The main novelty of the proposed framework is its complementarity to cross-industry reference architectures and generic traceability standards, and its stakeholder-centric approach. The framework is demonstrated in the three pork supply chain types that are also widely present in Vietnam and are representative of the pork supply chains of emerging markets in general. The applicability of the framework is described in detail in a case study of a collaborative supply chain of independent members, which is one of the three pork supply chain types. The case study is selected for detailed analysis because the members work closely together to provide safe and traceable pork meat to consumers.The three major meat supply chains in emerging markets are traditional wet markets, integrated supply chains, and the more recent collaborative supply chains. Customers in these markets are increasingly demanding safe and high-quality meat, which requires more transparency in the supply chain. This paper presents a generic framework for modelling and designing transparency systems in meat supply chains, with special attention to the needs of emerging markets like Vietnam where all the three supply chain types co-exist. The framework consists of  As a result, consumers in these countries can generally trust their meat products. However, the same cannot be said about meat products in emerging markets. The World Health Organization (WHO) estimates that about 600 million, almost 10 % of the world population, fall ill after eating contaminated food of which about 420 thousand people die every year [Meat supply chains in Southeast Asia, which are the focus area of this research, are vulnerable to disease outbreaks among consumers . DiseaseA major reason for meat safety concerns in Southeast Asia is not only a lack of proper safety systems but also a lack of accurate meat safety information ,10. MostSoutheast Asian countries are the largest producers and consumers of pork meat . As in mThere have been several initiatives to implement information systems in the meat supply chains . As the It is our observation that the initiative to adopt a transparency system in Vietnam is driven by major value chain stakeholders, such as feed suppliers, large farms, and meat processors, who often have the capacity to gather tracking-and-tracing data at their facilities and from their supply chain partners. Some of these actors are providing Farm Management Information Systems (FMISs) to their contracted farmers in order to have better visibility and quality control in their supply chain. Likewise, slaughterhouses are increasingly deploying modern slaughterhouse information systems and industrial meat processors and retailers often have advanced information systems in place.RQ1How can a framework for supply chain transparency be designed?RQ2What are the current transparency scenarios of the pork supply chain in Vietnam?RQ3How can the framework be applied to enhance transparency in the pork supply chain?While transparency systems are being deployed across the supply chain, the missing link to realizing chain-wide transparency is the lack of standardisation and the inability of the different systems to share data. Against this background, the aim of this study is to analyse the state of the pork supply chain in Vietnam, offer a framework for modelling and designing transparency systems, and demonstrate the framework by applying it in the design of a transparency system for a collaborative supply chain. Specifically, the research questions we address are.To address these research questions, we identified three types of pork supply chains in Vietnam, that vary from the traditional wet markets to modern collaborative supply chains, and modelled the supply chains in order to capture the design alternatives for transparency systems in emerging markets. The existing modelling approaches and abstractions were inadequate in modelling and representing chain-wide transparency in supply chains, and therefore, we adapted and extended existing modelling views. The views are demonstrated in detail in a case study of a collaborative supply chain. Our approach and results are described in the remainder as follows. Section 22.1Transparency and traceability are crucial for the functioning of meat supply chains. Transparency is a broader term and can be defined as “… the extent to which all its stakeholders have a shared understanding of, and access to, the product-related information that they request, without loss, noise, delay and distortion” . Traceab•High flow complexities characterise the food sector, as food supply chains consist of continuous product flows (bulk products traced in volumes or masses) and discrete product flows (packages traced in units). Traceability systems have to combine bulk products upstream of the supply chains with packaged products downstream of the supply chains.•Batches and lots of food products have to be processed separated to prevent cross-contamination, which is not easy for continuous flows in food supply chains.•Diverging and converging processes and flows characterise many food supply chains. Often food products are composed of many ingredients (including packaging) that all have to be tracked and traced, including many waste- and by-products.•Food supply chains are often highly connected to each other, as waste and by-products of one supply chain are sometimes used as inputs in other supply chains.•Food products are often highly perishable, especially fresh foods. Consumers and society impose stringent requirements on the quality and safety of food through food safety regulations and quality standards. Traceability data must therefore also refer to best-before-dates, country of origin of all ingredients, and certification of quality, veterinary, phytosanitary and ecological checks.•i.e., farms, processing companies and retailers) that interact with multinational companies providing input to the supply chain and doing its retail. To ensure year-round supply of products, supply chains are often international, which makes tracking and tracing even more complex.Food supply chains are complex networks consisting of small and medium size enterprises , a global supply chain standard setting organisation with over two million businesses as members, identifies three sets of technologies for realizing traceability systems: object identification, data capture, and data sharing technologies . Object what, when, where, and why of transparency data. Second, data capture is ideally done automatically, and the use of manual data entry must be minimized. Tag readers are being used throughout the logistics processes and increasingly sensors are being used to capture many optional but useful data items [Data capture refers to the registration (or capturing) of data about the object using the object identification mechanism. Data capture has two main characteristics. First, besides the object ID, data capture registers other relevant data, some of which are necessary, such as the datetime and place when the data is captured, and others are optional but useful, such as why data is captured, what happened to the object, etc. EPCIS  is a global standard of GS1 for creating and sharing visibility event data, both within and across enterprises, to enable users to gain a shared view of physical or digital objects. Events contain data about the identity of the product, the date and time of event occurrence, the location where it occurred, and the reason why the event occurred. These are commonly known as the ta items .Data sharing is a key element of traceability and requires not only the actual sharing of data (either by providing access to own data repository or sending the data), but, and most importantly, agreeing on a common standard for data and the interfaces through which data is shared. The GS1 set of standards and particularly the EPCIS standard are key requirements for data sharing .one-step-back, one-step-forward principle). According to this principle, data on food items and associated processes follow the same paths as the food products. Every supply chain actor is responsible for correctly passing the data it received and the additional information it generated to the next actor. Currently, this happens to a large extent paper-based using delivery notes. Every actor has access to traceability data about their customers and suppliers in their own internal systems [There are three traceability approaches that differ structurally based on the enabling technologies and standards they adopt for supply chain traceability . The fir systems .The second approach is based on a shared repository of traceability events. The shared repository captures all events of processing and movement of food items across the supply chain, store the events on one or more EPCIS repositories, and make them accessible on a portal with the help of software tools for querying, discovering, and aggregating data. Different types of tools cater to the needs of different types of stakeholders, such as food authorities, supply chain partners, and consumers. Supply chain partners are usually willing to share data only with those parties that they are directly dealing with. For this reason, data governance agreements are established and authorization, integrity, and security mechanisms are implemented .The third approach involves a distributed ledger in which traceability data are managed collaboratively by multiple participants. In this approach, the blockchain technology enables multiple participants to have an identical copy of the data and as a consequence, there is no need for a centralized database. Validation of the transactions is performed by the network based on a tamper-proof consensus mechanism, and not through a central intermediary. In a blockchain each transaction is encrypted and cannot be modified after approval, making this solution secure and tamper-proof. Due to these qualities of the blockchain technology, several studies have explored the transparency and security that the technology offers for ensuring food safety, integrity, and quality .2.3Meat traceability systems have to be able to deal with a high supply chain complexity. In order to get a grip on this complexity, it is essential to start the development of meat traceability systems with a systematic modelling of meat supply chains. Modelling frameworks are valuable means to do this in a precise and unambiguous manner. They define a coherent set of architectural views with the primary objective of creating a common understanding of the system among all stakeholders and guiding the development of the system ,34.A few modelling frameworks have been suggested to model supply chains. Probably, the most widely recognized and used framework is the SCOR (Supply Chain Operations Reference) model . The SCOTo establish a connection between business processes, management processes, and business strategies on one hand, and process control and data capture at the workplace on the other hand, a hierarchical view is essential. The process hierarchy view consists of the management information layer at the top, the operation execution layer in the middle and the process control layer at the bottom . The opeBusiness process modelling is a widely used and practical approach for capturing the activities and events that lead to a particular service or product outcome. The outcomes captured by BPMs are often simple but tangible, for instance, product is ordered, or product is shipped from store. The Business Process Model and Notations (BPMN) is a mature standard and widely used method to model BPMs .In this study we will use traceability standards and supply chain modelling approaches to offer a modelling framework for traceability systems. The framework will be applied in a Vietnamese pork supply chain case study.3The research is based on design-oriented and case study methodologies. A design aims to build purposeful artefacts that address heretofore unaddressed or unsolved problems ,40. A de3.1i.e., how to design, construct and apply a new design artefact for a class of problems [views.The design artefact developed in this paper is a framework for modelling transparency systems for meat supply chains in emerging markets. Design-oriented research typically deals with the ‘how’ questions, problems ,42. We dproblems  for a sti.e., the model must be an abstraction and not a representation of a specific case, (2) the model must be modular, i.e., it should be composed of smaller models that address specific needs of specific stakeholder type, (3) the model must be executable, i.e., besides having a visual representation, the model should be executable by, or convertible to, a software tool, (4) the model must be configurable. i.e., non-technical users should be able to tune the model to their needs by changing parameters, and (5) the model must be applicable in more than one application scenario or domain.In this study we adopt the five design criteria for designing a reference model for meat supply chains . These aWe present five hierarchical modelling views and generic models based on existing generic frameworks in order to fulfil the design criteria of genericity and modularity. The two major generic frameworks from which our modelling framework is derived are the SCOR model  for modedomain, product flow, business control, business process and data views. The domain and product flow views capture the supply chain system. The business control view enables the modelling of various configurations. Both the business control and business process views provide fine-grained models that allow modularity in the software system that implements the framework.Based on the framework modelling approach of Verdouw et al. (2010) we develop the modelling views that belong to 5 hierarchical levels, which are the 3.2A case study approach from a real-world application context  has beenIn Vietnam, but also in most emerging markets, the dominant sales channel for meat products is the wet market. The wet market supply chain is characterised by many small-scale actors, including small-scale feed mills, small-scale traditional farms , dealers, small and unsophisticated slaughterhouses, and small retailers. The resulting fresh meat products are sold in traditional open-air markets where vendors also sell various other products in addition to meat. Next to the shopping function the wet markets are often considered as cultural and social hubs where people gather to socialise and share information.In integrated supply chains, the central orchestrating companies control major aspects of the meat production supply chain, which include at least two of the following supply chain activities: breeding, fattening, feed production, slaughtering, meat processing and retailing. These integrators may also provide better pig breeds, technical support and credit lines to their contracted farmers. The emerging modern collaborative supply chains, which are the main focus of this case study, consist of specialized and decentralized meat product operators that are governed by a common set of quality standards.The case study is conducted at a feed company that is active in a modern collaborative supply chain. The company operates many production locations in Vietnam and is one of the top three animal feed producers in the country and has in recent years expanded into other Southeast Asian countries. Due to the concerns over the safety of meat products in Asia, the company is collaborating with various stakeholders to realize modern and transparent collaborative supply chains. As part of this effort, the company has developed and deployed a Farm Management Information System (an FMIS) for its customers (which are farmers) and a Slaughterhouse Information System (an SIS) for use in collaboration with another food operator in the operation of slaughterhouses. We monitored the development of the FMIS and other transparency solutions, as well as the transition towards a collaborative supply chain at the case study company, both as researchers and as the responsible manager of the systems.facility visits, interviews, and modelling. Four facilities, one of which is owned by the case company, were visited. The facilities were selected for visits because they were related to the case study company and the latest available information technologies were used in the facilities. The facilities represent the four key phases in meat supply chains, namely, feed production, breeding, fattening, and slaughtering. A chicken slaughterhouse was visited in order to gain additional insight into integrated supply chains in which the production of feed, breeding, fattening, and slaughtering is generally centrally planned and managed. The pig farm had, besides the breeding sows, also produces commercial hogs, works with contracted farmers, and operates own meat processing facility.The analysis done in the case study is based on Before, during, and after the facility visits, a number of interviews were conducted with six experts of the case study company who had five different roles: FMIS specialist, strategic purchaser, project manager, software specialist, and sales support. The interviews were conducted to gain insights into the existing transparency systems in place and the plans for the future. Some of the interviewed persons have detailed knowledge about the current state of transparency and traceability in Vietnam, while others were involved in traceability innovation projects. Several interviews were conducted with all interviewees to make a better sketch of the current situation of transparency and traceability and gain insight into possible future scenarios.We enhanced the modelling framework based on the insights and information gained through the case study. The results of facility visits and interviews were mainly used to model the current state and possible future scenarios based on the proposed framework. First, the main transparency scenarios of the case study were modelled. Two dominant supply chain types in Vietnam are the integrated supply chains and the traditional wet markets. They represent the existing (AS-IS) scenarios in our modelling. The emerging future scenarios consist of collaborative supply chains. They represent the desired (TO-BE) scenarios.4This chapter presents the designed framework by introducing its views. Each of the following subsections will shortly introduce a view and discuss a simplified model. A more detailed definition of the elements and relations of the models is provided in 4.1This view is meant for all stakeholders and serves to create the necessary initial common understanding among all stakeholders. A conceptual model is therefore a simple box-and-lines model where the meanings of the boxes and the lines are intuitive, and the model captures the essential elements of the supply chain. 4.2i.e., a system of interlinked business processes, each adding value to the product or service for the end customer [The product flow view is adopted from the supply chain framework of Verdouw et al. (2010) and is meant for all stakeholders involved in the actual tracking and tracing of products. It visualises a value chain, customer . In a sue.g., make = slaughtering). Producers also must include at least one make process. Every arrow represents a product or products (input or output). In the generic model presented in e.g., product = half carcass). Products can be stored in strategic stocks (depicted as upside-down triangles), and in this example, a storage of input material and a delivery buffer of end products are included. The value-added processes shown in 1.Shipping Unit (SU): an item or group of items that are delivered together to a location. SUs can be identified with Global Shipment Identification Number (GSIN). In addition, shipments include shipping and receipt locations, that are identified with Global Location Number (GLN).2.Logistics Unit (LU): mostly associated with a pallet represents an item composed of one or more products for the purpose of transport or storage. LUs are generally identified with Serial Shipping Container Codes (SSCC).3.Trade Unit (TU): a product, a package of identical products, or a composition of products that are traded between businesses. TUs are typically identified with Global Trade Item Numbers (GTIN). If each item or a batch of items has to be uniquely identified, GTIN in combination with a serial number or batch/lot number identifies the item or the batch uniquely. The combination of a serial number with GTIN is referred to as Serialized GTIN (SGTIN).4.Consumer Unit (CU): a product as it is sold to end customers. CUs are identified in the same as Tus.The product flows comprise different levels of granularity, and each can uniquely be identified by particular identification standards. In this model we adapt the traceability layers and related standards of GS1 .1.Shippi4.3i.e., how they are planned and managed in such a way that the business processes in a supply chain are controlled. A major control challenge is how to balance lean control that anticipates future demand and agile control that focusses on quick response to unpredictable demand [The business control view is based on thread diagrams in the framework of Verdouw et al. (2010) and provides a high-level process overview of the chosen supply chain configuration . The buse demand ,48. A kee demand ,50. The e demand .Fig. 3Bui.e., it defines the boundary between state-dependent and state-independent data [e.g., GTIN). A state-dependent identifier is needed to track and trace particular products in the supply chain . When moving towards more upstream CODP positions including make-to-order (MTO) product data become more dependent on customer orders.The CODP position is also a key factor for traceability because it defines where the link between material flow and customer order information ends, ent data . TraceabBesides, the traceability information dimension is added by visualising the source and destination of direct and indirect traceability information in data flows (dashed lines) and where traceability information is stored, either within a particular company or on a supply chain level (database symbols). 4.4•flow elements: events , activities, and gateways;•connecting elements (arrows): sequence flows, message flows, and associations;•swim lanes: pools and lanes; and•data elements: data objects (input and output), data stores and messages.The business process view depicts the details of the business processes identified in the business control view. It shows the sequence of activities and the flow of information along the flow of activities. The business process view is based on the Business Process Modelling Notation (BPMN) and this choice ensures a smooth translation of supply chain designs to information systems architecture . BPMN aims to provide a standard notation readily understandable by all business stakeholders, including business analysts who create and refine the processes, the technical developers responsible for implementing them and business managers who monitor and manage them. BPMN also provides a mechanism to generate an executable business process using Business Process Execution Language (BPEL) from the BPMN models. BPMN's four basic element categories are.This diagram zooms in on the ‘Deliver to Order’ business control model in In the latest version of BPMN (2.0) many specific variants of the elements are defined, mainly for technical reasons. In order to keep the models understandable for also business users, the method proposed in this paper uses only the basic BPMN elements, as defined in 4.5The data view is used to identify the data entities that constitute traceability information. The data model used in the framework is based on the EPCIS event model of GS1 and its extension developed at IoF2020 meat transparency and traceability use case ,53 and rID (the event ID that uniquely identifies the record of the event), eventTime and recordTime , bisStep and disposition , readPoint and bizLocation .The EPCIS standard defines five specialized event classes, namely: object, aggregation, transformation, association, and transaction events. The five event types have many common attributes which are depicted in the figure as a super class EPCISEvent. Common attributes across all event types are the ObjectEvent represents the observation of an object or objects and is by far the most common event. When pigs are purchased, piglets are weaned, and when they get ill, treated, loaded on a truck for transport, etc., the corresponding data are registered as object events. The second type of event that often occurs in the meat sector is the TransformationEvent, such as slaughtering or cutting. This event represents the transformation of one or more inputs into an entirely new product or products. The use of transformation event in practice is limited because it is a new addition to the standard and users have already workarounds in place that capture facts about transformation using object events. A common event during transport is the AggregationEvent, in which one or more objects are associated with the container . Somewhere in between the aggregation and transformation event is the AssociationEvent with which a strong physical association (or disassociation) of objects  with the parent object can be captured. Last, a TransactionEvent enables to capture the association (or disassociation) of objects to the business transaction and can be used to link the physical events to the management processes of business control  slaughterhouses, large professional slaughterhouses or directly to wet markets. When pigs are sold to small slaughterhouses there are only limited quality requirements, while large-scale professional slaughterhouses often have higher quality requirements.Small-scale traders also sell pigs onwards to larger-scale traders. Large-scale traders transport the pigs to large slaughterhouses, or they export the pigs. Meat from small slaughterhouses is supplied to the wet markets. While feed dealers play the role of retailer, pig traders play the opposite role, as aggregators. The other important actors in the wet market (and the other supply chain scenarios) are medication suppliers. In the wet market, there is little data and transparency in the use of medications, and the little information there is, it gets lost because dealers and traders generally track their transactions insufficiently and therewith form the weakest link in the supply chain. The domain model of the wet markets is depicted in The domain model shows that wet markets lack a supply chain coordinator or orchestrator, which is a defining characteristic of the integrated and collaborative markets. Furthermore, there are relatively many intermediary actors that trade and ship products between producers. Traceability is hardly possible in this scenario. Record keeping at feed dealers, pig traders, and retailers are virtually non-existent. If a traceability system is existing, it is generally weak. Generally, a record of the chain of custody is lost when dealers and traders are involved. Dealers and traders generally receive cash payments and may not keep the record of buyers  and sellers (in the case of pig traders).5.1.2In an integrated supply chain, one dominant actor performs, or has a significant influence on, a major part of the production activities of the supply chain. The characteristic feature of the product flow in the integrated market is that the dominant (the integrator) actor controls the production at key stages of the supply chain and gains significant insight into the chain of custody of products. A dominant actor that contracts out the fattening process to smallholder farmers play a major role in quality control. It achieves this, for instance, through its farm coaches, who guide farmers on how to implement best practices, and through an FMIS it supplies to farmers, which captures data at from the farmers and stores it centrally at its information system. The dominant actor may in some cases sell part of the meat products through its own retail channels.In the integrated supply chains, transparency is achieved in almost all cases with the help of a transaction system the dominant actor uses. The transaction system in use is generally a propriety information system, such as an ERP (Enterprise Resources Planning) system, in which the data is not necessarily captured in compliance with any transparency standards described in sections 5.2The rising health-conscious middle class in Vietnam is increasingly willing to pay more for safe high-quality meat products, and tends to shop in supermarkets instead of wet markets ,56. This5.2.1A simplified version of the collaborative pork supply chain of the case study is presented in the domain model depicted in 5.2.2suppliers. Likewise, the end consumers are represented undifferentiated by the vertical bar labelled end customers, but they could be consumers at home, at restaurants, or other places of meat product consumption.The product flow model for the collaborative supply chain of the case study is shown in The traceability units are presented only for the orchestrator's processes and describe two aspects of traceability: 1) internal traceability showing how input products to a meat product operator are linked to the output products of the same operator, and 2) external traceability showing how the output products of one set of meat product operators (suppliers) are linked to the input products of another set of operators (customers). For example, the FMIS system provided by the case study company to the farmers helps the farmers to track products  within their farms. External transparency is realized when data from the FMISs is shared to the SISs of the slaughterhouses, and vice versa, so that the identities of pigs at farms are linked to the identities of pigs received at slaughterhouses. The value-adding processes (depicted by rounded rectangles), and product flows (arrows) represent the two aspects of the transparency.The traceability units are as follows. The breeders (farmers producing piglets) keep a record of nearly all events that are related to the cycle of individual sows, including new arrival and removal, mating, farrowing, weaning, fostering (adoption) and the registration of medication. At each of these activities, the tag number that uniquely identifies the animal is registered. By arrival, the birth date, the arrival date, breed, location and the company name of the supplier are registered, which enables the tracing of the animal to the supplier. By removal, the removal date and the reason are registered. When documenting mating, the tag numbers of both the boar and sow are registered. When farrowing, the sow tag number and the date, along with the number of piglets born in 5 categories  are registered; the piglets are not individually identified at this stage. When weaning and fostering, the total number of piglets and their average weight are registered along with the tag number of the sow.raising slaughter pigs (or fattening). In the desired situation, the delivery of feed and the associated feed information is registered in the FMIS of the farm at delivery; and the same must happen for medication. Currently, the plan is to identify pigs per batch as the cost of registering and managing pigs individually using ear tags is prohibitive in emerging markets. To realize better traceability, the batches of pigs will not mix and both feed and medication usage are registered per batch of pigs. Once the pigs are fattened, they are sold directly to the slaughterhouse and mixing of pigs of different farms will not be allowed. However, external transport companies are used to transport the pigs from farms to slaughterhouses and the possible mixing of the animals during transport remains a challenge.The next major process is trading in pigs, executed by the orchestrator, will guarantee the information supplied by the farms are correct and are correctly delivered to slaughterhouses. At the slaughterhouse, detailed information about the individual pig and carcass is gathered and added to the data warehouse information system managed by the orchestrator. The slaughterhouse sells the products to wholesalers and supermarkets, and the labelling of the products ensures the individual product at the supermarket can be traced back to the batch of animals supplied by farmers.The next step of 5.2.3i.e., the inventory of slaughter-ready pigs in the farm stables. The farm produces the pigs based on its own estimations, which are based on forecast data of the association as well as supply-related data, including stable capacity. All physical steps  in this process are logged for tracking and tracing purposes. The traceability data are shared with a supply chain traceability system of the orchestrating company. Authorized users of the system can monitor the status and take action when problems arise.In the collaborative supply chain of the case study, an independent orchestrator has a key role in the coordination of demand fulfilment. Demand information is shared for the alignment of planning, control and operations of the involved actors. 5.2.4In this section, business processes at a farm and slaughterhouse are selected for modelling and analysis. Both business processes are complex and thus a significantly simplified versions of the processes are modelled. The first business process modelled is a long-lasting fattening business process. The second business process modelled is the intake at slaughterhouse, and the third is the slaughtering business process.The business process showing the delivery of pigs from farms and the intake of pigs at a slaughterhouse is depicted in The activities at the slaughterhouse are shown in the business process model depicted in In one pen, only pigs from the same truck are placed. It is possible that the content of one truck occupies more than one pen. All the pigs in one pen (or in one truck) are considered one batch. After the penning, where the pigs stay for at least 10–12 h with only water, the pigs are slaughtered. In the slaughtering process consists of several steps such as stunning, killing and cleaning, which are not included. The slaughtering process results in whole carcasses. After cleaning the carcass, products that are distinguished include the head, the blood, organs, and the carcass. The blood, which is treated as waste, and the processing of other by-products are not included in the model. The carcass is split up into two half carcasses and the half carcasses are weighed as a pair.A veterinarian checks if the quality of the carcasses is good, and if not the entire carcass or part of it can be rejected. The carcasses recognized as good quality receive a stamp with the batch number that serves as a quality certificate. The heads and organs are put in plastic bags or boxes with a label with a barcode to identify the batch number. Before dispatch, the products are provided with a QR code. An external order release is created when a product is sold. The QR code contains information that is stored in the orchestrator's database. When the QR code is scanned, information about the slaughterhouse, farm of origin, production date, and the expiration date is provided.5.2.5In ObjectEvent. In the slaughterhouse where several transformations occur, events used in the framework are derived the TransformationEvent. In the model shown in SlaughterEvent. The aggregation, transaction and associated events that occur within the pork chain are similar to those occurring in other sectors, and thus are not included in the figure. The events described below are initially modelled within the context of the IoF2020 project [Several of the events in the framework are derived from the  project .BirthEvent is registered when piglets are born. Piglets are not tagged individually; therefore, they are identified as a group using LGTIN, which is a combination of GTIN (product type ID) and a batch identifier. Once the pigs are weaned, they can be identified either by their batch (using LGTIN) or individually (using SGTIN) in PenUpEvent, VetEvent and DeathEvent when transferring or inspecting them, or when they die at the farm. When feed is supplied the batch number of the feed type encoded as LGTIN is recorded together with time and the location of delivery. The timestamp of the occurrence of the events, and often together with the time the event is recorded, is identified by the datetime and the time zone.Animals can be identified individually or as a group depending on the level of automation practised at the farm. The Most events occurring at a farm  are associated with a specific pen, barn or farm. Places at the farm are identified using the business identification number, GLN, or a specific location at the farm (barn or pen) using SGLN. The location for feed delivery is either the farm or the specific silo, which will be identified by GLN or SGLN.why the event is recorded) is captured as an Instance or Lot Master Data (ILMD). At birth, the gender and the generic information  of the piglets are recorded. When relocating pigs, the reasons for relocation are recorded. When feed is supplied, the supplier's information, type and quantity of feed are recorded. Extensive ILMD data is recorded during a veterinary visit or when an animal dies at the farm.Detailed information related to the event  and the IDs of the resulting products  are used as the what of the event. The place and time of the event are registered in a similar fashion as other events. The slaughter event involves a large amount of ILMD, including diverse quality and safety information.The 6In this paper, we presented a framework to model existing and desired future tracking and tracing systems in pork supply chains and used a case study in Vietnam to demonstrate the framework. Most pork meat samples sold in Vietnam do not meet national food quality standards, and consumers have legitimate meat quality and safety concerns ,16. The The framework presents five hierarchical model types namely the domain, product flow, business control, business process and data views. These views can be used to progressively model the pork value chain in more detail and help the various stakeholders collaborate in diagnosing the issues related to transparency and designing new solutions. The models vary from the high-level domain model that can be used by all stakeholders to the more detailed business process and data models which are essential for solution providers who implement the desired transparency system.A domain model is a simple and informative boxes-and-lines model that all actors can understand and collectively co-create in order to see how their supply chain functions or how it should operate in the future. The boxes-and-lines model is also widely used as a conceptual model. The product flow model helps to identify the interlinked value adding business processes. In this model product identification points are indicated showing whether products are identified as individual items or as a batch. The identification mechanism allows to see if product mixing or disaggregation takes place and how products are made from raw materials. Stakeholders responsible for labelling and identifying products benefit from product flow models.Change in the supply chain management leads to changes in the control model of the supply chain. Implementation of an improved transparency system requires the scrutiny of, and when necessary changes in, the control model. For instance, better tracking of customer specific products needs changes in the customer order decoupling point in the supply chain, which in turn may necessitate data related to the business control decisions  to be captured as part of the transparency information.birth, vet and slaughter events made the models more accessible and understandable to the stakeholders in the sector and made our communication with them easier.Business process and data models are essential inputs in the implementation of any information system, including transparency systems. Transparency requires collaboration and therefore adoption of global standards is essential in realizing transparency and traceability. Based on the SCOR and GS1 standards, we provided business process and data models for analysing existing and realizing new transparency systems in the pork chain. The framework provided data models that are unique to the sector. For instance, though the GS1 EPCIS events, such as the object and transformation events, were designed to be universal, the modelling of events such as the A key advantage of the framework is that it helps to model the architecture of a supply chain traceability system in a timely, punctual and coherent way. It combines domain-specific knowledge with knowledge from literature, generic standards, existing frameworks and reference models, and has added views that allow to model supply chain traceability systems. This paper builds on several previous studies ,36,52,58Another area for future research is to expand the number of views to encompass the entire software lifecycle. Currently, the views of the framework concentrate on the business part of software architecture, including data models. Models for the technical implementation such as the deployment and technical communication diagrams have not yet been included but can be adopted from existing architecture frameworks.7In this paper we presented a modelling framework for transparency systems in the pork sector with a specific focus on emerging markets. We described the designed framework and demonstrated how the framework can be applied through a case study. The framework presented provides a set of modelling views that address the concerns of the stakeholders in the sector. The views are adapted and extended from existing modelling views in order to adequately model and represent chain-wide transparency in supply chains.The application of the framework is based on a case study in Vietnam. It involves three supply chain scenarios found in the Vietnamese pork sector: (1) traditional wet market supply chains that are generally open for all stakeholders to join, but uncontrolled in terms of quality and food safety, (2) integrated supply chains that are largely controlled by a central controlling company, and (3) emerging modern collaborative supply chains, that differ from integrated supply chain in the sense that each supply chain participant can decide to join. A detailed design of the collaborative supply chain is included in the present paper, since it represents a desired future scenario of open supply chains of independent members that closely work together to provide safe and traceable pork meat to consumers in emerging markets. The applicability of the framework is however not only limited to the emerging markets or to the pork sector only. As demonstrated in a previous study, the transparency system developed for the pork sector has served as a foundation for implementing a transparency system in the fruit (table olives) sector. The framework presented in this study can serve as a foundation for other food products. To achieve this, future research should provide additional validations covering diverse case studies across multiple countries in emerging markets.The authors do not have permission to share interview transcripts. All other data is referenced in article.Ayalew Kassahun: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Cor Verdouw: Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Conceptualization. Jeroen Roomer: Writing – review & editing, Validation, Supervision, Software, Resources, Project administration, Investigation, Data curation, Conceptualization.Jeroen Roomer is the Chief Information Officer (CIO) of the case study company, De Heus Animal Nutrition B.V., Ede, The Netherlands (https://www.de-heus.nl/). He is also the responsible person who oversaw the development of the Farm Management Information Systems (FMIS) for farmers and the transparency system for the independent collaborative supply chain orchestrator, used in the case study.Cor Verdouw is also an Innovation Manager at Mprise Agriware B.V., Veenendaal, The Netherlands (https://www.mprise.nl/en/), besides his role as a Senior Scientist at Wageningen University.However, the manuscript is in no way aimed at promoting the case study company, or is related to any financial aspect of both companies. At most care has been given to make the case study as anonymously as possible while keeping the relevant details.The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:


Resilience in autonomous agent systems is about having the capacity to anticipate, respond to, adapt to, and recover from adverse and dynamic conditions in complex environments. It is associated with the intelligence possessed by the agents to preserve the functionality or to minimize the impact on functionality through a transformation, reconfiguration, or expansion performed across the system. Enhancing the resilience of systems could pave way toward higher autonomy allowing them to tackle intricate dynamic problems. The state-of-the-art systems have mostly focussed on improving the redundancy of the system, adopting decentralized control architectures, and utilizing distributed sensing capabilities. While machine learning approaches for efficient distribution and allocation of skills and tasks have enhanced the potential of these systems, they are still limited when presented with dynamic environments. To move beyond the current limitations, this paper advocates incorporating counterfactual learning models for agents to enable them with the ability to predict possible future conditions and adjust their behavior. Counterfactual learning is a topic that has recently been gaining attention as a model-agnostic and post-hoc technique to improve explainability in machine learning models. Using counterfactual causality can also help gain insights into unforeseen circumstances and make inferences about the probability of desired outcomes. We propose that this can be used in agent systems as a means to guide and prepare them to cope with unanticipated environmental conditions. This supplementary support for adaptation can enable the design of more intelligent and complex autonomous agent systems to address the multifaceted characteristics of real-world problem domains. In the fast-paced society of today, the application demands for collective intelligence are increasing. Applications of autonomous systems are often found in fields that require distributed sensing and action. These include both engineering-related disciplines that are interested in autonomous self-organizing artifacts and science-related disciplines that are interested in understanding the behaviors and evolution of the natural world. The benefits in safety, productivity, and in saving money and time has induced intelligent systems use to be more widespread across a range of domains  or to continue operations amid the presence of partial or individual failures, uncertainties, and disturbances to the environment , and recommender systems. It is used as a means to supplement incomplete data and partial feedback received from learning models to improve performance through estimations and inferences. In computer vision models, counterfactual analysis has been used to clarify the existences of biases. For example, Joo and Kärkkäinen  uses a mAmong these, the most prominent applications of counterfactuals in AI are perhaps seen in explainable AI (XAI) where it is commonly used as a model-agnostic and post-hoc technique to improve explainability in machine learning models. XAI is conceptualized in three phases of explanation which explore the aspects of: explanation generation, explanation communication, and explanation reception  is a widely adopted algorithm in solving large imperfect-information games  through simulation of multiple possibilities of interactions and events which is otherwise strenuous within the limits of human cognitive capacity. Mueller et al.  facilitating intelligent autonomous agents that can better perform in dynamic and complex environments. As expected of a robust system, the ability to preserve functionalities and continue operation amid partial failures can be ensured through the understanding of causal inferences of the current domain provided by counterfactual analysis. As discussed by Peysakhovich et al. , counterAs identified, counterfactual learning has the potential to advance the future of intelligent resilient agent systems. However, it is not without limitations. Specifically in agent system, there exists a requirement for the counterfactual explanations and new knowledge generated to represent the system's operation along causal, justificatory, and purposive lines (Arnold et al., The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.DS conceived the idea of the paper, prepared, and reviewed the article.


Participatory surveillance (PS) has been defined as the bidirectional process of transmitting and receiving data for action by directly engaging the target population. Often represented as self-reported symptoms directly from the public, PS can provide evidence of an emerging disease or concentration of symptoms in certain areas, potentially identifying signs of an early outbreak. The construction of sets of symptoms to represent various disease syndromes provides a mechanism for the early detection of multiple health threats. Global Flu View (GFV) is the first-ever system that merges influenza-like illness (ILI) data from more than 8 countries plus 1 region (Hong Kong) on 4 continents for global monitoring of this annual health threat. GFV provides a digital ecosystem for spatial and temporal visualization of syndromic aggregates compatible with ILI from the various systems currently participating in GFV in near real time, updated weekly. In 2018, the first prototype of a digital platform to combine data from several ILI PS programs was created. At that time, the priority was to have a digital environment that brought together different programs through an application program interface, providing a real time map of syndromic trends that could demonstrate where and when ILI was spreading in various regions of the globe. After 2 years running as an experimental model and incorporating feedback from partner programs, GFV was restructured to empower the community of public health practitioners, data scientists, and researchers by providing an open data channel among these contributors for sharing experiences across the network. GFV was redesigned to serve not only as a data hub but also as a dynamic knowledge network around participatory ILI surveillance by providing knowledge exchange among programs. Connectivity between existing PS systems enables a network of cooperation and collaboration with great potential for continuous public health impact. The exchange of knowledge within this network is not limited only to health professionals and researchers but also provides an opportunity for the general public to have an active voice in the collective construction of health settings. The focus on preparing the next generation of epidemiologists will be of great importance to scale innovative approaches like PS. GFV provides a useful example of the value of globally integrated PS data to help reduce the risks and damages of the next pandemic. Digital epidemiology has expanded significantly in recent years ,2, encomBy these evidences it seems that PS is an affordable and effective proxy for public health departments to estimate, for example, influenza-like illness (ILI) and COVID-19–like illness numbers, despite the fact that clinical surveillance efforts are receiving far less data.After the H1N1 pandemic emerged in 2009, PS for ILI became vital for monitoring this health challenge ,15,16. SIn 2018, the first prototype of a digital platform to combine data from several ILI PS programs was created . At thatGFV is the first ever system that merges ILI data from more than 8 countries plus 1 region (Hong Kong) on 4 continents for global monitoring of this annual health threat. GFV provides a digital ecosystem for spatial and temporal visualization of syndromic aggregates compatible with ILI from the various systems currently participating in GFV in near real time , updatedGFV may be best described as an ecosystem with three different layers: (1) program partners, (2) community, and (3) outcomes. PS programs for ILI were informed about the purpose of GFV, and after accepting the data sharing agreement, became part of the core layer by contributing to the availability of real-time data. In the second layer, the “community” of potential consumers of this data can be described in three categories: (1) public health practitioners focused on disease surveillance, (2) data consumers focused on the use and integration of the data for third-party systems and platforms, and (3) researchers, who may use the data for comparative, predictive, or characterization of global epidemiological scenarios about ILI outbreaks as well as the development of forecasting and modeling techniques. Finally, the outcomes layer demonstrates the impact that these data can generate on the knowledge of the current and future ILI landscapes; and the potential of information sharing for constructing and strengthening of the ILI PS community. Pooling data from different regions and integrating them into GFV generates a pivotal tool for participatory surveillance. The collaborative network formed from this international data integration not only fosters shared learning but also strengthens ties between researchers and public health practitioners globally. This community aspect provides a robust response to ILI on an international scale.The global perspective offered by GFV is especially valuable considering the nature of influenza epidemics. Tracking these patterns across various geographical regions provides critical insights into disease spread dynamics, contributing significantly to our understanding of these epidemics. Further, the collective data from diverse sources amplify the statistical power of analyses conducted using this platform. With a larger, more diverse data set at their disposal, researchers can draw more robust conclusions and gain key insights into influenza trends and variations across different settings. This enriched perspective provides a significant contribution to the global understanding and management of ILI.The list of program partners is periodically updated, giving opportunities for ILI programs from anywhere to be part of this ecosystem. GFV is also open to the general public, who can use the potential of this tool to understand the importance of their contribution in reports to program partners in the collective construction of information on global health. A community of PS enthusiasts can be connected with knowledge sharing in several ways. First, this community can provide a forum for practitioners to share their knowledge and expertise with each other. This can take the form of discussions and collaboration on specific projects or challenges, as well as the sharing of resources, such as research papers, technical manuals, and training materials. By sharing knowledge within the community, practitioners can learn from each other's experiences and can help to advance the field as a whole. Additionally, a community of health technology practitioners can also facilitate the sharing of knowledge with other stakeholders, such as policy makers, health care organizations, and the general public. For example, a community can organize conferences, workshops, and seminars where practitioners can present their research and findings to a broader audience. This can help to raise awareness of the latest developments in health technology and can encourage the dissemination of new ideas and best practices.The GFV ecosystem aims to strengthen the use of surveillance data among professionals in various fields. Professionals such as public health practitioners, epidemiologists, data scientists, and software developers may use the GFV data in the routine of disease surveillance. The platform encourages contribution of data analysis techniques, methods, and processes to promote knowledge sharing among partners and ensures that data are used in a reproducible manner.Interoperability for participatory disease surveillance data can help to ensure that data from different systems can be easily exchanged and integrated, leading to more effective and efficient processes. However, several challenges must be considered to achieve this goal, including the following: (1) lack of standardization: different PS programs may collect different types of data, at different granularities, use different data formats and structures, which can make it difficult to exchange data. Moreover, without standardized data definitions and formats, it can be difficult to ensure that data from different PS programs are comparable; (2) complexity of data integration: integrating data from diverse PS programs systems can be a complex and time-consuming process, especially if the systems are not designed to interoperate; (3) limited technical capacity: some PS systems may not have the technical capacity or expertise to integrate data from other systems; and (4) privacy and security concerns: ensuring the privacy and security of sensitive health or disease data is a key concern when integrating data from distinct surveillance systems and are subject to different national or international regulatory frameworks .To overcome these challenges, it is necessary to adopt data standards that provide a common framework for the exchange and integration of data from different surveillance systems. Data standards offer a set of rules and guidelines for defining data elements and formats, which can help to ensure that data from different systems are comparable and can be easily integrated.In developing GFV, the number of global platforms that use secondary epidemiologic data was considered. Documenting the processes and end points for accessing data on the platform is fundamental to the interoperability of this data with other data streams to allow connections for integration with GFV. Using an API to connect the various systems with GFV was deemed the most efficient mechanism.A data sharing agreement is required for any partner that provides its data to GFV (except for those programs which make the data publicly available). This agreement covers core principles such as (1) preserving data privacy by sharing deidentified data, (2) maintaining data integrity, and (3) respecting the governance structure and compliance required by each program partner.Data governance is an essential aspect of PS systems to safeguard the integrity, quality, and security of data collected. It includes policies, processes, and practices for data collection, management, and use, with defined roles and responsibilities for data quality assurance and security. Clear and transparent policies and practices can ensure that participants feel confident that their data will be used responsibly.The popularization of social media, added to the wave of “big data,” creates the opportunity for crowdsourcing participatory disease surveillance in GFV to expand the understanding of ILI and its impact across the globe ,20.Several initiatives are integrating diverse data streams to increasingly provide timely information to detect public health events. Low- and middle-income countries, in particular, are greatly benefited by these approaches. Examples of these initiatives include the Integrated Disease Surveillance and Response framework in sub-Saharan Africa. For 20 years it has demonstrated the importance of improving the detection and response of public health threats by combining data from diverse systems and laboratories . In ChinDuring the first years of the COVID-19 pandemic, the value of other types of data, from wearables, wastewater, and mobility through cellular data for early identification of risks, became evident -45. ApprCross-engagement is a term inherited from the marketing industry, which describes the use of multiple marketing channels to seamlessly leverage retention of a target audience . CollaboPrograms for monitoring and combating chronic diseases  can servSmart Health Communities has been another important trend to consider in the future of disease surveillance . The corIn this scenario, studies demonstrate how these programs reach the status of social contagion to achieve a collective good -55. ThisGiven these perspectives, the importance of the next-generation of digital epidemiologists actively participating in the ongoing digital transformation of public health surveillance cannot be overstated. Their role in this rapidly evolving landscape goes beyond merely applying existing tools and systems; they must also acquire a comprehensive understanding of this emerging field. In order to cultivate a new breed of professionals capable of steering this digital revolution, the inclusion of data science, software engineering, mathematical modeling, and data design in academic and technical training programs is vital. This multidisciplinary approach equips future digital epidemiologists with the broad spectrum of skills they require to be self-reliant and innovative in their roles. While collaboration and cross-pollination of ideas are indeed essential in this multifaceted field, it is equally important for these professionals to have the capability to make independent, informed decisions. The interdependence inherent in such a diverse field should not lead to an overreliance on others. These professionals will need to be well-versed in all aspects of digital epidemiology, from disease surveillance to the intricacies of digital public health. GFV is pioneering in this educational journey by providing a platform that fosters collaboration, knowledge exchange, and the cultivation of skills. The GFV Codelab, for instance, is an exemplary feature that demonstrates how digital platforms can be harnessed as digital learning environments. As we navigate this new digital landscape, it is incumbent upon us to ensure that the next generation of professionals is well-equipped to lead the field of digital epidemiology into the future.Further, the endorsement of participatory surveillance strategies by major health organizations, such as the World Health Organization , underscGFV has several limitations. First, the data in GFV are dependent on the quality of its partners’ data. Since PS programs rely on the active involvement of individuals and communities, GFV data are impacted by this reality that not all individuals and communities may be willing or able to participate in these programs. This can lead to incomplete or biased data, which can impact the accuracy and usefulness of the information collected through these programs. Second, data quality and integrity are an issue due to the characteristics of self-reported data, which can be subject to errors, biases, and inconsistencies. Third, the various programs contributing data to GFV often require significant resources, including funding, staff time, and technology. These resources may be limited, which can impact the ability of these programs to effectively collect data on an ongoing basis and share it with GFV.Connectivity between existing PS systems enables a network of cooperation and collaboration with great potential for continuous public health impact. Thus, it is crucial for professionals engaged in ILI studies, educators, students, and public health practitioners to engage with digital ecosystems. It is an opportunity to make use of these open data channels and contribute to knowledge, and help to enhance the depth and breadth of ILI surveillance. Moreover, it offers a platform for learning, interaction, and collaboration, fostering a more integrated and proactive approach toward public health challenges. The exchange of knowledge within this network is not limited only to health professionals and researchers, but also provides an opportunity for the general public to have an active voice in the collective construction of health settings. The focus on preparing the next generation of epidemiologists will be of great importance to scale innovative approaches like PS. GFV provides a useful example of the value of globally integrated PS data to help reduce the risks and damages of the next pandemic.


The stages were: select the concept; elaborate analytical questions; comprehensively review the literature; and determine concept structural components. Sixty-one documents were analyzed after a search carried out until October 2019.four temporal periods of contextual changes have occurred since the emergence of ideas of a systematization in the 1960s. This first lasted until 1990. It was followed by those from 1990 to 2002, from 2002 to 2009 and from 2009 onwards. Partial conceptual maturity was identified, operationalization over the years, based on multiple definitions, and, currently, a concept of Systematization of Nursing Care with managerial and organizational attributes.the concept of Systematization of Nursing Care is partially mature, presents multiple definitions, being operationalized in uncertain connections with other concepts. Such interpretations raise the growing interest of professionals and academics for the conceptual clarification of the term. However, debates and publications existing today tend not to precisely explain the SNC ontology or offer operational definitions, except for some contributions already presented in manuscript in pre-print format.Inaccurate conceptions of the Systematization of Nursing Care (SNC) term can lead to mistaken or limited interpretations, influencing its conceptual applicationAssociação Brasileira de Enfermagem), a scientific body in the area, has for some years been including, in its documents, goals to encourage the debate on the SNC and the NP. The Federal Council of Nursing , the body that regulates professional nursing practice in Brazil, established a working group to update the norm that regulates NP and SNC implementation in health units.In the contemporary professional context, nursing class entities and a network of researchers have used strategic actions to promote the SNC’s and the Nursing Process’ (NP) conceptual design. The Brazilian Association of Nursing (ABEn - -10. Nursing Process Research Network (REPPE - Rede de Pesquisa em Processo de Enfermagem) researchers, when reflecting on the NP concept in Brazil, expressed the need to expand the SNC concept’s conceptual and operational definitions so that there is no overlap between its definition and that of other concepts.In the academic context, for more than a decade, Brazilian researchers have shown the existence of different currents of thought for the mentioned terms, with the permanence of conceptual inaccuracies and terminological overlap in recent years,9-10,12, the temporal markers of conceptual evolution and SNC concept components still remain uncertain. Moreover, available research is insufficient to delineate or clarify the concepts and identify systematization meanings contained in original elements of nursing production, particularly between the 1970s and 1990s, a period prior to the first COFEN Resolution 272 of 2002 on the NCS.The issue that still represents a barrier to the advancement of knowledge on the subject refers to non-exploration of temporal origin and dynamics of overlapping SNC concept attributes, by most of available publications. Although there are already reflexive gains in the literature from the aggregation of evidence by reviewing the literature and qualitative research.There is a tendency to recognize that the SNC is a Brazilian construct, which leads researchers to investigate the concept fundamentally in Brazilian literature. Despite this, there are potentially related constructs in other countries, such as the Professional Practice Model, which, to a certain extent, would be a possible model of professional practice with convergent attributes to that of the SNC. Thus, the challenge of advancing knowledge about the SNC construct and producing extrapolations and comparisons with other international concepts depends on concept analysis.In this article, we present advances in the original findings of a pragmatic utility concept analysis, from doctoral research, and those brought in a pre-print article, with a view to reflecting changes that improve conceptual knowledge and facilitate the theoretical construction and verification of specific SNC attributes. Because of this, this article expands the interpretation of results currently available in the literature on the subject, investigating the process of conceptual maturation and being relevant for future research decisions on the subject.Findings from a pragmatic concept analysis can guide the future research decision, from qualitative approaches, for better description, clarification and design or indicate the possibility of advancing to quantitative studies, theory development and applicationTo analyze the SNC conceptual maturation from the perspective of pragmatic utility.. The results and preliminary analyzes were deposited on the platform in pre-print format, by the same team of authors, without having received comments until the time of submission of manuscript to this journal. However, this manuscript is substantially different in content from most sections, incorporating blind reviewer recommendations and supporting our SNC concept’s interpretative evolution.Ethical issues related to research with human beings are not applicable, however, the principle of authorship rights was respected, with due source citation and indication,18-19, with writing guided by the Standards for Reporting Qualitative Research (SRQR) checklist.This is a concept analysis study using the pragmatic utility method. In it, epistemological, pragmatic, linguistic and logical principles are applied. The epistemological guide the assessment of the concept’s internal structure, which must be clear, distinct, internally consistent and differentiated from others. The pragmatist points out that the concept must be applicable to the world or operable. The linguistic indicates the appropriate use of the concept in context. The logician postulates conceptual coherence and the relationship with other concepts. These last three principles contribute to assessing the concept’s external structure.Pragmatic utility is used to develop, clarify, delineate, compare, or correct concepts.The present study analyzes the SNC concept’s internal structure through definitions, attributes, boundaries, preconditions and outcomes developed over time. In external assessment, existing limits with the competing concept are investigated: the NP,18. In the first stage, the SNC concept was chosen, based on the diffuse scope of definitions and contextual changes over time already pointed out in the literature.We applied the method’s four operational stages: select the concept; elaborate analytical questions; comprehensively review the literature; and describe concept use in the scientific contextIn the second stage, analysts constructed two analytical questions: what changes occurred in the SNC concept definition in nursing literature in Brazil over the years, indicating the conceptual design? What conceptual and contextual evidence indicated the concept maturation process?. The aforementioned trail was obtained by combining a generic search using a search tool, access to literature data and selection of publications cited in the retrieved materials.The third stage was to comprehensively review the literature. The method employed requires tracing the concept back as far as possible to what would presumably be its origins® tool, until October 2019, using the term “SNC”, without establishing a time limit. After adding the words, not descriptors, in English and Spanish, “Sistematización de Enfermería”, “Systematization of Nursing Care”, the authors checked 15,209 materials.Initially, we performed a generic search with the Google Scholar. Concept analysis methods are not literature reviews per se, although they do review publications. They dispense with most of literature synthesis and summarization techniques required in empiricist-based reviews, and do not apply the same quality criteria as the bibliographic sources of systematized reviews, especially when one considers that a concept is built evolutionarily by maturing and in the multiplicity of opinions, such as pragmatic utility. Thus, in this research, Critical Theory’s philosophical foundation for material collection and selection for pragmatic utility analysis did not emphasize concept contextualism, as opposed to the observable evidence and universalization of the language of empiricist science.The inclusive extension of this search is compatible with the analytical principle of concept analysis methods, since it operates in a logic contrary to that of most literature review studies that tend towards synthesis and aggregationFrom the search to the preliminary analysis, 31 manuscripts that actually dealt with SNC were selected, excluding NP. Of these, 09 manuscripts were in the category regulations and professional consensus documents, namely: COFEN professional resolutions and synthesis documents of symposiums and congresses organized by ABEn. This procedure was useful to obtain old publications that could not be included in the literature databases and of other nature, essential to conceptual maturity analysis.Later, in 2021, the researchers carried out a second search, which was structured in Latin American and Caribbean Literature in Health Sciences (LILACS), through the Virtual Health Library (VHL) portal.sistematizacao OR organizacao OR organizacion OR sistematizacion)) AND ) AND ) AND  AND (db:(“LILACS”)).Aligned descriptors were chosen due to the absence of the descriptor “Systematization of Nursing Care”, namely: . Thus, the SNC, in its organizational perspective, will tend to support its own decisions to achieve a PTS and suffer particular interference arising from this multidisciplinary strategy.It seems essential to us to recognize the SNC as a construct specific to the organizational type and different from the NP, given the existence of multidisciplinary care systems that do not primarily use the NP, such as the Singular Therapeutic Project (PTS - . However, the organizational meaning of nursing practice involves situations that do not identify and are exclusive to the subject in health care, which makes it impossible to circumscribe all professional action to the NP. This issue demands an advance of reflections for the field of nursing and health management and invites to this debate researchers who are not exclusively those interested in NP and standardized language systems.Relating the SNC to the NP is the central aspect constructed for decades by the area, especially in the perspective that the NP confers scientific rationality to highlight the specificity of subject/profession’s know-how.Thus, the philosophical foundations and methodological procedures of pragmatic utility analysis seem to have been adequate to verify conceptual maturation concepts over the more than 40 years since the introduction of original ideas of a “systematization” for nursing care. However, the fragile consensus on definitions and structural elements of SNC reflects a process that is still in partial maturityThe authors recognize a study limitation in offering objectifiable elements that are essential to build an operational definition for the SNC.It is possible to understand the substantial challenges of currently producing research that validates SNC strategies in the sense of an organizational or management system, before resolving conceptual confusions. Also, concept elements are under construction in the implantation and implementation in different health units in the country, influenced by the absence of referential, attributes and robust constitutive definitions about the concept. Based on this, the authors recommend two guidelines for future studies: the first in relation to developing conceptual and empirical models for the SNC that would serve as a reference structure; the second in relation to exploratory and analytical studies that provide evidence of the application of individualized initiatives and of targeted interactions with the NP.This analysis contributes to the field of nursing, by providing evidence on different periods of SNC concept maturation, verifying its current point of partial maturity and proposing a general definition. In this regard, this study enables a better understanding of the phenomenon that the SNC concept represents in Brazilian nursing practice.The study presents a conceptual outline and clarification without abandoning the historical or temporal context in which the concept was developed. The findings of this research, in addition to contributing to current scientific debate and entities representing the professional category of nursing, give room for a possible and necessary adequacy of language and operationalization in the training and assistance spaces. The study can also serve as a reference for changes in policies and regulations that deal with and regulate the SNC.The SNC concept is still a partially mature concept, presenting multiple definitions, being operationalized in uncertain links with other concepts, implying the current state of knowledge in an elusive and confusing concept.Analysis by the pragmatic utility method was useful to verify the SNC concept maturation process in four time periods. It also helped in verifying the process’ conceptual maturation and characteristics and in the outlining of a definition that could be useful for its operationalization in the context.