Manufacturing

Document Processing for Procurement

A manufacturing group processing 500+ invoices monthly transformed its procurement workflow with AI powered document extraction.

The Challenge

The manufacturing group sourced materials from international suppliers across multiple currencies and jurisdictions. Each month, the procurement team manually processed over 500 invoices, keying in line items, verifying quantities against purchase orders, handling currency conversions, and routing documents through a multi-level approval chain.

The manual process produced an error rate of approximately 8%, resulting in payment disputes, delayed supplier relationships, and significant time spent on corrections. The approval workflow was bottlenecked by paper based routing, with invoices sometimes sitting in queues for days before reaching the right approver.

Our Approach

  1. Invoice format analysis: We catalogued all invoice formats received from suppliers, identifying common fields, variations in layout, and the most frequent sources of data entry errors.
  2. AI powered extraction: We deployed intelligent document processing that reads invoices in multiple formats and languages, extracting line items, amounts, currencies, tax details, and supplier information automatically.
  3. Validation rules engine: Extracted data runs through configurable validation rules, including three way matching against purchase orders and goods received notes, flagging discrepancies for human review.
  4. Automated approval routing: Invoices are automatically routed to the correct approver based on amount thresholds, cost center, and supplier category, with escalation rules for overdue approvals.
  5. ERP integration: Approved invoices flow directly into the existing ERP system, eliminating duplicate data entry and ensuring the general ledger stays current.

Results

70%
Time reduction in processing
8%
Previous error rate
<1%
Current error rate
500+
Invoices processed monthly
This case study has been published with client permission. All identifying details have been anonymized. Want to discuss a similar challenge? Contact us.

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