supply chain

Supply chain management is about communicating with suppliers and customers to reduce costs and increase customer satisfaction. In this research, in order to optimize and solve problems and conflicts in supply chain management, IoT networks combined with blockchain technology and artificial intelligence are used. In order to combine AI with the IoT network, it is done by initializing peripheral systems and after this stage, data is read from sensors. In the next stage, the output decision is received from the MLP NN model using sensor data and by preparing a message with decision-making, the output decision is published using the MQTT model and finally the AI is combined with the IoT network. In order to combine blockchain with the Internet of Things network, first, after the deployment of the Internet of Things nodes, the remaining energy of the nodes, the clustering mechanism, the maximum amount of remaining energy for selection are calculated and checked, then the cluster head is selected and activated, finally the role of the cluster head is published and the cluster head is activated, and the Internet of Things is combined with blockchain. The results obtained indicate that combining Internet of Things networks with blockchain technology and artificial intelligence in supply chain management will improve security, transparency, accuracy, performance improvement, quality improvement, cost optimization, supply chain management optimization, and customer satisfaction and trust.

Despite the importance of retirees' knowledge in resolving various complexities and challenges in the refinery supply chain, including risks arising from oil price and market fluctuations, lack of cooperation and coordination among supply chain members, oil challenges such as leaks and fires, etc., in recent years, a significant amount of knowledge has been lost with the retirement of employees. The purpose of this study is to analyze the return of retirees' knowledge in the Isfahan Refinery supply chain.

Design/methodology/approach: 

The present study is applied in terms of its purpose, descriptive-causal in terms of its nature and method, and a cross-sectional survey-type non-experimental study in terms of data collection. Initially, 12 factors affecting the return of retirees' knowledge were identified based on a literature review and research background and were approved by academic and industrial experts (Isfahan Refinery). In order to present the conceptual model of the research, the interpretive structural modeling approach was used. In this section, a paired comparison questionnaire was designed among the factors affecting the return of retirees' knowledge in the Isfahan Refinery supply chain. Then, using the judgmental sampling method and opinion polls from 15 academic and industrial experts, the relationship between the factors affecting the return of knowledge of retirees was identified and a conceptual model was presented. In order to confirm or reject the conceptual model, the structural equation modeling approach and Smart PLS3 software were used. Using the convenience sampling method, 300 questionnaires were distributed among the employees and managers of Isfahan Refinery, of which 243 questionnaires were returned. The validity of the research questionnaire was confirmed using convergent validity (factor loading coefficients and AVE criterion) and divergent validity (Fornell-Larker table). Also, the reliability of the research questionnaire was confirmed using Cronbach's alpha and composite reliability criteria.

The results of this study showed that government support with a path coefficient of 0.495 and knowledge and experience of retirees with a path coefficient of 0.416 have an effect on top management support, top management support with a path coefficient of 0.789 on financial incentives, and with a path coefficient of 0.854 on non-financial incentives. In addition, the results of the study showed that financial incentives with a path coefficient of 0.383 and non-financial incentives with a path coefficient of 0.522 have an effect on the culture of maintaining and promoting organizational knowledge, the culture of maintaining and promoting organizational knowledge with a path coefficient of 0.817 on the use of advanced technologies, the use of advanced technologies on the desirability of working environment with a path coefficient of 0.787, the desirability of the work environment with a path coefficient of 0.805 on employee participation and collaboration, and employee participation and collaboration with a path coefficient of 0.774 on organizational coherence and stability. Other results of this study include the effect of organizational cohesion and stability with a path coefficient of 0.797 on facilitating communication and the effect of communication facilitation on the quality of products and services with a path coefficient of 0.804. In addition, the results of the study showed that the return of knowledge of retirees plays a decisive role in resolving challenges in the supply chain of Isfahan Refinery. In addition, based on the conceptual model presented from the interpretive decision-making modeling approach, government support and the knowledge and experience of retirees have been identified as key factors in the return of knowledge of retirees in the supply chain of Isfahan Refinery.

Research limitations/implications: 

The present study, like many studies, has limitations in the field of conducting research processes. Among these limitations, we can mention the lack of examination of different cycles between stimuli, which has always existed as a structural limitation in the structural equation technique, and it is suggested that in future research, the return paths between the relationships formed in this study be examined. Also, considering the use of a questionnaire as a data collection tool and its distribution among Isfahan Refinery employees and managers and academic experts, their mindset and attitude in completing the questionnaire may have affected the results of the present study. In addition, the present study was related to the Isfahan Refinery supply chain, and its results should be generalized with caution to other similar organizations and industries.

Practical implications: 

This research has various implications for the refinery supply chain. Using retirees' knowledge can reduce various problems in the refinery supply chain, including oil field exploration, oil price fluctuations, lack of coordination and cooperation between supply chain members, high repair and maintenance costs, transportation costs, etc. In addition, the results shown in this research based on the use of retirees' knowledge in solving various supply chain problems and challenges can encourage other industries to use their retirees' knowledge. Also, the return of retirees’ knowledge can lead to improved crisis management and reduced risks in the supply chain, as these individuals usually have experience in critical situations and can help design and implement successful strategies. This process can lead to increased productivity and reduced costs in the supply chain, which is of particular importance for the refinery’s competitiveness. Furthermore, fostering collaboration between active employees and retirees can promote a culture of continuous learning and knowledge sharing that is critical to adapting to industry change. This engagement not only helps to facilitate the onboarding process for new employees, but also helps to preserve organizational knowledge that might otherwise be lost. Involving retirees in advisory roles can also help to strengthen organizational resilience, as their guidance can be invaluable during times of crisis or transition. Ultimately, these outcomes lead to increased productivity, reduced operating costs, and improved competitiveness, better positioning the refinery for sustained success in a dynamic marketplace.

Originality/value:

 Using the experiences and knowledge of retirees in the Isfahan Refinery supply chain can bring various benefits such as reducing costs and time wasted due to trial and error, reducing rework, improving performance, etc. Therefore, it is necessary to identify key factors in the return of retirees' knowledge, given the large amount of knowledge that is lost when people retire from the Isfahan Refinery.

The supply chain is the backbone of global commerce, ensuring the smooth and continuous flow of products and services between producers and domestic and industrial customers. An efficient supply chain enables businesses to meet customer needs promptly, reduce operational costs, and enhance their competitive edge effectively. Also, artificial intelligence (AI) and its subset technologies (such as machine learning and big data) have empowered businesses to make more accurate data-driven decisions regarding inventory levels, production, and delivery planning, thus optimizing route planning, and reducing costs and delivery times. Over the past decade, the issue of AI adoption in the supply chain and its potential implications has attracted the attention of both businesses and researchers in the field of supply chain and logistics management. Despite numerous studies conducted over the past decade, the diversity of these studies in the supply chains of various industries and production sectors, along with a wide range of identified factors and the lack of a comprehensive classification of these factors, have led to a lack of clarity in the findings regarding the antecedents and outcomes of AI adoption in the supply chain. Therefore, the main objective of this research is to create transparency in the results obtained from previous studies in this field.

The research method employed in the present study is qualitative and of the meta-synthesis type. This method is utilized to identify the antecedents and consequences of AI technology adoption in the improvement and strengthening of the distribution network, aiming to integrate multiple studies to create comprehensive and interpretive findings. The method proposed by Sandelowski and Barroso (2007) is one of the most prominent methods for conducting meta-synthesis and provides better results. Thus, this seven-step method has been used in this research. The research falls under applied research, and the data collection approach is based on library research. Data gathered from papers indexed in the research database Scopus and Web of Science, as well as two Persian databases of Comprehensive Portal of Humanities and the Scientific Information Center of Jihad Daneshgahi. Also, the time period related to English articles was 2013 to 2023 and the time period related to Persian articles was 1392 to 1402.

By examining the factors leading to and resulting from the adoption of artificial intelligence (AI) technology, a conceptual framework was established that classifies these elements into two main categories, with 16 factors and 121 components. The antecedents consist of organizational, environmental, technological, human, institutional, and economic factors. On the other hand, the consequences are divided into communication, knowledge, experiential, financial, product, workforce, organizational, supply chain, and environmental outcomes. The innovation of this research lies in the comprehensive identification and categorization of the diverse antecedents and consequences associated with AI adoption in the supply chain, addressing gaps left by previous studies. These studies often lacked clarity and coherence in their findings due to the absence of a unified classification. The results from the meta-synthesis indicate that organizational and environmental factors are the most frequently cited antecedents, while organizational and supply chain consequences are the predominant outcomes. This suggests that in order to foster AI adoption in the supply chain, special emphasis should be placed on organizational and human factors, as they are pivotal in shaping organizational systems. Additionally, the research highlights a notable gap in the literature concerning economic antecedents and knowledge-related outcomes, as well as workforce-related consequences, indicating limited existing knowledge in these areas.

the research provides a comprehensive framework for understanding the diverse antecedents and consequences of adopting artificial intelligence (AI) technology within the supply chain. By categorizing these elements into a structured model, the study highlights the significance of organizational and environmental factors as primary antecedents, while organizational and supply chain outcomes emerge as the most frequent consequences. This comprehensive approach addresses the gaps and inconsistencies found in previous studies, offering a clearer understanding of the dynamics at play in AI adoption within supply chains. Emphasizing the role of organizational and human factors, the research underscores their fundamental importance in shaping effective organizational systems. Additionally, the study identifies a notable lack of research in areas such as economic antecedents and knowledge-related outcomes, as well as workforce-related consequences. This gap in the existing literature points to the need for further investigation to enhance the current understanding and knowledge base in these fields. Overall, the research contributes to a more transparent and cohesive body of knowledge, facilitating better decision-making and strategic planning for businesses looking to integrate AI technology into their supply chain operations. This study not only addresses the gaps and inconsistencies in previous research but also paves the way for more informed decision-making and strategic planning, ultimately enhancing the integration of AI technology in supply chain operations.

The current research was conducted with the aim of identifying and evaluating the components of the supply chain in order to improve the effectiveness of the organization's performance. In terms of purpose, this research is fundamental-applied and also, in terms of the type of data, it is mixed (qualitative-quantitative) of the exploratory type, which is theoretical coding in the qualitative part and descriptive-survey in the quantitative part. The studied population in the qualitative section includes experts, specialists and knowledgeable people in the field of supply chain, and in the quantitative section it includes all managers and experts of home appliance industries in Tehran. The sample size in the qualitative section was 20 interviewees according to the principle of saturation and the purposeful sampling method, and in the quantitative section, 355 people were selected based on the calculation of the sample size in structural equations and the stepwise cluster random sampling method. The data collection tool is semi-structured interviews in the qualitative part and a 66-item researcher-made questionnaire derived from the theoretical foundations, research background and interviews with experts in the quantitative part. In the qualitative part, in order to calculate the validity according to the experts, and to calculate the reliability of the retest method and intra-subject agreement, in the quantitative part, in order to calculate the validity, content and construct validity were used, and in order to calculate the reliability,

In this research, using the double-objective mixed integer linear programming method, an optimal supply chain network for the collection and recycling of urban waste has been presented in terms of source separation and the uncertainty of per capita waste generation by citizens. Due to the uncertainty in the parameters of the problem, the two-stage stochastic programming method has been used to model the problem. The objective functions include an economic function to minimize investment costs and a social objective function to maximize the amount of recycling. In order to accurately solve the problem on a large scale, the Lagrange release method has been used. To validate and confirm the effectiveness of the model presented in this research, the model was implemented on a case study in the city of Karaj. According to the obtained results, to increase the amount of recycling in the waste supply chain network, more infrastructural and operational investments are needed. By increasing recycling, the harmful environmental and destructive effects of burying and burning waste will be reduced. The Lagrange release solution method can be used as a suitable method to reduce problem-solving time. In this research, it was observed that the Lagrange release method can solve large-scale problems with appropriate accuracy and in less time compared to the commercial CPLEX solver.

The perishable food supply chain is a critical system that includes the production, processing, distribution and consumption of food and ensures the availability and access to food for the population. Today, the complexities of the business environment, uncertainty and environmental fluctuations have made the supply chain vulnerable to various types of challenges. The aim of this research is to identify and prioritize the challenges of the perishable food supply chain and provide effective insights and solutions to reduce the impact of these challenges.The current research is Qualitative and Quantitative . Fuzzy Delphi method and Ordinal Priority Approach (OPA) were used for data analysis. At first, 18 challenges of the perishable food supply chain were extracted by reviewing the research literature in reliable scientific databases, then the final 13 challenges were selected using the fuzzy Delphi method. In the following, by preparing a questionnaire and experts' opinions, the challenge of the perishable food supply chain was ranked using the OPA method. The statistical population of the research is made up of academic experts, managers of the agricultural jihad and the food and drug organization of Kerman province. For this purpose, 10 people were selected as a sample using the purposeful sampling method. By prioritizing the challenges, the first to third place was awarded to "weak demand management", "lack of regulations and government support" and "lack of product quality control". The results of this research have been obtained based on scientific texts and the opinion of domestic experts. Therefore, it can be new and comprehensive insights for organizations, experts and researchers in this field.

Lack of trust in the supply chain is a major obstacle to the cooperation of supply chain members. The goal of this paper is to present a model for trust in the supply chain based on risk management in the pharmaceutical industry of Iran.

This research is survey and cross-sectional in terms of data collection method and in terms of the type of methods used and its nature is composite and mixed. At first, the results of previous researches were analyzed using Meta Synthesis. Twenty-six articles out of 100 articles found between 1990 and 2023 were analyzed. Next by conducting semi-structured interviews and targeted sampling with supply chain experts in Iran's pharmaceutical industry using thematic analysis approach. The results of meta-synthesis and thematic analysis were combined and risk factors of Iran's pharmaceutical industry supply chain was identified. After analyzing the validity of the results with the method of Content validity ratio, a model including ten factors was obtained as the final risk-creating factors of the Iranian pharmaceutical industry.

The first category, the factors in which there is trust, were: the complexity and incompatibility of information systems, low quality of raw materials and products, the technologies of the distribution sector, the increase in the price of raw materials and medicine, transportation and insurances. The second category risks are environmental factors, demand fluctuations, Lack of timely supply of raw materials, unavailability of medicine and its timely delivery, currency supply, foreign financial payments.

The results of the research showed that there is the highest level of trust in the factors: the increase in the price of raw materials and medicine, transportation and insurance and the lowest level of trust in: the complexity and incompatibility of information systems, the low quality of raw materials and products, as well as distribution technologies. In the factors, demand fluctuations, lack of timely supply of raw materials, unavailability of medicine and its timely delivery among the beneficiaries, there is no trust and the level of risk is low. Environmental factors, currency supply and foreign financial payments were the most important risks that should be prioritized in risk management.

The purpose of this research is the value-added model of the supply chain of petrochemical industries with a sustainable development approach. According to its purpose, the research method is practical, qualitative-quantitative in terms of implementation, and survey in nature. The statistical population of the research includes 25 managers and experts in the petrochemical industry, and the sampling was done as available judgment, and the interviews continued until theoretical saturation was reached. The tool for gathering information is an interview. To collect and analyze data, Delphi technique was used to identify components. MATLAB software was used for data analysis. In the library analysis, 16 main components were identified and entered into the Delphi technique, and finally all were confirmed. These sixteen criteria are political factors, cultural factors, legal factors, financial factors, individual factors, management factors, information sources, implementation, review, feedback analysis, performance evaluation factors, risk concepts, identification of environmental issues, identification of health issues, learning Issues of safety and added value in the supply chain of petrochemical industries with a sustainable development approach. Based on interpretive structural technique, these components were leveled. And they formed a model in 7 levels. The proposed model leads to the improvement of the economic and commercial performance of companies due to optimization of processes, reduction of wastage, improvement of product quality and reduction of costs. Considering the variety of issues such as safety, health, environment and social standards, this value-added model of the supply chain helps to realize sustainability and continuity.

The supply chain of machine-made carpets has faced several fluctuations in recent years. One of the most important fluctuations is the bullwhip effect. Future study of bullwhip effect in the supply chain can prepare decision makers to deal with possible scenarios. This research has been carried out in the Iranian machine-made carpet industry.

Future study of the bullwhip effect in the supply chain of Iran's machine-made carpet industry

The statistical population of this research is the experts of Iranian machine carpet industry who have been selected by judgment and snowball method. Theme analysis was used to analyze the interviews and Micmac and Scenariowizard software were used for future study.

The results of the interview showed that there are 10 drivers for bullwhip effect in the supply chain, 9 of which were selected as important drivers by Micmac software. Out of 1152 possible scenarios, 3 scenarios were consistent, which are a favorable scenario, an unfavorable scenario and a median scenario.

Finally, suggestions such as reducing start-up time, improving demand forecasting methods, changing the replenishment policy, volume-based discounts instead of batch-based discounts, and vendor inventory management were presented to achieve the desired scenario and avoid the unfavorable scenario.

The primary objective of this research is to design a sustainable supply chain agility model in the oil and gas industry of Bushehr Province. This study employs a mixed-method approach (qualitative-quantitative), content analysis, the fuzzy DEMATEL technique, and the Analytic Network Process (ANP). The statistical population consists of managers, officials, and mid- to senior-level experts from companies operating in the oil and gas industry of Bushehr Province. A sample size of 10 managers, experts, and industry specialists was selected for each stage. The qualitative findings revealed that sustainable supply chain agility indicators are comprised of environmental factors (green purchasing, eco-friendly and economic design and packaging, environmental preservation), economic factors (adoption of information technology, responsiveness, speed, competence, flexibility), and social factors (customer collaboration, ethical purchasing, social responsibility, compliance with laws, integration of relationships, and supplier collaboration). In the quantitative section, the first stage using the fuzzy DEMATEL approach showed that environmental factors fall under the category of effect criteria, while economic and social factors fall under the category of cause variables. ANP findings indicated that among the environmental factors, the green purchasing indicator holds the highest weight (0.33), followed by the environmental preservation indicator (0.087) and eco-friendly and economic design and packaging (0.086). Among the economic factors, the speed indicator has the highest weight (0.0524), followed by flexibility (0.0513) and competence (0.0505). Among the social factors, the customer collaboration indicator (0.0439) and compliance with laws (0.0438) have the highest weight.

Ripple effect has many destructive effects on supply chains because if it occurs, it can involve a large number of layers of the supply chain. After the ripple effect occurs, issues in the supply chain can act as its intensifiers. This issue has not been investigated in previous studies, so this study has identified and modeled them in the Iranian machine-made carpet supply chain. The statistical population of this study is machine-made carpet supply chain experts. The data collection tool in the first stage is a semi-structured interview and in the second stage is a researcher-made questionnaire. The data analysis method in the first stage is content analysis and in the second stage is interpretive structural modeling. The results indicate 40 intensifiers that were grouped into 12 categories, which are: "weakness in planning and foresight", "structural intensifiers", "upstream intensifiers", "weakness in supply", "purity", "operational intensifiers", "technological intensifiers", "weakness in market orientation", "structural weakness of the chain", "chain culture", "human resource intensifiers" and "knowledge intensifier". After identifying these factors, an interpretative structural model was presented. The structural model indicates the great importance of "upstream intensifiers", "structural intensifiers" and "weakness in planning and foresight". Developing different scenarios, creating rapid response teams, creating market monitoring systems and simplifying processes and regulations are among the solutions to deal with intensifiers.

Uncertainty in food supply in the country has heightened the focus on the supply chain of semi-prepared foods. Like many economic sectors, the food supply chain has not been immune to the impacts of the COVID-19 pandemic. Food supply, influenced by the behaviors of governments and customers during such crises, can exacerbate uncertainties in the supply chain, underlining the need for simulating the behavior of supply chain actors. Accordingly, the aim of this study is to develop an agent-based simulation to analyze the relationships between actors in the supply chain of ready and semi-prepared foods concerning raw material imports and final product exports during the COVID-19 pandemic.

This study is applied in its aim and descriptive-analytical in terms of data collection, utilizing agent-based simulation techniques. The case study for simulating pandemic conditions in the supply chain is one of the ready and semi-prepared food companies in Tehran Province with a production capacity of 216,000 tons per month. To identify factors, variables, and parameters for the agent-based model, the research employed thematic analysis, reviewing prior studies, and conducting interviews with 20 industry experts from the ready and semi-prepared food sector and supply chain experts from Amadeh-Laziz Company.

The findings identified three main actors in the food supply chain that play a role in exports: “Government”  (e.g., customs, national standards organization, consumer protection organizations, industry and mining banks, Iran Chamber of Commerce-Industries-Mines and Agriculture, and the Food and Drug Administration), “Supply Chain Companies” (e.g., domestic suppliers, foreign suppliers, manufacturers, and distributors), and “Final Customers” (e.g., domestic and foreign customers). The key uncertainty identified in interviews was governmental intervention or non-intervention in exports and imports.Accordingly, two scenarios were developed: “Governmental intervention in exports and imports”, and “Lack of governmental support in exports and imports during the pandemic”. These scenarios were analyzed using agent-based simulation to examine the relationships, characteristics, and decision-making behaviors of supply chain actors. Additionally, fuzzy DEMATEL was used as a complementary method to identify the most significant relationships among actors in the ready and semi-prepared food supply chain. Simulation parameters included: Import tariffs on raw materials (%), Export tariffs on final products (%), Total monthly domestic demand (in thousand tons), Total monthly foreign demand (in thousand tons), Total monthly demand (in thousand tons), Maximum production capacity (in thousand tons).Simulation variables included: Percentage change in total costs compared to the base price, Percentage of completed domestic customer orders, Percentage of completed foreign customer orders, Total completed orders (total sales volume), Profits from the domestic market, Profits from the foreign market, Percentage of overall profitability. The simulation was performed under the two scenarios of governmental non-intervention and governmental intervention in importing raw materials and exporting final products, with emphasis on two main variables: profitability and order fulfillment rate, which encapsulate the model’s overall outcomes.

The results indicate that under the governmental intervention scenario, profitability increased by 6%, and total order fulfillment improved by 14%. Based on the defined criteria for scenario comparison, the second scenario (governmental support) provides more favorable conditions for supporting domestic production, enhancing employment levels, and ensuring food security in both domestic and international markets.

This research aims to explain a model for evaluating factors affecting supply chain strategies in small and medium dairy companies in Khorasan province. The current research is a qualitative-quantitative study using the thematic analysis method to explain the mentioned model. The data collection method in the qualitative phase was a semi-structured interview. The study population of the qualitative research included the employees and elites of small and medium-sized production companies active in the dairy industry located in North, Razavi, and South Khorasan provinces, who had theoretical and practical knowledge in the field of the subject under study and sufficient knowledge and mastery over the phenomenon under investigation. The studied sample also consisted of people with at least 10 years of work experience and a complete understanding of the practical concepts of the supply chain. The participants were selected through the purposive sampling method. Since data saturation determines the sample size in the qualitative section, more samples were interviewed until the data was sufficient. Then, quantitative analysis was done to validate the obtained model, including exploratory and confirmatory analysis. A small part of the statistical population, including all dairy industry workers, was considered, and a list of all managers and activists of this industry was prepared. Sampling was done in a simple random way among the names. Data collection was carried out using a researcher-made questionnaire. Quantitative data analysis was also done using structural equation modeling. Based on the results obtained, seven effective factors in the supply chain strategy were identified: task factors, environmental green factors, economic factors, resilience, and its dimensions, including marketing mix, customer orientation, and organizational structure were identified. The results of the quantitative part, with the confirmation of the qualitative findings, indicate the significant impact of all factors on the supply chain strategy. The results show that the extracted factors of the supply chain were less observed in other studies and are appropriate for the Iranian landscape. The innovation of this research can be examined from many other aspects. For example, the resilience raised in connection with Iranian society is different from that in other countries. The style of dealing with this issue differs in Iran and other countries.

Improving the financial and operational performance of supply chains requires a strong focus on inventory management. Additionally, implementing financial measures at the operational level is crucial for enhancing overall performance. By establishing effective financial strategies at this level, the efficiency and profitability of the supply chain can be significantly increased. This article aims to apply the Atride-Sterling approach to enhance the financial and operational performance of supply chains.

The participants in this research were supply chain managers with a minimum of 15 years of teaching experience and a master's degree or higher. Participants were selected through purposive sampling, focusing on individuals with teaching experience at the elementary school level. A total of 22 experts participated in the study. Data collection was conducted using semi-structured interviews, which continued until theoretical saturation was reached. Thematic analysis, based on the Atride-Sterling model, was employed to analyze the qualitative data. To ensure validity, Holstein's coefficient, Scott's Pi coefficient, Cohen's kappa index, and Krippendorff's alpha were utilized, all of which were confirmed. Atlas.ti software was used for the thematic analysis.

The results of the thematic analysis indicate that from the 54 available indicators, 13 fundamental themes can be identified, leading to the emergence of two categories of constructive themes: financial performance and supply chain operations. The identified model encompasses these two constructive themes, which include 13 primary themes: inventory management, financial reporting, operational financial measures, coordination, risk management, supply strategy, demand planning, technology management, logistics strategy, customer engagement, flexibility, supplier relationship management, and quality assurance on a global scale. Given the critical importance of the supply chain to organizational success, enhancing the financial and operational performance of this supply chain is essential.

Optimizing coordination between internal and external stakeholders, along with timely logistics management and the sharing of financial information, will enhance productivity, reduce costs, and improve competitiveness. Also, recognizing and managing various risks such as price risk, supplier risk, and market risk, are crucial measures for effectively addressing environmental challenges and seizing opportunities.

In the current era, the competition between organizations has also been transferred to the supply chain. The stability of the supply chain is a necessary and sufficient condition for the stability of companies. The most basic condition for the sustainability of an organization is its profitability. Therefore, the aim of this research is to present a mathematical model based on game theory in order to maximize profit in the closed loop supply chain. The main goal in the closed loop supply chain is to eliminate waste and optimize the consumption of products in such a way that the products are separated into two groups, new product and used product, and there is the possibility of returning used product and reproduced products in the consumption cycle. has it. However, all members of the chain benefit. But the fact is that it is not clear which factors can maximize the benefits of the closed loop supply chain members. For this purpose, in the current research, a mathematical model based on game theory is designed in which cooperation between upstream and downstream members occurs. Finally, it is clear what effective factors such as demand, returned products, recycled products and remanufactured products have on the profit of the chain members. In other words, how can the total profit of chain members and the share of individual members be increased and under what conditions this increase is achieved.
The general results from the analysis of this research show that demand is a factor that can increase the profit of all members of the chain, and returned products have a positive effect on the profit of all members, while recycled products and remanufactured products, in If it increases, it only increases the profit of the reproducer and recycler and reduces the profit of other members significantly at different levels.
In this research, the profitability of the closed loop supply chain has been investigated using game theory and the parameters of demand, returned products, recycled products and remanufactured products affecting the profitability of the closed loop supply chain have been simultaneously investigated. Therefore, the innovation of this research includes the simultaneous examination of demand, returned products, recycled products and remanufactured products affecting the profitability of the closed loop supply chain.

The current research aims at the possible scenarios of the sustainable supply chain in the dairy industry with a future research approach. The method of qualitative and applied research and in terms of data collection is a descriptive-survey research. The statistical population of the research includes 11 expert professors (academic experts), experts and specialists working in the country's dairy industries (industry experts), and independent customers and researchers (public stakeholders). The non-probability sampling method is purposeful (judgmental) and snowball. Effective factors and key uncertainties in the dairy industry were identified by the fuzzy Delphi method, then three compatible scenarios were identified with the help of Wizard Scenario software. According to the findings of the research, the factors influencing the driving forces and key uncertainties on the future of the sustainable supply chain in the dairy industry were identified, which include: foreign trade and communication regulations, the state of competition laws, attention to health and safety issues and standards, attention to Inventory maintenance for each product unit, foreign exchange rate fluctuations, inflation rate fluctuations, the cost of raw material production, the cost of spoilage of dairy products, and the motivation and willingness of private investors to invest in the dairy industry. The results of the research showed that after the possible states for each of the key uncertainties were identified with the help of the Wizard Scenario software, three compatible scenarios were obtained with the titles "prosperous", "realistic" and "incurable". . On the other hand, it was determined that the foreign exchange rate and inflation have the greatest impact on other factors and the future of the dairy industry.