Implementing GS1 Standards in Your Manufacturing Process

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Why Manufacturers Must Embrace GS1 Standards for DPP Compliance

Manufacturing is where products come into existence, and it is at this critical point that the data foundation for a Digital Product Passport must be established. The identifiers assigned during production, the material data captured on the factory floor, and the quality records generated during testing all flow directly into the DPP. Without standardised identification and data capture at the manufacturing stage, the entire downstream passport ecosystem is compromised.

GS1 standards provide the globally recognised framework for product identification, data capture, and data sharing that the ESPR regulation builds upon. For manufacturers, implementing these standards is not merely a compliance exercise, it is an operational improvement that enhances traceability, reduces errors, and enables seamless data exchange with supply chain partners.

This article provides a practical guide for manufacturers looking to implement GS1 standards within their production processes to support Digital Product Passport requirements. It covers identification, marking, data capture, and integration with DPP systems, drawing on real-world implementation patterns that have proven effective across diverse manufacturing environments.

Establishing Your GS1 Identification Foundation

The first step in implementing GS1 standards is obtaining a GS1 Company Prefix through membership of your national GS1 member organisation. In the UK, this is GS1 UK. Your Company Prefix is a unique number from which you will allocate all of your GS1 identifiers, GTINs for products, GLNs for locations, and other keys as needed.

With your Company Prefix in hand, the next task is to allocate GTINs to all products that will require Digital Product Passports. Follow GS1 allocation rules carefully: each distinct product variant (differing in any trade-significant attribute) must receive its own GTIN. Maintain a master data repository that links each GTIN to its complete product description, including all the attributes that will be needed for the DPP.

Allocate GLNs to your manufacturing facilities, warehouses, and any other locations that will generate traceability data. Each location where events are captured should have its own GLN to ensure that the "where" dimension of your traceability data is precise and unambiguous. For larger facilities, consider sub-site GLN extensions that identify specific production lines, storage areas, or inspection stations.

If your products require serialisation, design your serial number allocation strategy before production begins. Decide on the format (numeric, alphanumeric), length, and generation method (sequential, random, algorithmic). Implement uniqueness validation to guarantee that no two products ever receive the same GTIN plus serial number combination. This validation is critical, duplicate serial numbers would create ambiguous Digital Product Passport records.

Product Marking: Applying Data Carriers on the Production Line

Once identifiers are established, they must be physically applied to products through data carriers, QR codes, RFID tags, NFC tags, or traditional barcodes. The marking process is a critical manufacturing step that directly affects DPP functionality.

For QR code marking, several production-line technologies are available:

• Inkjet printing, high-speed, non-contact printing suitable for direct application on product surfaces or packaging; ideal for serialised QR codes where each item's code is unique
• Laser marking, permanent, durable marking on metal, plastic, glass, or ceramic surfaces; suitable for products requiring long-lived data carriers
• Label application, pre-printed labels applied by automated applicators; suitable for products where direct marking is impractical
• Packaging integration, QR codes incorporated into packaging artwork during the pre-press stage; suitable for batch-identified products where the code does not change per item

Each QR code must encode a GS1 Digital Link URI containing the product's GTIN and, where required, its serial number. The QR code must be printed at a size and resolution that ensures reliable scanning. GS1 guidelines recommend a minimum module size (the smallest element of the QR code) of 0.5mm for close-range scanning, with larger sizes for greater scan distances or challenging environments.

Verification is essential after marking. Every data carrier applied to a product should be automatically scanned and validated on the production line to confirm that it encodes the correct identifier and meets the minimum quality threshold for reliable scanning. Products with unreadable or incorrect data carriers should be rejected and remarked. This verification step prevents defective data carriers from reaching the market, where they would render the product's Digital Product Passport inaccessible.

Data Capture on the Factory Floor

Manufacturing generates a wealth of data that contributes to Digital Product Passports, material inputs, process parameters, quality measurements, energy consumption, and more. Capturing this data in a structured, standardised format is essential for populating DPPs accurately and efficiently.

Material traceability begins at goods receipt. When raw materials and components arrive at the factory, they should be scanned and recorded against their GS1 identifiers (GTINs, batch/lot numbers). This receipt event captures which specific materials entered your facility and links them to the supplier's identifiers, establishing the first link in the material traceability chain.

During production, manufacturing execution systems (MES) or production tracking systems should record which input materials were consumed at each process stage and which output products were created. These transformation records, ideally structured as GS1 EPCIS TransformationEvents, establish the bill of materials at the batch or serial level, precisely the material composition data that DPPs require.

Quality and inspection data should be captured alongside production data. Test results, measurement values, and pass/fail determinations for each product or batch contribute to the DPP's quality and compliance sections. Where products are tested against specific standards or regulations, the test records should reference the applicable standard identifiers to enable automated compliance verification.

Environmental data, energy consumption, water usage, waste generation, emissions, captured during production directly supports the DPP's environmental impact disclosures. Increasingly, manufacturers are installing sub-metering and monitoring systems that can attribute resource consumption to specific production runs or even individual products, enabling granular environmental footprint calculations.

Integrating GS1 Data with DPP Systems

The data captured during manufacturing must flow into Digital Product Passport systems where it is assembled, validated, and published. This integration is a critical technical challenge that requires attention to data mapping, synchronisation, and quality assurance.

Most manufacturers will need integration between their existing systems (ERP, MES, QMS, WMS) and their DPP platform. The typical data flows include:

• Product master data (GTIN, description, classification, attributes) from ERP to DPP platform
• Serial number or batch number assignments from MES or production systems to DPP platform
• Material composition and bill-of-materials data from ERP or PLM systems to DPP platform
• Quality and test data from QMS or laboratory systems to DPP platform
• Environmental and sustainability data from monitoring systems or sustainability tools to DPP platform

APIs are the preferred integration mechanism, enabling near-real-time data synchronisation between manufacturing systems and DPP platforms. REST APIs with JSON payloads are the most widely supported format. For EPCIS event data, the EPCIS 2.0 REST API provides a standardised integration interface.

Data quality validation should be implemented at the integration boundary. The DPP platform should validate incoming data against expected schemas, check for completeness of required fields, and flag anomalies for review. Automated validation reduces the risk of incorrect or incomplete data reaching the published DPP, which could create compliance issues or erode consumer trust.

Platforms such as MyProductPassport provide APIs and import tools designed specifically for manufacturing data integration, simplifying the technical effort required to connect factory-floor systems with DPP publication.

Staff Training and Process Change Management

Implementing GS1 standards in manufacturing is as much a people challenge as a technology challenge. Production staff, quality engineers, warehouse operators, and IT teams all need to understand their role in the new identification and data capture processes.

Training should cover the fundamentals of GS1 identification, what GTINs, serial numbers, and GLNs are, why they matter, and how they are used. Staff responsible for product marking need hands-on training with the specific marking and verification equipment deployed on their production lines. Quality teams need to understand how their test data flows into Digital Product Passports and the importance of complete, accurate data capture.

Process documentation should be updated to incorporate GS1 identification and data capture steps. Standard operating procedures for production, goods receipt, quality inspection, and dispatch should all reflect the new requirements. For ISO-certified manufacturers, these changes will need to be incorporated into the quality management system documentation and may require an update audit.

Change management is particularly important for serialisation, which represents a fundamental shift in how many manufacturers think about their products. Moving from batch-oriented thinking to item-level thinking requires changes in mindset as well as systems and processes. Pilot programmes on individual production lines can help build confidence and identify practical issues before full-scale rollout.

Establishing clear ownership and accountability for DPP data quality is essential. Designate a data steward or team responsible for monitoring data completeness, accuracy, and timeliness, and empower them to halt shipments if data quality falls below acceptable thresholds.

Handling Multi-Site and Contract Manufacturing

Many businesses manufacture products across multiple sites or use contract manufacturers. GS1 standard implementation must account for these distributed production scenarios to ensure consistent identification and data capture regardless of where products are made.

For multi-site operations under a single brand, the brand owner typically holds the GS1 Company Prefix and allocates identifiers centrally. Each manufacturing site receives its own GLN but uses the brand owner's GTINs for the products it produces. Serial number ranges should be allocated to sites in blocks to prevent duplication, with centralised tracking of which ranges have been assigned to which facilities.

Contract manufacturing adds complexity because the contract manufacturer may have their own GS1 identifiers, systems, and processes. Clear contractual agreements should specify which party is responsible for identifier allocation, data carrier marking, and data capture. The brand owner should provide their GTINs and serial number ranges to the contract manufacturer and receive structured data files (ideally EPCIS events) documenting the production activity.

Data exchange with contract manufacturers should follow GS1 standards wherever possible. EPCIS 2.0 provides a ready-made format for exchanging production event data. Where contract manufacturers lack EPCIS capability, structured CSV or XML templates can serve as an interim solution, with the brand owner responsible for converting this data into the required DPP format.

Regardless of the manufacturing model, the brand owner retains ultimate responsibility for DPP accuracy and completeness. Implementing supplier audits, data quality checks, and verification processes for contract-manufactured products is essential for maintaining the integrity of the Digital Product Passport.

Measuring Success and Continuous Improvement

Implementing GS1 standards in manufacturing is not a one-time project but an ongoing capability that requires monitoring, measurement, and continuous improvement.

Key performance indicators for GS1 implementation in manufacturing include:

• Data carrier application rate, the percentage of products successfully marked with a readable, correct data carrier
• Data capture completeness, the percentage of required DPP data fields populated from manufacturing data
• Serialisation accuracy, the rate of correct, unique serial number assignments without duplicates or errors
• Integration latency, the time between data generation on the factory floor and availability in the DPP platform
• First-scan success rate, the percentage of data carriers that read correctly on first attempt in downstream supply chain scanning

Regular review of these metrics identifies areas for improvement and quantifies the return on GS1 implementation investment. Many manufacturers find that the discipline of structured identification and data capture delivers operational benefits, improved inventory accuracy, faster recall response, reduced counterfeiting, that extend well beyond DPP compliance.

As serialisation and DPP requirements expand to additional product categories, the foundation built through initial GS1 implementation can be extended and refined. Manufacturers that treat GS1 standards as a strategic capability rather than a compliance burden will be best positioned to adapt as requirements evolve and to capitalise on the competitive advantages that transparent, traceable products increasingly provide.