UK E-Commerce Retailer Cuts Monthly Reporting from 14 Days to 36 Hours

Background: A 1,200-Person UK E-Commerce Retailer

This case study is a composite drawn from patterns Forfis has observed across multiple e-commerce and retail engagements in the UK. No named customer appears. The company described here is a mid-market online retailer with roughly 1,200 employees, operating across three fulfilment centres in the Midlands and the North of England. It sells through its own website and two major marketplaces, processes around 40,000 supplier invoices per month, and runs a monthly operations report that feeds into board-level KPIs. The existing stack includes a mid-tier ERP, a legacy document management system, and Microsoft Teams as the primary internal communication channel. The finance and operations teams are separate, and the monthly report is a hand-built spreadsheet assembled from exports in three different formats.

The Challenge: 14 Days of Manual Reporting

The monthly operations report took the finance team 14 working days to assemble. The process started with exporting supplier invoices from the document management system, manually keying line items into a spreadsheet, reconciling them against the ERP purchase orders, and then formatting the output for the board pack. Two analysts spent roughly 60 hours per cycle on this task, and the error rate on manual data entry sat around 4 to 6 percent, meaning roughly 1,600 to 2,400 line items per month required correction before the report could be signed off. The operations team, meanwhile, had no real-time visibility into supplier performance because the data was locked in the spreadsheet until the report was published. The pressure was not regulatory; it was operational. The CFO had flagged the reporting lag in a board review, and the head of operations wanted supplier scorecards available within 48 hours of month-end close, not 14 days later.

Approach: Audit, Pilot, and n8n Orchestration

Forfis began with a two-week AI automation audit. The audit mapped the invoice-to-reporting flow end to end, identified 11 distinct manual touchpoints, and scored each on volume, error rate, and cycle time. The top candidate was the invoice extraction and reconciliation step, which accounted for 70 percent of the analyst hours. The pilot scope was fixed at eight weeks: build a document and data extraction pipeline that ingests supplier invoices from the document management system, extracts line items, PO references, and tax codes, and pushes structured data into the ERP via its REST API. On top of that, a retrieval-augmented knowledge assistant was built over the company’s operations documentation, historical reports, and CRM records, accessible through Microsoft Teams. The orchestration layer was n8n, self-hosted on the client’s own infrastructure, so no data transited a third-party SaaS boundary. The model layer used OpenAI’s API for extraction quality and an open-weight model for the RAG assistant, running on the client’s GPU server, because the operations documentation contained supplier contract terms that procurement wanted to keep on-premises.

Outcome: 36 Hours, Not 14 Days

The pilot shipped in seven and a half weeks, one day ahead of the eight-week deadline. The extraction pipeline processed 40,000 invoices per month with a field-level accuracy of 96.2 percent on the test set, up from the 94 to 96 percent baseline of manual entry. The monthly report cycle dropped from 14 working days to 36 hours: the pipeline ran overnight, the RAG assistant generated a draft narrative summary by 09:00 the next morning, and a finance analyst reviewed and approved the output by 12:00. The error rate on the final report fell to under 1 percent. The operations team gained access to supplier scorecards within 48 hours of month-end close, a 12-day improvement. The two analysts who previously spent 60 hours per cycle on this task were redeployed to supplier negotiation support. The n8n workflow was handed over with documentation, and the client’s own operations team could adjust routing rules without a developer. The RAG assistant was scoped to the indexed corpus only; it did not have internet access, and access was controlled at the Teams channel level.

Lessons for Similar Teams

• Fix the pilot scope before writing code. The eight-week timeline held because the audit deliverable defined exactly which invoices, which fields, and which ERP endpoints were in scope. Any new request during the pilot was treated as a change order with its own timeline, not a silent addition. Teams that skip this step routinely blow past their deadline by two to three weeks.
• Self-host the orchestration layer when procurement asks where data lives. n8n on the client’s own infrastructure answered that question in one sentence. A managed SaaS orchestrator would have required a data processing agreement and a security review that added three to four weeks to the timeline.
• Partition the RAG index by department. The operations assistant could not query finance data, and vice versa. This was enforced at the vector store level, not just at the Teams channel level. Without partitioning, a user in logistics could have pulled supplier contract terms from the finance index.
• Log every human approval with a timestamp and user ID. Even though no regulation mandated it, the audit trail became the first thing the CFO asked for in the post-pilot review. The log showed exactly who approved the report, when, and what the model had drafted before approval.
• Model-agnostic from day one. The client swapped the RAG model from OpenAI to the open-weight model in week three when procurement raised a data-residency concern. The n8n workflow did not change; only the model endpoint did. That swap cost two hours of configuration, not a re-architecture.