Introduction

In the past 2 decades, organizations have been focusing their business efforts on activities they know best, i.e., those that match their core competencies. Activities that are not cost-effective are assigned to other organizations [1, 2]. This strategy is called outsourcing. Organizations take a big step towards development and excellence by outsourcing non-core activities and focusing on core activities [3, 4]. The main reasons behind outsourcing are financial savings, improved competitiveness, strategic focus, access to superior technology, improved service levels, access to experts, and organizational policies [5, 6].

Many factors in the global market have motivated companies to focus on their supply chain, seeking competitive returns. Procurement is one of the most strategic activities in supply chain management as it enables companies to reduce costs and thus increase profits. An essential task in the procurement activity is the selection of the supplier [7, 8]. Selecting suitable suppliers is the key to the procurement process and represents a significant opportunity for companies to reduce costs throughout the supply chain. In high-tech companies, the services and materials purchased may account for 80% of the total cost of products in many industries [9, 10].

Being socially responsible means that people and organizations must behave ethically and be sensitive to social, cultural, and environmental issues. One of the main concepts that describe sustainable activities is corporate social responsibility (CSR), or being responsible for your impact on the environment and society [11, 12] corporate social responsibility. Striving for fulfilling social responsibility (CSR) helps individuals, organizations, and governments positively impact progress, work, and society. CSR is a set of duties and responsibilities that a company must fulfill to protect, care for, and help the community in which it operates [13]. CSR represents the coherence and unity between an organization's activities and values so that all stakeholders’ interests are considered [14]. CSR is strongly pursued by governments, corporations, civil society, international organizations, and scientific communities around the world in developed and open economies [13]. According to global studies, companies that can successfully fulfill CSR requirements have higher loyalty, motivation, and performance among their employees. However, this is not the only reason organizations pay attention to CSR [14]. Organizations’ managers are motivated to implement CSR procedures. Some of the advantages of this procedure are brand reputation and management, risk management, better access to capital resources, innovation and learning, cost savings, operational efficiency, competition and market penetration, social licenses to continue working, effective communication with government officials, continuous improvement, and organizational promotion [15, 16]. CSR in its modern sense has developed in line with the development and emergence of management systems based on standards and frameworks such as quality management, environmental management, social accountability, and Occupational Safety and Health (OSH) [17]. The quality management standard is the latest specific CSR standard and complements this process. Accordingly, this study seeks to examine the indicators of CSR assessment and propose a model for supplier selection that considers conventional criteria for selecting suppliers and considers the CSR indicators. To this end, the following questions are addressed in this study:

  • Why is the issue of CSR so important currently?

  • What are the key CSR indicators that can play a role in the supplier selection process?

  • What is the impact of simultaneous consideration of the conventional criteria and CSR criteria in the supplier selection process?

In the current dynamic business environment, choosing the right supplier can play a vital role in a company's business strategies. Companies need to select more effective suppliers to cope with the increasing production complexities, attract more customers, ensure the desired quality, provide special and better services, and deal with the increased competitive pressure. Choosing a supplier and benefiting from it is always the best strategy for creating socio-economic benefits. Moreover, a couple of theories consider the development of supplier selection as a requirement for continuous growth and improvement. Thus, an organization’s strategies, decisions, and actions to modify and develop its supplier selection process form the core of the socio-economic development process of that company. If an organization can lead this process systematically, it paves the way for success and continuous development and can hope for survival in the current competitive and turbulent market. This study aims to identify indicators affecting supplier selection and CSR practices to provide a dynamic model for supplier selection. The insights from this study can identify potential sources of risk and provide guidelines for implementing effective strategies through an integrated approach among supply chain members to increase CSR practices for supplier selection. This issue will help them to achieve more competitive power and satisfy customers optimally. The system dynamics (SD) model for supplier selection will be developed by taking into account profitability, productivity, social transparency, and customer satisfaction three stages. In the first stage, the indicators affecting supplier selection are developed based on the study’s theoretical framework and literature review. In the next stage, a subjective model is extracted for supplier selection by surveying the experts in the field, and the results of the model estimation are analyzed. In the third stage, the SD model for supplier selection is developed and simulated after performing the necessary analyses. The final output from the third stage will be a dynamic supplier selection model that takes into account CSR practices. Choosing an SD approach in this project enables examining changes over time and observing variables’ effect on target variables and other variables. SD includes cause–effect mapping and the development of computer simulations to understand system behavior. Finally, alternative scenarios are systematically tested to answer “what-if” questions.

Literature review

With the increasing globalization of the economy and the intensification of market competition, Green Supply Chain Management (GSCM) has emerged as a new management approach to pursuing economic benefits, integrated sustainable environmental development, and managing modern companies’ production operations. Evaluating and selecting a green supplier is the GSCM core, which can directly affect producer performance. Choosing a green supplier can be considered as a multi-criteria group decision-making problem.

Qin et al. [18] developed the TODIM technique to solve MCGDM problems using type-2 fuzzy sets (IT2FS) and assessed its applications for selecting a green supplier. In another study, Guo et al. [19] evaluated a methodological framework for assessing and selecting a green supplier in global garment production based on a fuzzy multi-criteria decision-making model. Cengiz et al. [20] analyzed supplier selection for (wall, cladding, and roofing) construction materials. Furthermore, Fallahpour et al. [21] integrated the hierarchical analysis process with multi-level planning to introduce a new evolutionary approach to evaluating and selecting suppliers and adapting to the previous problem. Luthra et al. [22] proposed a framework for evaluating sustainable supplier selection using an integrated analytic hierarchy process and the VIKOR method, a multi-criteria optimization, and a compromise solution approach.

Yazdani et al. [23] proposed an integrated approach to selecting a green supplier by considering different environmental performance conditions and criteria. In another study by Sinha and Anand [24], a framework was introduced for supplier selection from a sustainability perspective by specifying the supplier characteristics. Wang and Tsai [25] proposed the fuzzy MCDM approach, which incorporated the Fuzzy Analytic Hierarchy Process (FAHP) model and data envelopment analysis to evaluate and select a solar panel supplier to design the photovoltaic system in Taiwan.

Incorporating environmental criteria in supplier selection methods is essential for organizations seeking to improve green supply chain management. The challenges associated with green resource selection have been widely recognized by supplier procurement and management experts. Banaeian et al. [26] contributed to this field of knowledge by comparing the application of three popular multi-criteria supplier selection methods in a fuzzy environment.

With increasing awareness of environmental and social issues, Sustainable Supply Chain Management (SSCM) has received special attention in academia and industry. Supplier selection plays an important role in successfully implementing sustainable supply chain management as it can affect SSCM performance. A new approach was developed based on the rough set theory and ELimination Et Choix Traduisant la REalité (ELECTRE) to solve these problems by Lu et al. [27]. The new approach integrated the power of rough theory in dealing with ambiguity without prior knowledge and ELECTRE competence in modeling multi-criteria decision-making problems. Ishtiaq et al. [28] also tried to identify, validate, and rank the criteria required for selecting hospital waste management suppliers.

Demir et al. [29] presented a new method for VIKOR-based sorting called VIKORSORT to evaluate suppliers’ environmental performance and organize them into predefined classes. Wang et al. [30] proposed an MCDM model using Supply Chain Operations Reference (SCOR) criteria, Analytic Hierarchy Process (AHP), and the technique for order of preference by similarity to TOPSIS. Jain et al. [31] addressed a supplier selection problem in an Indian automotive company using the AHP and TOPSIS methods.

Sinha and Kumar [24] addressed supplier problems, evaluation competency, assessment, and selection processes to achieve the project objectives using the MCDM model. Given the uncertainties associated with the decision-making process in supplier selection, fuzzy techniques can provide more reliable and flexible decision-making solutions for supplier selection. Ahmadizadeh et al. [32] weighted the degree of the importance of customer needs and engineering needs using the fuzzy analytical network process (FANP) and quality function deployment (QFD) matrix in the fuzzy environment. In developing a high-speed train manufacturing (HSTM) industry, a key issue is evaluating suppliers’ performance, taking into account their specific characteristics. To address this issue, Xue et al. [33] introduced a multi-criteria decision-making (MCDM) method based on evidential reasoning. Liu et al. [34] proposed a new evidential ANP methodology based on game theory to manage the supplier in an uncertain environment effectively. Abdel-Basset et al. [5] analyzed the factors influencing the selection of SCM suppliers using the neutrosophic set for Decision-Making Trial and Evaluation Laboratory (DEMATEL).

Political-economic deregulation, new communication technologies, and cheap transportation have led companies to outsource their business activities to geographically distant countries increasingly. Awasthi et al. [35] developed a framework based on the integrated fuzzy AHP-VIKOR approach for selecting a global sustainable supplier, which considers sustainability risks from sub-suppliers.

Yoon et al. [36] evaluated a wide range of quantitative and qualitative risk factors in selecting a supplier and assessed the effectiveness of alternative risk reduction strategies. Vahidi et al. [37] introduced a two-stage probabilistic-random mixed planning model to address the sustainable supplier selection and order allocation under operational risks and disruptions.

The concept of sustainability has become a central issue for many industries and organizations due to their increased sensitivity to environmental protection and social responsibility. Since suppliers are the first entity and the main source of any supply chain, organizations must select their suppliers by carefully evaluating their critical success factors (CSFs). A business needs to consider its shareholders’ views to develop a successful supply chain management strategy. Following CSF theory and taking into account the multi-stakeholder perspective on sustainability, Kannan [38] proposed a decision-support system for Sustainable Supplier Selection (SSS) in the real-world textile industry in India’s emerging economy. Sustainability is of interest to academics and stakeholders due to increased stakeholder awareness of environmental and social issues. However, relatively few studies have addressed social sustainability in supply chain management in emerging economies. Mani et al. [39] examined the social issues related to suppliers and to identify measures and indicators of social sustainability in emerging economies.

Research on production and operation management and its relationship to environmental sustainability has grown significantly in recent years. Sarkis and Zhu [40] reviewed how research has progressed in the International Journal of Production Research alongside socio-environmental development. Over the past 2 decades, DM theories and techniques have continued to contribute to and assist in developing supplier selection applications. Maintaining rapid transmission speed in this field, Chai and Ngai [41] systematically reviewed the relevant articles published between 2013 and 2018.

Optimal supplier selection is an open and important issue in supply chain management that can be considered as an MCDM issue. Expert evaluation plays a very important role in the supplier selection process. However, existing techniques cannot deal with uncertainties effectively. Dempster–Shafer theory (DST) is widely used in modeling uncertainty, decision-making, and management due to its advantages in obtaining uncertain information. Fei et al. [39] extended the VICOR method into the DS-VIKOR method for solving supplier selection problems using the D–S theory.

Sustainability in the supply and demand chain is a challenge. Stević et al. [42] solved a problem for selecting a sustainable supplier based on all three aspects of sustainability. The evaluation was performed based on 21 criteria set in two levels and three groups. A new interval rough SAW method was also developed to demonstrate help in addressing MCDM problems.

Environmental concerns have encouraged green supply chain management worldwide. Phochanikorn and Tan [43] used the integrated multi-criteria decision-making (MCDM) method and the fuzzy DEMATEL method to consider cause-and-effect relationships and then used fuzzy ANP to determine the weight of each criterion. The concept of sustainability has become a core philosophy for many industrial sectors due to increased environmental protection and awareness of social commitments. Memari et al. [44] showed the effectiveness of a visual fuzzy TOPSIS method for selecting a sustainable source that meets the 9 criteria and 30 sub-criteria for an auto-parts manufacturer.

Environmental awareness of society and the global competitive market has increased significantly due to the current environmental problems. Yalcin and Kilic [45] used two robust MCDM methods, FAHP (IF AHP) and PROMETHEE, as an integrated method for the optimal control of selection problems. Choosing a flexible supplier is a key strategic decision in supply chain disaster management. Hosseini et al. [46] proposed an efficient solution for selecting a flexible supplier and optimal order allocation. Bai et al. [47] suggested a decision-making framework for the concept of social sustainability to evaluate and select sustainable social providers using the BWM and TODIM methods. Haeri and Rezaei [48] presented a comprehensive grey-based green supplier selection model covering economic and environmental criteria. The new specific weight model was presented with the best method and fuzzy gray cognitive maps to record the interdependence between the criteria.

Various initiatives have been taken for organizations to stay competitive, including supply chain decisions such as low-cost sourcing [49]. However, organizations face social problems resulting from their supply chain operations, typically from their suppliers [50].

Govindan et al. [51] proposed a model to select the best supplier based on their corporate social responsibility in three phases using DEMATEL-ANP and PROMETHEE. Shen et al. [52] revealed the factors that resist the implementation of CSR in the textiles industry with the assistance of a proposed model. The model was validated with a case industry situated in southern India. Govindan et al. [53] extended the scope of CSR implementation through varying multi-criteria decision-making tools and different environments such as grey, fuzzy, and other approaches. The additional drivers can also be validated with statistical work. Govindan et al. [54] explored the drivers and value-relevance of corporate social responsibility performance in the logistics sector by particularly focusing on board characteristics and ownership structure. CSR performance is measured with its three sub-dimensions between 2011 and 2018. Waqas Kamran et al. [55] conducted an empirical analysis about how corporate social responsibility contributes to company performance using AHP and fuzzy TOPSIS theory. Liu et al. [56] examined the impact mechanism of CSR on technological innovation performance from the mediating effect of CSC and the moderating effect of market competition intensity. Modak et al. [57] considered two CSR tools: social work donation and recycling investment. They integrated the CSR investments into a closed-loop supply chain where the stochastic demand depends on the sales price and social work donation. Fontana et al. [58] integrated the global value chain literature with the micro-organization literature on negative emotions to explore the drivers of fear and anger among supplier factory senior managers in apparel supply chains after Rana Plaza—a major industrial disaster—and their influence on decisions on CSR practices.

Zhang et al. [59] suggested a spherical fuzzy grey relational analysis for multi-attribute group decision-making problems. They gave some resources for coping with uncertain challenges and for future extension in decision-making contexts. In Thevenin et al. [60] paper, we look at how to employ robust optimization to solve the combined lot-sizing and supplier selection issue when lead time is unpredictable. We employ polyhedral budgeted uncertainty sets in particular. Tong et al. [61] developed a supplier selection assessment framework for small- and medium-sized businesses and offered an expanded PROMETHEE II approach for achieving a sustainable supplier selection process. Saputro et al. [62] developed a framework that gives direction on how to construct and approach supplier selection for different categories of commodities separated in Kraljic’s portfolio matrix and production strategies. Bai et al. [63] developed a complete set of criteria for supplier selection, monitoring, and development in the context of circularity. These measurements are available at the macro-, meso-, and micro-levels.

Research gap

Many studies have addressed supplier selection and evaluation. Many of them have focused on traditional business and economic criteria (e.g., Yazdani et al. [23], Kumar and Sinha [24], Jain et al. [31]). An increasing number of studies have focused on the criteria for environmental sustainability. Other studies have considered supplier selection with broader sustainability criteria (e.g., Sinha and Anand [24]; Awasthi et al. [35]; Vahidi et al. [37]). Few studies have covered social sustainability in the supply chain [36, 37]. A few of these studies [56] have not focused on selecting and evaluating suppliers solely based on social sustainability performance. Thus, given this gap in the literature, the present study aimed to develop an SD model for supplier selection by considering both conventional indicators (such as cost) and CSR indicators. Accordingly, the contributions of this study are detailed as follows:

  • Addressing the importance of CSR in the form of an SD model and its effects on revenue, customer satisfaction, and social transparency.

  • Determining influential indicators in supplier selection with an emphasis on CSR.

  • Developing an SD model for selecting suppliers by considering both conventional indicators (such as costs) and CSR indicators.

  • Establishing a connection between strategies and measurement criteria in selecting a supplier with an emphasis on CSR.

  • Planning a set of goals and formulating an innovative strategy to provide a better representation of the cause–effect structure of the system under analysis with a focus on the concept of the feedback loop and the use of tools such as cause–effect loop diagrams in supplier selection with an emphasis on CSR.

Methodology

SD models are used to analyze the structure and behavior of a system and develop efficient policies for system management. For example, using an SD decision-making model can help companies make the right decisions [64]. Previous studies in this field were first reviewed to identify the factors influencing supplier selection based on CSR criteria. In the next step, the experts confirmed the variables. Then, they determined the relationship between these variables and their effect on each other. Finally, VENSIM software was used to plot cause–effect loop diagrams.

Modeling time horizon

SD addresses the behavior of systems over time. However, time itself has been seldom discussed in the literature in this field. Alternative concepts of time are derived from Newtonian mechanics and modern thermodynamics. SD mixes the two concepts in a way that can be problematic. Another time-dependent issue is the choice of time horizon for model execution. The length of the time horizon affects the consequences of the simulation policies, but there are no clear rules for choosing the time horizon. These problems with time in modeling affect the practical applications of SD in policy management [65, 66]. With this in mind, it is vital to strike a balance between the two issues. Many researchers suggested that a period of 3–5 years is usually reasonable to review the results. Accordingly, given the broad range of variables and their calculations, a time horizon of 36 months is considered based on expert opinions and literature review. Thus, there is enough time for feedback to work.

Formulating a dynamic hypothesis

A dynamic hypothesis is a theory about what structure exists that generates the reference modes. A dynamics hypothesis can be stated verbally as a cause–effect loop diagram or a stock and flow diagram. The generated dynamic hypotheses can determine what will be kept in models and excluded [67]. A survey of the experts and reviewing the literature showed that some criteria are more effective, and others were removed due to similar effects to simplify the model. Three main hypotheses were developed and tested in this study:

Hypothesis 1

A SD model can be developed to identify the impact of important CSR indicators in the supplier selection process.

Hypothesis 2

A SD model can be developed to determine the optimal level of social transparency in selectin suppliers.

Hypothesis 3

A SD model can be developed to simultaneously examine the effects of the conventional indicators (such as cost) and CSR indicators.

Model structure

Model boundaries

One of the most basic modeling concepts is delineating the model boundaries and limiting them to a specific area for analysis and planning. Factors affecting and affected by each other are located in this area. Factors that affect but are not significantly affected are located outside the system, and other factors that are not significantly affecting or affected are removed from the model. Processes, information feedback, policy latency, and time are important elements of SD modeling. Given that endogenous and exogenous variables are determined only by the boundaries, boundary setting is equally important in SD modeling [68].

Given these variables and the cause–effect relationships between them, first, the effect diagram is plotted. Afterward, the stock-flow diagram is drawn using the cause–effect diagram. These two diagrams are shown in Fig. 1. Besides, the model components and their relationships are analyzed using the model loops as detailed in the following sections.

Fig. 1
figure 1

The SD model

Full size image

SD model and main loops

A problem or system can be represented as a cause–effect loop diagram in an SD model, a simple map of a system with all its components and interactions. For this purpose, given the relationships between variables, cause–effect loops are determined and confirmed by experts. A cause–effect model was used and converted into a flow chart to show the relationships between the influencing factors in this study. The cause-and-effect diagram and the state-flow diagram are shown in Fig. 1. Furthermore, Figs. 2, 3 and 4 display these diagrams in terms of social transparency, customer satisfaction, and profitability.

Fig. 2
figure 2

The stock-flow diagram for social transparency

Full size image
Fig. 3
figure 3

The stock-flow diagram for customer satisfaction

Full size image
Fig. 4
figure 4

The stock-flow diagram for profitability

Full size image

According to Awasthi et al. [35], the higher the social transparency, the greater the social legitimacy, and the higher the legitimacy, the greater the consumer trust. Furthermore, consumers are emotionally closer to and more trusting in organizations that have more humanitarian activities. As stated, to increase legitimacy and, as a result, trust, social transparency must be increased. Tsai et al. [69] suggested that the more social programs a business implements and the more it exposes to the public, the greater this transparency. Legal requirements and tax rates also affect this factor. Some government and commercial requirements, such as the presentation of certain financial instruments on the stock exchange and standard rules, force companies to engage in self-disclosure, which increases transparency. This issue is also the case with taxes, and companies are required to disclose some of their instruments. Besides, tax payment contributes to social responsibility. According to the definition of social responsibility, the payment of taxes enables the government to do humanitarian work, supporting it through taxes. Another issue pointed out by Awasthi et al. [35] is the protection of the environment. They believe that companies’ environmental and natural resource protection programs for future generations are part of social programs and ultimately increase social transparency. Finally, they state that social transparency gains a higher status by incorporating social effects and adapting local values into its social programs. Figure 3 shows the stock-flow diagram for customer satisfaction.

Lv et al. [70] argue that the higher the customer satisfaction, the higher their trust. Moreover, satisfaction with the supply chain and any other business activity is greatly affected by two factors, price and delivery time. The shorter the time and the lower the cost of customers receiving their orders, the more satisfied they are. Thus, delivery costs are affected by delivery time, so more expensive technologies are needed to quickly deliver goods and services to customers (e.g., shipping goods by air instead of rail). The delivery cost is also affected by the price rate. The higher the service price, the higher the delivery cost, which reduces customer satisfaction, assuming other factors are constant.

Lakerveld et al. [71] suggested that profitability is affected by supply chain factors. The higher the quality of the service provided by the supplier, the less the error and associated costs are reduced. Costs are also related to the supplier risk. The lower the risk occurs, the lower the cost and error, and the higher the profitability. Moreover, the lower the price of service prices, the higher the profitability. They are assuming a fixed income and a decrease in the cost. Finally, as demand increases, the income increases, and profits increase as sales increase. However, these four factors themselves are influenced by other factors. The more the consumer trusts the organization and its product, the higher their demand from the organization. The average quality of supplier services also depends on various factors. Increasing the experience of suppliers’ personnel increases the quality of their services. The number of suppliers affects the quality of services. The higher the number of suppliers, the more difficult it is to maintain the quality of services due to the different suppliers. Another factor is the adaptation to environmental and community values. If a supplier does not adhere to regional values, the quality of their services decreases. The community of that area also hinders them.

Concerning supplier risk, factors such as flexibility in payment methods, information security, reliability, percentage of latency, and product quality are also influential. One of the biggest issues for businesses is financing. When suppliers’ flexibility in payment methods increases and they have a high level of alignment with the organization, the organization’s likelihood of failing decreases. Flexibility is one of the most important factors for choosing the right supplier. Besides, a lot of information is exchanged between the organization and suppliers. When this information is shared with high security, that supplier is the right supplier. Generally, the more confidence in the supplier, the lower is the risk of working with them. As pointed out earlier, one of the most important factors in a chain is timely delivery, which depends partly on the supply chain and supplier tardiness. The fewer delays a supplier have, the lower the risk of working with him. Another factor affecting the supplier’s risk is the quality of the products offered by him. The higher the quality of the products, the lower the ancillary costs, such as after-sales services and returned goods, which reduce the risk of cooperation with the supplier.

As stated earlier, this study aims to model the selection of suppliers by taking into account CSR criteria. SD is used to do this. Besides, the dynamics between the studied indicators are considered to achieve the possible optimality. In the next step, the validity of the proposed algorithm for solving the problem is measured. Finally, after confirming the validity and efficiency of the proposed solution method, the study’s findings are summarized, and some suggestions are offered for future research (Fig. 5).

Fig. 5
figure 5

Model loops

Full size image

The higher customer satisfaction causes the higher trust in their organization (according to the literature review [70] and the first loop). Moreover, the more consumers trust the organization and its product, the higher their demand will be. As demand increases, the number of suppliers must increase to meet this demand at the right time. Furthermore, the availability of materials and products decreases the delivery time. By reducing this time, the average delivery cost decreases, and thus customer satisfaction increases. This loop is always in place. In the second loop [35], the higher social transparency, the greater the social legitimacy. Besides, as social legitimacy increases, the social effects of the organization also increase. By increasing these effects and creating the belief that it has already found its place, the organization begins to reduce the cost of implementing its social programs, which reduces social transparency. According to Lakerveld et al. [71], the third loop shows the lower the risk of suppliers, the lower the cost will be paid for risks and errors, and the lower the cost, the higher the profit. Finally, the higher the profit, the ability to support the supply chain increases.

Model validation

Identifying the structure underlying “correct” behavior is a multidimensional process: problem representation, logical structures, and mathematical and cause–effect relationships. Forrester and Senge [72] discussed some of the tests used to validate an SD model [65] structurally:

  • Boundary adequacy: Are the important concepts and structures for addressing the issue of politics endogenous?

  • Structural verification: Is the model structure compatible with the descriptive knowledge related to the modeling system?

  • Parameter verification: Are the model parameters compatible with the descriptive and numerical knowledge related to the system?

  • Behavior reproduction: Do any of the equations in the model correspond to the real system?

  • Boundary conditions: Do the selected parameters show a logical behavior when they reach the assigned boundary values?

Barlas [73] has shown that the behavioral sensitivity test, originally proposed by Forrester and Senge [72] as a behavioral validity test, can detect the major structural defects of the model. However, the model can establish exact behavioral patterns. This test is a structure-based behavior test that shows whether the real system is highly sensitive to the parameters to which the model behavior is highly sensitive. This analysis is called sensitivity analysis [74].

  1. A.

    Boundary adequacy test

Each variable is omitted to show its effect on the whole model to examine the impact of the model’s variables. Figure 6 shows the effect of eliminating the legal requirements rate. This factor affects social transparency. Omitting this variable means ignoring it in the simulation (not the absence of this variable in the real world). As can be seen, these variables are strongly influenced by each other. Figure 6 shows the extent to which social transparency is reduced by removing this factor.

Fig. 6
figure 6

The effect of removing the legal requirements rate on social transparency

Full size image

Figure 7 shows the effect of eliminating the cost of compliance with the region’s ethical values on customer satisfaction.

Fig. 7
figure 7

The effect of eliminating the cost of compliance with the region’s ethical values on customer satisfaction

Full size image

Skipping this factor also leads to reduced customer satisfaction. Accordingly, the more the ethical values of the region are ignored, their satisfaction with the organization’s services decreases due to the creation of moral conflicts between the organization and customers. Elimination of this factor can be equated with not achieving ethical compliance between the organization and customers, thus reducing customer satisfaction. Figure 8 shows the effect of eliminating the supplier risk on profits.

Fig. 8
figure 8

The effect of eliminating the supplier risk on profits

Full size image

As can be seen, when the supplier’s risk is low, costs such as the cost of shortages or delivery delays due to delays in the supply of goods by the supplier also increase, decreasing profits.

  1. B.

    Boundary conditions test

In this test, the model’s behavior is assessed when inputs are in the boundary conditions, i.e., when they are at their lowest or highest boundaries. This test shows whether the model is stable in these conditions or not. The boundary adequacy test examines variables in the infinite states (minimum and maximum values).

  • Condition 1: Increasing the supplier risk rate to zero (Fig. 9).

Fig. 9
figure 9

Model behavior when the supplier risk rate is close to zero

Full size image

Suppose the supplier’s risk decreases to a very low level. In that case, the costs of error, product shortage, or delivery delays due to delays in the supply of goods by the supplier will decrease, leading to higher profits. This issue is one of the most important goals pursued in this study.

  • Condition 2: Decreasing the supplier risk rate to zero (Fig. 10).

Fig. 10
figure 10

Model behavior at the average delivery time

Full size image

If the average delivery time decreases, as shown in Fig. 10, customer satisfaction increases. By reducing the delivery time, the customer receives their goods faster, and thus they are more satisfied with the services.

  • Condition 3: The cost of the social program is at the worst level (Fig. 11).

Fig. 11
figure 11

Model behavior when the cost of the social program is at the boundary conditions

Full size image

As shown in Fig. 11, if the cost of a social program reaches its lowest point, the level of social transparency also decreases dramatically. The reason is that when there are fewer social programs, the organization has less social contact with its customers, so it is socially unknown by customers.

  1. III.

    Behavior reproduction test

The behavior reproduction test is conducted to find out whether the research model can reproduce and display the system behavior in real conditions or not. Following an extensive review of the literature, the researcher believes this study addresses variables affecting profitability, customer satisfaction, and social transparency, so it can predict the system’s behavior after identifying the criteria. As shown in Fig. 12, social transparency can be increased by controlling and increasing the cost of social programs.

Fig. 12
figure 12

Social transparency

Full size image
  1. IV.

    Scenario making

After analyzing the model factors and assessing their impact on the main research variables, practical policies are developed by defining different scenarios. Following the experts’ opinions and the recent studies in the literature, two general scenarios were tested by taking into account different conditions governing the organization and the government (Fig. 13).

  • Scenario 1. The normal government actions in terms of environmental protection laws: under this scenario, the government does not impose any pressure or requirement for protecting the environment. To implement this scenario, the legal requirement rate, according to the literature and experts’ opinions, is about 35%, and no tax penalty is imposed on organizations that harm the environment when rendering their services.

  • Scenario 2. Mandatory government actions in terms of environmental protection laws: under this scenario, the government insists on environmental protection by imposing more legal requirements and levying higher tax rates to penalize environmentally harmful organizations based on the environmental assessment of organizations. In this scenario, the legal requirement rate is about 50%, and it is assumed that environmentally destructive companies will have to pay 5% more tax.

Fig. 13
figure 13

Testing the scenarios

Full size image

The research model is tested under these scenarios, and the changes of the variables of profitability, customer satisfaction, and social transparency are plotted without the realization of any specific policy (Fig. 13A–C).

Under any of these scenarios, different policies should be adopted to manage the organization and success in the intended goals, i.e., profitability and customer satisfaction, by taking into account social transparency. These policies are discussed to find out their outcomes and determine the best time for implementing each policy (Fig. 14).

  • Policy 1: Increasing the funds for implementing various social programs and adapting the organization’s services with the local values of the areas covered. According to the experts, the cost of social programs and adaptation to the moral values ​of the area will be increase by 3 and 5%, respectively, to implement this policy.

  • Policy 2: Regardless of social issues, the organization seeks to optimize its suppliers. According to experts, the average distance between suppliers is reduced by 1%, and two units increase the number of suppliers.

  • Policy 3: To cover both social issues and supplier optimization simultaneously, the funds for social programs and adhering to the region’s moral values will increase by 2, and 3%, respectively, and the distance between suppliers will decrease by 1%, and the number of suppliers will increase by 1 unit.

Fig. 14
figure 14

Different policies under the two scenarios

Full size image

As shown in Fig. 14, under the first scenario, the best policy to implement is Policy 3, and if Scenario 2 is realized, the best policy to implement is Policy 2. After assessing the possible scenarios for government actions (regarding environmental requirements for organizations and their activities and services), different policies are considered under both situations. The model is examined in all of these cases, as shown in Fig. 14. Accordingly, if the government maintains the status quo and does not impose any particular requirements, the third policy is implemented. This issue increases the funds for implementing various social programs and adapting the organization’s services to the local values of the community in question. Besides, the average distance among suppliers is reduced, and the number of suppliers is increased to reduce the delivery time and increase funds for implementing social programs. These actions improve profitability, customer satisfaction, and social transparency. Therefore, if the government’s behavior regarding environmental requirements does not change, the third policy is the best option as it can simultaneously improve profitability, customer satisfaction, and social transparency. However, if the government puts mandatory requirements for environmental protection into force, the second and third policies almost equally optimize social transparency. Besides, with the implementation of the second policy, i.e., reduction of the average distance between suppliers and the increase in the number of suppliers, customers assume the organization complies with the environmental requirements due to government pressure. This issue reduces the costs and delivery time due to reduced average distance between suppliers, thus improving profitability, customer satisfaction, and social transparency. Accordingly, if the government stipulates mandatory environmental requirements, it is suggested that the organization focus all its planning efforts on optimizing its suppliers.

Conclusion

In the current dynamic business environment, choosing the right supplier can play a vital role in a company’s business strategies. Companies need to select more effective suppliers to cope with the increasing production complexities, attract more customers, ensure the desired quality, provide special and better services, and deal with the increased competitive pressure. Choosing a supplier and benefiting from it is always the best strategy for creating socio-economic benefits. Moreover, a couple of theories consider the development of supplier selection as a requirement for continuous growth and improvement. Thus, an organization's strategies, decisions, and actions to modify and develop its supplier selection process form the core of the socio-economic development process of that company. If an organization can lead this process systematically, it paves the way for success and continuous development and can hope for survival in the current competitive and turbulent market. This study assessed indicators affecting supplier selection and CSR practices to provide a dynamic model for supplier selection. This mode can identify potential sources of risk and provide guidelines for implementing effective strategies through an integrated approach among supply chain members to increase CSR practices for supplier selection. This subject will help them to achieve more competitive power and satisfy customers optimally. The system dynamics model for supplier selection was developed by considering profitability, productivity, social transparency, and customer satisfaction three stages. In the first stage, the indicators affecting supplier selection were developed based on the study's theoretical framework and literature review. In the next stage, a subjective model was extracted for supplier selection by surveying the experts in the field, and the results of model estimation were analyzed. In the third stage, the SD model was developed and simulated after performing the necessary analyses.

The final output from the third stage was a dynamic model of the supplier selection system that takes into account CSR practices. Accordingly, if the government maintains the status quo and does not impose any particular requirements, the third policy is implemented. This increases the funds for implementing various social programs and adapting the organization's services to the local values of the community in question. Besides, the average distance among suppliers is reduced, and the number of suppliers is increased to reduce the delivery time and increase funds for implementing social programs. These actions improve profitability, customer satisfaction, and social transparency. Therefore, if the government's behavior regarding environmental requirements does not change, the third policy is the best option as it can simultaneously improve profitability, customer satisfaction, and social transparency. However, if the government puts mandatory requirements for environmental protection into force, the second and third policies almost equally optimize social transparency. Besides, with the implementation of the second policy, i.e., reduction of the average distance between suppliers and the increase in the number of suppliers, customers assume the organization complies with the environmental requirements due to government pressure. This issue reduces the costs and delivery time due to reduced average distance between suppliers, thus improving profitability, customer satisfaction, and social transparency. Accordingly, if the government stipulates mandatory environmental requirements, it is suggested that the organization focus all its planning efforts on optimizing its suppliers. With practical attention to the results obtained in this research, practical suggestions are presented to industry managers as follows:

  • Extracting and periodically updating each of the indicators affecting the selection of a supplier for each of the organizational units (involved in the selection of a supplier) and extracting the amount of impact specifically for use in possible cases.

  • Investigate the possibility of a set of factors that the industry should consider in selecting suppliers with different technological readiness levels (TRL).

  • Preparation of quality checklist of supplier selection according to evaluation criteria.

  • Prepare a quantitative checklist to update and quickly enter information into the SD model designed to select its supplier according to quantitative factors and related coefficients.

  • Identify regional and international opportunities and challenges to better select supplier selection criteria in the industry and examine their impact on supplier selection and allocation costs.