Why building materials is a difficult case for invoice processing
The challenge for building materials traders is not volume alone. It is the combination of factors that makes manual processing so labour-intensive. Supplier A uses its own item numbers, supplier B sends one invoice per project, supplier C lists discounts only in a footnote. On top of that come price escalation clauses for steel or timber, which cause the invoice price to deviate from what was agreed at the time of the order. The result: every invoice requires thinking and searching before it can be posted. A staff member processing twenty of those invoices a day spends a large part of their time investigating rather than approving.
What does document-AI actually do in invoice automation for building materials?
Document-AI such as dottle reads the incoming invoice regardless of the supplier's format. No template per sender required. The system extracts the relevant fields: supplier name, invoice number, invoice date, payment terms, line items with quantity and unit price, any discounts, and the total amount. dottle then matches those lines to the purchase order number shown on the invoice or identified through supplier and reference data. If the price deviates from the PO, or if the quantity does not match, dottle flags the discrepancy and presents the invoice to a staff member, along with the relevant context. That staff member can immediately see what is correct and what is not, without having to trace everything themselves. Invoices that do match are passed to the ERP or accounting package for final approval. The human decides; dottle does the reading.
How does the connection to purchase orders work?
PO matching is often the most critical component. In the building materials trade, purchase orders are sometimes created per project, sometimes as blanket orders per supplier, and sometimes not recorded in the system at all. dottle works best when the purchase order is traceable: a PO number on the invoice, or a combination of supplier reference and order line. If the order is available in the ERP or TMS, dottle can mirror the invoice lines against what was agreed, line by line. Three-way matching: receipt, order, and invoice. If the order is missing or the reference does not match, dottle puts the invoice aside as an exception for a staff member to handle. That is not a shortcoming of the system; that is the system working correctly. It does not push invoices through automatically when the check is inconclusive.
When does building materials invoice automation pay off?
The business case is strongest when a few conditions are met. First, there must be consistent volume: at least a few hundred incoming invoices per month from a fixed group of suppliers. Second, it helps when purchase orders are in the system before the invoice arrives. Third, it works better when invoices come in digitally, as a PDF via email, rather than as a scan of a paper receipt after the fact. If volume is lower, or if the work involves project-based one-off purchasing where every invoice tells its own story, automation is less compelling. Exception handling then costs nearly as much effort as manual processing would. To be direct: with thirty invoices a month from constantly changing suppliers, a well-organised manual process is often the better choice.
What are the trade-offs in automating supplier invoice processing?
Automation shifts the work; it does not eliminate it entirely. Invoices that match move through quickly. But the exceptions, the deviating price, the missing PO reference, the credit note with a different layout, still require attention. The advantage is that a staff member can give that attention more precisely: dottle presents the exception with context, rather than the staff member having to search through a pile themselves. It is also worth noting that new supplier formats sometimes need some adjustment before extraction is reliable enough. dottle does not learn new formats automatically; new rules and exceptions are added in a controlled way. That may sound like a limitation, but it makes the system predictable: you know what passes through and what does not.