5 Practical Ways to Get Delivery Notes into Excel: A Guide for Logistics & Warehouse Teams

If you work in logistics or warehouse operations, you already know the pain: a pile of delivery notes — some printed, some scanned, some emailed as PDFs — and a spreadsheet that needs to reflect all of it by end of day. Manually keying in every item, quantity, and reference number is slow, error-prone, and frankly a poor use of skilled people's time.

The good news is there are five practical approaches that can dramatically reduce that manual work. Some are low-tech, some are highly automated — and the best one for your team depends on your document volume and existing tools. Let's go through each one.

1. Copy-Paste from Digital PDFs (Works, But Has Limits)

If a supplier sends you a delivery note as a native digital PDF (not a scan), you can often select the text, copy it, and paste it into Excel. For low-volume operations handling just a handful of notes per day, this is sometimes enough.

The catch: table structures rarely survive the paste intact. You typically end up with jumbled data across merged cells, and you'll spend more time fixing the formatting than you saved. It also falls apart completely the moment someone sends you a scanned image instead of a digital file.

• Best for: Very low volume, digital-only PDFs
• Fails when: Documents are scanned, photographed, or have complex table layouts

2. Manual Data Entry with a Structured Template

This sounds counterintuitive to include as a "method," but a well-designed Excel template with dropdown validation, auto-fill, and named columns can make manual entry significantly faster and less error-prone. Pair it with a second monitor showing the delivery note and you can cut entry time substantially.

This approach works best for small teams where documents arrive infrequently. The downside is obvious: it still requires human attention for every single line item, and it scales poorly once volume grows.

• Best for: Small warehouses, occasional documents
• Fails when: Volume increases, or notes have inconsistent formats across suppliers

3. Desktop OCR Software

Optical Character Recognition (OCR) tools — like Adobe Acrobat Pro, ABBYY FineReader, or similar — can read scanned delivery notes and convert the text into an editable format. Some even have table-detection features that attempt to reconstruct rows and columns.

The results depend heavily on scan quality. Clean, high-resolution scans of standardized forms convert well. Handwritten fields, rotated pages, or low-contrast prints tend to produce errors that still need manual correction. Licensing costs can also be significant if you need multiple seats across a warehouse team.

• Best for: Teams already using Adobe or similar suites
• Fails when: Scan quality is inconsistent or documents vary widely in layout

4. Excel's Built-In Data Import (Power Query)

Excel's Power Query feature can pull data from certain structured file types, including some PDFs. If your delivery notes come from the same supplier system and follow a consistent, machine-readable structure, you can build a repeatable Power Query flow that refreshes with new files.

Setting this up requires comfort with Power Query's interface, and it's brittle — any change in the source document's layout can break the query. It also doesn't work with scanned PDFs at all, since Power Query needs actual text to parse, not images. Still, for teams already embedded in the Microsoft ecosystem, it's worth exploring.

• Best for: Consistent, digital-native PDFs from a single source system
• Fails when: Layouts vary, documents are scanned, or the team lacks Power Query experience

5. AI-Powered Extraction with Ready-Made Presets

This is where the real efficiency gains are. Modern AI extraction tools can read PDFs, scanned images, and photos of delivery notes — understand the table structure intelligently — and output clean, structured Excel data without any manual reformatting.

Tablola's Delivery Note to Excel preset is built specifically for this use case. Upload a delivery note (PDF, image, or scan), and the AI identifies columns like item description, quantity, unit, and reference codes, then exports a clean, ready-to-use spreadsheet. You can also use the İrsaliye preset for Turkish-format documents. For teams dealing with invoices alongside delivery notes, the invoice extraction preset covers that workflow too.

What makes this approach stand out for warehouse and logistics teams:

• Works on scanned documents — no perfect scan quality required
• Handles multiple supplier formats — the AI adapts, you don't have to configure templates per supplier
• Bulk processing — use the bulk documents to single table preset to merge multiple delivery notes from a day or week into one consolidated sheet
• No technical setup — no Power Query, no scripting, no per-seat OCR licenses

For a receiving team processing 30–50 delivery notes a day across multiple suppliers, switching to AI extraction can reclaim several hours of data entry per week — with higher accuracy than manual input.

Choosing the Right Method for Your Team

There's no single answer that fits every operation. If you're handling a handful of clean digital PDFs from one supplier, Power Query or even copy-paste might be sufficient. But if your team deals with mixed formats, scanned documents, or high daily volume — the AI extraction route will pay for itself quickly in time saved and errors avoided.

The most important thing is to stop treating manual re-entry as an unavoidable cost of doing business. The tools to automate it exist, they're accessible, and getting started takes less time than you'd expect.