Advanced Product Traceability: How to Capture Data from Non-Standardised Documents

A warehouse operator scans a pallet on arrival. The WMS logs a single line: a reference, a quantity, a location. On paper, everything is traced. In reality, nobody knows what condition the parcels arrived in, whether the serial numbers match the purchase order, or whether an item was swapped along the way.

This isn't an exception – it has become the norm in most warehouses running a capable WMS. The information system knows what should be there. It doesn't know what actually is. And it's precisely in that gap between theoretical data and physical reality that most supply chain disputes, non-conformities and traceability failures take root.

Supply chain: understanding how a WMS works

A well-configured WMS remains a powerful tool: in the UK, some companies have virtually eliminated picking errors – reducing error-related complaints to 0.07 per cent – and have achieved productivity gains of around 10 percent 1. But a WMS was designed to manage flows, not to document the physical reality of every object passing through the warehouse.

One pallet, one line: the structural limit of the scan-point

In most warehouses, traceability rests on a simple principle: an operator scans a barcode on arrival, another on departure. In between, the system knows that a pallet of X parcels ‘exists’. What it doesn't know precisely is what each parcel contains or what condition it's in. For a uniform flow, this is sufficient; it becomes inadequate as soon as a pallet mixes several references, serial numbers, or conformity states. Why? Because documenting that variability with conventional WMS tools would mean multiple manual entries for a single item.

Between the inbound and outbound dock: the documentation gap

This is where the real gap lies: between the warehouse's ‘in’ and ‘out’. The documents that travel with a pallet – delivery notes, supplier labels, transport documents, non-conformity reports – rarely share the same format from one supplier, country or carrier to the next.

The data exists, but it isn't standardised: handwritten on a label, printed on a box, stamped on a delivery note. It's rarely structured in a format the WMS or ERP can use directly. Frontline teams must interpret this mass of non-standardised documents today, often by hand, before the data can even reach the information system.

EAN, serial number, batch number: why a single identifier is no longer enough

Faced with this problem, the instinct is to assume a single identifier per item is enough to trace everything. This is where the distinction between EAN and serial number becomes strategic – and is too often misunderstood outside quality and compliance teams.

What the EAN knows – and doesn't – about your product

The EAN (the code scanned at the till) identifies a product model, not an individual item: every smartphone of the same model and configuration, for instance, shares the same EAN. That's fine for sales or catalogue purposes. It falls short, however, the moment you need to distinguish one physical unit from another – or work out which of the hundred smartphones on a pallet was issued to which employee.

The serial number: the identifier that changes everything

That's the role of the serial number, or the IMEI for a mobile device: a unique identifier per unit, not per reference. Take a common, concrete example.

When a new employee joins, they're issued a smartphone, a SIM card and a phone line. For the network operator to match the right number to the right line, it needs the exact IMEI of the device taken out of stock – not just its EAN. The same need applies to any asset whose individual history matters: equipment under warranty, test kit, a fleet vehicle. Capturing this level of data with conventional WMS tools means keying several fields manually per item, which quickly becomes unworkable at volume.

GS1 Sunrise 2027: a signal that confirms the problem

This isn’t specific to WizyVision: the global traceability ecosystem is moving towards the same next-generation barcode standard. GS1 UK explains that QR codes powered by GS1, encoded with a GS1 Digital Link, can connect a product’s GTIN to real-time online information and supply-chain data such as batch numbers, expiry dates and serial numbers. GS1 DataMatrix codes can also carry this type of product information 2. The industry’s global target, known as “Sunrise 2027”, is for retail point-of-sale systems to be able to scan 2D barcodes alongside existing EAN/UPC barcodes. Rather than disappearing overnight, traditional barcodes are expected to coexist with QR codes powered by GS1 and GS1 DataMatrix codes during the transition. 3

In other words, the classic EAN-13, in use for more than fifty years, no longer meets today's traceability requirements. Until 2D codes are adopted universally across suppliers, businesses that need to trace a serial number, a batch, or a condition today have to capture that data another way: through photos and AI.

Complex capture: turning a photo into structured data

This is precisely the blind spot WizyVision addresses: complex capture, meaning the ability to extract, from a single photo, every relevant piece of data about an item – EAN, serial number, supplier reference, visible condition – without the operator having to key each one in separately.

Product traceability: one scan, multiple identifiers

In practice: an operator takes a photo of a parcel or a tray of items. The embedded AI then:

• Reads the barcode;
• Extracts the serial number or IMEI printed on the label;
• Identifies the visible condition of the packaging;
• And structures this information so it flows into the WMS or ERP via an API, with no manual re-entry.

In short, what a WMS can only capture through repeated, individual scans, a single well-used photo documents in one movement. This doesn't replace the WMS: it closes the gap between what it knows in theory and what actually happens on the dock.

The concrete stakes of complex capture in the warehouse

Beyond the technology, complex capture addresses five concrete business challenges:

• Complex traceability: documenting condition, numbers and quantities precisely, rather than a single generic line per pallet.
• Proof: having time-stamped, geolocated visual evidence that holds up in a dispute with a supplier or carrier.
• Productivity: capturing the full contents of a mixed pallet in seconds, versus several minutes of manual, item-by-item entry.
• Quality: aiming for a success rate of up to 100%, with a notification confirming that every expected step has actually been carried out.
• Compliance: sharing extracted data instantly with quality teams, customers or regulators, with no re-keying and no delay.

With WizyVision, capture irrefutable visual evidence and automatically extract data from any label. The result: fewer disputes and greater compliance and security across your supply chain.

Discover our advanced traceability apps →

Reverse logistics: a testing ground for advanced traceability

Returns – or reverse logistics – are undoubtedly the most challenging area for testing the robustness of a traceability system: a returned product often no longer has a legible label or its original packaging. In the UK, online returns reached 900 million items in 2023, equivalent to around 2.5 million returns per day, according to Barclaycard 4. In certain categories, such as clothing, returns can account for a very significant proportion of the volume dispatched. The cost of processing a return is estimated at between £5 and £15 per item, depending on the complexity of the process 5. Documenting this process accurately, rather than treating it as a secondary matter, has a direct impact on profitability.

Problem and solution

How Altrad strengthened its shipping and returns processes

This is exactly the challenge Altrad Plettac Mefran, a scaffolding equipment manufacturer, resolved amid strong demand: securing shipping and returns to avoid costly equipment losses. With WizyVision, Altrad digitised its entire returns process, securing tracking and invoicing for parts to be cleaned, repaired, or replaced–with no training required for frontline teams, and photos now serving as proof for invoicing.

Read the Altrad case study →

How Findis manages non-conformities with WizyVision

Findis, France's leading B2B distributor of home equipment products, followed the same logic: its WMS tracked movements but not the actual physical condition of packaging. WizyVision deployed two apps that let staff scan references and photograph packaging condition in seconds; the Purchasing team now has real-time access to visual proof of non-conformities to raise documented claims.

See how Findis applies advanced traceability with WizyVision →

How Geopost identifies parcel contents from a single photo

Reverse logistics, then, isn't a secondary use case for WizyVision: it's a field of expertise in its own right, one where the same level of credibility can be built as with unidentified-parcel identification for Geopost.

The results:

• Inventory is now twice as fast;
• The successful rerouting rate has risen from 10% to over 60%;
• And customer satisfaction in the event of a claim has tripled.

Learn more about the Geopost case study →

How to set up complex data capture in your warehouse

Setting up complex capture doesn't require overhauling your existing information system. It's built alongside the WMS, in three steps:

• Identify the costliest documentation gap (receiving, shipping or returns) to use as a pilot case;
• Connect the capture to the existing WMS so AI-extracted data flows through automatically via API, without creating a new data silo;
• Then roll out more broadly once reliability is confirmed – a deployment that becomes a matter of configuration, not a new development cycle.

It's the same logic that has proven itself in building a no-code field app: starting from a precise need rather than trying to digitise everything at once.

Conclusion: closing the gap before it gets costly

The WMS remains the backbone of warehouse management. Still, it was never meant to document, on its own, the physical reality of every item moving between the inbound and outbound dock. Between the EAN, which identifies a model, and the serial number, which identifies a unit; between the non-standardised documents accompanying each delivery and the structured data the ERP needs, a documentation gap remains. That's the gap complex AI capture is designed to close – before the logistics sector rolls out GS1 2D codes more widely.

That's why advanced traceability is no longer a minor compliance matter: it's a competitiveness issue for operations leadership and a data governance issue for CIOs. Closing the gap between the WMS and the physical reality of the warehouse isn't about replacing existing systems – it's about giving them access to data they've never been able to capture on their own.

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