Shahid Beheshti UniversityJournal of Industrial Management Perspective2251-987414320240822Comprehensive Evaluation of E-business Satisfaction Variables and Indicators in B2C Model Using the Negotiation Decision Function MethodComprehensive Evaluation of E-business Satisfaction Variables and Indicators in B2C Model Using the Negotiation Decision Function Method92310464610.48308/jimp.14.3.9FAHamedFazlollahtabarAssociate Professor, Department of Industrial Engineering, Technical and Engineering Faculty, Damghan University, Damghan, Iran.Journal Article20230716<strong>Introduction and Objectives</strong>: E-commerce has transformed traditional business behaviors, as consumers can easily make purchases through e-commerce platforms. The internet offers countless products and services, but consumers may struggle to choose their preferred products. In recent years, the B2C customer-seller model has gained significant popularity and is one of the best options for group-buying customers. However, users believe that this method increases the demand for services, which in turn leads to price increases.<br /><strong>Methods</strong>:<br />Using an intelligent agent in negotiations can effectively reduce the efforts spent on gathering buyer information and the costs of transactions and negotiations with sellers. This study applies an intelligent agent to the B2C e-commerce process and evaluates the system through testing. Additionally, a questionnaire is used to assess the benefits of the proposed intelligent system. The innovations of this research include identifying and categorizing variables affecting the business to customer purchase, designing a negotiation mechanism, and evaluating business satisfaction. A key limitation of this research is the data collection process and the sample size studied.<br /><strong>Results and discussion: </strong>Analytical results indicate that the proposed intelligent system can reduce operational risk while increasing user satisfaction and perceived fairness. Fifty participants, aged 23 to 27, were involved in this experiment. The most common method for estimating the reliability of such scales is the α coefficient. Based on the minimum acceptable criterion, if α is greater than 0.7, it has higher reliability, and if α is less than 0.35, it has lower reliability and should be rejected. The analysis shows that α in this study is 0.844, which indicates higher reliability. In this study, the perceived value shows no significant difference before and after using this system, indicating that the intelligent agent can enhance the value of the business to customer trading system. When participants engage with the platform, shared value may not be the primary concern. The user can receive assistance from this experimental system, but participants did not have a specific goal for using the system, likely due to the limited time of the experiment. In such a short period, participants were unable to form clear objectives or expectations for using the system. Therefore, the analytical results suggest that the system offers higher satisfaction, greater perceived fairness, and lower perceived risk, but users have a neutral attitude toward the perceived value and use of the system.<br /><strong> </strong><strong>Conclusion: </strong>The value of this system is not yet fully understood, indicating that it requires more promotion in today's commerce to better reveal its benefits to consumers. From a practical perspective, it is recommended that e-commerce practitioners implement negotiation processes through intelligent systems to identify customer satisfaction aspects, which can then be used for re-engineering and redesigning processes and products. Moreover, this system is not only applicable to the B2C, but can also be extended to other e-commerce models, as the agent can facilitate negotiations between sellers and buyers.<br /> <strong>Introduction and Objectives</strong>: E-commerce has transformed traditional business behaviors, as consumers can easily make purchases through e-commerce platforms. The internet offers countless products and services, but consumers may struggle to choose their preferred products. In recent years, the B2C customer-seller model has gained significant popularity and is one of the best options for group-buying customers. However, users believe that this method increases the demand for services, which in turn leads to price increases.<br /><strong>Methods</strong>:<br />Using an intelligent agent in negotiations can effectively reduce the efforts spent on gathering buyer information and the costs of transactions and negotiations with sellers. This study applies an intelligent agent to the B2C e-commerce process and evaluates the system through testing. Additionally, a questionnaire is used to assess the benefits of the proposed intelligent system. The innovations of this research include identifying and categorizing variables affecting the business to customer purchase, designing a negotiation mechanism, and evaluating business satisfaction. A key limitation of this research is the data collection process and the sample size studied.<br /><strong>Results and discussion: </strong>Analytical results indicate that the proposed intelligent system can reduce operational risk while increasing user satisfaction and perceived fairness. Fifty participants, aged 23 to 27, were involved in this experiment. The most common method for estimating the reliability of such scales is the α coefficient. Based on the minimum acceptable criterion, if α is greater than 0.7, it has higher reliability, and if α is less than 0.35, it has lower reliability and should be rejected. The analysis shows that α in this study is 0.844, which indicates higher reliability. In this study, the perceived value shows no significant difference before and after using this system, indicating that the intelligent agent can enhance the value of the business to customer trading system. When participants engage with the platform, shared value may not be the primary concern. The user can receive assistance from this experimental system, but participants did not have a specific goal for using the system, likely due to the limited time of the experiment. In such a short period, participants were unable to form clear objectives or expectations for using the system. Therefore, the analytical results suggest that the system offers higher satisfaction, greater perceived fairness, and lower perceived risk, but users have a neutral attitude toward the perceived value and use of the system.<br /><strong> </strong><strong>Conclusion: </strong>The value of this system is not yet fully understood, indicating that it requires more promotion in today's commerce to better reveal its benefits to consumers. From a practical perspective, it is recommended that e-commerce practitioners implement negotiation processes through intelligent systems to identify customer satisfaction aspects, which can then be used for re-engineering and redesigning processes and products. Moreover, this system is not only applicable to the B2C, but can also be extended to other e-commerce models, as the agent can facilitate negotiations between sellers and buyers.<br /> https://jimp.sbu.ac.ir/article_104646_2e4588fd2cfe223c5e0fcc91dc597772.pdfShahid Beheshti UniversityJournal of Industrial Management Perspective2251-987414320240822A Novel Meta-Symthesis Model to Improve New Product Development Based on Risk Management of Fast-Moving Consumer GoodsA Novel Meta-Symthesis Model to Improve New Product Development Based on Risk Management of Fast-Moving Consumer Goods245510455710.48308/jimp.14.3.24FAFatemehSaghafiAssociate Professor, Faculty of Industrial Management &Technology, College of management, University of Tehran, Tehran, Iran.AliMohagharProfessor, Faculty of Industrial Management &Technology, College of management, University of Tehran, Tehran, Iran.ArefehMohammadzadehMSc., Mechanical Engineering Department, College of Engineering, University of Tehran, Tehran, Iran.0009-0009-1369-3401YousefRoghaniMSc., Faculty of Industrial Management &Technology, College of management, University of Tehran, Tehran, Iran.Journal Article20230411<strong>Introduction and objectives:</strong> With the advancement of Industry 4.0, customer preferences are continuously evolving, and product life cycles are becoming shorter. Consequently, new product development (NPD) processes have become indispensable for companies. Achieving sustainable industries necessitates a process where emerging risks are mitigated. In other words, integrating risk management concepts into the NPD process can significantly improve organizational performance. The primary goal of this research is to develop a framework for risk management within the NPD process specifically tailored for fast-moving consumer goods (FMCG).
<strong>Method: </strong>Initially, relevant research on NPD was reviewed, defining the concepts of NPD process, risk, and FMCG to clearly articulate the problem statement and the need for risk management in this domain. This section was presented in two main parts: theoretical literature and empirical literature. Subsequently, a detailed review of 18 risk management models was conducted, selected from a pool of 95 articles, to identify prominent studies in this area.
<strong>Findings: </strong>The results indicated that while these articles examined specific phases or steps of the five-phase risk management models, often introducing variations, none of them comprehensively covered all activities and phases. Employing the meta-synthesis method and utilizing Sandelowski and Barroso's seven-step model, while considering the shortcomings and strengths of the reviewed models, a novel risk management model was proposed for enhancing the NPD process. This model consists of six steps and introduces the implementation details of the stages, steps, and sub-steps. Beyond incorporating the strengths of existing models, this model also addresses their deficiencies. Its innovation lies in its in-depth articulation of the risk process steps and its focused attention on improving the path for determining risk response strategies. The FMCG industry's impact on this model is evident in the independence of risk management and risk monitoring. This research was derived from existing models and grounded in the meta-synthesis method. It was further refined through consultations with risk management experts, FMCG specialists, and a case study of the current product status at Golrang Company. The key criteria for assessing the validity and reliability of the measures were examined. Validity assessment was initially conducted through an extensive review of the literature. Subsequently, the identified components and indicators were evaluated by experts in this field and received final approval. Additionally, two sets of interviews were conducted to gather the necessary data. The first set of interviews aimed to identify existing risks within the NPD process. The second set focused on determining response strategies for each prioritized risk.
<strong>Conclusion:</strong><strong> </strong>The feedback loop is a critical factor for FMCG products, as ignoring feedback and failing to adjust the development process can lead to a product's competitive decline. Experts emphasize the need for independent risk management and risk monitoring in FMCG due to the high perishability of these products. Lost opportunities can result in irreparable losses, highlighting the critical importance of speed in risk management for FMCG. Given the short life cycle of these products, any delay in decision-making can lead to significant losses. Therefore, rapid risk management is essential for these products. To achieve the desired outcomes, developing data collection and analysis systems for assessing risks associated with these products is imperative.<strong>Introduction and objectives:</strong> With the advancement of Industry 4.0, customer preferences are continuously evolving, and product life cycles are becoming shorter. Consequently, new product development (NPD) processes have become indispensable for companies. Achieving sustainable industries necessitates a process where emerging risks are mitigated. In other words, integrating risk management concepts into the NPD process can significantly improve organizational performance. The primary goal of this research is to develop a framework for risk management within the NPD process specifically tailored for fast-moving consumer goods (FMCG).
<strong>Method: </strong>Initially, relevant research on NPD was reviewed, defining the concepts of NPD process, risk, and FMCG to clearly articulate the problem statement and the need for risk management in this domain. This section was presented in two main parts: theoretical literature and empirical literature. Subsequently, a detailed review of 18 risk management models was conducted, selected from a pool of 95 articles, to identify prominent studies in this area.
<strong>Findings: </strong>The results indicated that while these articles examined specific phases or steps of the five-phase risk management models, often introducing variations, none of them comprehensively covered all activities and phases. Employing the meta-synthesis method and utilizing Sandelowski and Barroso's seven-step model, while considering the shortcomings and strengths of the reviewed models, a novel risk management model was proposed for enhancing the NPD process. This model consists of six steps and introduces the implementation details of the stages, steps, and sub-steps. Beyond incorporating the strengths of existing models, this model also addresses their deficiencies. Its innovation lies in its in-depth articulation of the risk process steps and its focused attention on improving the path for determining risk response strategies. The FMCG industry's impact on this model is evident in the independence of risk management and risk monitoring. This research was derived from existing models and grounded in the meta-synthesis method. It was further refined through consultations with risk management experts, FMCG specialists, and a case study of the current product status at Golrang Company. The key criteria for assessing the validity and reliability of the measures were examined. Validity assessment was initially conducted through an extensive review of the literature. Subsequently, the identified components and indicators were evaluated by experts in this field and received final approval. Additionally, two sets of interviews were conducted to gather the necessary data. The first set of interviews aimed to identify existing risks within the NPD process. The second set focused on determining response strategies for each prioritized risk.
<strong>Conclusion:</strong><strong> </strong>The feedback loop is a critical factor for FMCG products, as ignoring feedback and failing to adjust the development process can lead to a product's competitive decline. Experts emphasize the need for independent risk management and risk monitoring in FMCG due to the high perishability of these products. Lost opportunities can result in irreparable losses, highlighting the critical importance of speed in risk management for FMCG. Given the short life cycle of these products, any delay in decision-making can lead to significant losses. Therefore, rapid risk management is essential for these products. To achieve the desired outcomes, developing data collection and analysis systems for assessing risks associated with these products is imperative.https://jimp.sbu.ac.ir/article_104557_8bb919cbb45c4683fe6151edccc88691.pdfShahid Beheshti UniversityJournal of Industrial Management Perspective2251-987414320240822A Robust Risk Management Model for the Blood Supply Chain in Corona Pandemic ConditionA Robust Risk Management Model for the Blood Supply Chain in Corona Pandemic Condition567810473610.48308/jimp.14.3.56FAAbolfazlBabazadeh RafieiPh.D. Student, Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.TahmouresSohrabiAssistant Professor, Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.MajidMotamediAssistant Professor, Department of Management, Nowshahr Branch, Islamic Azad University, Nowshahr, Iran.Mohammad HosseinDarvish MotevalliAssistant Professor, Depatment of Industrial Management, West Teharan Branch, Islamic Azad University, Tehran, Iran.Journal Article20231220<strong>Introduction and objectives:</strong> Supply chain risk management is a proactive approach to prevent potential and unexpected consequences. This research aims to develop a mathematical model to reduce the risk within the blood supply chain during pandemics. Specifically, a robust, multi-objective, scenario-based model has been proposed to mitigate the risk of the blood supply chain under critical conditions.
<strong>Methods:</strong> The COVID-19 pandemic disrupted the blood supply from donors, leading to a crisis in the blood supply chain. Unlike previous research that focused on increased demand due to crises like earthquakes or wars, this study addresses the disruption in supply. To overcome this uncertainty, a three-level model with two objectives was developed. The first objective is to minimize total cost, and the second is to maximize the reliability of the blood supply chain. The model is then made robust by considering uncertainty in blood supply. The novelty of this research lies in presenting a mathematical model that simultaneously optimizes the two conflicting objectives of cost and reliability while considering supply uncertainty. The weighted sum method was used to convert the multi-objective model into a single-objective one, and the model was solved using GAMS software and the BARON solver.
<strong>Results and discussion:</strong> To validate the model, the problem was tested under various scenarios using real-world data, and a sensitivity analysis was conducted to assess the model's stability against parameter changes. The total cost minimization in the robust model was calculated, and it was observed that as the weight of the cost minimization objective function increased, this objective function moved towards minimization and optimization, stabilizing at a weight of 0.1. By increasing the weight in the reliability maximization objective function, the value of this objective function stabilized at 0.5 and moved towards maximization, reaching its maximum at a weight of 1. The Pareto solutions for changes in the cost function and stable reliability are presented, showing that as the stability of the cost objective function increases, the stable reliability function decreases significantly, and vice versa. Additionally, the relationship between reliability and the number of blood collection facilities was directly proportional. However, the reliability of the system did not increase beyond a certain point (15 facilities). Consequently, constructing more than 15 blood collection facilities is not cost-effective, indicating increased efficiency in the supply chain at the level of blood collection facilities when using the proposed model. The findings show that the presented model can determine the optimal amount of blood collected from donors, the number of collection centers, the blood inventory level at blood centers and hospitals, as well as the units of blood sent from blood centers to hospitals, aiming to reduce risk and manage the blood supply chain effectively during critical blood supply conditions like the COVID-19 pandemic.
<strong>Conclusion:</strong> The COVID-19 pandemic highlighted the importance of blood supply chain risk management. Since the blood supply chain is vital for public health, organizations and institutions involved in this field should implement robust plans and strategies to manage risks and enhance the stability of the blood supply chain during crises like pandemics. Therefore, implementing a robust risk management model for the blood supply chain in the context of the COVID-19 pandemic will help organizations ensure their stability and performance, effectively addressing society's blood supply needs.<strong>Introduction and objectives:</strong> Supply chain risk management is a proactive approach to prevent potential and unexpected consequences. This research aims to develop a mathematical model to reduce the risk within the blood supply chain during pandemics. Specifically, a robust, multi-objective, scenario-based model has been proposed to mitigate the risk of the blood supply chain under critical conditions.
<strong>Methods:</strong> The COVID-19 pandemic disrupted the blood supply from donors, leading to a crisis in the blood supply chain. Unlike previous research that focused on increased demand due to crises like earthquakes or wars, this study addresses the disruption in supply. To overcome this uncertainty, a three-level model with two objectives was developed. The first objective is to minimize total cost, and the second is to maximize the reliability of the blood supply chain. The model is then made robust by considering uncertainty in blood supply. The novelty of this research lies in presenting a mathematical model that simultaneously optimizes the two conflicting objectives of cost and reliability while considering supply uncertainty. The weighted sum method was used to convert the multi-objective model into a single-objective one, and the model was solved using GAMS software and the BARON solver.
<strong>Results and discussion:</strong> To validate the model, the problem was tested under various scenarios using real-world data, and a sensitivity analysis was conducted to assess the model's stability against parameter changes. The total cost minimization in the robust model was calculated, and it was observed that as the weight of the cost minimization objective function increased, this objective function moved towards minimization and optimization, stabilizing at a weight of 0.1. By increasing the weight in the reliability maximization objective function, the value of this objective function stabilized at 0.5 and moved towards maximization, reaching its maximum at a weight of 1. The Pareto solutions for changes in the cost function and stable reliability are presented, showing that as the stability of the cost objective function increases, the stable reliability function decreases significantly, and vice versa. Additionally, the relationship between reliability and the number of blood collection facilities was directly proportional. However, the reliability of the system did not increase beyond a certain point (15 facilities). Consequently, constructing more than 15 blood collection facilities is not cost-effective, indicating increased efficiency in the supply chain at the level of blood collection facilities when using the proposed model. The findings show that the presented model can determine the optimal amount of blood collected from donors, the number of collection centers, the blood inventory level at blood centers and hospitals, as well as the units of blood sent from blood centers to hospitals, aiming to reduce risk and manage the blood supply chain effectively during critical blood supply conditions like the COVID-19 pandemic.
<strong>Conclusion:</strong> The COVID-19 pandemic highlighted the importance of blood supply chain risk management. Since the blood supply chain is vital for public health, organizations and institutions involved in this field should implement robust plans and strategies to manage risks and enhance the stability of the blood supply chain during crises like pandemics. Therefore, implementing a robust risk management model for the blood supply chain in the context of the COVID-19 pandemic will help organizations ensure their stability and performance, effectively addressing society's blood supply needs.https://jimp.sbu.ac.ir/article_104736_08d2b258d3e3d2e769abcb36904ef8e2.pdfShahid Beheshti UniversityJournal of Industrial Management Perspective2251-987414320240822Development of a mathematical programming model to redesign the supply chain network with the possibility of changing the usage of facilitiesDevelopment of a mathematical programming model to redesign the supply chain network with the possibility of changing the usage of facilities7911610473210.48308/jimp.14.3.79FAAliAbdoliMaster's degree, Department of Master of Business Administration (MBA), Faculty of Financial Sciences, Management and Entrepreneurship, University of Kashan, Kashan, Iran.HadiMokhtariAssociate Professor, Department of Industrial Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran.Journal Article20240503<strong>Introduction and objectives:</strong> Today, due to the competitive environment of the market, cooperation in the form of supply chain networks is necessary for the survival of businesses, and for efficient and effective cooperation between members, there is a need for coherent management of the supply chain. To realize this, there is a need for continuous coordination between supply chain performance on the one hand and market expectations on the other hand. One of the methods of maintaining this coordination is the continuous redesign of the supply chain network over time. In the supply chain network redesign problem, the goal is to improve an existing supply chain, while in the supply chain network design problem, a new supply chain is created from scratch. in real conditions; Often, the problem of redesigning the supply chain is more widely used than the problem of designing the supply chain, while in the literature, the vast majority of researches are focused on designing a supply chain from scratch. One of the decisions of the redesign problem that has been hidden from the attention of researchers is the decision to change the use of supply chain facilities. in other words; Changing the facility layer in the supply chain is considered as a new decision.
<strong>Methods:</strong> The decision to change the use of facilities in the traditional supply chain problem is challenging, because it changes not only the network flows but also the network structure (topology). Changing the basic structure of the supply chain network is a non-linear problem. In this research, an innovative innovation has been used to face this challenge, by first changing the perspective of the supply chain network from a traditional layered network to a rotating network and then presenting Innovative mathematical modeling based on the innovative perspective of the rotating supply chain network.
<strong>Findings:</strong> Redesigning the supply chain network with the possibility of changing the usage of facilities due to changes in the network structure is a non-linear problem. In this research, by changing the perspective and creating innovative variables and constraints, a linear mixed integer multi-period programming model has been presented for the problem. Also, in this model, the transition mode of changing the usage of facilities from one layer to another layer is considered, and achieving this capability is one of the amazing results of using the innovative perspective of the rotating network of the supply chain. This model was solved using an example in GAMS software with the CPLEX method, and MATLAB software has been used to show the results of this model and an innovative view of the supply chain.
<strong>Conclusion:</strong> In the past, supply chain managers faced with the decision to change the usage of facilities in the supply chain network due to the limitations of the traditional layer view for mathematical modeling and optimal redesign of the network under their management. They have faced a challenge, which now the managers have the possibility to face it with the help of this innovative model and changing the perspective towards the supply chain network. As a management proposal, we can point out the need to use the principles of optimization and supply chain management as a new management approach and paradigm. At the strategic level of the supply chain, due to its wide nature and dimensions, the amount of costs is high and small improvements in it will lead to a significant competitive advantage increase for the supply chain under management. for this reason; Chain managers are advised to use the model presented in this article for chains in which it is possible to change the use of facilities, to improve the chain under their management.<strong>Introduction and objectives:</strong> Today, due to the competitive environment of the market, cooperation in the form of supply chain networks is necessary for the survival of businesses, and for efficient and effective cooperation between members, there is a need for coherent management of the supply chain. To realize this, there is a need for continuous coordination between supply chain performance on the one hand and market expectations on the other hand. One of the methods of maintaining this coordination is the continuous redesign of the supply chain network over time. In the supply chain network redesign problem, the goal is to improve an existing supply chain, while in the supply chain network design problem, a new supply chain is created from scratch. in real conditions; Often, the problem of redesigning the supply chain is more widely used than the problem of designing the supply chain, while in the literature, the vast majority of researches are focused on designing a supply chain from scratch. One of the decisions of the redesign problem that has been hidden from the attention of researchers is the decision to change the use of supply chain facilities. in other words; Changing the facility layer in the supply chain is considered as a new decision.
<strong>Methods:</strong> The decision to change the use of facilities in the traditional supply chain problem is challenging, because it changes not only the network flows but also the network structure (topology). Changing the basic structure of the supply chain network is a non-linear problem. In this research, an innovative innovation has been used to face this challenge, by first changing the perspective of the supply chain network from a traditional layered network to a rotating network and then presenting Innovative mathematical modeling based on the innovative perspective of the rotating supply chain network.
<strong>Findings:</strong> Redesigning the supply chain network with the possibility of changing the usage of facilities due to changes in the network structure is a non-linear problem. In this research, by changing the perspective and creating innovative variables and constraints, a linear mixed integer multi-period programming model has been presented for the problem. Also, in this model, the transition mode of changing the usage of facilities from one layer to another layer is considered, and achieving this capability is one of the amazing results of using the innovative perspective of the rotating network of the supply chain. This model was solved using an example in GAMS software with the CPLEX method, and MATLAB software has been used to show the results of this model and an innovative view of the supply chain.
<strong>Conclusion:</strong> In the past, supply chain managers faced with the decision to change the usage of facilities in the supply chain network due to the limitations of the traditional layer view for mathematical modeling and optimal redesign of the network under their management. They have faced a challenge, which now the managers have the possibility to face it with the help of this innovative model and changing the perspective towards the supply chain network. As a management proposal, we can point out the need to use the principles of optimization and supply chain management as a new management approach and paradigm. At the strategic level of the supply chain, due to its wide nature and dimensions, the amount of costs is high and small improvements in it will lead to a significant competitive advantage increase for the supply chain under management. for this reason; Chain managers are advised to use the model presented in this article for chains in which it is possible to change the use of facilities, to improve the chain under their management.https://jimp.sbu.ac.ir/article_104732_60b91a9457f0707dbbe3ad7756e42b16.pdfShahid Beheshti UniversityJournal of Industrial Management Perspective2251-987414320240822Evaluation and ranking of Stock Funds using Opportunity Losses-Based Polar Coordinate Distance (OPLO-POCOD) TechniqueEvaluation and ranking of Stock Funds using Opportunity Losses-Based Polar Coordinate Distance (OPLO-POCOD) Technique11714010487710.48308/jimp.14.3.117FARezaSheikhAssociate Professor, Faculty of Industrial Engineering and Management, Shahrood University of Technology, Shahrood, Semnan, Iran.SoheilaSenfiMSc, MBA, Department of Business Management, Faculty of Industrial Engineering and Management, Shahrood University of Technology, Shahrood, Semnan, Iran.Journal Article20240310<strong>Introduction:</strong> Today, in the modern economy, due to the growth of the capital market, the importance of investment for people has increased. One of the main concerns of investors, in the first instance, is selecting the most appropriate investment option. Mutual funds are a type of investment that gathers investors' funds to invest in a diverse range of securities, thereby reducing investment risk and increasing returns. In the field of investment decision-making, the process of selecting the most appropriate fund from a wide range of options can be complex. In the literature, various criteria have been developed to evaluate the performance of mutual funds. In this research, the most important criteria have been identified and evaluated using a new technique in the field of multi-criteria decision-making.
<strong>Methods</strong>: Opportunity loss is a fundamental concept in economics and management, referring to the costs incurred from not choosing the best possible option in a particular situation. This concept can serve as the basis for determining the value associated with information and economic decision-making. In fact, opportunity loss helps us better understand the real costs of our choices, enabling us to make more informed decisions. In this research, based on the problem-solving assumptions, a new technique called Opportunity Loss-Based Polar Coordinate Distance (OPLO-POCOD) has been used for evaluating and ranking mutual funds. Due to its strong and scientific logic in analyzing opportunity losses, this technique is recognized as an efficient tool in the decision-making process. One of the notable advantages of this technique is its ability to conduct precise evaluations and comprehensive rankings of various options. Using this method, it is possible to systematically assess the opportunity losses of each option and evaluate their positions relative to the best available condition. This evaluation is based on distance in polar coordinate space, which allows analysts to identify the strengths and weaknesses of each option both intuitively and quantitatively. Overall, this research not only contributes to a better understanding of the concept of opportunity losses but also provides a novel method for evaluating and ranking mutual funds, assisting investors and decision-makers in making more informed choices. This approach can enhance decision-making processes in the fields of investment and financial management, ultimately leading to increased efficiency and effectiveness of investments.
<strong>Results and Discussion</strong>: In this research, using the filters of fund size and one-year, two-year, and three-year performance, and based on the information available on the website of the Financial Information Processing Center of Iran (Fipiran), 20 stock funds were selected from among various mutual funds with the most assets and best performance. The research results indicate that stock funds option 14 with a DOL value of 0.000841, option 15 with a DOL value of 0.017437, and option 19 with a DOL value of 0.03432 received the highest rankings.
<strong>Conclusion</strong>: Based on this research, in addition to a comprehensive ranking of the options, a more detailed analysis has been conducted on three dimensions: the characteristics of the mutual fund, the personality characteristics of the mutual fund manager, and the performance evaluation criteria of the mutual fund. Mutual fund managers, by becoming aware of their fund's ranking, can analyze the efficiency and inefficiency of the fund across various dimensions and based on top-performing funds.<strong>Introduction:</strong> Today, in the modern economy, due to the growth of the capital market, the importance of investment for people has increased. One of the main concerns of investors, in the first instance, is selecting the most appropriate investment option. Mutual funds are a type of investment that gathers investors' funds to invest in a diverse range of securities, thereby reducing investment risk and increasing returns. In the field of investment decision-making, the process of selecting the most appropriate fund from a wide range of options can be complex. In the literature, various criteria have been developed to evaluate the performance of mutual funds. In this research, the most important criteria have been identified and evaluated using a new technique in the field of multi-criteria decision-making.
<strong>Methods</strong>: Opportunity loss is a fundamental concept in economics and management, referring to the costs incurred from not choosing the best possible option in a particular situation. This concept can serve as the basis for determining the value associated with information and economic decision-making. In fact, opportunity loss helps us better understand the real costs of our choices, enabling us to make more informed decisions. In this research, based on the problem-solving assumptions, a new technique called Opportunity Loss-Based Polar Coordinate Distance (OPLO-POCOD) has been used for evaluating and ranking mutual funds. Due to its strong and scientific logic in analyzing opportunity losses, this technique is recognized as an efficient tool in the decision-making process. One of the notable advantages of this technique is its ability to conduct precise evaluations and comprehensive rankings of various options. Using this method, it is possible to systematically assess the opportunity losses of each option and evaluate their positions relative to the best available condition. This evaluation is based on distance in polar coordinate space, which allows analysts to identify the strengths and weaknesses of each option both intuitively and quantitatively. Overall, this research not only contributes to a better understanding of the concept of opportunity losses but also provides a novel method for evaluating and ranking mutual funds, assisting investors and decision-makers in making more informed choices. This approach can enhance decision-making processes in the fields of investment and financial management, ultimately leading to increased efficiency and effectiveness of investments.
<strong>Results and Discussion</strong>: In this research, using the filters of fund size and one-year, two-year, and three-year performance, and based on the information available on the website of the Financial Information Processing Center of Iran (Fipiran), 20 stock funds were selected from among various mutual funds with the most assets and best performance. The research results indicate that stock funds option 14 with a DOL value of 0.000841, option 15 with a DOL value of 0.017437, and option 19 with a DOL value of 0.03432 received the highest rankings.
<strong>Conclusion</strong>: Based on this research, in addition to a comprehensive ranking of the options, a more detailed analysis has been conducted on three dimensions: the characteristics of the mutual fund, the personality characteristics of the mutual fund manager, and the performance evaluation criteria of the mutual fund. Mutual fund managers, by becoming aware of their fund's ranking, can analyze the efficiency and inefficiency of the fund across various dimensions and based on top-performing funds.https://jimp.sbu.ac.ir/article_104877_e767a29fdbe7bf5341d2290b73e6ab9e.pdfShahid Beheshti UniversityJournal of Industrial Management Perspective2251-987414320240822Evaluation of Human Resource Productivity Risks, Fuzzy DEMATEL and System Dynamics Approach (Case Study: High-Rise Building Projects)Evaluation of Human Resource Productivity Risks, Fuzzy DEMATEL and System Dynamics Approach (Case Study: High-Rise Building Projects)14116810473110.48308/jimp.14.3.141FANahalGoodarziMSc., Department of Construction, Faculty of Architecture and Urban Planning, Shahid Beheshti University, Tehran, Iran.AhadNazariProfessor, Department of Construction, Faculty of Architecture and Urban Planning, Shahid Beheshti University, Tehran, Iran.Journal Article20240428<strong>Introduction:</strong> The productivity of skilled human resources is one of the key factors influencing the performance of construction projects, and neglecting this issue can result in irreparable damages to the projects. Low labor productivity is one of the fundamental challenges in the construction industry, as it has consistently been a cause of excessive increases in time and cost in construction projects. Research conducted on the risk factors affecting human resource productivity in projects has shortcomings, such as a lack of comprehensiveness in the risk identification process, failure to consider the interactions between risks, and the failure to account for risk factors and their impacts on productivity. Therefore, the purpose of this research is to identify the risk factors and the resulting risks, analyze, and prioritize them to manage critical risks more effectively.<br /><strong>Methods</strong>: This research is a case study, and the data collection tools include structured interviews and questionnaires. The first step of this research is to identify the productivity indicators of skilled human resources and the risks arising from these indicators. The process for completing this step includes a comprehensive review of the literature to identify the indicators and risks, followed by interviews with 10 experts in the field to verify the identified risks. In the second step, to analyze the risks, we first employ the fuzzy DEMATEL method to determine the causal relationships between system variables. Using the information obtained from this step, we apply the system dynamics approach to model the risks. After creating the stock-flow diagram and conducting simulations, we identify the sensitive and critical points of the system.<br /><strong>Results and Discussion</strong>: The results show that the risks of site execution interference, falling from height, unsafe operations, electrocution, non-compliance with plans and specifications, surface defects during execution, rework, machinery and equipment efficiency, communication and coordination, and material wastage are among the 10 key risks that contribute to decreased productivity in high-rise building projects.<br />The results of the risk analysis indicate that the risks of 1) site execution interference, 2) rework, 3) communication and coordination, and 4) falling from height have the most significant impact on construction projects. The findings also reveal that the risk of site execution interference, if it occurs, could reduce productivity to 190 units of work completed per month, while rework could reduce productivity to 300 units. On the other hand, the risk of falling from height reduces productivity to 380 units, whereas communication-related risks maintain productivity at around 300 units. Therefore, as the greatest reduction in productivity is related to the risk of site execution interference, this risk is identified as the most critical in the system.<br /><strong>Conclusions</strong>: The model developed in this research has significant potential to assist construction managers in systematically evaluating the negative impacts of productivity risks. Project managers can use the findings of this research to focus more on key risks, such as site execution interference, rework, communication and coordination, and falling from height, managing these risks more effectively. By doing so, they can optimize the productivity of human resources, a critical issue in the construction industry. The advantage of this research for the construction industry is that it helps managers better understand the dynamics of productivity risks and, by restoring the connections between risks and breaking key interactions in feedback loops, stop the escalating impact of risks on one another.<strong>Introduction:</strong> The productivity of skilled human resources is one of the key factors influencing the performance of construction projects, and neglecting this issue can result in irreparable damages to the projects. Low labor productivity is one of the fundamental challenges in the construction industry, as it has consistently been a cause of excessive increases in time and cost in construction projects. Research conducted on the risk factors affecting human resource productivity in projects has shortcomings, such as a lack of comprehensiveness in the risk identification process, failure to consider the interactions between risks, and the failure to account for risk factors and their impacts on productivity. Therefore, the purpose of this research is to identify the risk factors and the resulting risks, analyze, and prioritize them to manage critical risks more effectively.<br /><strong>Methods</strong>: This research is a case study, and the data collection tools include structured interviews and questionnaires. The first step of this research is to identify the productivity indicators of skilled human resources and the risks arising from these indicators. The process for completing this step includes a comprehensive review of the literature to identify the indicators and risks, followed by interviews with 10 experts in the field to verify the identified risks. In the second step, to analyze the risks, we first employ the fuzzy DEMATEL method to determine the causal relationships between system variables. Using the information obtained from this step, we apply the system dynamics approach to model the risks. After creating the stock-flow diagram and conducting simulations, we identify the sensitive and critical points of the system.<br /><strong>Results and Discussion</strong>: The results show that the risks of site execution interference, falling from height, unsafe operations, electrocution, non-compliance with plans and specifications, surface defects during execution, rework, machinery and equipment efficiency, communication and coordination, and material wastage are among the 10 key risks that contribute to decreased productivity in high-rise building projects.<br />The results of the risk analysis indicate that the risks of 1) site execution interference, 2) rework, 3) communication and coordination, and 4) falling from height have the most significant impact on construction projects. The findings also reveal that the risk of site execution interference, if it occurs, could reduce productivity to 190 units of work completed per month, while rework could reduce productivity to 300 units. On the other hand, the risk of falling from height reduces productivity to 380 units, whereas communication-related risks maintain productivity at around 300 units. Therefore, as the greatest reduction in productivity is related to the risk of site execution interference, this risk is identified as the most critical in the system.<br /><strong>Conclusions</strong>: The model developed in this research has significant potential to assist construction managers in systematically evaluating the negative impacts of productivity risks. Project managers can use the findings of this research to focus more on key risks, such as site execution interference, rework, communication and coordination, and falling from height, managing these risks more effectively. By doing so, they can optimize the productivity of human resources, a critical issue in the construction industry. The advantage of this research for the construction industry is that it helps managers better understand the dynamics of productivity risks and, by restoring the connections between risks and breaking key interactions in feedback loops, stop the escalating impact of risks on one another.https://jimp.sbu.ac.ir/article_104731_3c6deef7f52ebbbfa723edc9b18329b1.pdfShahid Beheshti UniversityJournal of Industrial Management Perspective2251-987414320240822A Quantitative Approach for Prioritizing Supply Chain Priorities in Smart Industries Using Data-Driven Prediction: Two Common Industrial Case StudiesA Quantitative Approach for Prioritizing Supply Chain Priorities in Smart Industries Using Data-Driven Prediction: Two Common Industrial Case Studies16918810492910.48308/jimp.14.3.169FAMaryamNooraei AbadehAssistant Professor, Department of Computer Engineering, Abadan Branch, Islamic Azad University, Abadan, Iran.SondosBahadoriAssistant Professor, Department of Computer Engineering, Ilam Branch, Islamic Azad University, Ilam, Iran.MansoorehMirzaeiAssistant Professor, Department of Computer Engineering, Golpayegan Faculty of Engineering, Isfahan University of Technology, Isfahan, Iran.Golpayegan, IranNargesEbrahimiAssistant Professor, Department of Business Management, Abadan Branch, Islamic Azad University, Abadan, Iran.Journal Article20240315<strong>Introduction:</strong> Determining supply chain priorities in smart industries with data-driven analysis and modeling methods is essential to achieve sufficient accuracy and identify key factors affecting supply chain efficiency. The use of this information improves the effectiveness of supply chain management. This article investigates and presents a quantitative approach for evaluating the priorities of the smart supply chain using data-driven prediction methods. The main objective of this paper is to provide a systematic and efficient method for determining priorities in the supply chain. In this approach, first, the key efficiency indicators in the supply chain are identified. Then, using data-driven prediction methods based on machine learning, the efficiency of each indicator is calculated for each element of the supply chain. The proposed approach has advantages such as systematicity, flexibility, practicality, and high accuracy. This method helps companies and organizations improve their management decisions by evaluating and determining supply chain priorities, optimizing performance, and enhancing processes.
<strong>Method:</strong> The innovation dimensions of this research include two main aspects. The first aspect focuses on two widely used industries equipped with Internet of Things (IoT) technology. The second aspect combines traditional supply chain analysis methods with machine learning algorithms. Initially, key performance indicators in the supply chain were identified. These indicators were extracted through a comprehensive search of articles in reputable scientific databases using keywords related to the smart supply chain. Then, using data-driven prediction methods, the efficiency of each indicator for each element of the supply chain was calculated. In this study, the DEMATEL matrix was used to analyze the interrelationships between indicators, and the prediction method using Support Vector Machines (SVM) was applied to assess the relationships between the criteria. Finally, the final weight of each indicator was determined by combining the results of DEMATEL and SVM, and the indicators in the supply chain were prioritized accordingly.
<strong>Findings:</strong> The results of this article show that flexibility is the most important criterion in the supply chain due to its ability to respond to changes and fluctuations in demand. The quality of the products and services provided ranks second, as higher quality increases customer satisfaction and trust in the brand. The total cost of the supply chain is third, and reducing costs improves profitability and competitiveness. Product delivery time is fourth, as fast and accurate delivery significantly impacts customer satisfaction. Finally, supply chain-related risks are ranked fifth, and effective risk management can mitigate potential issues. This prioritization helps organizations better allocate resources and improve supply chain performance.
<strong>Conclusion:</strong> Using systematic and precise approaches to prioritize supply chain criteria can serve as a practical guide for selecting and determining suppliers, implementing supply chain optimization strategies, and allocating resources. This research demonstrated that combining traditional supply chain analysis methods with machine learning algorithms such as SVM can improve the accuracy and efficiency of forecasting and decision-making. By enhancing the supply chain, organizations can improve their performance and optimize processes. Moreover, approaches such as Just-In-Time (JIT) strategy, Total Quality Management, and the use of new technologies can contribute to supply chain improvements. Building relationships with suppliers, analyzing data, and forecasting supply chain needs and challenges are also useful strategies.<strong>Introduction:</strong> Determining supply chain priorities in smart industries with data-driven analysis and modeling methods is essential to achieve sufficient accuracy and identify key factors affecting supply chain efficiency. The use of this information improves the effectiveness of supply chain management. This article investigates and presents a quantitative approach for evaluating the priorities of the smart supply chain using data-driven prediction methods. The main objective of this paper is to provide a systematic and efficient method for determining priorities in the supply chain. In this approach, first, the key efficiency indicators in the supply chain are identified. Then, using data-driven prediction methods based on machine learning, the efficiency of each indicator is calculated for each element of the supply chain. The proposed approach has advantages such as systematicity, flexibility, practicality, and high accuracy. This method helps companies and organizations improve their management decisions by evaluating and determining supply chain priorities, optimizing performance, and enhancing processes.
<strong>Method:</strong> The innovation dimensions of this research include two main aspects. The first aspect focuses on two widely used industries equipped with Internet of Things (IoT) technology. The second aspect combines traditional supply chain analysis methods with machine learning algorithms. Initially, key performance indicators in the supply chain were identified. These indicators were extracted through a comprehensive search of articles in reputable scientific databases using keywords related to the smart supply chain. Then, using data-driven prediction methods, the efficiency of each indicator for each element of the supply chain was calculated. In this study, the DEMATEL matrix was used to analyze the interrelationships between indicators, and the prediction method using Support Vector Machines (SVM) was applied to assess the relationships between the criteria. Finally, the final weight of each indicator was determined by combining the results of DEMATEL and SVM, and the indicators in the supply chain were prioritized accordingly.
<strong>Findings:</strong> The results of this article show that flexibility is the most important criterion in the supply chain due to its ability to respond to changes and fluctuations in demand. The quality of the products and services provided ranks second, as higher quality increases customer satisfaction and trust in the brand. The total cost of the supply chain is third, and reducing costs improves profitability and competitiveness. Product delivery time is fourth, as fast and accurate delivery significantly impacts customer satisfaction. Finally, supply chain-related risks are ranked fifth, and effective risk management can mitigate potential issues. This prioritization helps organizations better allocate resources and improve supply chain performance.
<strong>Conclusion:</strong> Using systematic and precise approaches to prioritize supply chain criteria can serve as a practical guide for selecting and determining suppliers, implementing supply chain optimization strategies, and allocating resources. This research demonstrated that combining traditional supply chain analysis methods with machine learning algorithms such as SVM can improve the accuracy and efficiency of forecasting and decision-making. By enhancing the supply chain, organizations can improve their performance and optimize processes. Moreover, approaches such as Just-In-Time (JIT) strategy, Total Quality Management, and the use of new technologies can contribute to supply chain improvements. Building relationships with suppliers, analyzing data, and forecasting supply chain needs and challenges are also useful strategies.https://jimp.sbu.ac.ir/article_104929_79ae2e3a37e62fafbfcdd6498cc88aa9.pdfShahid Beheshti UniversityJournal of Industrial Management Perspective2251-987414320240822Integration and Development of Fuzzy QFD for Evaluation and Selection of Biofuel Development StrategiesIntegration and Development of Fuzzy QFD for Evaluation and Selection of Biofuel Development Strategies18921110494310.48308/jimp.14.3.189FAElhamMohseniPh.D. Candidate, Department of Industrial Management, Faculty of Administrative and Economics, University of Isfahan, Isfahan, Iran.DariushMohammadi ZanjiraniAssociated Professor, Department of Industrial Management, Faculty of Administrative and Economics, University of Isfahan, Isfahan, Iran.Journal Article20240320<strong>Introduction:</strong> In order to achieve sustainable development goals and reduce dependence on fossil fuels, biofuels are considered a sustainable and environmentally friendly alternative. The biofuels industry can increase energy security by diversifying energy sources and reducing reliance on fossil fuel imports. However, evaluating and selecting the best solutions for its development requires the use of appropriate methodologies.
<strong>Metods:</strong> This research is practical in nature and can be considered descriptive-survey research, as it focuses on identifying and ranking the challenges, strategies, and solutions for the development of biofuels. In this article, the integration and development of the quality function deployment (QFD) method are used to evaluate and select biofuel development strategies. Based on this approach, after reviewing the theoretical framework and collecting expert opinions through the fuzzy Delphi method, the challenges, strategies, and operational solutions for biofuel development were identified. Then, using a two-stage fuzzy QFD, the importance of strategies against challenges and the weight of solutions against strategies were assessed. In the final stage, by integrating and developing QFD with the TOPSIS method, the ranking of biofuel development solutions was conducted.
<strong>Result and Discussion:</strong> A systematic review of the theoretical framework identified the most important challenges and obstacles to the sustainable development of biofuels, which were categorized into four dimensions: economic, technical, environmental, and socio-political. The 20 challenges extracted from the theoretical framework were screened using the fuzzy Delphi method and reduced to 13 challenges after aggregating expert opinions. In the next step, the experts selected the best and worst challenges and compared the importance of other challenges to them. After aggregating the opinions and formulating the non-linear mathematical model, the challenges' weights were calculated using Lingo software. Based on the results of the Best-Worst Method (BWM), "unstable supply of raw materials and energy" and "lack of cost-effective innovations and technologies" were identified as the most and least important challenges, respectively. In the first phase of the QFD method, the strategies for addressing these challenges were weighted. According to the experts, the strategies of "research and development of programs to improve the biofuel production process" and "research and development of new and advanced technologies for biofuel production" were identified as the most important strategies. In the final phase of QFD, 10 executive solutions for biofuel development were evaluated and ranked against the prioritized strategies. The three top-ranked solutions were: establishing and enhancing standards, supporting sustainable producers, and protecting biodiversity.
<strong>Conclusions:</strong> Biofuels will play a crucial role in the future, but their production faces certain challenges. The unstable supply of raw materials and energy is one of the most significant challenges identified in this study. The implementation solutions proposed in this study can contribute effectively to biofuel production. In general, promoting environmental sustainability involves actions such as increasing the use of renewable resources, reducing reliance on non-renewable resources, promoting biofuels, improving energy efficiency, and managing waste and industrial effluents effectively.<strong>Introduction:</strong> In order to achieve sustainable development goals and reduce dependence on fossil fuels, biofuels are considered a sustainable and environmentally friendly alternative. The biofuels industry can increase energy security by diversifying energy sources and reducing reliance on fossil fuel imports. However, evaluating and selecting the best solutions for its development requires the use of appropriate methodologies.
<strong>Metods:</strong> This research is practical in nature and can be considered descriptive-survey research, as it focuses on identifying and ranking the challenges, strategies, and solutions for the development of biofuels. In this article, the integration and development of the quality function deployment (QFD) method are used to evaluate and select biofuel development strategies. Based on this approach, after reviewing the theoretical framework and collecting expert opinions through the fuzzy Delphi method, the challenges, strategies, and operational solutions for biofuel development were identified. Then, using a two-stage fuzzy QFD, the importance of strategies against challenges and the weight of solutions against strategies were assessed. In the final stage, by integrating and developing QFD with the TOPSIS method, the ranking of biofuel development solutions was conducted.
<strong>Result and Discussion:</strong> A systematic review of the theoretical framework identified the most important challenges and obstacles to the sustainable development of biofuels, which were categorized into four dimensions: economic, technical, environmental, and socio-political. The 20 challenges extracted from the theoretical framework were screened using the fuzzy Delphi method and reduced to 13 challenges after aggregating expert opinions. In the next step, the experts selected the best and worst challenges and compared the importance of other challenges to them. After aggregating the opinions and formulating the non-linear mathematical model, the challenges' weights were calculated using Lingo software. Based on the results of the Best-Worst Method (BWM), "unstable supply of raw materials and energy" and "lack of cost-effective innovations and technologies" were identified as the most and least important challenges, respectively. In the first phase of the QFD method, the strategies for addressing these challenges were weighted. According to the experts, the strategies of "research and development of programs to improve the biofuel production process" and "research and development of new and advanced technologies for biofuel production" were identified as the most important strategies. In the final phase of QFD, 10 executive solutions for biofuel development were evaluated and ranked against the prioritized strategies. The three top-ranked solutions were: establishing and enhancing standards, supporting sustainable producers, and protecting biodiversity.
<strong>Conclusions:</strong> Biofuels will play a crucial role in the future, but their production faces certain challenges. The unstable supply of raw materials and energy is one of the most significant challenges identified in this study. The implementation solutions proposed in this study can contribute effectively to biofuel production. In general, promoting environmental sustainability involves actions such as increasing the use of renewable resources, reducing reliance on non-renewable resources, promoting biofuels, improving energy efficiency, and managing waste and industrial effluents effectively.https://jimp.sbu.ac.ir/article_104943_13b9c55400830256c8cd768e41a1e263.pdfShahid Beheshti UniversityJournal of Industrial Management Perspective2251-987414320240822Evaluating Environmental Efficiency of Iranian Provinces Using Fuzzy Window Data Envelopment Analysis (FWDEA) with Undesirable OutputEvaluating Environmental Efficiency of Iranian Provinces Using Fuzzy Window Data Envelopment Analysis (FWDEA) with Undesirable Output21223510494510.48308/jimp.14.3.212FAMohammadZarei MahmoudabadiAssociate Professor, Department of Industrial Management. Faculty of Humanities, Meybod University, Meybod, Iran.RezaNorouzi AverganiMaster’s student, Department of Industrial Management. Faculty of Humanities, Meybod University, Meybod, Iran.Journal Article20240525<strong>Introduction: </strong>Over the past three decades, environmental challenges have become a global concern due to growing worries about the irreversible consequences of environmental degradation. Consequently, addressing environmental issues has increasingly become a priority for policymakers. Today, all countries are striving to achieve a balance between environmental protection and socioeconomic stability through the development of effective policies.
<strong>Objective:</strong> This study aims to examine and evaluate the environmental efficiency of Iran's provinces over different time periods, considering the inherent uncertainty in the data. In the real world, data is not always precise and deterministic, and deviations in data can significantly alter the results of efficiency evaluations. Therefore, it is essential to employ suitable methods to address data uncertainty when assessing efficiency. In this research, a Fuzzy Window Data Envelopment Analysis (FWDEA) model is utilized to evaluate the environmental efficiency of Iran's provinces. This model effectively accounts for data uncertainty and provides more accurate and reliable results.
<strong>Methodology: </strong>The approach used in this study incorporates undesirable outputs and can be applied to various structures in fuzzy data envelopment analysis. Based on a literature review, consultations with experts in the fields of the environment and data envelopment analysis, and available data, the input variables of the study were determined to be per capita energy consumption and per capita vehicles, while the output variable was defined as per capita pollutant emissions. Given the uncertainty about whether all units operate at optimal scale, the BCC model was employed. Furthermore, since it is easier to control outputs compared to inputs, an output-oriented data envelopment analysis model with variable returns to scale was assumed. Finally, the proposed fuzzy window data envelopment analysis model was implemented for 29 provinces of Iran over four time periods from 2017 to 2020, and the results were analyzed.
<strong>Findings: </strong>Data analysis using the proposed model revealed that East Azerbaijan province had the best environmental performance with an efficiency score of 0.837407, while Hormozgan province had the worst performance with an efficiency score of 0.332543 during the four years of the study. Examining the annual average efficiency of the provinces indicated that the trend of efficiency improvement or decline varied across provinces over the years and was not stable. Additionally, the results of the fuzzy window data envelopment analysis model showed that the efficiency of provinces varied in each consecutive time window and did not follow a fixed pattern.
<strong>Conclusion:</strong> In this study, a fuzzy DEA approach was employed to evaluate efficiency considering the ambiguous, unavailable, and imprecise nature of the data. Overall, the use of fuzzy window DEA in assessing the environmental efficiency of Iran's provinces is one of the best methods due to its high accuracy, ability to model fuzzy data, and identification of complex patterns. This approach can assist policymakers in identifying the strengths and weaknesses of provinces in terms of environmental efficiency and formulating appropriate policies to improve environmental performance. To enhance the environmental efficiency of provinces, it is necessary to develop dynamic environmental policies tailored to the specific conditions of each province. Moreover, given that environmental challenges are global, the application of similar approaches to evaluate environmental efficiency in other countries can also contribute to improving global environmental performance.<strong>Introduction: </strong>Over the past three decades, environmental challenges have become a global concern due to growing worries about the irreversible consequences of environmental degradation. Consequently, addressing environmental issues has increasingly become a priority for policymakers. Today, all countries are striving to achieve a balance between environmental protection and socioeconomic stability through the development of effective policies.
<strong>Objective:</strong> This study aims to examine and evaluate the environmental efficiency of Iran's provinces over different time periods, considering the inherent uncertainty in the data. In the real world, data is not always precise and deterministic, and deviations in data can significantly alter the results of efficiency evaluations. Therefore, it is essential to employ suitable methods to address data uncertainty when assessing efficiency. In this research, a Fuzzy Window Data Envelopment Analysis (FWDEA) model is utilized to evaluate the environmental efficiency of Iran's provinces. This model effectively accounts for data uncertainty and provides more accurate and reliable results.
<strong>Methodology: </strong>The approach used in this study incorporates undesirable outputs and can be applied to various structures in fuzzy data envelopment analysis. Based on a literature review, consultations with experts in the fields of the environment and data envelopment analysis, and available data, the input variables of the study were determined to be per capita energy consumption and per capita vehicles, while the output variable was defined as per capita pollutant emissions. Given the uncertainty about whether all units operate at optimal scale, the BCC model was employed. Furthermore, since it is easier to control outputs compared to inputs, an output-oriented data envelopment analysis model with variable returns to scale was assumed. Finally, the proposed fuzzy window data envelopment analysis model was implemented for 29 provinces of Iran over four time periods from 2017 to 2020, and the results were analyzed.
<strong>Findings: </strong>Data analysis using the proposed model revealed that East Azerbaijan province had the best environmental performance with an efficiency score of 0.837407, while Hormozgan province had the worst performance with an efficiency score of 0.332543 during the four years of the study. Examining the annual average efficiency of the provinces indicated that the trend of efficiency improvement or decline varied across provinces over the years and was not stable. Additionally, the results of the fuzzy window data envelopment analysis model showed that the efficiency of provinces varied in each consecutive time window and did not follow a fixed pattern.
<strong>Conclusion:</strong> In this study, a fuzzy DEA approach was employed to evaluate efficiency considering the ambiguous, unavailable, and imprecise nature of the data. Overall, the use of fuzzy window DEA in assessing the environmental efficiency of Iran's provinces is one of the best methods due to its high accuracy, ability to model fuzzy data, and identification of complex patterns. This approach can assist policymakers in identifying the strengths and weaknesses of provinces in terms of environmental efficiency and formulating appropriate policies to improve environmental performance. To enhance the environmental efficiency of provinces, it is necessary to develop dynamic environmental policies tailored to the specific conditions of each province. Moreover, given that environmental challenges are global, the application of similar approaches to evaluate environmental efficiency in other countries can also contribute to improving global environmental performance.https://jimp.sbu.ac.ir/article_104945_d67344f804e692a932b956f295edfd47.pdfShahid Beheshti UniversityJournal of Industrial Management Perspective2251-987414320240822A Simulation-Based Bi-Objective Optimization Model for Supply Chain Inventory Replenishment: A Case Study of the Electric IndustryA Simulation-Based Bi-Objective Optimization Model for Supply Chain Inventory Replenishment: A Case Study of the Electric Industry23626010494710.48308/jimp.14.3.236FABehzadMoghimi ShahriPh.D. student, Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabatabai University, Tehran, Iran.AliKhatami FirouzabadiProfessor, Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabatabai University, Tehran, Iran.MaghsoudAmiriProfessor, Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabatabai University, Tehran, Iran.Parha,mAzimiAssociate Professor, Department of Industrial Engineering, School of Industrial and Mechanical Engineering, Islamic Azad University, Qazvin Branch, Qazvin, Iran.Journal Article20240120<strong>Introduction:</strong> The electricity sector plays a crucial role in the country's economy. Therefore, any disruptions in the supply chain of this industry can result in the loss of economic benefits and decrease the competitiveness of industries dependent on this sector. The industry structure analysis of the electricity sector has shown that the lack of proper relationships between entities involved in the supply of goods and equipment leads to disruptions in the electricity supply chain. On the other hand, the specific political and economic conditions of the country, the presence of natural disasters, and high levels of change in the Middle East region have had significant impacts on increasing uncertainty at various levels of the supply chain in this sector. Considering the high uncertainty in the procurement of components in this industry, this study focuses on presenting a set of scenarios for replenishing goods in the supply chain entities of this sector.<br /><strong>Methods: </strong>To achieve this, a probabilistic four-echelon model consisting of a supplier, distributor, retailer, and customer was presented to minimize total inventory costs and the ratio of unmet customer demand based on the (R, Q) policy. Furthermore, by searching organizational documents, interviewing industry experts, and utilizing warehouse management software, data for the model was collected. Subsequently, through experimental design, initial solutions were provided for the differential evolutionary algorithm, and based on this algorithm, different values for reorder points and order quantities were determined. By employing simulation methods, the model's objective values were estimated, and the set of solutions was illustrated in a Pareto chart.<br /><strong>Result and Discussion: </strong>Research findings have shown that increasing the average inventory levels of retailers' warehouses leads to a decrease in the proportion of unmet customer demand. This occurs when different reorder point values for two retailers have high levels, but considering the probabilistic demand function, the order quantity can vary. On the other hand, reducing ordering and inventory costs leads to an increase in unmet customer demand. In other words, when reorder point values and low order quantities lead to inventory reduction, customer dissatisfaction increases. The Differential Evolution Algorithm used in this study has accelerated the process of finding solutions and improved model efficiency. This algorithm considers values between high and low levels of reorder points and order quantities, presenting multiple objective function values. Utilizing simulation methods to estimate the probabilistic objective functions employed in the model has increased the speed of executing multiple scenarios, aiding in cost reduction and model execution time.<br /><strong>Conclusions:</strong> Based on the results of this research, electric equipment with high innovation should have a low reorder point and a high order quantity in the supply chain. This is because the short product lifespan renders the product obsolete along the chain, lacking customer demand and consequently increasing the chain's costs. The computational results of this study indicate that a 105% increase in inventory leads to a 104% increase in customer satisfaction and a 95% decrease in lost sales costs. However, considering the high purchasing costs and large product volume during ordering, a scenario aligned with financial conditions and warehouse capacity should be selected for chain entities.<strong>Introduction:</strong> The electricity sector plays a crucial role in the country's economy. Therefore, any disruptions in the supply chain of this industry can result in the loss of economic benefits and decrease the competitiveness of industries dependent on this sector. The industry structure analysis of the electricity sector has shown that the lack of proper relationships between entities involved in the supply of goods and equipment leads to disruptions in the electricity supply chain. On the other hand, the specific political and economic conditions of the country, the presence of natural disasters, and high levels of change in the Middle East region have had significant impacts on increasing uncertainty at various levels of the supply chain in this sector. Considering the high uncertainty in the procurement of components in this industry, this study focuses on presenting a set of scenarios for replenishing goods in the supply chain entities of this sector.<br /><strong>Methods: </strong>To achieve this, a probabilistic four-echelon model consisting of a supplier, distributor, retailer, and customer was presented to minimize total inventory costs and the ratio of unmet customer demand based on the (R, Q) policy. Furthermore, by searching organizational documents, interviewing industry experts, and utilizing warehouse management software, data for the model was collected. Subsequently, through experimental design, initial solutions were provided for the differential evolutionary algorithm, and based on this algorithm, different values for reorder points and order quantities were determined. By employing simulation methods, the model's objective values were estimated, and the set of solutions was illustrated in a Pareto chart.<br /><strong>Result and Discussion: </strong>Research findings have shown that increasing the average inventory levels of retailers' warehouses leads to a decrease in the proportion of unmet customer demand. This occurs when different reorder point values for two retailers have high levels, but considering the probabilistic demand function, the order quantity can vary. On the other hand, reducing ordering and inventory costs leads to an increase in unmet customer demand. In other words, when reorder point values and low order quantities lead to inventory reduction, customer dissatisfaction increases. The Differential Evolution Algorithm used in this study has accelerated the process of finding solutions and improved model efficiency. This algorithm considers values between high and low levels of reorder points and order quantities, presenting multiple objective function values. Utilizing simulation methods to estimate the probabilistic objective functions employed in the model has increased the speed of executing multiple scenarios, aiding in cost reduction and model execution time.<br /><strong>Conclusions:</strong> Based on the results of this research, electric equipment with high innovation should have a low reorder point and a high order quantity in the supply chain. This is because the short product lifespan renders the product obsolete along the chain, lacking customer demand and consequently increasing the chain's costs. The computational results of this study indicate that a 105% increase in inventory leads to a 104% increase in customer satisfaction and a 95% decrease in lost sales costs. However, considering the high purchasing costs and large product volume during ordering, a scenario aligned with financial conditions and warehouse capacity should be selected for chain entities.https://jimp.sbu.ac.ir/article_104947_b607af4c1c9c7930640d7dee2415d998.pdf