Process assessment
Map invoice sources, exception types, company codes, approval policies and posting requirements.
A hypothetical Oil & Gas enterprise designs a controlled invoice process for contractor, transportation, engineering and equipment supplier invoices.
These illustrative scenarios are based on common industry challenges. Client profiles, project scopes and outcome targets are hypothetical and are not representations of completed customer engagements.
Oil & Gas enterprise processing high volumes of contractor, transportation, engineering and equipment supplier invoices.
20,000 invoices per month · Multiple company codes · Shared services finance · Six-month implementation scenario
Manual processing creates approval delays, duplicate invoice risk, payment exceptions and limited visibility into outstanding liabilities.
High manual data-entry effort
Approval delays and unclear ownership
Duplicate and exception risks
Limited invoice status transparency
Design an invoice automation framework for ingestion, extraction, validation, PO and non-PO routing, approval determination, exception handling, vendor-master validation and SAP posting.
A conceptual architecture for discussion; final design depends on the target landscape, interfaces, controls and delivery scope.
Map invoice sources, exception types, company codes, approval policies and posting requirements.
Define capture, validation, matching, approval and exception states with finance owners.
Configure VIM and supported capture integrations with SAP FI/MM and test sample document classes.
Use queue monitoring, user training and exception analysis to improve the operating model.
Document capture
Rule-based validation
Workflow approvals
Visible exception queues
These are illustrative planning targets only, not verified project results or customer claims.
50% illustrative reduction in manual invoice handling
40% illustrative acceleration of approval cycles
Greater invoice status transparency
More traceable exception and audit handling
Illustrative targets only — actual outcomes depend on scope, baseline, adoption, data quality and operating conditions.
Share your landscape and we’ll help shape the next practical step.