Traceability data originates from a variety of functions and processes within companies, including design and quality control data for the product; production process data; procurement data and logistics and distribution data. Several departments may need to be involved and several internal systems may need to play a role. Some of the data can be commercially sensitive and may require special processing or partial redaction before being made available to third parties.
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Some data will be more stable over time (e.g., master data) and can be communicated in advance of receipt of traceable objects. Other data will be added whenever relevant critical tracking events or transactions occur.
The volume of traceability data that is collected over time can be quite significant, creating challenges in terms of time and cost to collect, store and provide access to the data. Preparation for data retention should be considered to manage these challenges.
There are four sources of data that contribute to what we define as “traceability data”. These four data sources may be managed in different systems of an organisation, but together they provide the information that is critical in understanding the full context of traceability data.
■Master data: Master data are the single source of common business data used across all systems, applications, and processes for an entire organisation.
□Static master data (referred to as “static data” throughout this document) typically exists to describe products, parties, locations, and assets.
□Master data about supply chain relations (referred to as “relation data” throughout this document) typically exist to describe the supply chain partners of an organisation (i.e. its suppliers and customers), specified by product category and location. When relation data are linked across organisations, this enables the creation of a complete map of the supply chain (upstream and downstream). And, when relation data are enhanced with qualitative data such as certifications, this helps organisations to gain insight in environmental, ethical and safety aspects of the supply chain.
■Transaction data: Transaction data are recorded as a result of business transactions, such as the completion of a transfer of ownership (e.g. orders, invoices) or a transfer of custody (e.g. transport instruction, proof-of-delivery). Transaction data may be recorded with the aid of electronic data exchange (EDI) and AIDC techniques (e.g., POS scanning, bedside scanning).
■Visibility event data: Visibility event data are records of the completion of business process steps in which physical or digital entities are handled. Each visibility event captures what objects participated in the process, when the process took place, where the objects were and will be afterwards, and why (that is, what was the business context in which the process took place). Unlike the other types of data, visibility event data are often specifically recorded for visibility and traceability purposes. Visibility event data will often be captured using AIDC techniques such as barcodes or RFID.
Figure 3‑3 Sources of traceability data
The precision of traceability data is determined by two main dimensions:
1.The level of identification of the traceable objects (products and resources),
2.The granularity at which traceability data is recorded.
Together these two dimensions provide organisations a way to establish the optimal level of precision.
As illustrated in figure 3-4 the combinations with the lowest precision help to provide transparency, which is a basis for traceability. The combinations with the highest precision help to provide traceability, enabling organisations to locate specific traceable objects in a supply chain.
Visibility event data recorded at serial level provide the highest fidelity in the sense that:
1.Visibility event data record the completion of each business process step, including 'internal' processing steps that do not directly refer to specific transactions between trading parties.
2.A serialised object can only exist in one place at any point in time, so it makes a single unambiguous path through the supply chain network.
Figure 3‑4 Precision of traceability data
The following example illustrates how these combinations apply to trade items:
■ Trade items may be identified at class-level (GTIN), lot-level (GTIN + batch/lot ID) or instance-level (GTIN + serial ID).
■ Data may be available about the relations, transactions and visibility events that involve the trade item.
This leads to the following possible levels of precision in the available traceability data:
Table 3‑1 Precision of traceability data for trade items
An important aspect to consider is the potential sensitivity of the traceability data that an organisation may choose to share with other parties.
Generally, a distinction can be made between internal data —data not suitable for sharing with other parties, for example due to commercial or privacy reasons— and external data —data suitable for sharing with other parties if certain pre-defined conditions are met—. See Table 3‑2 for examples of data that may be treated as internal vs external by an organisation.
Note: The organisation will also need to consider access restrictions to any internal data that may be shared across organisational lines. Internal access restrictions vary widely across industries and regions.
Table 3‑2 Example of internal vs external data of an organisation
Sensitivity of data
Master data
Transactional data
Visibility event data
External data
Static data:
¡ Locations
¡ Catalogue items
¡ Assets
¡ …
Relation data:
¡ Suppliers
¡ Customers
¡ 3rd party service providers
¡ …
¡ Purchase orders
¡ Despatch notifications
¡ Transport instructions
¡ …
¡ Producing
¡ Picking
¡ Packing
¡ Shipping
¡ Receiving
¡ …
Internal data
¡ Product design
¡ Production process
¡ Personnel
¡ …
¡ Contracts
¡ …
¡ Quality inspection data
¡ Lab analysis results
¡ ..
¡ Inspecting
¡ Collecting
¡ Holding
¡ …
As such, this standard focuses mainly on the sharing of external traceability data.
The quality of data provided by each trading partner is critical because inaccurate data which is shared between trading partners could affect other business processes like a trace request or recall activity.
Establishing and maintaining a good quality level of traceability data is a major challenge. Some important aspects are:
■ Completeness: Are all relevant data recorded?
■ Accuracy: Are the recorded data accurately reflecting what happened?
■ Consistency: Are the data aligned across systems
■ Validity: Are the data time-stamped, to ensure the validity timeframe of data is clear?
When it comes to providing access to traceability data to supply chain partners or other stakeholders, five main models (traceability choreographies) can be distinguished. They result from the different ways in which traceability data can be systematically stored and made available to other parties.
The following example illustrates how the five different traceability choreographies compare, based on a simple scenario that involves three different organisations:
Figure 3‑5 Traceability choreographies
In the one step up-one step down model the parties keep the traceability data in their own local system. Information requests are exchanged between immediate trading partners upstream or downstream. This model enables traceability data to be exchanged and partially checked between each pair of trading partners, and further upstream or downstream one step at a time.
In the centralised model the parties share the traceability data in a central repository and send their information requests to it.
Note that some centralised repositories (e.g. those operated by a regulatory authority) may only provide a capture interface but might not make the query interface available to all contributing parties, instead preferring to limit query access to the owner of the repository. Other centralised repositories may support different access control policies, providing query access to all parties – or based on supply chain role – or whether the querying party can prove that they are on the chain of custody / ownership / transactions for the objects specified in their query.
In the networked model the parties keep the traceability data in their own local system and stage it in a way that enables all supply chain partners (not only immediate trading partners) to query the data.
Networked models may differ according to the access control permissions about who is allowed to retrieve the data. In some models, any member of the community or network of supply chain partners may be entitled to query and retrieve data. In other models, access may depend on supply chain role, e.g. to prevent one manufacturer from querying a rival manufacturer’s data; or access may depend on whether the querying party can prove that they are on the actual chain of custody / ownership / transactions for the objects specified in their query.
The cumulative scenario is a push method where the traceability data is systematically enhanced and pushed forward to the next party in the chain in parallel of the product flow. It enables sharing of upstream data with parties further downstream, but not the opposite.
This approach results in highly asymmetric visibility across the supply chain, in which downstream parties receive a complete copy of all relevant upstream data, while the upstream parties have no visibility downstream beyond their immediate 1-down customer. This approach can also be quite challenging for downstream parties to receive and process large volumes of traceability data, especially if the processing involves checking of multiple nested digital signatures.
The fully decentralised and replicated scenario is a mix of the cumulative scenario and networked scenario, and typical for the blockchain technology. The traceability data is systematically enhanced and all supply chain partners involved in the network keep a local copy of all data.
See section 4.3 for more information on the way the GS1 data sharing standards relate to the traceability choreographies.
In this section a generic example is given that illustrates the functions of a standards based traceability system. In the example, globally unique identifiers are used for the trade items, logistic units, parties and locations. Automatic data capture techniques such as barcodes are used across the supply chain to gather the traceability data based on the activities in the supply chain.
Note: See appendix D for sector-specific traceability examples
Figure 3-7 provides an overview of the supply chain. It illustrates how ingredients and packaging are supplied, transformed into products and distributed to the final customers.
Figure 3‑7 Supply chain overview
On the next pages, figures 3-8 and figure 3-9 illustrate some of the business process steps that will occur at various points in the supply chain. Each step will lead to one or more critical tracking event (CTEs) for which key data elements (KDEs) need to be recorded.
Figure 3‑8 Traceability data collection in business process steps (1)
Harvesting:
The producer harvests the crop and packs the products into in cases. Each of the cases gets a label with GTIN + batch/lot ID, and the related data are recorded.
Manufacturing:
The manufacturer transforms ingredients into final products. After that, the manufacturer packs the products into cases.
To maintain traceability the inputs and outputs of the process are recorded on batch/lot level.
Shipping:
The warehouse department picks the goods and packs them onto pallets.
To maintain traceability the warehouse records the links between product IDs (GTIN + batch/lot ID) and pallet IDs (SSCC).
Subsequently, the pallets are moved to the outbound staging area to be collected by the carrier.
Figure 3‑9 Traceability data collection in business process steps (2)
Transporting:
The carrier arrives and loads the pallets onto the truck. The driver uses his mobile device to identify each of the pallets. The link between the pallets and the truck is recorded. Now, by tracking the truck also the pallets and goods can be tracked.
Receiving:
The pallets arrive in the retail distribution centre.
The incoming goods department inspects the received goods by scanning the SSCCs on the pallet label and comparing the data against the pre-registered information in the system.
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When all checks are ok, the goods will be marked as available in the inventory management system.
Selling:
The products have arrived at the store and have been placed on the shelves.
A consumer has decided to buy two products. At the checkout, the clerk scans the barcode on the products. The system automatically checks the expiry date.
The sales are recorded, in addition to the GTIN also the batch/lot ID is registered.
On this page, two examples are included that illustrate the way traceability data can be applied.
In the first example, figure 3-10, a retailer needs to find upstream information about the origin of a particular ingredient, which growers were involved and the quality certificates they have. By following the chain of custody upstream, the grower is located, and the required information is retrieved.
Figure 3‑10 Data usage: Upstream query
In the second example in figure 3-11, a manufacturer needs to locate products of a specific batch/lot that need to be recalled from the distribution network. By following the chain of custody downstream all points in the distribution network where instances of the batch/lot were observed are identified, enabling a targeted recall.
Figure 3‑11 Data usage: Downstream query
A traceable object is a physical or digital object whose supply chain path can and needs to be determined. The table below lists the GS1 keys that are available for the identification of traceable objects.
Table 4‑1 GS1 identification keys for traceable objects
Trade item identification
The GS1 system provides globally unambiguous identification keys to provide a common language for the communication of product information from company to company. The GS1 identification key for products is the GS1 Global Trade Item Number (GTIN). For decades, this GS1 identification key has facilitated the sharing and communication of product information among supply chain partners. Moreover, it has provided the foundation for innovative improvements in supply chain management for many industries.
GS1 standards provide a choice regarding the granularity of trade item identification, leading to varying degrees of precision as it pertains to traceability that can be achieved, as summarized in the following table.
Table 4‑2 Trade item identification - precision levels
Reading from top to bottom, each choice gives increased ability to trace products in the supply chain, though at the cost of increased bookkeeping and cost of product marking.
Class-level identification (GTIN) provides the ability to see where different products are used in the supply chain, and to gather data based on counting products. This includes many inventory applications, sales analysis, etc. However, at this level, all instances of a given product are indistinguishable, which prevents real traceability.
Batch/lot-level identification (GTIN + batch/lot ID) provides the ability to distinguish products in one batch/lot from another batch/lot. This is especially useful in business processes that deal with quality issues that tend to occur on a batch-by-batch basis, such as a product recall of a contaminated batch/lot. Batch/lot-level traceability lets you identify all the places in the supply chain where a given batch/lot has reached, and confirm the quantity of items present from that batch/lot.
Instance-level, or fully serialised identification (GTIN + serial ID) provides the ability to identify each product instance individually. This allows each product instance to be tracked or traced individually, and therefore to precisely correlate observations at different times in the supply chain. This is for example useful for products with a long product lifecycle, where traceability requirements extend to business processes related to the use and maintenance of the product.
Instance-level identification has the unique advantage that the identifier represents one individual instance that can only exist in one location at a particular point in time. The other identification levels allow multiple instances or quantities (fixed or variable measure) with the same identifier to exist in multiple locations at a particular point in time, which limits the amount of knowledge about the instance(s). For example, the specific chain of custody of an object can be evaluated precisely if instance-level identifiers are used – but otherwise can only be estimated in a probabilistic manner.
Note: Supply chain and logistics systems will often only support batch/lot-level traceability, since they are designed to concurrently handle a wide range of products. This means that even when each final product instance is assigned a serialised identifier during manufacturing, it may be advisable to also include a batch/lot identifier, both on the product as well as on the outer packaging.
Traceable objects in distribution and logistics
In distribution and logistics, trade items are often aggregated (grouped, packaged, loaded). This leads to other traceable objects needing to be tracked and traced, for example logistic units, containers, trucks and vessels. The diagram below illustrates how these types of objects relate to each other and how the GS1 identification keys can be applied.
Figure 4‑1 Traceable object aggregation levels and identification keys
In order to preserve the relation between the higher aggregation levels and the contained trade items, the links between the various aggregation levels will need to be recorded. This is one of the essential elements of a traceability system, and key for establishing connections between the traceability systems of the various parties (including logistic service providers).
There is a difference in nature between the GTIN and SSCC on the one hand and GIAI and GRAI on the other hand. Whereas SSCC and GTIN identify goods or products including their packaging, the GIAI and GRAI identify the T&L asset independent of its contents. A special situation occurs when assets themselves are being distributed, for example empty pallets. In such cases asset IDs can also be used to identify the contents.
Note: Not depicted in figure 4-1 are the GSIN and GINC. These GS1 identification keys serve to identify groupings of logistic units and are used in combination with an SSCC.
In any traceability system, it is important to distinguish the various actors who play a role in the chain of custody or ownership of a supply chain. Examples of parties in the supply chain might include a manufacturer, a broker, a distributor, a carrier, or a retailer. In order to understand the full context of traceability, understanding WHO played a role and sometimes their relationship to each other in the chain is essential. Identification of parties can be accomplished with the Global Location Number (GLN). In some cases, especially when identifying individuals involved, the Global Service Relation Number (GSRN) can also play a role.
Table 4‑3 GS1 identification keys for traceability parties
A traceability location is a designated physical area that has been selected to be in scope of a traceability system.
Physical locations defined by an organisation for their business operations can be identified using the Global Location Number (GLN).
Table 4‑4 GS1 identification keys for physical locations
Key
Full name
Type of supply chain information
GLN
Global Location Number
Locations
Global Location Number + GLN extension component
(internal) Locations within a site
The GLN can be used to identify business locations as defined by a specific party. In traceability systems other locations can also be of importance. For this reason, the GS1 standards support additional ways to identify locations, such as geographic coordinates.
In cases where references to documents or transactions need to be shared across parties, globally unique identifiers enable unambiguous identification across systems of parties. For example, globally unique identifiers could be applied to quality certificates assigned by certification bodies.
Table 4‑5 GS1 identification keys for documents and transactions
Traceable objects —and in some cases also parties, locations, transactions and documents— will need to be physically identified to enable traceability.
Traceability systems can use GS1-approved barcode symbologies and EPC/RFID tags to encode GS1 identification keys that uniquely identify products, trade items, logistic units, locations, assets, and service relations worldwide. Additional information such as best-before-dates, serial numbers, and lot numbers may also be encoded into barcodes or EPC/RFID.
Besides barcodes and EPC/RFID, other carrier-based technologies (such as digital watermarks) and carrier-less technologies (such as image recognition) may also play a role.
In addition to the data that is captured from objects, data provided by the equipment used to scan or read the data —such as date & time, read-point and user (operator)— will be important in determining the who, where, when and why dimensions.
Figure 4‑2 Data capture technologies and the 5 dimensions
Barcodes
The marking of traceable objects is driven by the level of identification. Batch/lot-level or serialised identification are dynamic data and therefore cannot be included in the artwork of the packaging. This means that adding dynamic data in a barcode will have an impact on printing and packaging speeds.
Traditionally, barcodes on consumer units were used for POS scanning and only contained the Global Trade Item number (GTIN), also known as EAN or UPC. With evolving product safety regulations and product information requirements, other types of data are making their way to the barcodes on consumer products. Besides the batch/lot ID and/or serial ID these may also include the expiry date, best before date, etc. The proper linkage of the barcode, the related data and the produced instances of the trade item, is a key aspect.
Looking at trade item groupings such as outer cases, traditionally barcodes containing a GTIN were applied, in some cases pre-printed on the case, but also quite often included on a label. In recent years, dynamic data have made their way to case labels causing such barcodes to be increasingly printed inline.
For logistic units the barcodes have always been based on the SSCC, which is a serialised identifier. This means that logistics labels will be printed when the goods are packaged, and that the link between data and label will be secured that way.
Table 4‑6 GS1-barcodes overview
1D symbols
EAN/UPC, ITF-14, GS1 DataBar (non-expanded)
GS1-128, GS1 DataBar (expanded)
2D symbols
GS1 QR Code, GS1 DataMatrix
Note: Matrix symbols (2D) require image-based scanners. Linear symbols (1D) can be read by laser as well as image-based scanners.
EPC/RFID
EPC/RFID tags are by definition serialised. A special aspect is that EPC/RFID tags will often be pre-written, requiring the link between the issued serialised identifier and the associated data to be recorded afterwards.
Example
The example below illustrates the entities that need to be automatically identified in the healthcare sector, and which carrier techniques are applied.
Figure 4‑3 Example of GS1 barcodes and EPC/RFID tags as applied in healthcare
When it comes to capturing the data, the main questions are:
1.Which process steps need to be captured?
2.What is the most cost effective way to capture the data?
Usually the first step will be scanning of logistic units upon receipt. For barcodes this is often done using handheld devices. For EPC/RFID tags, fixed readers can be used. Other process steps where data will be captured are storing, picking, packing, shipping, transporting, selling. Often a combination of fixed mounted scanners or readers and hand held devices will be applied to capture the critical tracking events.
The emergence of mobile devices deserves a special mention here, since it increases the availability of scanning capability (making scanning as pervasive as the barcode) and so may make it feasible to record additional events at limited additional cost.
The collection of traceability data from other parties and the provision of data to other parties are essential components in distributed traceability systems. The five traceability choreographies (see section 3.3.5 ) all pose different requirements when it comes to the standards-based exchange of data.
An important principle is the separation of data content from the way the data is exchanged (the communication method).
In terms of data content, GS1 standards for business data pertain to three categories of business data that are shared between end users:
■Master data that provide descriptive attributes of real-world entities identified by GS1 identification keys, including trade items, parties, and physical locations.
■Transaction data that consist of trade transactions, triggering or confirming the execution of a function within a business process as defined by an explicit business agreement (e.g., a supply contract) or an implicit one (e.g., customs processing), from the start of the business process (e.g., ordering the product) to the end of it (e.g., final settlement), also making use of GS1 identification keys.
■Visibility event data provide details about activity in the supply chain of products and other physical or digital assets, identified by keys, detailing where these objects are in time, and why; not just within one company’s four walls, but throughout the supply chain. It makes it possible to track and trace goods with live data along the process.
The communication methods applied in the GS1 standards may be broadly classified in two groups:
■Push methods, where one party unilaterally transfers data to another in the absence of a prior request. Push methods may be further classified as:
□Bilateral party-to-party push, where one party transfers data directly to another party.
□Publish/subscribe, where one party transfers data to a data pool or repository, which in turn pushes the data to other parties who have previously expressed interest in that data by registering a subscription (“selective push”).
□Broadcast, where a party publishes business data in a well-known or publicly-accessible place such as a World Wide Web page, where it may be retrieved by any interested party.
Broadcast does not necessarily mean that the data is available to anyone; the data may be encrypted for a specific intended user or the broadcast channel (e.g. website) may require the receiving party to authenticate and may only grant access to the broadcast data according to specific access control policies.
■Pull or query methods, where one party makes a request for specific data to another party, who in turn responds with the desired data. Note that in the above classification of Push methods, the Broadcast method may also involve a Pull query, in order to retrieve the data from a publicly-accessible place (such as a website).
See the GS1 System Architecture document [ARCH] for more information.
GS1 offers several standards and services, based on the types of data and communication methods described above. All GS1 data exchange standards and services are based on the use of GS1 identification keys, rather than relying on internal identifiers or descriptive elements. The use of globally unique keys greatly simplifies implementations between trading partners, since they provide interoperability across the various systems.
Table 4‑7 Overview of GS1 data sharing standards
The traceability choreographies mentioned in section 3.3.5 all apply to the three types of data content. However, each choreography applies a different mix of communication methods, as illustrated in the table below.
Table 4‑8 Traceability choreography – applicable communication methods
Access control
All of the choreographies are in principle capable of selectively restricting access to the meaning of the exchanged data on a need-to-know basis, although they differ in the mechanisms used and in the ability to control whether a receiving party shares the data with additional parties:
■ Some of the choreographies involve bilateral communication between an information requesting party (querying party) and an information providing party, which may be the original contributor of the data or a shared repository holding the data. Privacy of such bilateral communications can be assured via mutual authentication, use of secure communication channels and potential encryption of the data payload or messages.
■ Decentralised and replicated choreographies can involve a different approach to selectively restricting access to the meaning of the data. In the case of a blockchain ledger, trust in the ledger is assured if everyone is able to independently inspect the entire ledger including all of its data, in order to be assured that no historic transaction data has been subsequently altered. Although this openness necessarily means that anyone can read all the data in the ledger, it is still possible to hide the meaning of sensitive data either by encrypting such data or by storing a hash value in the ledger. If hash values are stored in a blockchain ledger, the original data is typically stored elsewhere and exchanged by another mechanism, while the hash value recorded in the blockchain ledger effectively archives a ‘tamper-evident seal’ that corresponds to what the data originally looked like.
Data discovery
It is sometimes desirable to share data between parties who have no direct relationship, but who are connected through a chain of custody, chain of ownership, chain of transactions, or some combination of these.
All traceability choreographies (see 3.3.5 ) enable this in one way or another. The "data discovery problem" is applicable when going beyond the one step up-one step down scenario, and specifically in the case of the networked model. It is concerned with how to directly share data between parties that are connected in a chain but do not have a direct relationship.
Elements of the discovery problem include:
■Chaining: how does Company A find out which other companies are connected to it by a chain (and who therefore may have data of interest)?
■Trust: if Company A and Company C discover they are connected by a chain, but have no direct relationship, how can they establish the conditions of trust necessary to share data with each other? Are they able to do this in an automated manner, without human intervention by intermediate companies such as Company B?
■Data transfer: once companies have discovered each other and established trust, how do they accomplish the sharing of data?
GS1 technical standards to address the data discovery and trust aspects are currently being developed but are not yet ready for market. Much good work has been done to develop the concepts of discovery services (including basic data model and functional requirements). See also section 4.4 .
Note: Please contact GS1 for implementation advice on the topic of data discovery, trust and access control. https://www.gs1.org/contact
The enormous growth of sensors and actuators in physical devices, and the fact that these devices are increasingly connected to the internet (the Internet of Things) leads to a new category of timestamped event data that is much broader than the visibility event data currently handled by the GS1 ALE (Application Level Events) and EPCIS standards.
Sensors passively record changes in the state of the physical world and the objects contained within it, such as a food temperature sensor in a truck recording events outside an acceptable temperature range. Event data from actuators record a history of intentional changes, such as the opening of a valve for a water reservoir or gas pipeline or the locking/unlocking of a door.
GS1 has started to engage in IoT-related standardisation initiatives, in particular when it comes to the use of identifiers, the interpretation (semantics) and fusion (adding business context) of event data from devices and sensors.
Event data from devices and sensors are expected to be very similar in nature to visibility event data. The data will be enriched with business context —including the five dimensions that define the who, what, when, where and why— and shared using networked or decentralised choreographies (see section 3.3.5 ).
Figure 4‑4 Visibility event data and event data from devices and sensors
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