Integration of SCOR model and Social Network Analysis: improving supplier management in complex supply chains

Integração do modelo SCOR e Análise de Redes Sociais: aperfeiçoando a gestão de fornecedores em cadeias de suprimentos complexas

Abstract

Abstract  The integration of the SCOR model (Supply Chain Operations Reference) with Social Network Analysis (SNA) is proposed in this study as a strategic, data-driven approach to enhance supplier management in complex supply chains. The research was conducted through a case study in a molecule purification plant using the Roster-Call method, interviews with managers, ERP system data collection, and analysis with UCINET and NetDraw software. Centrality indicators (degree, closeness, and betweenness) allowed the identification of the focal company's critical centrality while also revealing structural weaknesses, such as excessive dependence on a single node and low connectivity among peripheral actors. The SWOT analysis, combined with the SCOR Plan and Source stages, made it possible to relate the network’s strengths, weaknesses, opportunities, and threats, while the proposal of performance indicators such as Order Fulfillment Cycle Time, Supply Chain Responsiveness, and Sourcing Flexibility expanded monitoring and management capabilities. The results demonstrate that the combination of analytical tools (SNA and SWOT) with reference models (SCOR) provides a hybrid framework capable of mapping relational structures, identifying critical points, and proposing practical metrics that strengthen supply chain resilience and efficiency. As a limitation, the study highlights its application to a single case and the absence of practical validation of the proposals, recommending caution in generalizations. Nevertheless, the work contributes both to literature by advancing the methodological integration of analytical models and to managerial practice by supporting decisions grounded in structural and strategic data.

Keywords:
SCOR; Social Network Analysis; Supply chain management; Performance indicators; Roster-Call

Resumo

Resumo  A integração do modelo SCOR (Supply Chain Operations Reference) com a Análise de Redes Sociais (SNA) é proposta neste estudo como uma abordagem estratégica e orientada por dados para aprimorar a gestão de fornecedores em cadeias de suprimentos complexas. A pesquisa foi conduzida por meio de um estudo de caso em uma planta de purificação de moléculas, utilizando o método Roster-Call, entrevistas com gestores, coleta de dados em sistema ERP e análise com os softwares UCINET e NetDraw. Os indicadores de centralidade (grau, proximidade e intermediação) permitiram identificar a centralidade crítica da empresa focal, mas também evidenciaram fragilidades estruturais, como a dependência excessiva de um único nó e a baixa conectividade entre atores periféricos. A análise SWOT, associada às etapas Plan e Source do SCOR, possibilitou relacionar forças, fraquezas, oportunidades e ameaças da rede, enquanto a proposição de indicadores de desempenho como Order Fulfillment Cycle Time, Supply Chain Responsiveness e Sourcing Flexibility ampliou a capacidade de monitoramento e gestão. Os resultados demonstram que a combinação de ferramentas analíticas (SNA e SWOT) com modelos de referência (SCOR) oferece um arcabouço híbrido capaz de mapear estruturas relacionais, identificar pontos críticos e propor métricas práticas que fortalecem a resiliência e eficiência da cadeia de suprimentos. Como limitação, destaca-se a aplicação em um único caso e a ausência de validação prática das propostas, recomendando cautela em generalizações. Ainda assim, o trabalho contribui tanto para a literatura, ao avançar metodologicamente na integração de modelos de análise, quanto para a prática gerencial, ao apoiar decisões fundamentadas em dados estruturais e estratégicos.

Palavras-chave:
SCOR; Análise de Redes Sociais; Gestão de cadeia de suprimentos; Indicadores de desempenho; Roster-call

1 Introduction

In supply chains, the complexity and interdependence of suppliers are determining factors for the success or disruption of the supply network. The need to ensure efficiency requires companies to maintain detailed control over the supply chain (Ahmed et al., 2024). However, traditional management practices often fail to provide a strategic and integrated view of the structure and vulnerabilities of the supplier network, especially in sectors that require operational continuity, such as molecule purification (Maleki Vishkaei & De Giovanni, 2024). The SCOR model (Supply Chain Operations Reference) is a well-established reference in supply chain management, structuring the main processes into stages such as Plan, Source, Make, Deliver, and Return (Kottala & Herbert, 2020).

Although the SCOR model offers guidelines and metrics for internal supply chain processes, it has limitations in capturing the dynamics and interdependencies of suppliers in complex networks (Lemghari et al., 2018). In this context, Social Network Analysis (SNA) emerges as a complementary tool, offering a structured view of relationships among supply chain actors and supporting the identification of risks and opportunities throughout the network (Bento et al., 2024). Evidence of the use of SNA in mapping complex networks can be found in studies by Welege et al. (2021), which present the application of SNA in mapping complex relational networks in modern micro- and macro-level environments, given recent advances in SNA software packages and analytical methods. Furthermore, the study by Adami et al. (2021) analyzed the structural complexity of six supply chains in the Brazilian wind power industry using a similar systematic approach, focusing on an alternative method for addressing the structural perspective of social networks.

SNA and the roster-call method make it possible to map and quantify these interactions, offering insights into the position and relevance of suppliers within the supply network. These techniques help identify central and intermediary nodes in the network, making it easier to understand the interdependencies and vulnerabilities (Bento et al., 2024; Rodríguez-Rodríguez & Leon, 2016). When integrated with SCOR, these tools enable the creation of new performance indicators based on network structure, providing more strategic and efficient supply management (Han et al., 2020).

In this context, this study aims to integrate knowledge from the SCOR model with the modeling and analysis of the supplier network, seeking to improve supply management through the use of performance indicators that make this management more strategic. To achieve this, the study seeks aims to map and model the company's supplier network, identifying critical actors in the supply structure through the roster-call method, using the UCINET and NetDraw software. In addition, the study proposes analyzing the performance indicators currently used by the target company and developing new indicators based on network structure and centrality metrics to improve supply management.

2 Literature review

Supply chain management (SCM) in organizations aims to meet customer demands quickly and with quality, at the lowest possible cost, improving their production processes, organizing their operations, working with minimal stock levels, among other factors that directly impact operational performance (Sharma & Modgil, 2020).

The SCOR tool provides a common language for all participants in the supply chain across five decision areas: Plan, Source, Make, Deliver, and Return (Oliveira & Gonzalez, 2022; Jahanbakhsh Javid & Amini, 2023). It is a model with a structure that enables organizations to develop their own systems for managing and planning their supply chain, offering solutions to monitor and measure performance (Kottala & Herbert, 2020).

2.1 Supply chain management

The supply chain encompasses all stakeholders directly or indirectly connected to an organization’s processes and final objectives, including all parties and functions involved in meeting existing demand. This is made possible through the connection between the organization’s suppliers and its customers, integrating value-added operations. Effective supply chain management strengthens a company’s competitiveness in relation to its products or services (Becker, 2024).

Suppliers are essential components of organizational performance, as they directly influence product quality, operational agility, and cost control. Therefore, to better leverage the potential of the supply market, supplier management becomes fundamental for organizations, ensuring improved outcomes throughout the supply chain (Barbosa & Musetti, 2012; Masood et al., 2024). Supplier trust is considered a strategic parameter in today’s globalized and interconnected environment, defined as the degree to which a company assesses the reliability and honesty of its key suppliers. Such trust contributes to strengthening cooperation, reducing uncertainty in actions, and minimizing opportunistic behavior (Zhao et al., 2018).

To better manage and control their supply chain, organizations carry out performance monitoring in order to identify the results achieved, verify whether customer needs are being met, and support the organization’s understanding of its processes; confirming existing knowledge while also identifying possible gaps and ensuring that decisions are based on concrete data (Langarizadeh et al., 2024).

2.2 SCOR model

Since SCM has become an essential activity for meeting internal and market demands, and to achieve even more effective results, management and activity-monitoring tools have expanded the scope of organizational operations and improved coordination of the supply chain (Samaranayake et al., 2024). In this regard, the Supply Chain Operations Reference (SCOR) model enables better supplier monitoring because it provides performance evaluation metrics developed through indicators, making it a system applicable not only to supply network planning (Zangeneh & Banaeian, 2024).

The characterization and correlation with the operations related to the five decision areas are shown in Table 1.

Table 1
Characterization and operations for decision areas.

Decision Area	Characterization
Plan	It involves supply chain planning at the strategic, tactical, and operational levels. It aims to improve supply chain performance, and information plays a key role as it facilitates integration across the chain.
Source	By achieving a better balance between cost and lead time that meets demand, select the best suppliers that fulfill these requirements.
Make	It occurs through the transformation of raw materials into finished products, developed using process improvement techniques.
Deliver	It completes the supply chain by connecting the flow of finished products from a supply point to a demand point.
Return	Involves the return of excess or defective products, as well as repair, inspection, or maintenance.

• Source: Adapted from Kottala & Herbert (2020).

2.3 Social Network Analysis (SNA)

Network theory has been widely used to understand the development of relationships between companies and their suppliers, as well as the impact of these interactions on organizational performance. This approach highlights the relevance of the structural position of organizations within the network and their associated strategic characteristics (Hald & Spring, 2023).

In this sense, organizations depend directly on their connection with suppliers to meet technological development demands and ensure quick customer service. Suppliers represent significant components within the organization’s strategic context, and their efficient management reflects the company’s competitiveness and ability to maintain a strong market presence (Wen et al., 2020). Their management does not occur in isolation. It requires connection among all suppliers, considering their interdependencies, since companies make use of different resources from different origins to assemble the product to be offered. These characteristics, combined with resource management, enable organizations to remain healthy and grow (Ghosh et al., 2023).

Fassnacht et al. (2024) state that the relational perspectives among the assets participating in the economic relationships that structure the way companies organize themselves are therefore referred to as networks. Barbosa & Musetti (2012) highlight that, for SMEs, the efficient articulation of these networks is crucial to overcoming their logistical limitations.

The existence of multiple actors participating in a social network, whether individuals or organizations, results in relationships that keep them connected. To analyze the existing patterns and structure of this network, the Social Network Analysis (SNA) methodology provides perspectives oriented toward understanding how actors in the network interact, providing tools that enable its examination (Wichmann & Kaufmann, 2016). This methodology has been expanded and applied to the management of organizational supply chains.

For the analysis of network structuring, there are metrics that support its evaluation, either through its nodes or the network. These metrics and their characteristics are shown in Table 2.

Table 2
Network Evaluation Characteristics.

Analysis Level	Metric		Characteristic	Classification
Node	Centrality	Degree	Determines the number of ties linking one of the network actors to the other members. The higher the number of ties, the more centralized the point of analysis (focal actor) will be.	Internal	represents the number of direct ties from the network actors to the focal actor.
				External	represents the number of direct ties from the focal actor to the other network participants.
		Closeness	Indicates how close one actor is to the other participants in the network. The greater the closeness, the easier the interaction between connected actors.	Inbound	Lower values of closeness centrality tend to receive information more quickly due to the actor’s strategic position, which reduces the average distance to the other nodes in the network.
				Outbound	The flow of information/products occurs simultaneously through parallel and shorter paths, improving efficiency in transmission and distribution within the network.
		Intermediation	Indicates how the actors involved in the network depend on the intermediation of another actor to interact with the network.	Measures the number of geodesic paths that pass through a node, indicating the volume of traffic it controls in the network and its ability to influence the flow by acting as an intermediary.
Network	Centrality	Density	Indicates how well the network is connected, measuring the total number of existing ties in relation to the number of possible ties.
		Centralization	Determines the concentration among central actors.
		Complexity	Determines the number of relationships that exist in the network, whether dependent or not, and is influenced by the number of participating actors and their reach.

• Source: Adapted from Wichmann & Kaufmann (2016).

2.4 Network properties

A more efficient structure of information flow is associated with companies that hold a central position in the network, facilitating the identification of opportunities and the establishment of partnerships with suppliers capable of providing materials or services aligned with the needs of the central company (Xue et al., 2024).

Centrality metrics such as degree, closeness, and betweenness assess the relative relevance of nodes within a network. The analysis of these indicators enables a deeper understanding of the functions performed by the actors, highlighting that nodes with high centrality have easier access to information and greater capacity for dissemination within the system (Evans & Chen, 2022).

2.4.1 Structural embeddedness

Structural embeddedness is related to the overall configuration of a network, characterized by the presence or absence of ties as well as the delimitation of its boundaries, often defined by the degree of network closure. Strong ties are associated with trust and the consistent exchange of information among actors; while weak ties facilitate access to new information, mainly through direct connections (Su et al., 2023).

Density can promote cooperative benefits among network members, since in networks that exhibit this characteristic of elevated centrality, participating individuals are strongly interconnected and each behavior is observed, trust is strengthened, and perceived risk decreases due to collective monitoring and the application of social norms (Li et al., 2023).

According to Capioto et al. (2019), degree centrality indicates the level of connection of a company within the network. This is defined by the number of edges incident on a vertex (v) based on the number of neighbors connected to it, denoted as dv. The average degree of the network is indicated by dG and is calculated as shown in Equation 1.

$d_G=\frac{{{\displaystyle \sum }}_{i=1}^n{d}_G\left(vi\right)}{n}$ (1)

Since closeness is related to the path between two nodes, its calculation is performed through the ratio between the node being analyzed and the remaining nodes in the network, and the sum of the distances between it and the nodes to which it is connected (Evans & Chen, 2022; Capioto et al., 2019), as shown in Equation 2.

$Cc\left(v\right)=\frac{n-1}{\sum t\in \frac{V}{vdg}\left(v,t\right)}$ (2)

Betweenness centrality determines the importance of an actor based on how frequently it connects different groups, considering geodesic paths. Thus, the greater the flow of information passing through that actor, the higher its centrality. It is calculated through the ratio between the number of shortest paths that include it and the total number of existing paths in the network (Evans & Chen, 2022), as demonstrated in Equation 3.

$Bet\left(v\right)=\sum i\ne j\ne v,i\ne v\frac{\sigma i,j\left(v\right)}{\sigma i,j}$ (3)

where σi,j represents the number of geodesic paths connecting nodes i and j, and σi,j(v) corresponds to the number of geodesic paths connecting nodes i and j that pass-through v.

Cohesion and density are mutually connected within networks, since cohesion determines the intensity of interactions between pairs of actors, ranging from strong to weak, and is associated with information gains, tacit knowledge, and reciprocity among network members. Density is related to the overall structure of the network and is defined as the proportion between observed ties and possible connections, reflecting the global level of interaction among participants. Its values range from 0 to 1, whereby a value of 1 represents a fully connected network. Although higher density may suggest greater network cohesion, elevated values can hinder the flow of information needed for adequate performance. On the other hand, density analysis makes it easier to identify isolated components and to detect structural holes in the network (Capioto et al., 2019). Its calculation is given in Equation 4 (Capioto et al., 2019).

$D=\frac{2T}{n\left(n-1\right)}$ (4)

where T represents the number of ties in the network and n represents the number of nodes.

2.4.2 Relational embeddedness

Relational embeddedness can be analyzed from three perspectives. The first views it as a direct and cohesive tie between two companies. The second conception relates to the continuous relationship established through interactions among the actors involved, based on trust, obligation, and reciprocity. Finally, the third perspective highlights that relational embeddedness is characterized by the quality and strength developed through the relationship between the company and its suppliers (Hald & Spring, 2023).

The concept of relational embeddedness is connected to the direct ties previously established between companies, facilitating the exchange of information on their capabilities and reliability. This strengthens the existing trust relationship between them, minimizing risks in strategic partnerships. In addition to its direct effects, this concept also plays a regulatory role in shaping the strategic position within the network, which enhances the flow of information (Xue et al., 2024).

3 Methodology

The first stage, referring to the literature review, was conducted between January and March 2024, with the aim of identifying studies that linked supply chain management, the SCOR model and social network analysis (SNA). The databases consulted included Web of Science, Scopus, ScienceDirect and Emerald Insight, as they are recognized as leading sources for scientific publications in the fields of Production Engineering and Supply Chain Management. Combinations of Portuguese and English keywords were used, such as “Supply Chain Management”, “SCOR Model”, “Social Network Analysis”, “Supplier Management”, “Key Performance Indicators” and “Supply Chain Resilience”.

It is important to note that this study adopts a qualitative approach with network analysis, using the case study method to investigate the supplier network of a molecule purification plant in Paraná, Brazil. The methodology involves modeling and analyzing the supplier network through the SCOR model (focusing on the Plan and Source stages), integrating techniques from Social Network Analysis (SNA) and the Roster-Call method. The objective is to characterize the structure of the network, identify critical suppliers and propose metrics that contribute to strategic and efficient supply chain management. An overview of the company and its suppliers is shown in Figure 1.

Figure 1
Organization of the company and its suppliers. Source: Authors, 2024.

The case study was chosen as the methodological approach due to its ability to provide an in-depth understanding of the specific context of the purification plant’s supplier network. This method is particularly suitable for investigations involving multiple variables and interdependent relationships, such as those found in complex supply chains, allowing for detailed analysis of supplier interactions and characteristics in a real environment (Yin, 2018).

To guide each phase of the research in a structured and transparent manner, a detailed research protocol was developed. This protocol, shown in Table 3, defines the stages of data collection, analysis and validation, ensuring consistency and methodological rigor throughout the study (Yin, 2015).

Table 3
Case Study Protocol.

Case study protocol
Research question	How can the integration of the SCOR model and social network analysis characterize the structure and interdependencies of the supplier network in a company in the molecular purification sector, identifying critical actors and proposing strategic metrics for performance management?
Unit of analysis	Supplier network, with a focus on the critical actors identified in the supply structure.
Organization	Molecular purification plant (Downstream Process – DSP), located in the state of Paraná, Brazil.
Period of analysis	2023 á 2024
Data sources and reliability	Interviews with managers, direct observation, internal databases, and corporate documents.
Construct validity	Multiple sources of evidence (direct observations, interviews, documents): validation of responses by interviewees and comparison with data and secondary sources.
Internal validity	Comparison and triangulation among different data sources (interviews, observations, and documents) to ensure accuracy of information about the structure and interdependencies of the supplier network. The results will be analyzed in light of the SCOR model and social network analysis centrality metrics to confirm the relevance of the critical actors identified.
Examples of key questions	Who are the critical suppliers for the company? Do they participate in collaborative practices? How does the structure of the supplier network impact supply chain efficiency? How can centrality metrics improve strategic management?

• Source: Adapted from Yin (2015).

After defining the unit of analysis and the research objectives, the next methodological step was to select and structure the supplier network. This process began with a complete survey of the company’s suppliers extracted from the SAP ERP system, which provided an initial list of all partners involved in supplying the molecule purification plant since the beginning of its operations in 2018. Due to the large number of suppliers, it was necessary to establish a prioritization criterion, selecting those with a financial volume exceeding 100 thousand reais over a five-year period (2018-2023). This filter made it possible to focus on suppliers with greater financial impact, resulting in a more representative and manageable network for analysis.

After selection, suppliers were classified based on their degree of interaction with the company, ranging from 1 to 3, according to their level of collaboration and integration. The characteristics of each level are described in Table 4 (Saikouk et al., 2021).

Table 4
Criteria for defining the degree of interaction between companies.

Degree of interaction	Description
Low	Purchase and sales relationship, with interaction limited to commercial transactions.
Medium	Commercial relationship with additional partnerships, including equipment provision on loan, process evaluation, and improvement suggestions.
High	High-level collaboration, with intensive information exchange and involvement in process research and development.

• Source: Adapted from Saikouk et al. (2021).

The classification criterion presented in Table 4 allowed suppliers to be differentiated not only by the financial volume transacted but primarily by their level of strategic involvement in the supply chain. Suppliers classified with a low degree operate transactionally, restricted to occasional buying and selling operations. Those with a medium degree establish more consistent ties, adding value through partnerships, equipment lending agreements and contributions to process improvement. High-degree suppliers are considered strategic, since in addition to maintaining a high intensity of information exchange, they actively participate in research and development activities, directly influencing innovation and competitiveness of the focal company (Saikouk et al., 2021).

Second and third-tier suppliers were assigned an interaction degree of 1, due to the inability to access detailed information about their level of collaboration with the central company. This categorization served as the basis for the interaction matrix used to model the network in UCINET software 6.357 and NetDraw 2.114 (Zhu et al., 2022), generating a structured representation of the relationships among supply chain actors.

In addition to creating the network matrix, a survey of the performance indicators currently used by the company was carried out. The company employs only three indicators, as shown in Table 5, which will be analyzed and discussed in the results section, aiming to understand how integrating network data can contribute to improving supply management.

Table 5
Indicators currently used by the focal company.

Indicator	Description	Calculation	Target
Stockout	Hours of production halted in the month due to lack of raw materials or inputs.	Sum of halted hours / Available hours in the month	≤ 5%
Nutrient Shortage Risk	Measures the risk of running out of nutrients (inputs) for production.	(Days with stock below the minimum replenishment level) / (Total days in the month)	≤ 2%
Inventory Accuracy	Measures the accuracy between the inventory recorded in the SAP system and the physical warehouse stock.	Items with discrepancies / Total items	≤ 2%

• Source: Authors, 2024.

Table 5 shows the three performance indicators currently used by the focal company, all related to monitoring inventory and operational continuity. The stockout indicator measures the proportion of production hours halted due to a lack of inputs and is used to ensure that downtime does not exceed 5 percent of the total available hours in a month. Similarly, the nutrient shortage risk indicator tracks the frequency of days in which inventory levels remain below the minimum replenishment point, aiming to keep this percentage below 2 percent during the analyzed period. Finally, the inventory accuracy indicator measures the proportion of items showing discrepancies between the SAP system and the physical inventory, seeking to limit these occurrences to no more than 2 percent of total items. Thus, the company’s established targets reflect a performance logic oriented toward reducing undesirable events, prioritizing operational continuity and the reliability of information. However, these indicators focus predominantly on operational and transactional aspects, highlighting the need for complementary metrics that capture structural characteristics of the supply network, such as centrality, connectivity and flexibility, in order to anticipate risks and promote greater supply chain resilience.

The entire process was carried out in eight stages, illustrated in the flowchart in Figure 2, which visually describes each step taken; from defining the research problem to the conclusion. To ensure consistency, the stages described in the flowchart were aligned with those shown in Table 6, which details the procedures and tools used in each phase.

Figure 2
Research Protocol Flowchart. Source: Authors, 2024.

Table 6
Detailed procedures and tools.

Stage	Steps	Procedures and tools
1	Definition of the Research Problem	Establishing study objectives, identifying theoretical gaps, and conducting a targeted literature review (Scopus, Web of Science, ScienceDirect, Emerald Insight, and Google Scholar).
2	Data Collection with the Company	Conducting a technical visit, semi-structured interviews with managers, and collecting data from the focal company’s ERP system.
3	Characterization of the Supplier Network	Selecting relevant suppliers, classifying them by degree of interaction, and organizing data in a relational matrix.
4	Supplier Network Modeling	Structuring and visualizing the network using UCINET 6.357 and NetDraw 2.114 software.
5	Generation of Centrality Indicators	Calculating degree, closeness, and betweenness metrics in UCINET to identify critical nodes and relevant interactions.
6	Network Evaluation	Analyzing centrality results in comparison with literature and diagnosing strengths and vulnerabilities.
7	Suggestion of Indicators	Proposing performance indicators aligned with the SCOR model (Plan and Source) to monitor the observed weaknesses.
8	Conclusion	Systematizing results, discussing study limitations, and presenting recommendations for future research.

• Source: Authors, 2024.

The eight stages of the protocol are shown in Table 6, specifying the procedures and tools used in each of them.

The methodology described in this study was designed in a structured and detailed manner, allowing its application to other supplier networks. The use of the research protocol presented, combined with the SCOR model focused on the Plan and Source stages, and Social Network Analysis (SNA) techniques using the Roster-Call method, provides a solid and replicable foundation.

4 Results and discussion

To better understand the supplier network, Graph 1 shows the classification of actors according to the type of supply they provide. It is observed that approximately 31% of the suppliers are concentrated in the provision of raw materials and inputs, while 37% operate with packaging materials. Additionally, 15% are suppliers of chemical products, considered complementary technological inputs, and another 15% supply materials for quality control laboratories. Finally, only 4% of the network is composed of suppliers of industrial parts and equipment.

Graph 1. Classification of network suppliers by market segment. Source: Authors, 2024.

This distribution highlights a strong dependence on two segments: packaging and raw materials. Together, they account for 68% of the supply network, suggesting that potential failures or interruptions in these sectors could significantly compromise the operation of the focal company. This corroborates previous studies (Zhao et al., 2018; Maleki Vishkaei & De Giovanni, 2024), which emphasize the importance of diversifying suppliers and reducing concentration in critical categories to mitigate disruption risks.

Figure 3 shows the graphical representation of the modeled network, in which the target company was designated as actor M. The other actors were assigned letters corresponding to their segment (as described in Table 1), followed by a sequential number. The size of the spheres was defined according to each actor’s degree centrality, making it possible to identify the most connected ones. The edges (lines) vary in thickness according to the intensity of the relationship, and color is used to differentiate the segments, facilitating visual interpretation.

Figure 3
Graphical representation of the modeled network. Source: Authors, 2024.

The joint analysis of Figure 3 and Table 7 enables an understanding of both the structural position of the focal company and the strategic distribution of the segments. Visually, Figure 3 highlights the absolute centrality of actor M, who connects virtually the entire network, confirming its function as a supply “hub.” Table 1, in turn, supports interpretation by showing the predominance of packaging (P) and raw material (R) suppliers, while also highlighting the presence of less numerous actors, such as laboratories (L) and equipment suppliers (S). This combination reveals a structural imbalance: while some segments are highly represented, others appear in smaller numbers, which may compromise redundancy and increase vulnerability in failure scenarios. Moreover, chemical (C) and laboratory (L) actors, even in smaller quantities, play intermediate bridging roles, functioning as connection points between distinct groups in the network. This finding suggests that supply chain management should prioritize both reducing excessive dependence on the central node and strengthening horizontal connections among segments, thereby enhancing the resilience and flexibility of the supply system.

Table 7
Attributes of the network actors.

Actors	Notation
Packaging Material	P
Raw Materials and inputs	R
Parts and Equipment	S
Chemicals	C
Laboratory	L
Main Actor	M

• Source: Authors, 2024.

Figure 4 shows the network centrality indicators generated using the UCINET software. These indicators allow the assessment of different structural dimensions of the network: degree centrality, which measures the number of direct connections of each actor; closeness, which evaluates how accessible a node is in relation to the others; and betweenness, which indicates the capacity of an actor to act as a bridge between different groups. The ordering of the data highlights not only the dominant position of the focal company (M) but also the role of some intermediary suppliers which, although not numerous, hold strategic relevance due to their position on the network.

Figure 4
Network Centrality Measures. Source: Authors, 2024.

The analysis of these indicators shows that the focal company (M) exhibits extremely high degree, closeness, and betweenness centrality, confirming its role as the hub of the network. Although this concentration provides efficient control and coordination, it also generates vulnerability: the potential failure of this node could fragment the network into several isolated groups. Furthermore, suppliers such as R18 and C1 appear with significant betweenness values, indicating that they function as secondary linking nodes capable of reducing the overload on the central actor and increasing communication flow. Conversely, the low centrality of most peripheral nodes indicates limited integration and constraints in leveraging synergies among suppliers. These results reinforce the need to diversify flows and strengthen horizontal connections to reduce risks, aligning with studies by Adami et al. (2021) and Bento et al. (2024), which highlight the role of decentralized networks in promoting resilience and flexibility in complex supply chains.

5 Structural analysis

The network is composed of 57 actors, including the focal company. In Figure 4, it is possible to observe the degree, closeness, and betweenness centrality indicators that were generated. With a degree centrality of 63, the focal company maintains the central position within the network, directly connecting to most of the nodes. This confirms its strategic role as the main integration point among suppliers.

Actor R18, with a degree of 22, is the second most connected node. This position suggests that R18 is a relevant supplier directly engaged in many flows of the chain. This actor represents a biotechnology company whose main focus is on research and development of new products, which helps explain its importance in the network, as it requires partners who collaborate in research and production scaling.

Actors C1 and R1 present degree centrality values of 12 and 13, respectively, and even though they show an interaction degree of 1 with the focal company, they are multinational firms in the chemical sector and therefore well-positioned within the supply chain context. They exhibit significant connectivity, indicating important intermediary roles, as they are responsible for specific and strategic flows.

Shifting the focus to the closeness centrality indicator where lower values indicate better positioning within the network we note that the focal company is the most accessible node, reinforcing its ability to interact quickly with other actors. Other well-positioned actors include R18, R8, and C1, indicating that they have good accessibility to the other nodes, which may help reduce response times, improve information exchange agility, and optimize logistics costs.

In regard to betweenness, the focal company shows an extremely high value (1441.567), confirming its position as an essential hub for network connectivity. Its removal would fragment the structure, indicating excessive dependence. Figure 5 shows the network after the removal of the focal actor, highlighting how several nodes become isolated and lose functional relevance within the structure.

Figure 5
Intermediation Relationship Between the Focal Company and Suppliers. Source: Authors, 2024.

Other nodes such as C1, R18 and R8 also present relevant betweenness values and therefore play “bridge” roles between different parts of the network. They help relieve excessive flow through the focal company and facilitate communication and transport.

To better understand the modeled network, a SWOT analysis was carried out, relating the structural characteristics of the network evidenced by the centrality indicators to internal strengths and weaknesses, as well as external opportunities and threats. This approach made it possible to identify improvement points and areas requiring attention, highlighting aspects such as the efficient centralization of flows as a strength, the excessive dependence on a single node as a weakness, the potential for integration among suppliers as an opportunity, and the risk of external disruptions as a threat. The SWOT matrix, shown in Table 8, organizes these factors strategically, guiding actions to improve supplier network management.

Table 8
SWOT Matrix.

Strengths	Weaknesses
Focal company centrality (M): The highest degree, closeness, and betweenness centrality ensures control over material and information flows.	Overreliance on the focal company: M’s high centrality makes the network vulnerable to disruptions at this single critical point.
Relevant intermediary suppliers: Nodes such as R18 and C1 show significant betweenness, allowing smoother communication.	Low connectivity among peripheral nodes: Many nodes have a low degree (≤ 4), indicating limited relationships and collaboration challenges.
High closeness of most nodes: The network has a short average response time, facilitating operational agility.	Isolated peripheral nodes: Nodes like P1, L2, and R5 have low betweenness, limiting their strategic usefulness.
Identification of strategic critical nodes: R18 and C1 are important nodes that can be leveraged as reliable intermediaries.	Linear structure and low redundancy: The absence of alternative paths generates risks of disruptions if a critical node fails.
Opportunities	Threats
Network decentralization: Strengthen connections among intermediary nodes (e.g., R8, R1) to reduce the load on the focal company.	Central node failure (M): An operational disruption at M could fragment the network and cause critical supply interruptions.
Strengthening strategic nodes: Expand the role of nodes such as R18, C1, and R8 to increase network resilience.	Concentration of critical suppliers: R18 and C1 play important roles, but their failure could also significantly impact the network.
Integration of peripheral nodes: Create new connections to leverage the potential of nodes such as P1, L2, and R6, reducing isolation.	Lack of sourcing flexibility: Low redundancy among suppliers may hinder replacement in case of disruptions.
Increased collaboration: Encourage partnerships and horizontal exchanges among suppliers to reduce costs and improve efficiency.	Communication difficulties with distant nodes: High closeness (≥ 110) of many peripheral nodes may cause delays in critical situations.

• Source: Authors, 2024.

The SWOT matrix revealed important structural characteristics of the supplier network, highlighting how network modeling tools (SNA), strategic analysis (SWOT), and the SCOR model can be integrated to enhance supply chain management. Among the main observations, the efficient centralization of flows in the focal company stands out as a strength, although the excessive dependence on this node also constitutes a weakness that may compromise network resilience. The low connectivity among peripheral suppliers and the absence of structural redundancy reinforce the need for opportunities such as decentralization and collaboration, which are essential to mitigate external threats such as supply failures.

The integration between the SCOR model and the SWOT matrix is essential for translating the strategic factors identified into specific indicators that monitor and control the network. SCOR indicators focused on the Plan and Source stages allow the measurement of aspects such as reliability, agility, and flexibility, aligning centrality analysis with practical performance objectives. This approach unifies the strategic perspective provided by the SWOT analysis with the quantitative and structural data from SNA, offering a solid foundation for strategic and tactical decision-making in supply chain management.

The SWOT matrix is also directly connected to the indicators currently used by the focal company, such as stockout rate, nutrient shortage risk, and inventory accuracy, which monitor critical aspects related to the network’s strengths, weaknesses, opportunities, and threats. These indicators reflect some of the weaknesses identified, such as excessive dependence on a single node and low connectivity among suppliers, but they have a reactive focus, measuring the effects of problems rather than their underlying causes. For example, the stockout indicator aligns with the threat of supply failures but does not address issues such as supplier diversification or sourcing flexibility. The SWOT analysis suggests that these indicators can be complemented or refined with metrics based on the SCOR model, which broaden the strategic perspective for monitoring and controlling network vulnerabilities, promoting greater resilience and efficiency.

Next, Table 9 shows the weaknesses identified and the SCOR indicators suggested for monitoring and continuous improvement.

Table 9
Weaknesses vs. SCOR indicators.

Weakness	Evidence	SCOR Indicator	Equation/ Calculation	Explanation
Dependence on a single central node	High centrality of the focal company, making it a critical failure point.	Order Fulfillment Cycle Time	Sum of times from order to delivery	Measures the total time to fulfill orders, allowing assessment of network resilience in the face of centralization.
Isolation of peripheral suppliers	Low degree and betweenness of many nodes in the network, limiting connectivity and collaboration.	Supply Chain Responsiveness	Average response time to plan changes	Assesses the network’s ability to react quickly to demand or changes, promoting greater integration with peripheral suppliers.
Lack of supply redundancy	Dependence on few suppliers for critical inputs, with no alternative paths in case of failure.	Sourcing Flexibility	Maximum number of suppliers that can meet an X% increase in demand without delays	Measures the ability to adjust supply quickly, indicating the degree of flexibility and resilience of the network.
Low horizontal connectivity	Peripheral nodes have low collaboration among themselves, limiting direct exchanges and collective efficiency.	Collaboration Rate	(Number of Collaborative Suppliers ÷ Total Suppliers) × 100	Evaluates the proportion of suppliers actively participating in collaborative planning, promoting higher connectivity and horizontal information exchange.
Outdated or unreliable data	Current indicators monitor effects (stockouts) but do not predict or prevent problems.	Forecast Accuracy	(Correct Forecasted Demand ÷ Total Forecasted Demand) × 100	Measures the accuracy of demand forecasts, contributing to more assertive planning and helping prevent stockouts.

• Source: Authors, 2024.

Based on the weaknesses identified in the SWOT matrix and considering the Plan and Source stages of the SCOR model, a table was developed suggesting the best indicators to monitor and control these vulnerabilities in the supplier network. These indicators were selected from a detailed analysis of the SCOR metrics available on the official ASCM SCOR Model website (Association for Supply Chain Management, 2024), focusing on aspects such as flexibility, responsiveness, collaboration, and predictability, which are fundamental to strengthening supply chain resilience and efficiency.

Table 3 shows each observed weakness, the supporting evidence, the recommended SCOR indicator, its calculation formula, and an explanation of how it can be applied to mitigate network fragilities. This approach integrates social network analysis (SNA), the SWOT matrix, and the SCOR model, enabling the use of indicators for strategic and data-driven supply network management.

6 Conclusion

This study demonstrated how the integration of tools such as the SCOR model, Social Network Analysis (SNA), and the SWOT matrix can provide a strategic, data-driven approach to supplier network management. The analysis of the modeled network revealed the centrality of the focal company as a significant control point but also highlighted vulnerabilities such as excessive dependence on this node and low connectivity among peripheral actors. The SWOT matrix emphasized both the strengths of the network, such as the proximity among central nodes, and critical weaknesses, such as the lack of redundancy and flexibility in sourcing.

The application of the SCOR model, focused on the Plan and Source stages, made it possible to identify specific indicators to monitor and improve the weaknesses identified. Indicators such as Order Fulfillment Cycle Time, Supply Chain Responsiveness, and Sourcing Flexibility offer a practical perspective for mitigating threats and leveraging opportunities, fostering a more resilient and efficient supply chain management approach.

However, it is important to acknowledge the limitations of this research. The study focused on a single case and did not incorporate the practical implementation of the proposed actions within the operational routine of the focal company. Thus, although the model shows potential for replication in other contexts, its successful application will depend on the specific characteristics of each supply chain, requiring caution when making generalizations. Moreover, the proposed methodology does not encompass all dimensions of supply chain management and should be viewed as complementary to other analytical and managerial tools.

From an academic perspective, this work contributes by demonstrating that the articulation between structured methods (SCOR), relational analyses (SNA), and strategic approaches (SWOT) can generate a hybrid framework capable of capturing both quantitative and qualitative aspects of supplier networks. From a practical perspective, the research reinforces the importance for managers not only to monitor internal performance indicators but also to understand the structural interdependencies of the network, which may represent invisible sources of risk or competitive advantage.

For future research, it is suggested to expand the analysis to include the full SCOR model, incorporating the Make, Deliver, and Return stages. This would allow optimization of supply chain performance in processes such as manufacturing, delivery and returns, providing a more integrated and strategic view of the supplier network. Another possibility is the use of the TOWS Matrix as a complementary methodology to SWOT analysis, helping advance toward the structured formulation of strategies by systematically combining previously identified SWOT factors. This could lead to concrete strategies such as supplier diversification combined with the use of contingency contracts in cases involving weaknesses related to dependence on a central supplier and threats associated with the risk of logistical disruption. Finally, it is suggested to explore integration with advanced technologies, such as artificial intelligence and blockchain, which can enhance resilience, collaboration, and visibility in supplier networks, offering innovative solutions to optimize logistics processes and increase transparency in supply chain management.

Acknowledgements

This study was carried out with the support of the Coordination for the Improvement of Higher Education Personnel – Brazil (CAPES) – Funding Code 001. The authors declare that there are no relevant conflicts of interest.

Statement on Data Availability

The data used in this article are fully presented throughout the text and in the attached tables. Therefore, there are no additional data available or required beyond those already presented in the article.

• Financial support:
  Coordination for the Improvement of Higher Education Personnel – Brazil (CAPES) – Funding Code 001.
• How to cite:
  Soares, L. C., Bonini, A. P., Garcia, M. V., & Barbosa, D. H. (2026). Integration of SCOR model and Social Network Analysis: improving supplier management in complex supply chains. Gestão & Produção, 33, e3325. https://doi.org/10.1590/1806-9649-2026v33e3325

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• Editor-in-Chief
  Pedro Munari

Publication Dates

• Publication in this collection
  22 May 2026
• Date of issue
  2026

History

• Received
  08 May 2025
• Reviewed
  07 Jan 2026
• Accepted
  08 Mar 2026