The work is transcription, and transcription is machine work
In distribution, wholesale, and manufacturing, a large share of orders still arrive unstructured. A customer emails a purchase order as a PDF, or pastes line items into the message body, or attaches their own spreadsheet template. Somebody in your team reads it and types it into the ERP.
This is the clearest kind of automation candidate there is. The inputs vary in format but not in meaning, the target structure is fixed, the volume is high, and the errors are expensive in a way everyone can already describe.
Product matching is where it gets hard
Parsing the document is the easy half. The difficulty is resolving what the customer wrote to what you actually sell. Customers use their own part numbers, old codes, abbreviations, and descriptions that match three of your SKUs. A human does this from memory and context.
This is precisely where a confidence threshold earns its keep. High-confidence matches post automatically. Ambiguous ones go to an exception queue with the candidates ranked and the source line shown. Over time the resolved ambiguities become mapping rules, and the queue shrinks. A system that guesses instead of queuing will ship the wrong item, and one wrong pallet costs more than a month of the saved labour.
Validate against reality before posting
Before an order is written, it should be checked against the things that make it valid: does the price match the customer's agreed terms, is the quantity within a sane range for this account, is the item still active, is the delivery address one you hold. These checks are cheap and they catch both parsing errors and genuine customer mistakes.
Validation failures should behave like match ambiguities: queued, visible, and attributed to a person, never silently corrected. The point of the system is that a human sees fewer orders, not that a human sees no orders.
Use this before you buy or build.
- All inbound order formats are inventoried before the build, including the awkward ones.
- Product resolution uses a confidence threshold with an exception queue, not a best guess.
- Price, quantity, item status, and address are validated before the order is posted.
- Resolved exceptions feed back into mapping rules so the queue shrinks over time.
The parsing is commodity. The value is in disciplined product matching and pre-post validation, with everything uncertain queued for a person instead of guessed.
