Manufacturing companies process high volumes of remittance advices, vendor invoices, packing slips, bills of materials, and shop floor documents that need to flow into ERP systems like Global Shop Solutions, SAP, or NetSuite. AI document extraction reads these documents in any format without templates, pulls structured data into rows and columns, and outputs CSV or Excel files formatted for direct ERP import. This replaces the manual data entry that typically occupies dedicated staff in accounts receivable, accounts payable, and operations.
Manufacturing runs on documents. Purchase orders from customers, invoices from vendors, remittance advices attached to incoming payments, packing slips on shipments, inspection certificates, bills of materials, work orders. Each one carries data that has to end up in an ERP system before anything downstream can happen: before a payment can be posted, before a shipment can be reconciled, before a job can be closed out.
The problem is that most of these documents arrive in formats the ERP can't read directly. They're PDFs, scanned paper, emails with data pasted into the body, Excel files from a customer's system that don't match your field names. Someone has to read each one, identify the relevant data, and type it into the ERP manually. In many manufacturing companies, this is what a surprising number of people spend their days doing.
One manufacturing company we work with had their AR team printing remittance advices, hand-keying payment data into Global Shop Solutions, and filing the paper copies in a physical cabinet. Their new CFO's first priority was figuring out why this was still happening in 2026.
Manufacturing generates more document variety than most industries. The data entry burden doesn't come from one document type. It comes from the sheer number of different formats flowing through different departments, all needing to reach the same ERP.
Remittance advices are among the worst offenders. Every customer sends them in a different format. One customer sends a clean single-page PDF. Another sends a six-page FedEx statement with two columns of invoice lines per page. A third pastes the payment detail into the body of an email. The AR team has to extract invoice numbers, payment amounts, discounts, and check numbers from all of these, match them against open invoices, and post cash receipts. For a company with hundreds of customers, this is often one person's entire job.
Vendor invoices are the AP-side equivalent. Material suppliers, freight carriers, tooling vendors, MRO suppliers -- each has their own invoice format. The AP team extracts vendor name, invoice number, line items, quantities, unit prices, tax, and totals, then codes them to GL accounts and enters them for approval. Multi-page invoices with dozens of line items are common in manufacturing, especially for raw material and component orders.
Packing slips and shipping documents arrive with every inbound shipment. Receiving needs to match them against POs to verify that what was ordered is what showed up. This data often has to be entered into the ERP's receiving module before the goods can be put away or the invoice can be approved for payment.
Bills of materials and work orders define what goes into a finished product and what the shop floor needs to produce. When these come from customers or engineering in PDF format, someone has to extract component lists, quantities, and specifications into the ERP's production planning module.
Inspection and compliance certificates (material test reports, certificates of conformance, MSDS sheets) accompany raw materials and need to be logged against the corresponding purchase order or lot number. In regulated manufacturing -- aerospace, medical devices, food processing -- this paperwork is mandatory and audited.
Each of these document types has its own department, its own workflow, and its own set of people doing manual data entry. The total labor cost across all of them is substantial.
Most manufacturing companies run industry-specific ERPs: Global Shop Solutions, Epicor, Infor, JobBOSS, IQMS (now DELMIAWorks), or SAP Business One. These systems are built around manufacturing workflows -- job costing, BOM management, shop floor scheduling, quality tracking. They're good at what they do.
What they are not good at is getting external data in. ERP import tools are rigid about formatting. Column headers have to match exactly. Dates need to be in the specific format the system expects. Amounts can't have dollar signs or commas. Vendor IDs need leading zeros preserved. A single formatting mismatch rejects the entire import file.
Global Shop Solutions, for example, recently added a CSV import option for cash receipts. That's progress. But the CSV has to match the exact field structure their import module expects. If your extracted data has "Invoice Date" and the import template expects "InvDate", it fails. If your amounts come through as "$1,250.00" instead of "1250.00", it fails.
The result is a two-step manual process. First, someone keys the data from the source document into a spreadsheet. Then they reformat the spreadsheet to match the ERP's import requirements. Each step introduces opportunities for error, and this happens for every batch of documents processed.
For a deeper look at ERP-specific formatting requirements and how to eliminate the reformatting step, see our post on getting extracted data into your ERP.
Cash application is where we see the most immediate ROI in manufacturing. The workflow is straightforward to automate and the time savings show up in the first week.
Here's what the manual process looks like at a typical manufacturer: a remittance advice arrives by email, someone on the AR team opens the PDF, reads the invoice numbers and payment amounts, looks up the customer in the ERP, navigates to the cash receipts screen, and types each line. If the remittance covers 15 invoices, that's 15 rows of data entry. If the customer name on the document doesn't exactly match the ERP's customer record ("iTool" vs. "iTool Co." vs. "I-Tool Company"), someone has to figure out who it is and look up the right customer number before anything can be posted.
AI extraction changes this to: remittance arrives, gets processed automatically (either by uploading or via email forwarding), and outputs a CSV with one row per invoice line -- formatted for the ERP's cash receipt import. Invoice numbers, amounts, discounts, check numbers, and customer IDs are all populated. The vendor name gets matched against a customer master list automatically through context documents, so even when "Federal Express Corporation" on the RA needs to map to "FedEx" in the ERP, it happens without manual lookup.
For the manufacturing company mentioned earlier, this eliminated a dedicated AR role's worth of manual work. The person who previously spent most of their day on remittance entry now reviews extracted batches and handles exceptions -- payments where a field came back blank or an invoice number didn't match anything open in the system.
We wrote a detailed walkthrough of this workflow in our post on how to automate cash application.
The AP side follows the same pattern but with different document characteristics. Manufacturing vendor invoices tend to be longer and more complex than invoices in service industries. A raw materials order might have 50 line items. A components invoice might reference multiple POs. A freight invoice might include accessorial charges, fuel surcharges, and detention fees on top of the base rate.
AI extraction handles this by pulling every line item from the invoice into a separate row, regardless of how many pages the invoice runs or how the table is formatted. The output includes vendor name, invoice number, date, PO reference, line-item descriptions, quantities, unit prices, and totals -- all mapped to your AP import template columns.
Where manufacturing AP gets especially messy is PO matching. The invoice says the vendor shipped 500 units at $4.25 each. The PO says 500 units at $4.20 each. That $0.05 discrepancy across 500 units is $25 that needs to be flagged, investigated, and resolved before payment. Catching these discrepancies manually requires someone to pull up the PO, compare it line by line against the invoice, and note any mismatches. Lido's extraction can pull both documents into structured data that makes automated PO-to-invoice matching possible.
For manufacturers processing high volumes of vendor invoices, the extraction step is where automation starts. GL coding, approval routing, and payment scheduling can be layered on top, but the bottleneck is almost always getting accurate data out of the invoice in the first place.
Not all manufacturing document processing happens in the finance office. The shop floor generates and consumes documents too, and many of these still involve manual data entry.
Traveler sheets (also called route cards or job travelers) follow a part through production, accumulating data at each operation: start times, end times, operator IDs, measurements, pass/fail results. In shops that still use paper travelers, someone eventually has to enter all of that data into the ERP's shop floor module. AI extraction can read these forms, even handwritten ones, and output structured data for import.
Receiving documents -- packing slips, bills of lading, commercial invoices on imported materials -- need to be matched against POs and entered into the ERP receiving module. This is the manufacturing version of the three-way match: PO vs. packing slip vs. vendor invoice. Getting structured data from all three documents is the prerequisite for automating that match.
Quality documents -- certificates of analysis, material test reports, inspection records -- need to be logged and associated with specific lots, purchase orders, or work orders in the quality module. In aerospace (AS9100) and medical device (ISO 13485) manufacturing, this documentation is audited and must be traceable. AI extraction doesn't replace the quality system, but it eliminates the manual entry step that gets the data into the system.
This is where manufacturing differs from most industries. A significant portion of shop floor documentation is still handwritten. Travelers with handwritten measurements. Inspection forms filled out with a pen at the machine. Receiving notes scribbled on packing slips.
Most OCR tools fail on handwriting. Traditional OCR relies on character recognition algorithms trained on printed text, and handwriting introduces too much variation -- different people's writing, different pen types, smudges, inconsistent spacing.
AI-based extraction (specifically, vision-model-based OCR) handles handwriting at roughly the level a human reader can. If you can read it, the AI can read it. This includes handwritten numbers on inspection forms, handwritten quantities on shop floor travelers, and handwritten notes on receiving documents. For degraded inputs -- smudged, partially illegible, or written on stained paper -- the AI extracts what it can and leaves ambiguous fields blank rather than guessing incorrectly.
One manufacturing company we spoke with identified handwriting extraction as an immediate second use case the moment they heard about it. Their shop floor still uses paper forms for certain operations, and the data entry to get those forms into the ERP was a known pain point.
The practical advantage of AI extraction in manufacturing is that one tool handles everything. You don't need separate software for remittance advices, vendor invoices, packing slips, and inspection certificates. Each document type gets its own extraction template (its own set of column headers and formatting rules), but they all run through the same system.
In Lido, each template is a separate sheet in a workbook. The remittance advice template has columns for invoice number, payment amount, discount, check number. The vendor invoice template has columns for vendor name, PO number, line-item description, quantity, unit price. The packing slip template has columns for PO number, part number, quantity shipped, lot number. Each sheet has its own email address for automated intake, its own formatting rules, and its own export settings.
This means the AR team, the AP team, the receiving dock, and the quality department can each have their own templates configured for their specific ERP import requirements, all running on a single platform. Sharing controls let you give view access to people who need to see the data without letting them modify the template configuration.
When the CFO or controller wants to expand from one use case to the next -- say, starting with remittance advice extraction and then adding vendor invoices -- there's no new software to evaluate or deploy. It's another sheet in the same workbook.
The pattern we see in manufacturing is almost always the same: start with one document type, prove the ROI, then expand.
Cash application is the most common starting point because the pain is concentrated in one person or team, the documents are relatively simple (compared to multi-page BOMs), and the time savings are immediately visible. A manufacturing company processing 100+ remittances per month can typically validate the tool in a day and be running production volume within a week.
The setup process: create a template with column headers matching your ERP's cash receipt import format, upload your customer master as a context document for automatic name-to-ID matching, set formatting rules (date format, no currency symbols, preserve leading zeros), and test with a handful of real remittance advices from your most varied customers. Once the output imports cleanly into your ERP, set up email forwarding for automated intake.
From there, the most common expansion path is vendor invoices (AP), then receiving documents, then shop floor paperwork. Each new document type takes a few hours to set up because the extraction platform is already in place. You're just building a new template.
For manufacturing companies evaluating the ROI: count the hours per week your team spends on document data entry across all departments, not just finance. Include the AR clerk keying remittance data, the AP clerk entering vendor invoices, the receiving clerk matching packing slips, and the quality team logging inspection certificates. The total often exceeds 30 hours a week once you add receiving and quality on top of finance, because the work is distributed across the organization rather than concentrated in one place.
Lido is an AI document extraction platform that handles remittance advices, vendor invoices, packing slips, inspection certificates, and other manufacturing documents. We work with manufacturers running Global Shop Solutions, SAP, Epicor, and other industry-specific ERPs to eliminate manual data entry from document-dependent workflows.
AI extraction handles remittance advices, vendor invoices, purchase orders, packing slips, bills of materials, work orders, inspection certificates, material test reports, and shop floor travelers. Each document type gets its own extraction template with specific column mappings, but all run through the same platform.
Yes. Any ERP that accepts CSV or Excel imports can receive AI-extracted data. You configure your extraction template column headers to match the ERP import format exactly, including date formats, number formats, and field names. Global Shop Solutions, SAP Business One, Epicor, Infor, and JobBOSS all support file-based imports that work with extracted output.
Yes. Vision-model-based AI extraction reads handwriting at roughly the level a human reader can. This includes handwritten measurements on inspection forms, quantities on shop floor travelers, and notes on receiving documents. If the handwriting is legible to a person, the AI can extract it.
Most manufacturers start with cash application, extracting data from remittance advices for posting to the ERP. The workflow is simple to set up, the time savings are immediate, and the ROI is easy to measure. From there, the most common expansion path is vendor invoices, then receiving documents, then shop floor paperwork.
Upload your customer master or vendor master list as a context document. The AI cross-references names from incoming documents against your master list and returns the correct internal ID, even when the name on the document does not match your ERP records exactly. This eliminates the manual lookup step that slows down cash application and AP processing.
Count the hours per week your team spends on document data entry across all departments: AR staff keying remittance data, AP clerks entering vendor invoices, receiving clerks matching packing slips, and quality teams logging inspection certificates. The total often exceeds 30 hours per week. AI extraction typically reduces this by 70-80%, with the tool paying for itself within the first month for most mid-size manufacturers.
Yes. AI extraction pulls every line item from multi-page documents into separate rows in your output file. A 50-line-item raw materials invoice spread across three pages is processed the same way as a single-page invoice. Page breaks mid-table are handled automatically.
No. A single AI extraction platform handles all manufacturing document types. Each document type gets its own template with specific columns and formatting rules. Templates are separate sheets in a workbook, each with its own email address for automated intake. AR, AP, receiving, and quality can each have their own templates on one platform.
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