Commercial Operations Team
Cuts Supplier Order Processing Friction Across Daily Purchasing Workflows

Daily supplier paperwork was arriving in inconsistent formats and being processed manually by staff who were already stretched across purchasing, job costing, and project delivery.

The format problem alone was significant. Orders arrived as structured PDFs, scanned images with variable quality, multi-page email attachments, and occasionally hand-completed forms. Each format demanded a different interpretation approach, and there was no standardised intake process to handle the variation. Staff adapted on the fly, which introduced inconsistency from the first step.

Job coding errors were a persistent downstream consequence. When line items were manually transcribed, cost codes were applied from memory rather than verified against the job record. Those coding mistakes did not always surface immediately — they compounded through the costing and invoicing cycle, sometimes only becoming visible when a job closed and the margins looked wrong.

Approval bottlenecks added further pressure. When the staff member responsible for sign-off was unavailable — on site, in meetings, or managing delivery — orders queued without a clear escalation path. The business needed a workflow that could take messy inbound documents, turn them into structured data, and still keep a human approval step before anything was exported downstream.

We designed a document-processing workflow that turns inbound supplier paperwork into structured order records ready for review.

The decision to retain a human approval step was deliberate and was not a compromise. Full automation — extracting, coding, and exporting without review — would have removed the one point in the process where operational context could catch extraction errors before they reached the operating system. In a business where job coding affects downstream costing and contract reporting, that oversight layer matters.

The queue-based review flow puts extracted records in front of the right person at the right time, rather than routing approvals through whoever is available. Each record arrives pre-structured with the extracted fields visible alongside the source document, so the reviewer is confirming rather than interpreting. Exceptions and low-confidence extractions are flagged automatically, drawing attention where it is actually needed rather than requiring a full manual review of every line.

Technology Stack

• Document ingestion: Uploads are received, normalised, and separated into reviewable source records before extraction begins.
• Document AI: Structured fields, references, and line items are extracted from supplier paperwork with exception handling for low-confidence results.
• Workflow engine: Each record moves through a queue-based approval flow so users can review, correct, and approve before export.
• Operational platform: Approved records are pushed into the downstream operating system in a consistent, audit-friendly format.
• Data store: Source files, extracted values, review states, and export outcomes remain traceable for follow-up and reporting.

Error rates from manual transcription dropped sharply once extraction replaced hand entry. Job coding mistakes that previously accumulated through the billing cycle are now caught at the review stage, where the source document is immediately visible for comparison.

The more significant operational shift is what the team now does with recovered time. Rather than spending the first part of each day working through inbound paperwork, operations staff manage exceptions, follow up on supplier queries, and focus on the purchasing decisions that actually require judgement. The workflow handles the mechanical processing; people handle the things that need context.

Approval bottlenecks that previously formed when key staff were unavailable are reduced by the queue structure. Records do not depend on a single person to be in the office — they sit in a reviewable state until the appropriate approver is available, with status visible across the team.