Shahid Beheshti UniversityJournal of Industrial Management Perspective2251-987414420241121Agent-Based Simulation of Agent Relationships in the Ready and Semi-Prepared Food Supply Chain for Export During the COVID-19 Pandemic (Case Study: Amadeh-Laziz Company)Agent-Based Simulation of Agent Relationships in the Ready and Semi-Prepared Food Supply Chain for Export During the COVID-19 Pandemic (Case Study: Amadeh-Laziz Company)93610467210.48308/jimp.14.4.9FAAliMohagharProfessor, Faculty of Industrial Management and Technology, College of Management, University of Tehran, Tehran, Iran.RohollahGhasemiAssistant Professor, Faculty of Industrial Management and Technology, College of Management, University of Tehran.AliAskarianPh.D. student, Faculty of Industrial Management and Technology, College of Management, University of Tehran, Tehran, Iran.Journal Article20240301<strong>Introduction: </strong>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.
<strong>Methods: </strong>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.
<strong>Results and Discussion: </strong>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.
<strong>Conclusions:</strong> 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.<strong>Introduction: </strong>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.
<strong>Methods: </strong>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.
<strong>Results and Discussion: </strong>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.
<strong>Conclusions:</strong> 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.https://jimp.sbu.ac.ir/article_104672_6a7b53511f3fd1352efeffd0952e8869.pdfShahid Beheshti UniversityJournal of Industrial Management Perspective2251-987414420241121Analysis of Consumer Perspectives in End-of-Life Management of Waste Electrical and Electronic Equipment (WEEE)
Using Twitter DataAnalysis of Consumer Perspectives in End-of-Life Management of Waste Electrical and Electronic Equipment (WEEE)
Using Twitter Data376710509410.48308/jimp.14.4.37FASaraShafahiPh. D Candidate, Management & Accounting Faculty, Shahid Beheshti University.0000-0001-6387-1071AkbarAlem TabrizProfessor, Management & Accounting Faculty, Shahid Beheshti University.SajjadShokouyarAssistant Professor of Management at the Faculty of Management and Accounting, Shahid Beheshti UniversityJournal Article20231129<strong>Introduction:</strong><strong> </strong>In today’s digital age, the rapid increase in the use of electrical and electronic devices has led to massive production of Waste Electrical and Electronic Equipment (WEEE). This trend poses serious challenges for solid waste management, making proper disposal of such waste a critical issue. According to global reports, the volume of WEEE is steadily increasing, emphasizing the need for effective waste management practices. Studies have shown that the rise in WEEE not only harms the environment but also has negative impacts on public health and natural resources. Consumers, as primary generators of this waste, play a pivotal role in its management. Therefore, understanding consumers' perspectives and behaviors regarding the disposal of WEEE is crucial for developing effective waste management strategies. This study aims to identify appropriate options for managing electrical and electronic equipment at their end-of-life stage. It seeks to analyze and identify best practices for managing such waste.
<strong>Methods:</strong> This research adopts a mixed-methods approach, combining quantitative and qualitative methods. In the quantitative phase, data collected from Twitter (approximately 2,905,579 tweets) between May 2019 and April 2022 were analyzed. These data included consumers’ opinions and perspectives on WEEE management. Furthermore, the study employed the Fuzzy Analytical Hierarchy Process (FAHP) to evaluate different end-of-life options. This technique enables a more precise analysis of factors influencing consumer choices and identifies the best options. Based on the findings, various factors—such as accessibility to recycling centers, awareness of disposal methods, distrust in governments, financial incentives, charitable contributions, and concerns about data security—directly influence consumers’ decisions.
These factors significantly shape consumer behavior regarding WEEE and, consequently, their end-of-life choices. Additionally, the analyses reveal that awareness of proper disposal methods and the availability of suitable recycling infrastructure can significantly impact consumer decision-making.
<strong>Results and discussion</strong>: The study identifies four end-of-life options for WEEE: reuse, repair, recycling, and disposal. Using the FAHP technique, the relationships between these factors were examined, and the most suitable end-of-life option was identified based on factors influencing consumer participation in waste management. The results serve as a valuable tool for managing WEEE and contribute to the development of effective end-of-life strategies. These strategies will foster a more sustainable approach to WEEE management.
<strong>Conclusions</strong>: Finally, the research recommends that policymakers and relevant stakeholders design comprehensive educational and awareness programs to enhance consumer knowledge about WEEE management methods. Such initiatives can help promote a culture of sustainable waste management and encourage consumers to adopt more environmentally friendly options. These efforts will play a key role in reducing the negative impacts of WEEE on the environment and public health.<strong>Introduction:</strong><strong> </strong>In today’s digital age, the rapid increase in the use of electrical and electronic devices has led to massive production of Waste Electrical and Electronic Equipment (WEEE). This trend poses serious challenges for solid waste management, making proper disposal of such waste a critical issue. According to global reports, the volume of WEEE is steadily increasing, emphasizing the need for effective waste management practices. Studies have shown that the rise in WEEE not only harms the environment but also has negative impacts on public health and natural resources. Consumers, as primary generators of this waste, play a pivotal role in its management. Therefore, understanding consumers' perspectives and behaviors regarding the disposal of WEEE is crucial for developing effective waste management strategies. This study aims to identify appropriate options for managing electrical and electronic equipment at their end-of-life stage. It seeks to analyze and identify best practices for managing such waste.
<strong>Methods:</strong> This research adopts a mixed-methods approach, combining quantitative and qualitative methods. In the quantitative phase, data collected from Twitter (approximately 2,905,579 tweets) between May 2019 and April 2022 were analyzed. These data included consumers’ opinions and perspectives on WEEE management. Furthermore, the study employed the Fuzzy Analytical Hierarchy Process (FAHP) to evaluate different end-of-life options. This technique enables a more precise analysis of factors influencing consumer choices and identifies the best options. Based on the findings, various factors—such as accessibility to recycling centers, awareness of disposal methods, distrust in governments, financial incentives, charitable contributions, and concerns about data security—directly influence consumers’ decisions.
These factors significantly shape consumer behavior regarding WEEE and, consequently, their end-of-life choices. Additionally, the analyses reveal that awareness of proper disposal methods and the availability of suitable recycling infrastructure can significantly impact consumer decision-making.
<strong>Results and discussion</strong>: The study identifies four end-of-life options for WEEE: reuse, repair, recycling, and disposal. Using the FAHP technique, the relationships between these factors were examined, and the most suitable end-of-life option was identified based on factors influencing consumer participation in waste management. The results serve as a valuable tool for managing WEEE and contribute to the development of effective end-of-life strategies. These strategies will foster a more sustainable approach to WEEE management.
<strong>Conclusions</strong>: Finally, the research recommends that policymakers and relevant stakeholders design comprehensive educational and awareness programs to enhance consumer knowledge about WEEE management methods. Such initiatives can help promote a culture of sustainable waste management and encourage consumers to adopt more environmentally friendly options. These efforts will play a key role in reducing the negative impacts of WEEE on the environment and public health.https://jimp.sbu.ac.ir/article_105094_c3ec3afc74c889f6eb957749ad04f459.pdfShahid Beheshti UniversityJournal of Industrial Management Perspective2251-987414420241121Analyzing and Improving Production Line Efficiency Using Simulation in the Auto Parts IndustryAnalyzing and Improving Production Line Efficiency Using Simulation in the Auto Parts Industry689710510010.48308/jimp.14.4.68FAEhsanPoorali MalabadMSc, Department of Management, Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran.0009-0007-7542-7681NasserMotahari FarimaniAssociate Professor, Department of Management, Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran.AzamModaresPhD, Department of Management, Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran.KimiaAbdariMSc Student, Department of Management, Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran.0009-0005-3968-6091Journal Article20240217<strong>Introduction: </strong>Paying attention to the production and productivity of industries can accelerate industrial growth and development while guiding it on a correct and sustainable path. Evaluating production efficiency and striving to improve it play a crucial role in the progress and advancement of industries. This study proposes an innovative approach to evaluate and improve the efficiency of a production line using simulation as the primary tool and also addresses the reengineering of production line processes. The main objectives of this research include identifying bottlenecks in the production process, analyzing production cycle times, evaluating buffer capacities within specified time intervals, and determining the optimal resource capacities required in the factory.
<strong> </strong><strong>Methods: </strong>This research examines and models a fully automated production line and provides a systematic framework based on discrete-event simulation. The modeling process was conducted in two stages. In the first stage, rework and separation activities were excluded from consideration, while in the second stage, these details were incorporated into the model. In this phase, real data collected from a case study were applied to the model. To ensure the accuracy of the designed model, the logic of the modeled process was continuously reviewed, and the model's outputs were compared with actual system data. After identifying the factors contributing to reduced production line efficiency, four improvement scenarios were proposed and analyzed using the simulation model. Arena software was utilized to evaluate the scenarios and conduct sensitivity analyses.
<strong>Results and discussion:</strong> The results reveal that incorporating details such as operator break times and downtimes into the simulation model—bringing it closer to reality—reduced the production line efficiency from 80% to 57%. Rework and separation activities also significantly impacted the efficiency. Four improvement scenarios were designed and evaluated within the optimized model. In the first scenario, changes in resource capacities related to the main processes were thoroughly examined, leading to a significant reduction in waiting times in process queues and overall process duration. In the second scenario, reducing the percentage of parts sent to rework and separation resulted in a considerable improvement in production efficiency. The third scenario focused on minimizing process time by determining optimal control variable values, while the fourth scenario aimed to maximize efficiency by optimizing resource capacities. In all scenarios, increasing resources at bottleneck activities, through logical and balanced combinations, significantly enhanced process efficiency. Sensitivity analysis confirmed the practical applicability of the improvement scenarios in real-world conditions.
<strong>Conclusion:</strong> The findings indicate that discrete-event simulation is an effective tool for managers, enabling them to make informed decisions about improving production efficiency without incurring irreversible costs. Additionally, the results align closely with prior studies that have utilized discrete-event simulation to optimize various organizational processes, further confirming the positive impact of this approach on improving process performance.<strong>Introduction: </strong>Paying attention to the production and productivity of industries can accelerate industrial growth and development while guiding it on a correct and sustainable path. Evaluating production efficiency and striving to improve it play a crucial role in the progress and advancement of industries. This study proposes an innovative approach to evaluate and improve the efficiency of a production line using simulation as the primary tool and also addresses the reengineering of production line processes. The main objectives of this research include identifying bottlenecks in the production process, analyzing production cycle times, evaluating buffer capacities within specified time intervals, and determining the optimal resource capacities required in the factory.
<strong> </strong><strong>Methods: </strong>This research examines and models a fully automated production line and provides a systematic framework based on discrete-event simulation. The modeling process was conducted in two stages. In the first stage, rework and separation activities were excluded from consideration, while in the second stage, these details were incorporated into the model. In this phase, real data collected from a case study were applied to the model. To ensure the accuracy of the designed model, the logic of the modeled process was continuously reviewed, and the model's outputs were compared with actual system data. After identifying the factors contributing to reduced production line efficiency, four improvement scenarios were proposed and analyzed using the simulation model. Arena software was utilized to evaluate the scenarios and conduct sensitivity analyses.
<strong>Results and discussion:</strong> The results reveal that incorporating details such as operator break times and downtimes into the simulation model—bringing it closer to reality—reduced the production line efficiency from 80% to 57%. Rework and separation activities also significantly impacted the efficiency. Four improvement scenarios were designed and evaluated within the optimized model. In the first scenario, changes in resource capacities related to the main processes were thoroughly examined, leading to a significant reduction in waiting times in process queues and overall process duration. In the second scenario, reducing the percentage of parts sent to rework and separation resulted in a considerable improvement in production efficiency. The third scenario focused on minimizing process time by determining optimal control variable values, while the fourth scenario aimed to maximize efficiency by optimizing resource capacities. In all scenarios, increasing resources at bottleneck activities, through logical and balanced combinations, significantly enhanced process efficiency. Sensitivity analysis confirmed the practical applicability of the improvement scenarios in real-world conditions.
<strong>Conclusion:</strong> The findings indicate that discrete-event simulation is an effective tool for managers, enabling them to make informed decisions about improving production efficiency without incurring irreversible costs. Additionally, the results align closely with prior studies that have utilized discrete-event simulation to optimize various organizational processes, further confirming the positive impact of this approach on improving process performance.https://jimp.sbu.ac.ir/article_105100_603d8c7c8f25f57c0405d33a82ca032f.pdfShahid Beheshti UniversityJournal of Industrial Management Perspective2251-987414420241121A bi-objective optimization model to design a cold and green network of the multi-product, multi-level and multi-period pharmaceutical supply chain, taking into account the time value of money and imports of pharmaceutical productsA bi-objective optimization model to design a cold and green network of the multi-product, multi-level and multi-period pharmaceutical supply chain, taking into account the time value of money and imports of pharmaceutical products9812010508510.48308/jimp.14.4.98FAFaezeAminizadePh.D. student, Department of Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran.AliRajabzadehProfessor, Department of Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran.0000-0002-8470-3568MahmoudDehghan NayeriAssociate Professor, Department of Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran.0000-0002-7648-2937Journal Article20240516<strong>Introduction: </strong>Due to the specific characteristics of pharmaceutical products such as the specific half-life, the need to observe temperature controls; maintaining the safety of medicines at all stages, complying with international regulations and standards and providing the best level of service to consumers; The pharmaceutical supply chain is very important. Today, in developing countries, the chalenges that exist in the production, storage, import and distribution of pharmaceutical products have reduced the sustainability and increased costs of the pharmaceutical supply and distribution chain network. Sustainability, which seeks to meet today's economic and social needs without jeopardizing the environment and the needs of future generations, is defined as a concept consisting of three economic, social and ecological dimensions.<br /><strong>Methods: </strong>This study by developing an integer bi-objective optimization model; while integrating import factors, time value of money (TVM) and temperature control requirements; It guarantees operational efficiency and economic and environmental sustainability of the pharmaceutical network. In other words, with synergy between cold chain logistics and green principles, while maintaining the quality and integrity of temperature-sensitive pharmaceutical products, it seeks to minimize costs and environmental effects. Also, taking into account the economic consequences of TVM, the inherent challenges and complexities of the international import of pharmaceutical products, it helps to make more cost-effective decisions and more accurate financial feasibility over time. In fact, the present study aims to achieve a harmonious balance between operational excellence, economic viability and sustainable practices, and helps the managers and stakeholders of this industry in navigating the multifaceted challenges of global drug distribution.<br /><strong>Results and discussion: </strong>After solving the model with the help of GAMS and the comprehensive benchmarking method, it has been determined by using numerical examples that (a) pharmaceutical import plays the biggest role in creating costs for its cold and green network. In this regard, it is important to pay attention to the fact that although domestically produced drugs have a lower price than the imported ones, their quality and effect on treatment is also much lower, and this issue makes imported pharmaceutical products more popular among people. In addition, since even the production of pharmaceutical products is dependent on the import of raw materials and legal requirements such as price restrictions are an obstacle for producers, it seems that revision of existing requirements and regulations and Such improvement of production technologies in the long term can reduce the costs of the chain to a great extent, although it may bring costs to the network in the short term; (b) Compliance with temperature controls, which is inevitable during the operation of a strategic product chain such as medicine, will definitely lead to an increase in its costs and a warming of the earth, and it is necessary for the decision-makers of this industry, in short spend enough time and take appropriate measures to improve maintenance technologies and continuously monitor the health and quality of refrigerated items in order to enjoy more benefits in the long term; (c) Transportation, while constituting the smallest part of the costs, is considered the most effective operation of the network in warming the earth. This issue, rather than being affected by the quality, health and capacity of vehicles, is a result of the lack of proper management of supply and demand and improper management of inventory; (d) The time value of money and issues such as inflation, exchange rate fluctuations, and sanctions, although in the short-term and medium-term it will cost more for the chain, but it will not play a decisive role in the long term.<br /><strong>Conclusions: </strong>In fact, the above two-objective model shows that the improvement of economic and environmental performance in the long term and the realization of sustainable management of the pharmaceutical supply chain can be achieved by revising the legal requirements and production strategies, correct supply and demand management, and proper inventory management.<strong>Introduction: </strong>Due to the specific characteristics of pharmaceutical products such as the specific half-life, the need to observe temperature controls; maintaining the safety of medicines at all stages, complying with international regulations and standards and providing the best level of service to consumers; The pharmaceutical supply chain is very important. Today, in developing countries, the chalenges that exist in the production, storage, import and distribution of pharmaceutical products have reduced the sustainability and increased costs of the pharmaceutical supply and distribution chain network. Sustainability, which seeks to meet today's economic and social needs without jeopardizing the environment and the needs of future generations, is defined as a concept consisting of three economic, social and ecological dimensions.<br /><strong>Methods: </strong>This study by developing an integer bi-objective optimization model; while integrating import factors, time value of money (TVM) and temperature control requirements; It guarantees operational efficiency and economic and environmental sustainability of the pharmaceutical network. In other words, with synergy between cold chain logistics and green principles, while maintaining the quality and integrity of temperature-sensitive pharmaceutical products, it seeks to minimize costs and environmental effects. Also, taking into account the economic consequences of TVM, the inherent challenges and complexities of the international import of pharmaceutical products, it helps to make more cost-effective decisions and more accurate financial feasibility over time. In fact, the present study aims to achieve a harmonious balance between operational excellence, economic viability and sustainable practices, and helps the managers and stakeholders of this industry in navigating the multifaceted challenges of global drug distribution.<br /><strong>Results and discussion: </strong>After solving the model with the help of GAMS and the comprehensive benchmarking method, it has been determined by using numerical examples that (a) pharmaceutical import plays the biggest role in creating costs for its cold and green network. In this regard, it is important to pay attention to the fact that although domestically produced drugs have a lower price than the imported ones, their quality and effect on treatment is also much lower, and this issue makes imported pharmaceutical products more popular among people. In addition, since even the production of pharmaceutical products is dependent on the import of raw materials and legal requirements such as price restrictions are an obstacle for producers, it seems that revision of existing requirements and regulations and Such improvement of production technologies in the long term can reduce the costs of the chain to a great extent, although it may bring costs to the network in the short term; (b) Compliance with temperature controls, which is inevitable during the operation of a strategic product chain such as medicine, will definitely lead to an increase in its costs and a warming of the earth, and it is necessary for the decision-makers of this industry, in short spend enough time and take appropriate measures to improve maintenance technologies and continuously monitor the health and quality of refrigerated items in order to enjoy more benefits in the long term; (c) Transportation, while constituting the smallest part of the costs, is considered the most effective operation of the network in warming the earth. This issue, rather than being affected by the quality, health and capacity of vehicles, is a result of the lack of proper management of supply and demand and improper management of inventory; (d) The time value of money and issues such as inflation, exchange rate fluctuations, and sanctions, although in the short-term and medium-term it will cost more for the chain, but it will not play a decisive role in the long term.<br /><strong>Conclusions: </strong>In fact, the above two-objective model shows that the improvement of economic and environmental performance in the long term and the realization of sustainable management of the pharmaceutical supply chain can be achieved by revising the legal requirements and production strategies, correct supply and demand management, and proper inventory management.https://jimp.sbu.ac.ir/article_105085_fd04d27d6887e802d9897a0aa9f23170.pdfShahid Beheshti UniversityJournal of Industrial Management Perspective2251-987414420241121Estimating and Assessment of Technical Efficiency in Listed Petrochemical Companies: Bootstrap-DEA ApproachEstimating and Assessment of Technical Efficiency in Listed Petrochemical Companies: Bootstrap-DEA Approach12114110510310.48308/jimp.14.4.121FAMohammadjavadSamieifardPh.D. Student, Department of Economics, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran.MehdiFathabadiAssistant professor, Department of Economic, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran.MasoodSoufi MajidpourAssistant professor, Department of Economic, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran.SalehGhavidel DoostkoueiAssistant professor, Department of Economic, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran.Journal Article20240719<strong>Introduction and purpose: </strong>The petrochemical industry in Iran, as a flagship sector, plays a pivotal role in the national economy, supplying a substantial share of its output to downstream industries. In recent decades, advancements in technology and research within the petrochemical sector have led to substantial improvements, reducing costs and enhancing efficiency. Therefore, considering the importance of the petrochemical industry and its role in economic growth, this article aims to evaluate the efficiency of petrochemical companies using the bootstrap-DEA approach. This method provides statistical inference regarding technical efficiency criteria in non-parametric frontier models.
<strong>Methods: </strong>To achieve this objective, a comprehensive two-stage approach was adopted. First, input-oriented efficiency scores were estimated under the assumptions of constant, variable, and non-increasing returns to scale for all companies, using radial (Debro-Farrell efficiency) and non-radial (Russell efficiency) methodologies to provide a robust evaluation. Then, a non-parametric independence test was conducted to identify the appropriate bootstrap implementation approach. Finally, considering the test results, the efficiency scores of petrochemical companies were estimated using the bootstrap-DEA approach. The bootstrap process allows for bias estimation and the calculation of confidence intervals for initial estimates.
<strong>Findings: </strong>The results indicate that Tondgoyan Company achieved Debro-Farrell efficiency under all three scenarios: constant, variable, and non-increasing returns to scale. Additionally, Zagros and Fanavaran companies exhibited Debro-Farrell efficiency under variable returns to scale. However, 18 other companies were inefficient, with Ghadir Petrochemical Company displaying the worst performance, achieving an efficiency score of 0.68 under constant returns to scale. Bias-corrected radial technical efficiency scores across homogeneous, heterogeneous, and subsampling bootstrap methods revealed that none of the 21 petrochemical companies exhibited full technical efficiency. Among them, Fanavaran and Tondgoyan showed better performance, while Aria, Pardis, Shiraz, and Isfahan Oil companies demonstrated the lowest input-oriented technical efficiency. Output-oriented technical efficiency findings indicated that Tondgoyan and Zagros were efficient, while Ghadir had the worst performance, with an efficiency score of 1.37, signifying 37% excess input consumption. The results further demonstrate that radial efficiency estimates are prone to overestimation, as confirmed by heterogeneous smooth bootstrap and subsampling techniques.
<strong>Conclusion: </strong>One of the primary objectives of Iran's 7th Development Plan is achieving 8% economic growth, with 2.8% attributed to productivity and efficiency improvements in production factors, including capital, human resources, technology, and management. Emphasis has been placed on bolstering the value-added chain within the petrochemical industry. Therefore, attention to the development of midstream and downstream petrochemical industries through reinvesting revenues from the export of upstream products is crucial for completing the value chain of the petrochemical sector.<strong>Introduction and purpose: </strong>The petrochemical industry in Iran, as a flagship sector, plays a pivotal role in the national economy, supplying a substantial share of its output to downstream industries. In recent decades, advancements in technology and research within the petrochemical sector have led to substantial improvements, reducing costs and enhancing efficiency. Therefore, considering the importance of the petrochemical industry and its role in economic growth, this article aims to evaluate the efficiency of petrochemical companies using the bootstrap-DEA approach. This method provides statistical inference regarding technical efficiency criteria in non-parametric frontier models.
<strong>Methods: </strong>To achieve this objective, a comprehensive two-stage approach was adopted. First, input-oriented efficiency scores were estimated under the assumptions of constant, variable, and non-increasing returns to scale for all companies, using radial (Debro-Farrell efficiency) and non-radial (Russell efficiency) methodologies to provide a robust evaluation. Then, a non-parametric independence test was conducted to identify the appropriate bootstrap implementation approach. Finally, considering the test results, the efficiency scores of petrochemical companies were estimated using the bootstrap-DEA approach. The bootstrap process allows for bias estimation and the calculation of confidence intervals for initial estimates.
<strong>Findings: </strong>The results indicate that Tondgoyan Company achieved Debro-Farrell efficiency under all three scenarios: constant, variable, and non-increasing returns to scale. Additionally, Zagros and Fanavaran companies exhibited Debro-Farrell efficiency under variable returns to scale. However, 18 other companies were inefficient, with Ghadir Petrochemical Company displaying the worst performance, achieving an efficiency score of 0.68 under constant returns to scale. Bias-corrected radial technical efficiency scores across homogeneous, heterogeneous, and subsampling bootstrap methods revealed that none of the 21 petrochemical companies exhibited full technical efficiency. Among them, Fanavaran and Tondgoyan showed better performance, while Aria, Pardis, Shiraz, and Isfahan Oil companies demonstrated the lowest input-oriented technical efficiency. Output-oriented technical efficiency findings indicated that Tondgoyan and Zagros were efficient, while Ghadir had the worst performance, with an efficiency score of 1.37, signifying 37% excess input consumption. The results further demonstrate that radial efficiency estimates are prone to overestimation, as confirmed by heterogeneous smooth bootstrap and subsampling techniques.
<strong>Conclusion: </strong>One of the primary objectives of Iran's 7th Development Plan is achieving 8% economic growth, with 2.8% attributed to productivity and efficiency improvements in production factors, including capital, human resources, technology, and management. Emphasis has been placed on bolstering the value-added chain within the petrochemical industry. Therefore, attention to the development of midstream and downstream petrochemical industries through reinvesting revenues from the export of upstream products is crucial for completing the value chain of the petrochemical sector.https://jimp.sbu.ac.ir/article_105103_6991eb031a03edbd62c3451c1df3b773.pdfShahid Beheshti UniversityJournal of Industrial Management Perspective2251-987414420241121Designing a Bi-objective Post-Disaster Relief Logistics Model Considering Cost and Time of Utilizing Helicopters and Chance-Constraint ProgrammingDesigning a Bi-objective Post-Disaster Relief Logistics Model Considering Cost and Time of Utilizing Helicopters and Chance-Constraint Programming14216410511010.48308/jimp.14.4.142FASeyranGhadimiPh.D. Student, Department of Industrial Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran.MehdiSeifbarghyProfessor, Department of Industrial Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran.Journal Article20240717<strong>Introduction:</strong> Disaster management in the post-disaster phase is crucial for minimizing damages. Relief logistics enable the rapid deployment of relief personnel and necessary materials to affected areas and the rescue of victims. During natural disasters like earthquakes, physical infrastructures such as roads and bridges are often destroyed, making access to affected areas extremely difficult or even impossible. For this reason, helicopters are the most suitable means of transport to assist the injured. In this context, another critical issue is the difference in service times between helicopters. Naturally, shorter service times result in higher costs. Therefore, it is essential to strike a balance between the dual objectives of time and cost.
<strong>Methods:</strong> This paper proposes a mathematical model for post-disaster relief logistics following a catastrophic earthquake in a mountainous region. The model aims to plan the deployment of helicopters to affected areas and manage the rescue and transportation of injured individuals to temporary facilities. The issue of uncertainty regarding the affected population and the demand for rescue personnel is also addressed. Initially, a deterministic mathematical model is proposed. Subsequently, the model is adapted using the chance-constraint programming method to incorporate the stochastic nature of the aforementioned parameters, converting them into deterministic constraints. Additionally, two approaches, the LP-metric method and the epsilon constraint method, are employed to solve the bi-objective model concerning time and cost.
<strong>Results and discussions:</strong> A key finding of this research is the formulation of decision variables in the mathematical model. One critical decision variable is the capacity allocated for preparing and dispatching relief personnel at each temporary facility. This designed capacity must not exceed a maximum allowable value due to technical constraints and, operationally, must also accommodate the total number of personnel deployed by all helicopters from the facility across multiple trips. Similar constraints apply to the capacity for treating injured individuals at each temporary facility. Specifically, this capacity must not exceed a predefined maximum and must also meet or exceed the population of injured individuals transported to the facility by various helicopters over multiple trips. Two additional important constraints addressed in this research include adherence to the maximum flight hours of each helicopter and ensuring a minimum level of service to cover the affected population. These constraints are made feasible through the defined decision variables. Another significant finding pertains to the modeling of the trade-off between helicopter service costs and times, represented as a bi-objective model. Moreover, given the uncertainty of the two exogenous parameters—relief force demand and the population of affected areas—these parameters are assumed to follow a normal distribution with specific means and standard deviations, and their associated constraints are ultimately converted into deterministic forms.
<strong>Conclusions:</strong> The proposed model is solved using the epsilon constraint method and the LP-metric method for a case study involving the Tabriz fault. In a pilot scenario, 13 affected areas were considered, with 3 potential locations for establishing temporary facilities and 5 types of helicopters. The results indicate that increased demand for relief personnel and affected areas leads to higher costs and longer service times, demonstrating the logical functionality of the developed model.<strong>Introduction:</strong> Disaster management in the post-disaster phase is crucial for minimizing damages. Relief logistics enable the rapid deployment of relief personnel and necessary materials to affected areas and the rescue of victims. During natural disasters like earthquakes, physical infrastructures such as roads and bridges are often destroyed, making access to affected areas extremely difficult or even impossible. For this reason, helicopters are the most suitable means of transport to assist the injured. In this context, another critical issue is the difference in service times between helicopters. Naturally, shorter service times result in higher costs. Therefore, it is essential to strike a balance between the dual objectives of time and cost.
<strong>Methods:</strong> This paper proposes a mathematical model for post-disaster relief logistics following a catastrophic earthquake in a mountainous region. The model aims to plan the deployment of helicopters to affected areas and manage the rescue and transportation of injured individuals to temporary facilities. The issue of uncertainty regarding the affected population and the demand for rescue personnel is also addressed. Initially, a deterministic mathematical model is proposed. Subsequently, the model is adapted using the chance-constraint programming method to incorporate the stochastic nature of the aforementioned parameters, converting them into deterministic constraints. Additionally, two approaches, the LP-metric method and the epsilon constraint method, are employed to solve the bi-objective model concerning time and cost.
<strong>Results and discussions:</strong> A key finding of this research is the formulation of decision variables in the mathematical model. One critical decision variable is the capacity allocated for preparing and dispatching relief personnel at each temporary facility. This designed capacity must not exceed a maximum allowable value due to technical constraints and, operationally, must also accommodate the total number of personnel deployed by all helicopters from the facility across multiple trips. Similar constraints apply to the capacity for treating injured individuals at each temporary facility. Specifically, this capacity must not exceed a predefined maximum and must also meet or exceed the population of injured individuals transported to the facility by various helicopters over multiple trips. Two additional important constraints addressed in this research include adherence to the maximum flight hours of each helicopter and ensuring a minimum level of service to cover the affected population. These constraints are made feasible through the defined decision variables. Another significant finding pertains to the modeling of the trade-off between helicopter service costs and times, represented as a bi-objective model. Moreover, given the uncertainty of the two exogenous parameters—relief force demand and the population of affected areas—these parameters are assumed to follow a normal distribution with specific means and standard deviations, and their associated constraints are ultimately converted into deterministic forms.
<strong>Conclusions:</strong> The proposed model is solved using the epsilon constraint method and the LP-metric method for a case study involving the Tabriz fault. In a pilot scenario, 13 affected areas were considered, with 3 potential locations for establishing temporary facilities and 5 types of helicopters. The results indicate that increased demand for relief personnel and affected areas leads to higher costs and longer service times, demonstrating the logical functionality of the developed model.https://jimp.sbu.ac.ir/article_105110_9e88692fff9c871e2fc02e9630d3611c.pdfShahid Beheshti UniversityJournal of Industrial Management Perspective2251-987414420241121Two-Echelon Location-Routing under Uncertainty Considering ReliabilityTwo-Echelon Location-Routing under Uncertainty Considering Reliability16520010510910.48308/jimp.14.4.165FARaminBehbamzadehPh. D. Student, Department of Operation Management and Information Technology, Management and Accounting Faculty, Allameh Tabataba’i University, Tehran, Iran.MaghsoudAmiriProfessor, Department of Operation Management and Information Technology, Management and Accounting Faculty, Allameh Tabataba’i University, Tehran, Iran.Mohammad TaghiTaghavifardProfessor, Department of Operation Management and Information Technology, Management and Accounting Faculty, Allameh Tabataba’i University, Tehran, Iran.AmirYousefliAssociate Professor, Department of Industrial Engineering, Faculty of Engineering, University of Zanjan, Zanjan, Iran.Journal Article20240807<strong>Introduction and objectives</strong>: Urban development and the increase in intercity and intracity transportation have led to a significant rise in transportation costs. This rise in cost increases the final product price and consequently the product's market price. Additionally, the economic aspect is not the only concern; increased transportation results in higher CO2 emissions. These issues have driven the design of various transportation and vehicle routing models, including the vehicle location-routing problem. Facility location is a strategic decision due to the high costs associated with constructing and locating facilities. On the other hand, routing decisions depend on location decisions and are considered mid-term and short-term decisions.<br />In this study, a mathematical model for vehicle location-routing under uncertainty conditions and considering reliability is developed to simultaneously optimize sustainability objective functions while maintaining minimum reliability levels. The sustainability objectives in this study include minimizing total costs, minimizing CO2 emissions, and maximizing job opportunities based on integrated strategic and tactical decisions. The primary focus of this paper is decision-making for optimal transportation routing, considering time windows and the reliability of facility location based on their failure rates.<br /><strong>Methods</strong>: Given the uncertainty of the mathematical model parameters, various fuzzy and robust possibilistic programming methods were employed to formulate the model. Four different models were used to manage the uncertainty of demand parameters and transportation costs, and the results were compared. Additionally, two methods—an exact solution approach and a meta-heuristic algorithm—were employed to solve the multi-objective mathematical model. The enhanced epsilon constraint method was used to analyze small-scale mathematical models and conduct sensitivity analyses, while the NSGA-II algorithm was applied to solve larger-scale numerical examples. Furthermore, an initial solution based on prioritization was utilized for the meta-heuristic algorithm.<br /><strong>Results and discussion</strong>: The results indicate that although an increase in uncertainty rates leads to more job opportunities, it also raises total costs and greenhouse gas emissions. Additionally, the analysis reveals that the RPP-III method achieves the highest model robustness cost with the lowest standard deviation. In the reliability analysis, it was observed that higher facility failure rates result in an increased number of located production and distribution centers. This, in turn, leads to higher total costs, increased CO2 emissions, and more job opportunities. By analyzing 15 numerical examples, it was also found that NSGA-II is highly efficient in solving the mathematical model compared to the enhanced epsilon constraint method.<br /><strong>Conclusion</strong>: The findings of this research assist managers in making strategic decisions such as facility location and tactical decisions such as vehicle routing in the context of market uncertainty. Given that the model incorporates various realistic decisions and assumptions, the mathematical model can be effectively applied in distribution industries, particularly for pharmaceutical and electronic goods.<strong>Introduction and objectives</strong>: Urban development and the increase in intercity and intracity transportation have led to a significant rise in transportation costs. This rise in cost increases the final product price and consequently the product's market price. Additionally, the economic aspect is not the only concern; increased transportation results in higher CO2 emissions. These issues have driven the design of various transportation and vehicle routing models, including the vehicle location-routing problem. Facility location is a strategic decision due to the high costs associated with constructing and locating facilities. On the other hand, routing decisions depend on location decisions and are considered mid-term and short-term decisions.<br />In this study, a mathematical model for vehicle location-routing under uncertainty conditions and considering reliability is developed to simultaneously optimize sustainability objective functions while maintaining minimum reliability levels. The sustainability objectives in this study include minimizing total costs, minimizing CO2 emissions, and maximizing job opportunities based on integrated strategic and tactical decisions. The primary focus of this paper is decision-making for optimal transportation routing, considering time windows and the reliability of facility location based on their failure rates.<br /><strong>Methods</strong>: Given the uncertainty of the mathematical model parameters, various fuzzy and robust possibilistic programming methods were employed to formulate the model. Four different models were used to manage the uncertainty of demand parameters and transportation costs, and the results were compared. Additionally, two methods—an exact solution approach and a meta-heuristic algorithm—were employed to solve the multi-objective mathematical model. The enhanced epsilon constraint method was used to analyze small-scale mathematical models and conduct sensitivity analyses, while the NSGA-II algorithm was applied to solve larger-scale numerical examples. Furthermore, an initial solution based on prioritization was utilized for the meta-heuristic algorithm.<br /><strong>Results and discussion</strong>: The results indicate that although an increase in uncertainty rates leads to more job opportunities, it also raises total costs and greenhouse gas emissions. Additionally, the analysis reveals that the RPP-III method achieves the highest model robustness cost with the lowest standard deviation. In the reliability analysis, it was observed that higher facility failure rates result in an increased number of located production and distribution centers. This, in turn, leads to higher total costs, increased CO2 emissions, and more job opportunities. By analyzing 15 numerical examples, it was also found that NSGA-II is highly efficient in solving the mathematical model compared to the enhanced epsilon constraint method.<br /><strong>Conclusion</strong>: The findings of this research assist managers in making strategic decisions such as facility location and tactical decisions such as vehicle routing in the context of market uncertainty. Given that the model incorporates various realistic decisions and assumptions, the mathematical model can be effectively applied in distribution industries, particularly for pharmaceutical and electronic goods.https://jimp.sbu.ac.ir/article_105109_dec3119d882f7c5a7f5b3ab27e1b7e6f.pdfShahid Beheshti UniversityJournal of Industrial Management Perspective2251-987414420241121Designing the Competency Model of the Workforce in Manufacturing Industries During the Fourth Industrial RevolutionDesigning the Competency Model of the Workforce in Manufacturing Industries During the Fourth Industrial Revolution20122210525810.48308/jimp.14.4.201FAAliMirzaei EslamlouPhD Student, Department of Industrial Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran.MajidBagherzadeh KhajehAssistant Professor, Department of Industrial Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran.MortezaMahmoudzadehAssistant Professor, Department of Industrial Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran.MojtabaRamazaniAssistant Professor, Department of Business Management, Bonab Branch, Islamic Azad University, Bonab, Iran.Journal Article20230715<strong>Introduction:</strong> Despite the technological revolution transforming work processes, digital transformation requires inherent human skills to ensure the usability and efficiency of digital technologies. Accordingly, the labor market must adapt to these related demands. The Fourth Industrial Revolution has significantly impacted the workforce, leading to major shifts in job profiles. The aim of this research is to design a competency model for the workforce during the Fourth Industrial Revolution and to test this model in large manufacturing companies in East Azerbaijan Province.
<strong>Methods:</strong> This study is classified as applied-developmental research and employs an exploratory mixed-method approach (qualitative-quantitative). In the qualitative phase, the grounded theory method and NVivo software were utilized to determine the structure and components of the workforce competency model in manufacturing industries. In the quantitative phase, structural equation modeling was employed. The statistical population in the qualitative phase included academic experts and industrial managers, while in the quantitative phase, industrial managers formed the population (302 individuals). The qualitative sample was selected based on theoretical saturation, resulting in 17 expert participants for interviews. In the quantitative phase, 90 managers were selected using the rule of thumb sampling method. The study's spatial domain included large manufacturing industries in East Azerbaijan Province, and the temporal domain was the year 1401 (2022-2023). Research tools included semi-structured interviews for the qualitative phase and questionnaires based on qualitative findings for the quantitative phase. Data analysis in the qualitative phase was conducted using open, axial, and selective coding, while structural equation modeling was employed for quantitative analysis.
<strong>Results and discussion:</strong> The findings indicate that causal, contextual, and intervention conditions directly influence the design of a workforce competency model for the Fourth Industrial Revolution. Causal conditions, such as the dynamic technological environment and the need for new skills, push the workforce towards continuous learning and skill updates. Contextual conditions, including cultural support and infrastructure investment, strengthen learning processes and competency development. Intervention conditions, such as managerial interventions and training programs, enhance workforce capabilities in technical and social domains. These factors contribute to achieving organizational goals and improving job satisfaction by enhancing organizational performance and work quality. The findings underscore the necessity of adopting appropriate strategies to continuously improve workforce competencies, enabling organizations to succeed in today’s competitive environment.
<strong>Conclusion:</strong> The research results highlight the importance of identifying key skills for designing a workforce competency model in the Fourth Industrial Revolution. These skills include mastery of new technologies, social skills and collaboration, adaptability and flexibility, and critical and creative thinking. These competencies are crucial for strengthening workforce capabilities and addressing new workplace challenges. Furthermore, the study emphasizes the need for supportive cultural and governmental infrastructures to facilitate successful implementation. Consistent training and development programs should be employed to enhance employee skills. Ultimately, this competency model supports organizations in increasing productivity, improving work quality, and enhancing job satisfaction and talent retention.<strong>Introduction:</strong> Despite the technological revolution transforming work processes, digital transformation requires inherent human skills to ensure the usability and efficiency of digital technologies. Accordingly, the labor market must adapt to these related demands. The Fourth Industrial Revolution has significantly impacted the workforce, leading to major shifts in job profiles. The aim of this research is to design a competency model for the workforce during the Fourth Industrial Revolution and to test this model in large manufacturing companies in East Azerbaijan Province.
<strong>Methods:</strong> This study is classified as applied-developmental research and employs an exploratory mixed-method approach (qualitative-quantitative). In the qualitative phase, the grounded theory method and NVivo software were utilized to determine the structure and components of the workforce competency model in manufacturing industries. In the quantitative phase, structural equation modeling was employed. The statistical population in the qualitative phase included academic experts and industrial managers, while in the quantitative phase, industrial managers formed the population (302 individuals). The qualitative sample was selected based on theoretical saturation, resulting in 17 expert participants for interviews. In the quantitative phase, 90 managers were selected using the rule of thumb sampling method. The study's spatial domain included large manufacturing industries in East Azerbaijan Province, and the temporal domain was the year 1401 (2022-2023). Research tools included semi-structured interviews for the qualitative phase and questionnaires based on qualitative findings for the quantitative phase. Data analysis in the qualitative phase was conducted using open, axial, and selective coding, while structural equation modeling was employed for quantitative analysis.
<strong>Results and discussion:</strong> The findings indicate that causal, contextual, and intervention conditions directly influence the design of a workforce competency model for the Fourth Industrial Revolution. Causal conditions, such as the dynamic technological environment and the need for new skills, push the workforce towards continuous learning and skill updates. Contextual conditions, including cultural support and infrastructure investment, strengthen learning processes and competency development. Intervention conditions, such as managerial interventions and training programs, enhance workforce capabilities in technical and social domains. These factors contribute to achieving organizational goals and improving job satisfaction by enhancing organizational performance and work quality. The findings underscore the necessity of adopting appropriate strategies to continuously improve workforce competencies, enabling organizations to succeed in today’s competitive environment.
<strong>Conclusion:</strong> The research results highlight the importance of identifying key skills for designing a workforce competency model in the Fourth Industrial Revolution. These skills include mastery of new technologies, social skills and collaboration, adaptability and flexibility, and critical and creative thinking. These competencies are crucial for strengthening workforce capabilities and addressing new workplace challenges. Furthermore, the study emphasizes the need for supportive cultural and governmental infrastructures to facilitate successful implementation. Consistent training and development programs should be employed to enhance employee skills. Ultimately, this competency model supports organizations in increasing productivity, improving work quality, and enhancing job satisfaction and talent retention.https://jimp.sbu.ac.ir/article_105258_ddb57a36767d5821552342a2227f01b2.pdfShahid Beheshti UniversityJournal of Industrial Management Perspective2251-987414420241121Risk Modeling in Banking Services for the Blind Using Fuzzy FMEA and Graph Neural Network (GNN)Risk Modeling in Banking Services for the Blind Using Fuzzy FMEA and Graph Neural Network (GNN)22325510531010.48308/jimp.14.4.223FAAhmadJafarnejad ChaghoshiProfessor, Faculty of Industrial Management and Technology, College of Management, University of Tehran, Tehran, Iran.Amir MohammadKhaniPh.D. candidate, Faculty of Industrial Management and Technology, College of Management, University of Tehran, Tehran, Iran.ArmanRezasoltani,Ph.D. candidate, Faculty of Industrial Management and Technology, College of Management, University of Tehran, Tehran, Iran.Journal Article20240922<strong>Introduction and objectives:</strong> In today's world, accessibility and security of banking services for all members of society, particularly vulnerable groups such as the blind, are of utmost importance. With the increasing significance of digital banking, identifying and assessing risks related to accessibility and security of banking services for the blind has become a fundamental priority. This research aims to identify, evaluate, and prioritize the main risks associated with providing banking services to the blind and propose solutions to mitigate these risks. The goal is to improve the infrastructure and technologies used to significantly facilitate access to banking services for the blind. This study employs a combination of two methods: fuzzy Failure Mode and Effects Analysis (FMEA) and Graph Neural Networks (GNN), to more accurately and comprehensively identify the relationships and interactions among risks.
<strong>Methods:</strong> This study was conducted in two main stages. In the first stage, the fuzzy FMEA method was used to identify and evaluate risks. Due to its capability to work with fuzzy numbers, this method is particularly suitable for analyzing the criteria of severity, occurrence, and detectability of risks under conditions of uncertainty. After collecting experts' opinions, these criteria were defuzzified into crisp values, and the risks were prioritized. The second stage involved applying the Graph Neural Network (GNN) method to model and analyze the complex dependencies and interrelationships among the risks. GNN, as a powerful machine learning tool, enables the examination of interdependencies among different criteria and nodes. The research data were gathered through surveys conducted with 12 experts in banking and specialized services for the blind. Each expert was presented with a questionnaire containing various pairs of risk criteria and was asked to assign a score between 0 and 4 to each pair. To reduce the impact of individual opinions and achieve a comprehensive assessment, the average scores given by the experts were used as the final weights of the relationships among the criteria in the graph.
<strong>Findings:</strong> The results of the fuzzy FMEA analysis revealed that "physical access," "economic inequalities," "digital divide," and "technological barriers" are among the most significant risks to the accessibility of banking services for the blind. The non-fuzzy Risk Priority Number (RPN) results indicated that the risks "physical access" and "economic inequalities" require the highest priority attention and demand special focus. The GNN analysis confirmed that some risks, such as physical access and technological barriers, have complex and mutual effects on other risks and play a crucial role in the network of relationships among criteria. Specifically, the criteria "economic inequalities" and "technological barriers" were identified as key influencing factors within the graph network. Addressing these risks can significantly improve the accessibility and banking experience for the blind. Furthermore, the findings emphasized that focusing solely on economic and technological aspects is insufficient; the interactions among these criteria must also be considered.
<strong>Conclusion:</strong> Enhancing access to banking services for the blind requires a multifaceted approach that simultaneously focuses on improving physical infrastructure, reducing economic inequalities, raising awareness and providing training on banking technologies, and strengthening information security. The findings of this study demonstrate that integrating fuzzy FMEA and GNN can effectively identify interactions and prioritize risks more accurately, providing a foundation for designing more comprehensive and impactful solutions to improve the accessibility of banking services for the blind. It is recommended that banks and financial institutions utilize these findings to implement inclusive solutions that enhance accessibility and user experience for the blind. Such efforts can ultimately increase customer satisfaction and trust, improving the credibility and social responsibility of banks.<strong>Introduction and objectives:</strong> In today's world, accessibility and security of banking services for all members of society, particularly vulnerable groups such as the blind, are of utmost importance. With the increasing significance of digital banking, identifying and assessing risks related to accessibility and security of banking services for the blind has become a fundamental priority. This research aims to identify, evaluate, and prioritize the main risks associated with providing banking services to the blind and propose solutions to mitigate these risks. The goal is to improve the infrastructure and technologies used to significantly facilitate access to banking services for the blind. This study employs a combination of two methods: fuzzy Failure Mode and Effects Analysis (FMEA) and Graph Neural Networks (GNN), to more accurately and comprehensively identify the relationships and interactions among risks.
<strong>Methods:</strong> This study was conducted in two main stages. In the first stage, the fuzzy FMEA method was used to identify and evaluate risks. Due to its capability to work with fuzzy numbers, this method is particularly suitable for analyzing the criteria of severity, occurrence, and detectability of risks under conditions of uncertainty. After collecting experts' opinions, these criteria were defuzzified into crisp values, and the risks were prioritized. The second stage involved applying the Graph Neural Network (GNN) method to model and analyze the complex dependencies and interrelationships among the risks. GNN, as a powerful machine learning tool, enables the examination of interdependencies among different criteria and nodes. The research data were gathered through surveys conducted with 12 experts in banking and specialized services for the blind. Each expert was presented with a questionnaire containing various pairs of risk criteria and was asked to assign a score between 0 and 4 to each pair. To reduce the impact of individual opinions and achieve a comprehensive assessment, the average scores given by the experts were used as the final weights of the relationships among the criteria in the graph.
<strong>Findings:</strong> The results of the fuzzy FMEA analysis revealed that "physical access," "economic inequalities," "digital divide," and "technological barriers" are among the most significant risks to the accessibility of banking services for the blind. The non-fuzzy Risk Priority Number (RPN) results indicated that the risks "physical access" and "economic inequalities" require the highest priority attention and demand special focus. The GNN analysis confirmed that some risks, such as physical access and technological barriers, have complex and mutual effects on other risks and play a crucial role in the network of relationships among criteria. Specifically, the criteria "economic inequalities" and "technological barriers" were identified as key influencing factors within the graph network. Addressing these risks can significantly improve the accessibility and banking experience for the blind. Furthermore, the findings emphasized that focusing solely on economic and technological aspects is insufficient; the interactions among these criteria must also be considered.
<strong>Conclusion:</strong> Enhancing access to banking services for the blind requires a multifaceted approach that simultaneously focuses on improving physical infrastructure, reducing economic inequalities, raising awareness and providing training on banking technologies, and strengthening information security. The findings of this study demonstrate that integrating fuzzy FMEA and GNN can effectively identify interactions and prioritize risks more accurately, providing a foundation for designing more comprehensive and impactful solutions to improve the accessibility of banking services for the blind. It is recommended that banks and financial institutions utilize these findings to implement inclusive solutions that enhance accessibility and user experience for the blind. Such efforts can ultimately increase customer satisfaction and trust, improving the credibility and social responsibility of banks.https://jimp.sbu.ac.ir/article_105310_8d666843a8eafcc98f3af87d4ccd7f50.pdfShahid Beheshti UniversityJournal of Industrial Management Perspective2251-987414420241121Comprehensive Risk Identification and Prioritization for Engineering, Procurement, and Construction (EPC) Projects:
A Case of Karoon Oil and Gas Exploitation CompanyComprehensive Risk Identification and Prioritization for Engineering, Procurement, and Construction (EPC) Projects:
A Case of Karoon Oil and Gas Exploitation Company25729210545510.48308/jimp.14.4.257FAAsgarKhademvataniAssistant Professor, Department of Energy Economics and Management, Petroleum University of Technology, Tehran, Iran.Mohammad RezaShokouhiAssistant Professor, Department of Energy Economics and Management, Petroleum University of Technology, Tehran, Iran.FatemehNaamiM.Sc. student, Department of Energy Economics and Management, Petroleum University of Technology, Tehran, Iran.Journal Article20240501<strong>Introduction and Objectives: </strong>One of the prominent contract types for project execution is the Engineering, Procurement, and Construction (EPC) method, which serves as a mechanism for transferring project activities and associated risks to contractors. Given the significance of risk assessment in oil and gas projects, particularly for contractors, the primary objective of this research is to identify and rank the risks surrounding EPC projects in Karoon Oil and Gas Exploitation Company. Additionally, this study seeks to propose effective strategies for managing these risks.
<strong> </strong><strong>Methods: </strong>The research methodology consists of several stages. Initially, relevant risks in EPC projects are identified, extracted, and categorized through a literature review and expert opinions. At this stage, the research team compiled a list of potential risks by reviewing scientific sources and conducting interviews with experts in the oil and gas industry. Subsequently, a fuzzy Delphi method, based on the insights of five experts and 15 criteria, was applied to select the key risks. The experts' opinions were collected and analyzed using fuzzy numbers to reach a relative consensus on the primary risks. In the next stage, the selected risks were assessed and ranked using a fuzzy analytic hierarchy process (FAHP) based on their probability of occurrence and severity of impact. To enhance the accuracy of expert evaluations and minimize subjective biases, a fuzzy system was employed, utilizing fuzzy numbers instead of precise values to represent expert opinions.
<strong>Findings: </strong>The results of this study reveal that the key risks in the company's EPC projects, ranked in order of priority, include inflation rate, employer pressure to halt execution, rising equipment costs, delays in financial payments, the presence of incompatible factors and the use of substandard materials, deficiencies in initial design, and ineffective engineering management. These findings help contractors recognize and plan for the management of major risks. Additionally, a scenario-based cognitive map has been developed to address the identified risks, allowing contractors to devise appropriate strategies for risk mitigation. By identifying and prioritizing these risks, contractors can formulate scenario-based strategies to manage them effectively, thereby reducing the likelihood of adverse consequences in Karoon Oil and Gas Exploitation Company. This cognitive map serves as a roadmap for contractors in managing risks in EPC projects. For instance, recommended strategies include meticulous planning, contract management, financial management, procurement management, and stakeholder management. The primary goal of these strategies is to prevent potential challenges in project execution.
<strong> </strong><strong>Conclusion: </strong>This study underscores the critical importance of risk assessment in EPC projects. Contractors must identify major risks and implement appropriate management strategies to mitigate potential challenges. Analytical tools such as the fuzzy analytic hierarchy process and scenario-based cognitive maps significantly aid contractors in this regard and play a crucial role in the successful execution of EPC projects.<strong>Introduction and Objectives: </strong>One of the prominent contract types for project execution is the Engineering, Procurement, and Construction (EPC) method, which serves as a mechanism for transferring project activities and associated risks to contractors. Given the significance of risk assessment in oil and gas projects, particularly for contractors, the primary objective of this research is to identify and rank the risks surrounding EPC projects in Karoon Oil and Gas Exploitation Company. Additionally, this study seeks to propose effective strategies for managing these risks.
<strong> </strong><strong>Methods: </strong>The research methodology consists of several stages. Initially, relevant risks in EPC projects are identified, extracted, and categorized through a literature review and expert opinions. At this stage, the research team compiled a list of potential risks by reviewing scientific sources and conducting interviews with experts in the oil and gas industry. Subsequently, a fuzzy Delphi method, based on the insights of five experts and 15 criteria, was applied to select the key risks. The experts' opinions were collected and analyzed using fuzzy numbers to reach a relative consensus on the primary risks. In the next stage, the selected risks were assessed and ranked using a fuzzy analytic hierarchy process (FAHP) based on their probability of occurrence and severity of impact. To enhance the accuracy of expert evaluations and minimize subjective biases, a fuzzy system was employed, utilizing fuzzy numbers instead of precise values to represent expert opinions.
<strong>Findings: </strong>The results of this study reveal that the key risks in the company's EPC projects, ranked in order of priority, include inflation rate, employer pressure to halt execution, rising equipment costs, delays in financial payments, the presence of incompatible factors and the use of substandard materials, deficiencies in initial design, and ineffective engineering management. These findings help contractors recognize and plan for the management of major risks. Additionally, a scenario-based cognitive map has been developed to address the identified risks, allowing contractors to devise appropriate strategies for risk mitigation. By identifying and prioritizing these risks, contractors can formulate scenario-based strategies to manage them effectively, thereby reducing the likelihood of adverse consequences in Karoon Oil and Gas Exploitation Company. This cognitive map serves as a roadmap for contractors in managing risks in EPC projects. For instance, recommended strategies include meticulous planning, contract management, financial management, procurement management, and stakeholder management. The primary goal of these strategies is to prevent potential challenges in project execution.
<strong> </strong><strong>Conclusion: </strong>This study underscores the critical importance of risk assessment in EPC projects. Contractors must identify major risks and implement appropriate management strategies to mitigate potential challenges. Analytical tools such as the fuzzy analytic hierarchy process and scenario-based cognitive maps significantly aid contractors in this regard and play a crucial role in the successful execution of EPC projects.https://jimp.sbu.ac.ir/article_105455_d13c1acd680f1c1c116c1e51ec0363c7.pdf