How to prepare CSV data for production labels

Start with the manufacturing profile’s headings

A spreadsheet can look correct while the exported values are wrong. Start with the expected file structure, preserve identifiers as text and check one representative order.

Use the manufacturing profile’s complete set of 31 column headings, listed below. The importer accepts those named columns in any order, but rejects missing, extra or duplicate headings.

Use UTF-8 text with comma-separated fields. Keep optional columns even when their values are blank. A product name containing a comma must be quoted; spreadsheet CSV export normally handles that quoting for you.

SmartFACT imports CSV rather than native Excel workbooks. Export the relevant sheet and check the resulting text file. Regional spreadsheet settings can change the delimiter, so verify that fields are separated by commas. Microsoft’s guidance on importing and exporting text and CSV files in Excel explains the options.

The headings cover source and job references, customer and destination fields, line and product details, and batch values. The CSV to labels page shows how selected fields appear on the label.

• Source and job: source_system, source_revision, job_id, job_reference, job_status, job_stage, purchase_order, job_created_at, job_due_at.
• Customer and destination: customer_id, customer_code, customer_name, customer_label_profile, destination_country_code, destination_site_code.
• Line and product: line_id, product_id, quantity, product_sku, product_name, product_range, product_finish, product_gtin, product_country_of_origin.
• Product size and weight: product_length_mm, product_width_mm, product_height_mm, product_net_weight_kg.
• Batch: batch_code, batch_manufactured_on, batch_expires_on.

Preserve product codes as text

A reference such as 000017 is an identifier. If a spreadsheet converts it to the number 17, the original value has already been changed before SmartFACT sees the file.

In Excel, import through Data → From Text/CSV and set identifier columns to Text in the transformation steps before loading. Check that an earlier automatic type-conversion step has not already removed zeros. If it has, return to the original source. Applying a display format afterwards cannot recover lost digits. Microsoft explains this in its guide to keeping leading zeros and large numbers.

Check internal product codes, customer identifiers, product identifiers and any GTIN values. A saved customer mapping resolves the customer product code separately; customer_sku is not an extra CSV column to add.

Check quantities, dates and decimals

Use plain values in the exported file. Currency signs, thousands separators and locale-specific date formats do not belong in these fields. Leave a permitted optional value empty when it is unknown; do not invent a date or product code to get past a validation error.

Examples for the manufacturing profile:

• quantity, such as 3: a positive whole number, without decimal places or leading zeros.
• job_due_at, such as 2026-09-28T16:00:00Z: a UTC timestamp in this exact format.
• batch_manufactured_on, such as 2026-09-24: a calendar date using year, month and day.
• product_length_mm, such as 120.00: a non-negative value with two decimal places.
• product_net_weight_kg, such as 0.250: a non-negative value with three decimal places.

Keep an order’s identity consistent

Rows belonging to the same source, job and revision must agree on shared job, customer and destination values. Give each line in that order a distinct line_id.

Customer mappings use customer_id and product_id. A familiar customer name or a matching-looking internal SKU does not replace those keys. Check the identifiers against the saved mapping, particularly when moving data from another system.

An identical repeat import under the same configuration reuses its saved snapshot. Changed data needs a new source revision. A revised source does not grant another original print run: extra copies belong in the reprint workflow.

Review the result before printing

A supervisor imports the file. The importer validates the complete CSV before saving its results; a malformed row is not silently skipped. File limits are 10 MB and 10,000 data rows, with further limits applied by the selected profile.

Importing does not print. Review the imported order, resolved customer code, selected layout, quantity and actual label preview. A required customer code that cannot be resolved holds the Acme label until corrected.

Once the data and preview are right, print on a qualified station. The file check and the physical label check answer different questions.

Before you import

Check each of these before importing a file:

• All 31 headings are present exactly once.
• The exported file uses UTF-8 and commas.
• Identifier text, including leading zeros, matches the original source.
• Quantities, timestamps, dates and decimals use the expected formats.
• Job fields agree across lines, and line identifiers are unique within the order.
• The customer and product identifiers match the intended configuration.

Practise with the real screens

Practise printing a job’s labels, handling uncertain output, authorising an exact reprint and drafting, checking and publishing a rule, using made-up orders and customers.

Read the guides on any device. Interactive demonstrations run on a desktop or laptop, showing the office screens or the station screens used on a handheld or scan station.

Open LabelRules training