench scores were calculated before and after engine execution: source fit 100%, AI match 100.0%, mapping readiness 100%, diagnostic confidence 92.5%.
  • Public sample report: deterministic AI2COE sample data was used. Replace with uploaded customer data for customer-specific findings.

Limitations

  • Results are diagnostic signals, not final accounting entries.
  • Low-confidence findings require human review before remediation.
  • Missing source fields reduce confidence and may suppress some analyzers.
  • Benchmarks are labelled assumptions unless validated by uploaded data.