multiple-criteria decision-making
Sara Asili; Ebrahim Teimoury
Abstract
Considering the competitive environment between suppliers, the issue of choosing them based on important criteria is very important for decision makers, especially in the LARS supply chain, which is a combination of sustainable and LARG supply chains. The aim of this paper is to present a multi-objective ...
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Considering the competitive environment between suppliers, the issue of choosing them based on important criteria is very important for decision makers, especially in the LARS supply chain, which is a combination of sustainable and LARG supply chains. The aim of this paper is to present a multi-objective mathematical model for selecting suppliers based on criteria related to the LARS supply chain concepts. The innovations of the presented model include simultaneous consideration of multiple objectives, multiple periods, multiple products, and master production schedule. The quality of the presented model has been examined on a case study in the country's automotive industry. In this paper, at first, the most important criteria have been extracted from the literature, then finalized by experts in the country's automotive industry, and DEMATEL approach has been used to examine the internal relationships of the criteria in each category of criteria. Secondly, the network of criteria is determined, the importance of each criterion relative to the others was determined using a pairwise comparison matrix and considered as input to the Super Decision software. At last, a mathematical model for optimal supplier selection is presented. Based on the results obtained, the ordering cost had the greatest impact on the objective function. Also, considering the concept of backlog demand has led to flexibility in production volume and, as a result, reduced overall costs.IntroductionLARG Supply Chain Management represents an integrated approach that combines lean, agile, resilient, and green paradigms in supplier relationship management. This holistic framework enables organizations to simultaneously leverage the advantages of each approach while compensating for their inherent limitations. For example, lean supply chain management focuses on minimizing inventory levels to reduce waste and cost; agile supply chain management emphasizes responsiveness and flexibility to meet dynamic market demands; resilient supply chain management enhances the ability to withstand and recover from disruptions; and green supply chain management aims to minimize environmental impacts and promote sustainability. According to Babaei et al. (2017), in an increasingly volatile and uncertain global environment, organizations are placing greater emphasis on supply chain resilience as a key capability for survival and competitiveness. Resilient supply chains are characterized by their capacity to absorb shocks and maintain operational continuity. Meanwhile, the concept of sustainable supply chain management—which addresses environmental, social, and economic concerns—has attracted growing interest among both academics and practitioners. More recently, researchers have begun to explore the LARS framework as a comprehensive model that overlaps with sustainability initiatives, particularly through its emphasis on green practices. However, despite their similarities, LARS and sustainability are distinct in scope and application. This study aims to identify a comprehensive set of criteria for supplier selection under the LARS framework, thereby supporting more informed and strategic decision-making in supply chain management. To this end, relevant criteria will be extracted through expert input from a leading firm in the Iranian automotive industry. In parallel, an extensive literature review will be conducted to incorporate practical and validated indicators associated with lean, agile, resilient, and sustainable supply chain practices. A Likert-scale-based questionnaire will be developed to assess suppliers against these criteria, and the resulting scores will serve as input for a multi-objective mathematical model.The remainder of this paper is structured as follows: Section 2 presents a review of the relevant literature. Section 3 introduces the proposed mathematical model. Section 4 details the case study and analyzes the results. Finally, Section 5 concludes the paper with key findings and managerial implications.MethodsIn this study, the most critical criteria were initially extracted through a thorough review of the existing literature and subsequently refined based on expert insights from the Iranian automotive industry, with a case study focused on Zamiad Company. To analyze the interrelationships among the criteria within each category, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was employed. Following the identification of the criteria interaction network, the relative importance of each criterion with respect to the others was determined using pairwise comparison matrices. These comparisons served as input data for the Super Decisions software, facilitating the application of the Analytic Network Process (ANP). In the next phase of the study, a mathematical model was developed to support the optimal selection of suppliers.Discussion and resultsIn this research, the required data for all ten products were collected from the internal information systems of Zamiad Company. All collected data were evaluated in terms of consistency ratio, which was confirmed as being below 0.1, ensuring the reliability of the pairwise comparisons. After entering the data into Super Decisions software, the output—including the weights of each supplier for each product—were obtained. The results were reviewed and validated by experts at Zamiad Company.Although the proposed model is inherently nonlinear, it was linearized to reduce computational complexity. This adjustment significantly decreased the solution time, which is crucial when solving large-scale problems.The developed mathematical model aims to optimize the selection of suppliers for each product in each production period and to determine the allocated quantity to each supplier. The model incorporates various cost factors, including production, ordering, inventory holding, product shortage, and supplier switching costs. While the parameters of the model were intended to reflect actual company data, due to the confidentiality policies of Zamiad Company, some parameters were not accessible to the researchers. Therefore, these values were generated using normal distributions within predefined intervals based on expert judgment.The model was implemented over two six-month production periods, in alignment with Zamiad’s typical contractual arrangements with its suppliers. Based on the obtained results, ordering cost had the greatest impact on the objective function. Moreover, considering the concept of backlog demand has introduced flexibility in production volume, thereby reducing the overall costs.ConclusionThis study aimed to identify key criteria affecting supplier selection and to determine their relative importance within the integrated LARS supply chain approach. Based on the obtained results, incorporating the production planning process reduces the diversity of suppliers across different periods. This can be attributed to production integration and the model’s preference for maintaining existing contracts over frequent changes. Given the current economic conditions in the production environment, it is essential to consider all relevant parameters in the supplier selection process simultaneously. The findings indicate that accounting for production planning within the LARS framework leads to more effective supplier selection. Among the evaluated parameters, production cost and product ordering cost had a greater impact on the overall performance of the proposed model compared to other factors. Therefore, managerial strategies should focus on controlling these key cost components. This research has contributed by identifying and evaluating these critical parameters.
Industrial management
Sara Bagherzadeh Rahmani; Javad Rezaeian; Ahmad Ebrahimi
Abstract
In today’s project-based organizations, where multiple projects are executed concurrently within work teams, human resources play a crucial role in the success or failure of these organizations. Consequently, human resources are recognized as one of the most essential resources for these organizations, ...
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In today’s project-based organizations, where multiple projects are executed concurrently within work teams, human resources play a crucial role in the success or failure of these organizations. Consequently, human resources are recognized as one of the most essential resources for these organizations, and their optimization can significantly increase productivity while reducing organizational time and costs. This underscores the importance of effective human resource management and highlights the need for special attention to this area. Therefore, this study presents a mixed-integer nonlinear programming model for the multi-objective project scheduling problem with resource constraints, multi-skilled personnel allocation and the assignment of projects to work teams. The mathematical model of this research includes the multiple objectives of simultaneous minimization of the total costs of setting up work teams and the use of human resources and the total flow time of projects. To make the model more realistic, the effect of learning is also considered. Subsequently, a diverse set of test problems at varying scales was designed. Then, the Multi-Objective Artificial Immune System (MOAIS) algorithm and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) were utilized to solve the problems. The results demonstrate the superior performance of the NSGA-II algorithm compared to the MOAIS algorithm.
Introduction
Human resource management is one of the fundamental pillars of organizational success and, alongside financial and technological resources, plays a crucial role in process optimization and achieving strategic objectives. Optimal workforce allocation and effective project scheduling enhance productivity, reduce costs, and ensure the efficient utilization of resources. Teamwork and knowledge sharing facilitate learning and skill development, which, in complex projects with limited resources, lead to shorter project completion times and improved organizational efficiency.
Accordingly, this study addresses the project scheduling problem by considering human resource constraints, multi-skilled work teams, setup times, varying project start times, and work-team dependent learning effect. The primary objective is to simultaneously minimize the total costs of setting up work teams and the use of human resources, as well as the total project flow time. To achieve this, a Mixed-Integer Linear Programming (MILP) model is developed.
Given that the problem is NP-hard, employing metaheuristic algorithms is essential for obtaining near-optimal solutions within a reasonable computational time. This research utilizes two metaheuristic algorithms, NSGA-II and MOAIS.
The findings of this study provide valuable insights for project managers and decision-makers, aiding in optimized project scheduling, efficient workforce allocation, and enhanced organizational productivity.
Research Background
In this section, we mention only a few of the most relevant studies to the current one. Su et al. (2021) explored team formation in project scheduling and presented a simple mathematical model for task scheduling in single-skilled workgroups with restricted access to resources, aiming to minimize makespan (Cmax). In their model, workers were assigned to fixed groups, and tasks were allocated based on processing time and workforce availability. They proposed a hybrid genetic algorithm with a bin-packing strategy to solve the problem.
Mozhdehi et al. (2024) developed a mixed-integer mathematical model for multi-project scheduling with limited resources and multi-skilled workforce. They considered workforce agility, which improves either through collaborative teamwork and knowledge-sharing with more skilled colleagues or by dedicating more time to skill development. Their results indicated that incorporating workforce agility into project scheduling models significantly reduces project completion time.
Methods
In this study, a Mixed-Integer Linear Programming (MILP) model was developed to address the multi-project scheduling problem with multi-skilled work teams. The model integrates human resource constraints, setup times, and work-team dependent learning effect, ensuring a practical and efficient scheduling framework.
For solving the model, the single-objective version was first handled using the Branch and Bound algorithm in Lingo software. Then, for the multi-objective version, two metaheuristic algorithms, NSGA-II and MOAIS, were implemented to generate high-quality trade-off solutions.
To assess and compare the performance of these algorithms, a set of test problems of different scales (small, medium, and large) was designed and solved. The Taguchi Experimental Design Method was employed to fine-tune the key algorithm parameters, optimizing efficiency and accuracy.
Evaluating the performance of multi-objective metaheuristic algorithms is more complex than that of single-objective optimization due to the presence of non-dominated solutions that cannot be strictly ranked. In this study, the following key metrics were used to assess solution quality and diversity:
Number of Pareto Solutions (NPS)
Mean Ideal Distance (MID)
Diversity Metric (DM)
Spread of Non-dominated Solutions (SNS)
Discussion and Results
Results of sensitivity analysis reveals that increasing the learning rate of work teams significantly reduces project completion time. This finding underscores the importance of incorporating learning effects in multi-skilled workforce scheduling models. With a higher learning rate, teams execute tasks more efficiently and in less time, directly contributing to organizational productivity improvements.
Furthermore, computational results indicate that in small to medium-sized problems, there is no significant performance difference between NSGA-II and MOAIS. However, in large-scale problems, NSGA-II outperforms MOAIS. This superiority is attributed to NSGA-II’s population evolution mechanism, which enables a broader exploration of the solution space and prevents premature convergence to local optima. In contrast, MOAIS, due to its elitist nature, primarily focuses on replicating high-quality solutions, avoiding exploration in other regions of the search space. This increases the likelihood of getting trapped in local optima, thereby reducing search diversity. Furthermore, performance comparison results indicate that NSGA-II demonstrates superior Pareto front coverage and convergence to optimal solutions compared to MOAIS.
Conclusion
This study investigated the project scheduling problem considering human resource constraints, multi-skilled work teams, setup times, varying project start times, and work-team dependent learning effects. The primary objective was the simultaneous minimization of the total costs of setting up work teams and the use of human resources and the total flowtime of projects. To achieve this, a Mixed-Integer Linear Programming (MILP) model was developed, and its performance was evaluated through sensitivity analyses and numerical experiments. The results demonstrated that the proposed model performed effectively under various constraints and exhibited high accuracy and efficiency.
Given the NP-hard nature and multi-objective characteristics of the problem, two metaheuristic algorithms, NSGA-II and MOAIS, were implemented to solve it. The algorithm parameters were fine-tuned using the Taguchi Experimental Design Method, and their performance was compared across different problem sizes. Computational results indicated that while both algorithms performed similarly in small to medium-sized problems, NSGA-II outperformed MOAIS in large-scale instances. Further analysis revealed that MOAIS, due to its elitist-based nature, primarily focuses on replicating high-quality solutions, often avoiding broader exploration within the solution space. This characteristic increases the likelihood of getting trapped in local optima, reducing solution diversity. In contrast, NSGA-II, through its non-dominated sorting mechanism, allows lower-fitness solutions to participate in the evolution process, leading to broader solution space exploration and preventing premature convergence.
For future research, it is recommended to extend the model by incorporating additional operational assumptions, such as activity failure probabilities, simultaneous consideration of learning and forgetting effects, rework processes, and uncertainty of certain parameters in the problem. Furthermore, exploring more advanced heuristic and hybrid metaheuristic algorithms is suggested to enhance the efficiency of the solution approach.
Industrial management
Mohsen Keramatpanah; Mohammad Taghi Taghavifard; Maghsoud Amiri; Mehdi Mehdi Shamizanjani
Abstract
With the expansion of digital technologies and fundamental changes in the financial services ecosystem, the banking industry is facing new challenges on the path toward sustainable development. In this regard, the present study aims to propose a model for achieving sustainability in digital banking within ...
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With the expansion of digital technologies and fundamental changes in the financial services ecosystem, the banking industry is facing new challenges on the path toward sustainable development. In this regard, the present study aims to propose a model for achieving sustainability in digital banking within Iran’s private banking sector. To this end, key variables were initially identified through a review of the relevant literature and subsequently validated using the fuzzy Delphi method with input from subject matter experts. To analyze the relationships among the variables and determine their systemic structure, Interpretive Structural Modeling (ISM) was employed. Subsequently, a comprehensive digital banking sustainability model was developed and simulated using a system dynamics approach and related software tools. The results of the model analysis indicate that the development of technological infrastructure, enhancement of information security, improvement of customer experience, effective collaboration with regulatory bodies, and standardization of digital processes are key drivers in realizing sustainability in digital banking. Scenario analysis further reveals that simultaneous improvement of these components, by reinforcing positive feedback loops, can lead to sustainable growth and lasting competitive advantage for private banks. In this context, attention to sustainability requirements in digital banking and the development of related models can contribute to reducing operational costs in the banking network, optimizing energy consumption, increasing financial inclusion, and delivering more efficient services, thereby facilitating sustainable development. The primary contribution of this study is the development of an analytical and practical framework based on system dynamics modeling, which can support private banks in formulating strategies for sustainable digital transformation and serve as a roadmap for improving their economic, social, and environmental performance. The findings of this research can assist policymakers, banking executives, and researchers in the digital banking domain to better understand the factors influencing sustainability and to design effective interventions.IntroductionAs the banking industry experiences rapid digitalization driven by emerging technologies and evolving customer expectations, the necessity of aligning digital transformation with sustainability imperatives has become increasingly evident. This shift transcends traditional financial performance and calls for banking institutions to engage with social and environmental responsibilities while sustaining innovation. Particularly in developing economies such as Iran, the dual challenge of digital modernization and sustainable development presents a unique strategic frontier. In response to these dynamics, the present study proposes a comprehensive and dynamic model of sustainable digital banking, focusing on the private banking sector in Iran.Research BackgroundWhile digitalization and sustainability have been widely studied, there exists a significant gap in integrated models that account for the systemic interactions between the two domains. The prevailing literature tends to analyze digital banking from a technological perspective, often overlooking its environmental and social consequences (Del Carmen, 2022; Migliorelli & Dessertine, 2023). Similarly, sustainability-focused frameworks rarely include the disruptive potential of digital technologies such as blockchain, artificial intelligence (AI), and mobile banking. Some studies have suggested the relevance of digital sustainability in areas like waste reduction, financial inclusion, and eco-efficiency (Castro et al., 2021; Di Vaio et al., 2021), yet empirical applications remain sparse. By building on these foundations, this research aims to bridge conceptual and practical gaps by designing a policy-oriented, simulation-based model grounded in a system thinking approach.Materials and MethodsThis study adopted a hybrid methodology by integrating three complementary techniques—Fuzzy Delphi, Interpretive Structural Modeling (ISM), and System Dynamics (SD). Initially, a structured literature review spanning 2016 to 2023 was conducted using scholarly databases such as Scopus and Google Scholar. This led to the identification of 26 preliminary variables, which were refined and validated through expert consensus using the Fuzzy Delphi method. Experts were selected based on their experience in digital transformation initiatives and their roles in IT, innovation, or strategic planning within private banks. The Delphi rounds applied fuzzy logic to quantify agreement levels, ensuring rigor in variable selection. To structure the relationships among the validated variables, ISM was employed. This method facilitated the construction of a hierarchical framework to determine which variables serve as inputs, intermediaries, and outputs in the sustainability process. The final phase involved constructing a system dynamics model with feedback loops and flow diagrams to simulate the behavior of key sustainability indicators over time and under different scenarios. The sustainability model incorporated variables reflecting organizational, technological, social, and environmental dimensions. These include elements such as process improvement, trust, profitability, service quality, digital culture, smart leadership, electronic KYC, stakeholder satisfaction, employee welfare, and business model adaptability. By embedding these interconnected elements into the system structure, the model is designed to capture the complexity of digital sustainability transitions in a banking context.Data Analysis and FindingsThe system dynamics model as shown in Figure (1) was simulated across multiple scenarios to understand how different policy strategies would influence long-term outcomes. The simulation process ensured that model behavior aligned with historical data and logical expectations. Scenario analysis provided valuable insights into how banks can prioritize investments and strategic decisions to balance economic growth with sustainability. For instance, increasing investment in digital infrastructure and employee training led to noticeable gains in trust, customer acquisition, and organizational resilience. Similarly, enhancing authentication systems and E-KYC protocols improved operational efficiency while supporting environmental and social performance through reduced paperwork and increased accessibility. Findings confirmed that sustainability in digital banking is best achieved through a synergistic approach that combines internal cultural transformation with technological innovation and regulatory collaboration. Variables such as digital culture, smart leadership, and business model flexibility played a central role in facilitating systemic resilience. The results emphasize that merely focusing on digital upgrades is insufficient without aligning human capital and governance frameworks with sustainability objectives. Scenario comparisons further revealed that a balanced investment strategy targeting both operational excellence and environmental awareness yields the most robust outcomes.Figure 1:SD model ConclusionThe results of this research highlight the imperative for private banks in Iran and similar emerging markets to adopt integrated strategies that harmonize digital transformation with sustainability goals. This study makes a significant contribution by constructing and validating a system dynamics model that incorporates 26 interdependent variables derived from theory and practice, offering a holistic view of the sustainability landscape in digital banking. The model is unique in that it not only captures the technological enablers of sustainable banking but also embeds social and environmental dimensions, reflecting the true complexity of this transformation. The integration of Fuzzy Delphi and ISM methods with SD modeling allows for both structural clarity and dynamic simulation, enabling banks to test and adjust strategies before implementation. By embedding feedback loops and behavioral equations, the model delivers actionable insights into how various factors—such as trust, service quality, organizational agility, and stakeholder alignment—can collectively drive sustainability in the digital era. This comprehensive approach moves beyond linear planning, providing a real-time decision-making tool for managers, regulators, and researchers alike.Further Research IdeasFuture research can extend the current model by integrating real-time behavioral data through machine learning algorithms or agent-based modeling, offering more granular and predictive insights. Comparative studies across multiple banking systems or geographic regions would also enhance generalizability. In addition, introducing ESG indicators and green finance parameters could further enrich the environmental scope of the model. Finally, longitudinal studies using historical banking data can help validate the dynamic behavior of the system across different economic cycles.Managerial SuggestionsFrom a managerial perspective, several strategic recommendations emerge. First, banks should prioritize long-term investment in digital literacy and workforce development to foster a resilient culture aligned with sustainability values. Second, integrating advanced E-KYC and biometric verification tools can enhance customer trust and operational transparency. Third, collaborative governance involving fintechs, third parties, and regulatory bodies is essential for ecosystem resilience. Fourth, it is crucial to continuously monitor and update business models to reflect technological, societal, and environmental shifts. Finally, the adoption of sustainability-oriented performance metrics should be institutionalized to ensure that growth and digitalization are guided by ethical and ecological responsibility
safety,risk and reliability
Reza Masoudi; Mohammad Reza Zahedy; Morteza piri
Abstract
This study aims to enhance the performance of maintenance teams by proposing a mathematical programming model for team formation, focusing on the maximization of knowledge sharing. In this model, key factors influencing knowledge sharing — including knowledge absorptive capacity, knowledge-sharing ...
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This study aims to enhance the performance of maintenance teams by proposing a mathematical programming model for team formation, focusing on the maximization of knowledge sharing. In this model, key factors influencing knowledge sharing — including knowledge absorptive capacity, knowledge-sharing capability, willingness to share knowledge, and motivation to acquire knowledge — along with the required expertise for maintenance activities, are identified and incorporated into the optimization process. The data used to develop the model were collected through performance evaluation questionnaires and expert interviews. The implementation of the proposed model in an organization with extensive physical assets demonstrates that optimizing team formation leads to significant improvements in maintenance key performance indicators. The findings of this study indicate that knowledge sharing not only improves individual and team skills but also plays a key role in improving the performance of maintenance teams. The proposed model can be used as an efficient decision-making tool for maintenance.IntroductionIn today's competitive environment, organizations are constantly exploring innovative approaches to enhance their productivity and performance. One of the key areas that plays a critical role in achieving this goal is industrial maintenance. Effective performance in this domain can have a direct impact on reducing costs, extending the lifespan of equipment, and improving organizational reliability (Okirie, 2024). Consequently, the use of innovative approaches for more efficient management of maintenance activities has gained significant importance.One of the main challenges in maintenance management is the formation of optimal work teams that can efficiently carry out their assigned tasks. Teams composed of knowledgeable and skilled individuals not only enhance the quality of task execution but can also foster problem-solving and innovation through collaborative knowledge sharing (Alharbi & Aloud, 2024). This is particularly crucial in asset-intensive industries.The process of team formation can vary depending on factors such as individual characteristics, task types, and organizational context. One of the key elements in effectively guiding team formation is the willingness and ability of members to share knowledge (Stavrou et al., 2023). Knowledge sharing among team members facilitates the transfer of experiences and critical information, which improves skills and helps prevent the repetition of past mistakes. This process transforms tacit knowledge into collective assets and supports informed decision-making. Institutionalizing a culture of knowledge sharing not only enhances collaboration and innovation but also increases organizational resilience to environmental changes, thereby laying the foundation for sustainability and competitive advantage (Hamill, 2025).However, many organizations face challenges in identifying and leveraging their organizational knowledge capacities (Zamiri & Esmaeili, 2024). This study presents a model for forming optimal maintenance work teams with the goal of maximizing knowledge sharing. While maintenance tasks are often performed based solely on job roles, with less emphasis on team formation according to the specific issue at hand, this research integrates knowledge management concepts with optimization principles to offer an operational and quantitative model for improving the performance of technical maintenance teams.In this regard, the factors affecting knowledge sharing and the required expertise for performing maintenance activities have been identified using data collected from performance evaluation questionnaires and expert interviews. The identification process utilizes the Motivation-Opportunity-Ability (MOA) framework as its theoretical foundation, enabling a deeper analysis of the factors influencing knowledge sharing within work teams. The questionnaires designed based on this model assess team members' motivation to share knowledge, their technical and communication capabilities, and the existing opportunities for knowledge sharing. This information helps managers identify team strengths and weaknesses and adopt a data-driven approach to decision-making.The proposed model in this study employs mathematical programming. The implementation of this model in an organization has shown that forming optimal work teams leads to significant improvements in key performance indicators, such as average repair time and equipment availability (Rahman et al., 2022). The findings of this research highlight that knowledge sharing can serve as a fundamental driver for enhancing the performance of maintenance teams. This study represents an important step in redefining the role of team formation in the efficiency of maintenance processes.MethodsThis study adopts a quantitative approach to optimize the formation of maintenance teams. In the first phase, a nonlinear integer programming model is developed to maximize knowledge sharing among team members while considering the assumptions and constraints relevant to equipment maintenance. To evaluate the model's performance, it is applied within an asset-intensive industry. Required data were collected via expert interviews, as well as performance evaluation questionnaires. Following data collection and parameter calculation, an industry-specific mathematical model was formulated. The model was solved using the branch-and-bound method implemented in MATLAB software. As a result, the most suitable maintenance teams for carrying out designated activities were proposed. Finally, the performance of the formed teams was assessed using key indicators such as mean time to repair (MTTR) and equipment availability. This evaluation was conducted over six-month intervals using a Computerized Maintenance Management System (CMMS).Discussion and resultsTo illustrate the proposed model, a case study was conducted for forming maintenance teams for floating equipment. The selected equipment and the corresponding maintenance activities were recommended by industry experts. Based on these recommendations, key indicators — including technical expertise of maintenance personnel and Motivation-Opportunity-Ability parameters — were evaluated using a 360-degree assessment method. This was carried out through questionnaires employing a five-point Likert scale and distributed among individuals (self-assessment), managers (top-down assessment), and peers. Additionally, the required expertise levels for each maintenance task, as well as the opportunities for knowledge sharing and absorption, were identified through expert surveys. The collected data on individuals’ expertise were then normalized based on the maximum level of expertise needed for the maintenance tasks.Finally, to assess the impact of the implemented policies on equipment performance, two key indicators were analyzed: Mean Time Between Failures (MTBF), representing equipment reliability, and Mean Time to Repair (MTTR), reflecting the skill level of maintenance personnel. These indicators were monitored over a six-month period prior to implementation and three subsequent six-month intervals post-implementation. Simultaneous improvement in both indicators suggests that maintenance activities were conducted with greater speed and higher quality.ConclusionThe findings of this study underscore the critical role of knowledge sharing in enhancing the performance of maintenance teams. The proposed optimization model demonstrated that forming optimal work teams can effectively reduce repair time, manage maintenance costs, and improve key performance indicators. Based on these results, several practical measures can be implemented to enhance maintenance performance through effective knowledge sharing and optimal team formation. First, organizations are encouraged to adopt a performance evaluation system grounded in the Motivation–Opportunity–Ability (MOA) framework to better assess and develop individual and team capabilities. In parallel, targeted incentive schemes should be designed to actively promote knowledge sharing among team members. Additionally, establishing structured and interactive platforms within maintenance processes can facilitate communication and collaboration. Strategic investment in training programs aimed at improving both technical and team-based skills is also essential. Finally, ongoing monitoring of team performance and the use of data-driven decision-making can ensure continuous improvement and alignment with organizational goals. Collectively, these actions provide a comprehensive approach to enhancing the efficiency and effectiveness of maintenance operations
perfomance management
Maryam Sharifi; Sohrab Kordrostami; Leila Khoshandam
Abstract
In production technology, studying the effect of an indicator on one or more other indicators while maintaining efficiency, named marginal rate, can provide valuable information to managers for better management of the system. In this paper, the aim is to study the effect of meaningful indicators on ...
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In production technology, studying the effect of an indicator on one or more other indicators while maintaining efficiency, named marginal rate, can provide valuable information to managers for better management of the system. In this paper, the aim is to study the effect of meaningful indicators on each other in a specific two-stage structure with undesirable outputs. In this study, production technology is divided into two sub-technologies in a two-stage structure, and then, focusing on the application issue, first, the effect of a specific input from the first stage on the intermediate indicator is measured, and then, by calculating the changes made in this indicator, its effect on the specific final output is measured as a transmission factor. In this paper, focusing on data collected from 21 provincial power plants consisting of interdependent "generation" and "transmission" sections, each unit has a similar structure to the stated structure. Considering the total technology distribution, the effect of increasing or decreasing the fuel type component is taken as the first-stage input on the electricity flow, and then the changes in electricity flow are measured on the total system revenue.IntroductionIn today’s world, one of the appropriate tools for measuring and evaluating productivity is data envelopment analysis (DEA). In classical DEA models, the main objective of the increase in output per unit cost is to increase the output, but in some processes with undesirable outputs, other outputs are produced as undesirable outputs. In DEA, different views have been expressed in addressing these outputs. The weak disposability of Kuosmanen (2005), one of the most common ways, is used in this study. In the traditional DEA approach, decision-making units were considered as black boxes. However, in the real world, we are faced with manufacturing processes that have two or more stages to produce final products with network structures. Therefore, network data envelopment analysis (NDEA) is introduced. The network models not only provide overall efficiency for the production processes but also provide the efficiency score for each individual stage. In production theory, calculating the effect of an index on another index can provide valuable information so that managers can access the desired performance by maintaining the efficiency and the exchange between the inputs and outputs. Due to inherent complexity in most of the manufacturing processes, a decision parameter cannot be changed without affecting one or more parameters. Exchanges between inputs and outputs are known as marginal rates and marginal productivity in economics and partial derivatives in mathematics. Although in DEA models, the ratio of optimal coefficients provides this information, due to the nature of the linear segmentation of the border and also due to the multiplicity of these coefficients, the marginal rates will not be unique. Researchers have provided solutions in this field, which are discussed in the following sections. In general, by knowing these rates, valuable suggestions can be made to the managers in order to improve the overall performance of the system. As far as we know, very little research has been done in the field of studying marginal rates and marginal productivity in two-stage production networks with the presence of unfavorable factors. Therefore, this topic is still seen as a research gap in the DEA literature. In this paper, a four-stage process is presented to obtain marginal productivity in a two-stage system in the presence of unfavorable factors. For this purpose, an efficiency measurement model has been proposed to evaluate the efficiency score of two-stage systems in the presence of undesirable outputs. Then, with the idea of Asmild’s method, which was used in 2006 to calculate the right and left marginal rates, the marginal productivity was calculated in the proposed network.MethodologyTo show the practical nature of the proposed method, the model and process were analyzed on the collected data related to 21 provincial power plants of the country. The electricity generation process in each power plant is a two-stage process, which includes the production and transmission sectors, respectively. The flow of electricity was used as an intermediate product in the production sector with an output nature and in the transmission sector with an input nature. Personnel costs and types of fuel, respectively, are defined as the first and second inputs, and pollutants as the undesirable output of the production sector. In the second stage, i.e., the transmission part, the expected outputs including the total income and the covered area have been considered. In the study of production processes, the effect of changing one indicator on one or more other indicators can provide managers with valuable information. Such exchanges between inputs or outputs are known as marginal rates and marginal productivity. Therefore, the estimation of such rates is a significant issue.ResultsPower plants of each country are considered as one of the most important pillars of growth and development of the country and have a significant impact on increasing the welfare of people. In this paper, 21 provincial power stations have been studied.In this way, by considering a two-stage structure and with undesirable outputs, the effect of an index on the intermediate product and then the effect of this change on the final output of the system has been studied. For this purpose, during a four-step process, marginal productivity has been calculated for each component of the production process. As can be seen from the results, in a two-step process, any change in the first component can be transferred to the second component through the intermediate product. In this study, the aim is to measure the amount of personnel cost changes on the total income of power plants.ConclusionIn production theory, studying the effect of one indicator on one or more other indicators is important as long as we are addressing multi-stage processes. Such exchanges in economics are known as marginal rate and marginal productivity. In this paper, focusing on 21 provincial power plants that have two-stage structures and undesirable outputs, for the first time, through a four-stage process, the effect of changes in the input indicators on the intermediate index and then its effect on the outputs is addressed. According to this study, any increase or decrease in the inputs of the first component of the two-stage network can be transferred to the final product through changes in the intermediate index, so it is possible to manage the final outputs of the entire system by changing the inputs of the first component and maintaining the efficiency. In order to provide suggestions for future studies, it is recommended to study networks with more components and a combination of series and parallel modes. Also, the studied network should be analyzed in other industries with a similar structure.
project management
mohammad forozandeh; Amirali Foukerdi; majid salamati
Abstract
Navigating the complexities of modern financial markets requires a comprehensive understanding of the future of venture capital fund management. This involves innovative design and the adoption of new methodologies for managing knowledge-based projects. A promising strategy is the establishment of a ...
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Navigating the complexities of modern financial markets requires a comprehensive understanding of the future of venture capital fund management. This involves innovative design and the adoption of new methodologies for managing knowledge-based projects. A promising strategy is the establishment of a Project Management Office (PMO). This article aims to develop a robust implementation framework for the PMO, enhancing the management of knowledge-based projects, improving the investment ecosystem, and fostering strategic partnerships with co-investors. The study assesses the current maturity within a fund, evaluating related processes and infrastructures from the stakeholders' viewpoints. Data were gathered through interviews with five experts and analyzed using the Organizational Project Management Maturity Model. The results indicate that creating an effective PMO framework necessitates a thorough situational analysis, maturity assessment, and identification of improvement starting points, structured into four key steps: team preparation, current state assessment, maturity level identification, and action planning to achieve organizational objectives.IntroductionIn contemporary finance, investment funds are tasked with establishing and managing diverse portfolios aimed at profit generation. Fund management involves a systematic approach, encompassing several phases from the initial evaluation of proposals to the execution of investment contracts, followed by ongoing management and exit strategies. The development of effective mechanisms throughout these processes is crucial, as they are regarded as key success factors that can enhance the fund's financial capital and credibility within the investment ecosystem. The implementation of a structured system for planning, monitoring, and controlling investments is vital. Typically, this is facilitated by a Project Management Office (PMO), which acts as a dynamic management framework to achieve the strategic objectives of the fund. The PMO oversees the definition, implementation, supervision, and control of projects at various organizational levels, thereby maximizing coordination and efficiency among units and enhancing the effectiveness of achieving organizational goals.National economic growth and development hinge on the successful execution of projects and the support of diverse business sectors. In the context of Iran's economy, empowering knowledge-based enterprises has emerged as a strategic response to economic challenges. National policies, including those outlined in Article 44 of the Constitution and various development plans, emphasize the knowledge-based economy as a priority area. However, the advancement of such businesses faces significant barriers, including limited financial resources, inadequate market knowledge, insufficient governmental support for technology transfer, and challenges related to branding and market expansion (Gholami et al., 2018).The purpose of this paper is to present a Project Management Office framework in a venture capital fund. For this purpose, the maturity level of the organization and the current project management processes examined in the fund under study are assessed, and superior solutions are presented to increase the maturity level of project management processes. This framework is designed based on the primary and secondary functions of the Project Management Office in the venture fund. In this regard, first, by reviewing the research background in the two areas of venture capital funds and Project Management Offices, the general and specialized functions, implementation processes, prominent models used, and their adaptation for use in the investment industry are investigated. Then, the steps of using the Project Management Office in an applied case are followed and implemented.MethodsThe present study is in the field of applied research from the perspective of the objective and follows a case study method. The research uses the opinions and views of experts and, therefore, from the perspective of type, is considered descriptive survey research. The elements of the research method are presented in several steps. In this study, the self-assessment method based on the OPM3 cycle was used. Due to the need for decision-making, resource allocation, and review and modification of business goals and strategies, this study includes only the first to third steps of implementation; the follow-up of the next steps falls outside the scope of this research. One of the important assumptions of this research was the need to focus the study on the scope of project management and portfolio management, excluding the scope of program management from the research domain.In the first step, a comprehensive analysis of the current environmental situation of the organization, including human skills, capital, and tools available to the organization, was conducted. The output of this step is to determine the requirements and framework for the implementation of the Project Management Office in the organization and to develop an improvement plan in the third step of implementation.Discussion and resultsIn this paper, Bamdad Capital Management Company's venture fund was considered as the spatial domain of the research. This company began its activity in 2019 with the participation of Bahman Entrepreneurship Development Company (a subsidiary of Barakat Knowledge-Based Holding), with the aim of investing in, leading, and financing various projects. The company invests in health, energy, metal and mining industries, agriculture, and food. Currently, Bamdad Capital Management is responsible for leading and managing three investment funds, one of which is the 200-billion-toman venture fund for the development of entrepreneurship. This fund has so far invested in 12 projects over a seven-year period.The results show the overall maturity status of the organization by project and portfolio domains. Due to the heterogeneity of expert opinions, the percentage of maturity and immaturity of the processes has been calculated using the weighted average of "yes" and "no" answers given by the five experts. Also, in the standardization stage, no superior solution proposed by the OPM3 standard has been fully and continuously implemented in the project management processes (project scope) of the studied company.ConclusionIn the studied fund, the processes of risk, cost, and time management (with a maturity of more than 50%) have been considered by managers from the very beginning. Examining how much time (investment) and the amount of risk of a project can achieve the expected goals has led to an increase in maturity in the processes related to risk management, cost management, and time management compared to other areas. However, the organization has not yet reached full maturity in these areas. In addition to these knowledge areas that show the highest level of maturity in the organization, increasing the maturity of other areas—especially communication management and stakeholder knowledge (with maturity less than 20%), which relates to how to communicate and interact with various stakeholders, especially investors and entrepreneurs—should be seriously considered in planning for organizational improvement. Other knowledge areas such as scope management, integration, logistics, quality, and human resources (with maturity between 20% and 50%) also require tailored improvement plans. The best solutions for each field should be identified and implemented to elevate maturity levels. The results of this research can be compared with similar studies in other industries to identify influential components. Future researchers can use models and simulations to investigate the impact of effective components and characteristics on this research topic. Moreover, future research can explore the influence of external factors such as economic and political changes on venture capital funds.
modeling and simulation
Afshin Aminipour; Akbar Bagheri; Hamidreza Kordlooie
Abstract
The aim of this research is to analyze the impact of banking fintech on the risk levels of selected banks listed on the Tehran Stock Exchange. The statistical population consists of 41 banks and credit institutions listed on the Tehran Stock Exchange, according to the latest report by the Central Bank ...
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The aim of this research is to analyze the impact of banking fintech on the risk levels of selected banks listed on the Tehran Stock Exchange. The statistical population consists of 41 banks and credit institutions listed on the Tehran Stock Exchange, according to the latest report by the Central Bank of the Islamic Republic of Iran in May 2022. Using a systematic elimination method and covering the period from 2019 to 2022, a sample of 8 banks was chosen. The results of hypothesis testing, based on the first and second models of the study (with Z-Score and business risk of the studied banks as the dependent variables, respectively), showed that in the first model, under the dynamic condition (with lag), the banking fintech variable does not have a significant effect on the risk management index of the selected banks. Additionally, it was found that in the non-lagged model, the fintech innovation variable—through improved operational performance—and the variable of operational revenues, as well as each bank’s share of total transaction values from mobile payment acceptance tools, significantly impact the risk management index. Other findings also showed that in both models, under both lagged and non-lagged conditions, variables related to each bank’s share of transaction values from mobile, POS, and internet payment acceptance tools, as well as fintech innovation variables related to improved operational performance and capital adequacy, do not significantly affect the risk management index of the selected banks listed on the Tehran Stock Exchange.IntroductionA review of the literature suggests that, while the academic community generally agrees on the impact of FinTech on commercial bank risks, unresolved issues still require further investigation. There remains significant disagreement over whether FinTech’s influence on commercial bank risk is "favorable" or "unfavorable." This divergence may be attributed to the varying socioeconomic and institutional contexts in which FinTech operates, as previous studies have not adequately captured these dynamic effects. Therefore, further research is needed to explore these variations and examine the nonlinear effects of FinTech on commercial bank risks. As a result, further discussion on the potential relationship between FinTech risks and commercial bank risk is necessary. Given the significance of this issue, the present study aims to address this critical topic.Literature ReviewWith the rapid growth of financial technologies, fintech has emerged as a transformative force in financial and banking services, significantly impacting areas such as investment management, digital payments, service personalization, cryptocurrencies, artificial intelligence, big data, and blockchain. Its high agility has facilitated access to financial services, reduced costs, and improved the efficiency of banks. However, the boundary between fintech and traditional electronic banking is often unclear, and fintech's entry into the financial sector has introduced risks such as data security threats, transaction instability, and challenges to conventional banking structures, which require thorough evaluation. Most previous studies have focused on the benefits and innovative aspects of fintech, while empirical investigations into the impact of fintech innovation on operational, technological, and structural risks—especially in the context of Iranian banks—remain limited and fragmented. This study aims to identify and analyze these risks, addressing the existing research gap and supporting more informed decision-making by banking managers in the face of modern financial technologies. Table 1 presents a summary of the research background based on selected international studies. The review of these studies primarily focuses on the credit and liquidity risks of banks in relation to lending objectives and default risk.Table 1: presents a summary of the research backgroundStudy ResultsResearchers / YearRowThe development of fintech leads to a reduction in bank-generated liquidity and contributes to the diversification of banking services.Tang et al. (2024)1Greater fintech presence is associated with higher risk-taking by financial intermediaries, and the findings support the competition fragility hypothesis.Alkhodair et al. (2024)2Fintech products reduce banks' risk-taking behavior by improving operational efficiency. Path analysis results also showed that operational efficiency mediates the relationship between fintech products and bank risk-taking behavior in emerging countries.Sajid et al. (2023)4An empirical analysis of the relationship between fintech, digital transformation, and bank risk in 32 Chinese banks revealed that fintech increases bank risk and has a stronger effect on banks with lower levels of digitalization.Li et al. (2021)5Payment and settlement technology (PST), capital raising technology (CRT), and investment management technology (IMT) are positively correlated with bank risk-taking.Zhao et al. (2022)8Reference: Research FindingsMethodologyThis study is applied in terms of results, causal-correlational in terms of purpose, retrospective in terms of time, quantitative in terms of execution process, and follows a combination of deduction and induction (scientific research method) in terms of logic. This study is quasi-experimental. In such studies, the researcher has no control over the data generation process. The required data for this study, which results from various processes within companies and the transactions in the securities market, are obtained from databases. The research hypotheses are then tested using the available data. From another perspective, this research follows a positivist approach, meaning that the researcher seeks to discover what exists rather than prescribing any specific recommendations. Instead, suggestions are provided based on the discovered findings.ResultsNowadays, banks have begun competing beyond financial services in response to the increasing competition from non-banking institutions. As a result, traditional banks have lost a portion of their market share. The recent developments in financial services require banks to increase investment in fintech, reconsider service delivery channels, and enhance standardization in administrative and financial operations. Improving bank profitability and diversifying income sources can also lead to reduced risk-taking behavior. DiscussionDue to the significant impact of banking FinTech measured by each bank’s POS transaction share on risk management in Model 1 (no lag), banks are advised to actively pursue digital transformation strategies. As FinTech innovation, measured through operational revenue improvements, has a significant positive impact on risk management in Model 1, efforts should focus on strengthening this factor to boost risk control. Since the effect of FinTech via mobile and internet tools on risk management is insignificant, banks should prioritize the development of these tools to enhance commercial risk oversight. Given the lack of significant impact from FinTech innovation in terms of operational performance and capital adequacy in both models, reinforcing these areas is recommended to support risk control, regardless of model dynamics.
