production and operations management
Mohammad Rahim Ramazanian; Marjan Khodaparast Nodehei; Reza Sheikh
Abstract
Digital transformation and Industry 4.0 have emerged as key drivers for enhancing competitiveness and improving product quality across various industries, particularly in the automotive parts sector. This research focuses on Guilan Province, examining the extent of Industry 4.0 technologies’ implementation ...
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Digital transformation and Industry 4.0 have emerged as key drivers for enhancing competitiveness and improving product quality across various industries, particularly in the automotive parts sector. This research focuses on Guilan Province, examining the extent of Industry 4.0 technologies’ implementation aimed at increasing product longevity. Initially, through qualitative content analysis of 35 articles published between 2016 and 2024, 16 sub-criteria were identified within four main groups. Subsequently, using the novel OPLO-POCOD method (Opportunity Lost Assessment Based on Distance in Polar Coordinate Space) and surveying 14 experts from 10 parts manufacturing companies, the performance of these companies was analyzed. The results indicated that criteria such as automated warehousing systems, inventory management automation, and blockchain-based tracking had the highest impact on increasing product lifespan, with the lowest opportunity loss values of 0.0231, 0.0242, and 0.0253, respectively. On the other hand, the physical-information integration of the supply chain using cloud computing is still in the early stages of implementation. This research uniquely combines qualitative analyses with the innovative OPLO-POCOD method, enabling precise ranking of companies and identification of execution gaps. The findings emphasize the importance of focusing on smart technologies to achieve more sustainable and competitive production, assisting managers and policymakers in prioritizing Industry 4.0 strategies. Overall, while automotive parts industries in Guilan have made progress in areas such as automated warehousing, there is a need to accelerate the implementation of new technologies like cloud computing to fully realize the benefits of Industry 4.0 and complete the digital transformation process.IntroductionIn the contemporary competitive landscape, enhancing production quality and adopting sustainable supply chain management strategies—particularly with an emphasis on extending product lifespan—have evolved into strategic imperatives. Increasing product longevity not only alleviates pressure on natural resources and mitigates environmental impacts but also significantly enhances the economic value of products. Industry 4.0, as a transformative paradigm, leverages smart technologies such as additive manufacturing, the Internet of Things (IoT), robotics, and artificial intelligence to provide unprecedented potential for achieving these objectives. This digital transformation, enabling real-time tracking throughout the entire product lifecycle, predictive maintenance optimization, and production personalization, directly contributes to extending the useful life of products. The integration of this concept with Industry 4.0 smart technologies substantially enhances the capacity to realize these goals. This is particularly critical in complex and capital-intensive industries such as automotive and auto parts manufacturing, where production quality is directly linked to safety and competitiveness. However, despite prevailing assertions regarding the role of Industry 4.0 in sustainable development, few studies have specifically examined the impact of smart technologies on product lifespan extension. Aiming to address this research gap, this study focuses on the auto parts manufacturing industry to identify key components for enhancing product longevity within the Industry 4.0 framework. Employing an innovative methodology—the Lost Opportunity Technique based on distance in polar coordinate space—it investigates the extent of implementation of these factors within the industry. The findings of this research are poised to provide industrial managers and policymakers with a strategic roadmap for developing more sustainable and competitive products.MethodologyThe present study is applied in purpose and descriptive-survey in terms of data collection, adopting a multiple case study approach. This research was conducted using a mixed-methods (qualitative-quantitative) approach in two phases. In the qualitative phase, content analysis and a systematic review of library sources and reputable databases from 2016 to 2024 were employed to identify the most influential factors affecting product lifespan extension, with an emphasis on smart technologies within Industry 4.0. In the quantitative phase, a researcher-developed questionnaire based on a ten-point scale and the novel “Opportunity Losses-Based Polar Coordinate Distance (OPLO-POCOD)” was used to collect field data from 10 active companies in the automotive parts industry in Guilan Province. Sampling was performed using targeted and snowball sampling methods, and the questionnaires were completed by 14 experts (production managers, IT managers, and production line supervisors) with at least five years of professional experience and familiarity with Industry 4.0 concepts. The reliability of the questionnaire was confirmed with a Cohen’s kappa coefficient of 0.743, and its validity was endorsed by specialists. Finally, the collected data were analyzed using the OPLO-POCOD technique, and the companies under study were ranked based on the identified criteria.FindingsThe findings of this study, conducted using a mixed-methods approach (qualitative content analysis and the OPLO-POCOD technique), reveal that the adoption of Industry 4.0 technologies plays a significant role in enhancing product longevity in the automotive parts manufacturing industry. In the qualitative phase, which involved the analysis of 35 studies published between 2016 and 2024, 16 key concepts were identified across four main criteria: additive manufacturing, the Internet of Things (IoT), robotics, and smart supply chain management. The reliability of this analysis was confirmed with a Cohen’s kappa coefficient of 0.743. In the quantitative phase, employing the novel OPLO-POCOD technique and surveys of 14 experts across 10 automotive parts manufacturing companies, the studied companies were ranked based on their level of adoption of Industry 4.0 technologies. The results indicated that Companies 4, 3, and 5 achieved the highest percentages of opportunity gained (94.4%, 94.3%, and 93.9%, respectively) and the lowest levels of lost opportunity (below 0.061), securing the top ranks. In contrast, Companies 7, 2, and 8, with the highest levels of lost opportunity (between 0.131 and 0.174), demonstrated the weakest performance in adopting these technologies. These findings underscore the direct impact of implementing Industry 4.0 technologies—particularly in additive manufacturing, the Internet of Things, and smart supply chain management—on extending product lifespan.Discussion and conclusionThe present study aimed to assess the level of Industry 4.0 implementation in automotive parts manufacturing companies in Guilan Province. The findings revealed that three criteria—automated warehouse systems, inventory management automation, and blockchain-based traceability, all falling within the domain of data transparency and tracking—were identified as the most effective factors in enhancing product longevity. These results indicate that in Iran’s industrial context, the most fundamental layers of digitalization serve as essential prerequisites for achieving higher-level objectives such as the circular economy. On the other hand, the findings demonstrate that despite relative progress in certain foundational technologies, significant challenges persist in smart supply chain management, rooted in infrastructural limitations and weaknesses in strategic coordination among supply chain actors. This study contributes to the academic discourse in several ways, including the development of a prioritized operational framework (OPLO-POCOD) for assessing industrial readiness, the identification of specific mechanisms effective within each criterion, and the adaptation of global findings to Iran’s specific industrial context. By bridging theoretical literature with practical industry requirements, this research provides a valuable roadmap for industrial managers and policymakers. Based on the research findings, it is recommended that industrial managers allocate resources toward improving smart supply chain management. Furthermore, policymakers are advised to facilitate the successful implementation of Industry 4.0 by developing national cloud platforms for supply chain integration, providing targeted incentives to companies committed to national standards, and establishing mandatory data exchange standards. For future research, it is suggested that emerging criteria such as human-robot collaboration be examined, the study be replicated in other national industries, fuzzy logic-based methods be employed, and causal methods be utilized to analyze relationships among the identified criteria.
uncertainty
Iman Ebrahimi; Hadi Mokhtari; Mohammad Taghi Rezvan
Abstract
In today’s world, investment in the cryptocurrency market is regarded as one of the most attractive yet high-risk opportunities. Given the rapid growth of this market and its impact on the global economy, examining the importance and challenges associated with cryptocurrency investments is essential. ...
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In today’s world, investment in the cryptocurrency market is regarded as one of the most attractive yet high-risk opportunities. Given the rapid growth of this market and its impact on the global economy, examining the importance and challenges associated with cryptocurrency investments is essential. This paper, as a novel contribution, simultaneously explores the design of a cryptocurrency portfolio and the subsequent optimal allocation of investments in this market, considering both risk and return. In the first stage, the focus is on constructing a portfolio in the cryptocurrency market using relevant indices. After forming this initial portfolio, capital allocation is optimized. This approach allows for the creation of a portfolio that aligns with the investor’s risk preferences while also considering passive portfolio management. In the second stage, equally weighted portfolios are created relative to the market portfolio, as well as other portfolios selected based on criteria such as the highest market capitalization and the best risk-return ratios, using the Markowitz, Sharpe, and Sortino models. For the optimal allocation of these portfolios, metaheuristic algorithms based on particle swarm optimization are employed. The results show that portfolios including cryptocurrencies with the highest market capitalization exhibit lower risk, and the strategy based on the Sharpe model outperforms the other models.IntroductionThe rapid evolution of cryptocurrency markets has created both opportunities and challenges for investors. Unlike traditional assets, cryptocurrencies are highly volatile, decentralized, and driven by technological innovations such as blockchain. These unique features demand new approaches to portfolio design and optimization. Traditional models, such as Markowitz’s mean-variance theory, provide valuable foundations but are insufficient for capturing the complexity of crypto markets. Recent advances in heuristic and metaheuristic optimization have introduced promising tools to address these challenges. This study contributes by integrating portfolio formation with optimal allocation strategies while considering both risk and return. By employing passive management approaches alongside advanced optimization models, it seeks to create robust portfolios that reflect investors’ preferences. The research particularly emphasizes evaluating the performance of different models, including Markowitz, Sharpe, and Sortino, in combination with metaheuristic methods, offering practical insights for effective decision-making in cryptocurrency investments.Research BackgroundPortfolio optimization has long been a central topic in financial management, evolving from classical mean-variance models to more advanced risk-adjusted approaches. With the emergence of cryptocurrencies, scholars have increasingly focused on adapting these models to highly volatile, fast-growing markets. Prior studies have explored strategies such as equal-weight portfolios, maximum Sharpe ratio, and risk-parity allocations, often with mixed results. Additionally, machine learning and heuristic methods, including genetic algorithms and deep learning, have been applied to enhance prediction and asset allocation. However, most research addresses either portfolio formation or allocation optimization in isolation. A gap remains in simultaneously considering both aspects under realistic conditions. Furthermore, studies on passive portfolio management in cryptocurrency markets are limited. This research addresses these gaps by combining index-based portfolio construction with optimal allocation through particle swarm optimization (PSO), benchmarking results against classical genetic algorithms and well-established financial models.MethodThe study employs a two-stage methodology. First, cryptocurrency portfolios are constructed using different strategies: market index portfolios, equally weighted portfolios, and portfolios based on criteria such as the highest market capitalization and superior return-to-risk ratios. These portfolios are then evaluated using three established financial models: Markowitz’s mean-variance, Sharpe ratio, and Sortino ratio. In the second stage, the allocation of assets within each portfolio is optimized using the particle swarm optimization (PSO) algorithm, chosen for its ability to handle non-linear, NP-hard optimization problems. The PSO implementation is fine-tuned to balance exploration and exploitation, ensuring reliable convergence. To validate robustness, results are compared against those obtained from classical genetic algorithms. Data on the top 50 cryptocurrencies were collected from CRIX and Yahoo Finance over multiple horizons (30, 90, 180, and 365 days). Key performance metrics include return, variance, standard deviation, and risk-adjusted ratios, providing a comprehensive view of portfolio efficiency.Discussion and ResultsThe findings indicate that portfolios composed of cryptocurrencies with the highest market capitalization consistently exhibit lower risk levels compared to alternatives. Among the evaluation models, the Sharpe-based strategy outperformed others, delivering superior risk-adjusted returns. The Sortino model also proved effective in emphasizing downside risk, aligning with investor concerns in volatile markets. Conversely, some Markowitz-based portfolios produced higher variance, highlighting the limitations of variance as a sole risk measure in cryptocurrency investments. Across different time horizons, return-to-standard deviation ratios provided robust selection criteria, particularly when optimized through PSO. Comparisons with the genetic algorithm demonstrated PSO’s efficiency in convergence and accuracy, especially in capturing optimal weight distributions. The results suggest that integrating metaheuristic optimization with traditional financial models significantly improves portfolio performance. Overall, evidence supports the importance of market capitalization and model choice in shaping effective investment strategies, with PSO-based Sharpe optimization yielding the most promising outcomes.ConclusionThis research demonstrates that simultaneous portfolio formation and optimal allocation, when supported by advanced optimization techniques, can effectively balance risk and return in cryptocurrency investments. Portfolios weighted toward large-cap cryptocurrencies proved less risky, while the Sharpe model consistently delivered superior outcomes compared to Markowitz and Sortino. The application of particle swarm optimization enabled efficient identification of optimal weights, outperforming classical genetic algorithms in accuracy and stability. Importantly, the study highlights the relevance of passive portfolio management strategies in volatile digital markets, providing investors with practical tools to mitigate risks while maximizing returns. The findings emphasize the necessity of adopting scientific, data-driven methods rather than speculative approaches in crypto trading. Future research may extend this work by incorporating multi-objective optimization, hybrid algorithms, or alternative risk measures, offering deeper insights into dynamic portfolio strategies in evolving digital financial ecosystems.
multiple-criteria decision-making
Ali Memarpour Ghiaci; morteza abbasi; Jafar Gheidar-Kheljani
Abstract
Abstract
Supplier evaluation plays a pivotal role in the success of modular megaprojects, as these projects require capable suppliers due to the necessity for complex coordination among various subsystems and the precise integration of modules. This study proposes an integrated framework for the evaluation ...
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Abstract
Supplier evaluation plays a pivotal role in the success of modular megaprojects, as these projects require capable suppliers due to the necessity for complex coordination among various subsystems and the precise integration of modules. This study proposes an integrated framework for the evaluation of suppliers in modular megaprojects. For the first time, this research applies a novel integrated approach based on the LOPCOW and ARTASI methods, extended using spherical fuzzy sets (SF-LOPCOW and SF-ARTASI) for supplier evaluation. Based on this approach, 31 sustainability-oriented criteria have been identified for evaluating suppliers in modular megaprojects. The criteria are first weighted using the SF-LOPCOW method. Subsequently, in a case study, 12 suppliers identified for a modular megaproject are evaluated and prioritized using the SF-ARTASI method. A comparison of the SF-ARTASI results with other existing multi-criteria decision-making methods in the literature, along with a sensitivity analysis, demonstrates the effectiveness of the proposed approach and the robustness of its results under different scenarios.
Introduction
With the rapid expansion of the global economy, investment in large-scale projects worldwide has increased markedly over the past few decades. Projects with costs of one billion dollars or more are recognized as megaprojects. Megaprojects are inherently associated with growth, development, and competitiveness, acting as the infrastructure of globalization. Modularization is a key driver for reducing the time and cost of megaprojects. With the modularization of megaprojects, the evaluation and selection of suppliers acquire particular importance. The question therefore arises: how can suppliers for modular megaprojects be evaluated in the long term while concurrently reducing project delays? The present study concentrates on this critical issue, which can assist project and megaproject managers from a sustainable development perspective. First, it is essential to collect core criteria from various dimensions—economic, environmental, and social—to evaluate a sustainable supplier; then, by employing a multi-criteria decision-making (MCDM) method, the relative importance of these criteria is determined, and suppliers are subsequently evaluated and prioritized. The supplier evaluation problem is complex and involves uncertainty across all sustainability dimensions (economic, environmental, and social).
The main objective of this study is to evaluate and prioritize suppliers of modular megaprojects by proposing a novel approach under uncertainty. This study intends, for the first time, to apply the developed SF-LOPCOW-ARTASI method to the supplier evaluation problem. This method is capable of handling both uncertainty and group decision-making simultaneously. In this research, the supplier evaluation problem for megaprojects is, for the first time, conducted based on sustainability dimensions within a spherical fuzzy environment. The approach is presented for the first time by using the LOPCOW method developed on the basis of spherical fuzzy sets (SF-LOPCOW) to weight the criteria, and the ARTASI method developed on the basis of spherical fuzzy sets (SF-ARTASI) to prioritize suppliers of modular megaprojects.
Method
The present study employs an integrated approach. In the first stage, supplier evaluation criteria are identified and, after defining the alternatives, data derived from the judgments of the decision-making team are collected as linguistic variables based on spherical fuzzy sets. Subsequently, following the evaluation of suppliers against the identified criteria, the criteria weights are calculated using the SF-LOPCOW method. Finally, by implementing the SF-ARTASI method, suppliers are assessed according to the criteria and prioritized. Using purposive sampling, the decision-making team consisted of eleven experts with experience and specialization in management systems implementation consultancy, engineering, and project and megaproject management. Information on the members indicates that the majority of the expert team have between eight and fourteen years of professional experience.
Discussion and Results
To illustrate the applicability of the proposed approach, suppliers for modular megaprojects were evaluated and prioritized using this approach. In this study, twelve suppliers were assessed and ranked using 31 evaluation criteria. First, each supplier was evaluated by the decision-making team according to the identified criteria using linguistic variables based on spherical fuzzy sets. Given the uncertainty inherent in the evaluation criteria, spherical fuzzy sets were employed to address this uncertainty. The relative importance of the criteria was then determined using the developed LOPCOW method based on spherical fuzzy sets. According to this method, cost, strategy and organization, and the amount of waste generated received the highest importance weights of 0.087, 0.083, and 0.079, respectively. Subsequently, using the proposed approach, suppliers were evaluated and prioritized by applying the developed ARTASI method based on spherical fuzzy sets, taking into account the evaluation criteria and their importance degrees. The results indicate that S3, S9, and S7 ranked first through third, respectively.
Finally, a sensitivity analysis was designed in the form of multiple scenarios to examine the relationship between the outcomes produced by the proposed approach under varying conditions and the study’s findings. This analysis investigated the variation in the final utility function and the resulting ranking of alternatives as the values of φ and α changed; in both cases, the ranges of variation were negligible and not statistically significant.
Conclusion
Due to the need for complex coordination among subsystems and precise integration of modules, the success of modular megaprojects largely depends on the evaluation and selection of capable suppliers. The present study introduces an integrated approach for supplier evaluation in modular megaprojects. Accordingly, a comprehensive list of sustainability criteria for evaluating and prioritizing suppliers of modular megaprojects was identified. The relative importance of these criteria was then determined using the SF-LOPCOW method. Subsequently, following the proposed approach, suppliers were evaluated and prioritized according to the criteria and their importance weights by applying the SF-ARTASI method. The limited number of experts in the field of megaproject management and the absence of weighting expert judgments according to their knowledge and experience represent limitations of this study. The use of aggregation operators to integrate expert judgments, such as the spherical weighted arithmetic mean (SWAM) operator, and the development and comparison of multi-criteria decision-making methods in other uncertain environments (e.g., Pythagorean fuzzy, q-rung, and Fermatean fuzzy sets), and comparing their results with the methods developed in the present study, are suggested for future research. Regardless of the case used to implement the proposed approach, the method is applicable to various supplier evaluation and selection scenarios for megaprojects. In future work, we will extend our research to optimize scheduling and reduce the completion time of modular megaprojects through the employment of appropriate suppliers.
project management
Mohammad Amin Dorosti; Farhad Saiedi; Saied Yousefi
Abstract
Abstract
Qualified contractors are the main pillar of sustainable construction projects and the technical arm of project implementation, playing a significant role in achieving sustainable development (economic, social, and environmental) goals. Evaluating competence and selecting the best contractor ...
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Abstract
Qualified contractors are the main pillar of sustainable construction projects and the technical arm of project implementation, playing a significant role in achieving sustainable development (economic, social, and environmental) goals. Evaluating competence and selecting the best contractor are among the concerns of employer companies when outsourcing the implementation of such projects. In this regard, company managers and policymakers need criteria to evaluate qualified contractors. The current study aims to identify the evaluation criteria for competence and to select contractors for sustainable construction projects from the perspective of employer companies. This study is applied in terms of purpose and qualitative in terms of data type. In this study, the construction industry in Kish Island is examined, and an expert panel comprising project managers and experts from employer companies is formed, and sampling was carried out non-randomly and purposefully. After surveying 12 experts through semi-structured interviews, the content of the interviews was coded and analyzed using the thematic analysis approach based on the six-step model of Braun and Clarke (2006) with an inductive approach. A total of 73 codes with a frequency of 979 repetitions were counted in 12 sub-themes and 3 main themes. The results showed that, unlike traditional approaches to evaluating candidate contractors in tender meetings, carrying out this process in sustainable construction projects requires a comprehensive approach by the decision-makers of the employer companies and their simultaneous attention to 12 key criteria that are categorized into three economic, social, and environmental dimensions.
Introduction
The construction industry plays a vital role in enhancing the quality of life and supporting national growth, yet it is also among the largest contributors to environmental degradation through high greenhouse gas emissions and waste generation. Consequently, the transition toward sustainable construction practices has become imperative to mitigate negative environmental impacts. Sustainable construction emphasizes the use of recyclable materials, energy efficiency, waste minimization, and environmental protection, while also attracting increasing attention from governments, scholars, and leading companies as a strategic business priority. In Iran, and particularly in Kish Island, adopting modern construction methods, engaging qualified professionals, leveraging advanced technologies, and reducing bureaucratic inefficiencies have become critical concerns for stakeholders. Within this context, the selection of competent contractors is pivotal, as their capabilities directly influence project outcomes in terms of quality, cost, and timely delivery. However, existing evaluation processes in the country have predominantly focused on traditional economic indicators, neglecting social and environmental dimensions of sustainability. International studies highlight that the absence of comprehensive criteria for contractor assessment often leads to cost overruns, delays, and reduced quality. Therefore, conducting a field-based study to explore and define the dimensions and criteria of contractor competence in sustainable construction projects on Kish Island addresses a critical research gap.
Research Background
The selection of competent contractors is a critical factor in the success of construction projects and has been examined from multiple perspectives in the literature. The concept of “organizational competence” in project management refers to an organization’s ability to integrate resources, processes, and culture in alignment with its mission and strategy, which plays a pivotal role in achieving sustainable development goals. Recent international studies have highlighted that contractors are crucial in attaining sustainability objectives and mitigating environmental and social risks, making transparent and multi-criteria evaluation essential. To this end, numerous studies have considered technical, financial, managerial, safety, past performance, and bid price criteria; however, reliance solely on the “lowest bid” remains a significant weakness in tendering systems. Domestic research indicates that, although ranking systems and prequalification assessments partially improve contractor selection, sustainability criteria are still largely neglected. Evidence from international studies in Egypt, India, Jordan, and China underscores the necessity of integrating environmental, social, and economic indicators alongside traditional criteria, whereas in Iran, final selection often remains price-driven. This gap motivates the present study to identify and comprehensively analyze the competency criteria for contractors in sustainable construction projects on Kish Island, aiming to provide a localized framework that enhances the competitiveness of domestic construction firms and facilitates their entry into international markets.
Method
The present study is applied in nature, qualitative in terms of data type and analytical approach, and falls within the descriptive-survey category regarding data collection. The content analysis of the interviews was conducted using an inductive thematic coding approach. The analysis followed Braun and Clarke’s (2006) six-step framework, including familiarization with the data, generation of initial codes, theme extraction, review of sub-themes, definition and naming of main themes, and reporting. The study population consisted of 12 project managers and experts from client companies active in the construction industry on Kish Island, purposefully selected through non-probabilistic sampling. Semi-structured interviews were conducted between winter 2023 and summer 2024. Data validity was ensured through systematic data recording, increasing participant diversity, iterative data review, and oversight by external experts and faculty members.
Results and Discussion
Through open coding and content analysis of 12 interviews with experts, a total of 73 codes with 979 occurrences were identified, organized into 12 sub-themes and 3 main themes. The findings showed that the most important criteria for contractor competence and selection in these types of projects are structured around three primary categories, including economic, social, and environmental, which constitute the main themes and serve as the foundational pillars of sustainable development. The findings indicate that a comprehensive evaluation of these three dimensions facilitates the selection of competent contractors, reduces operational risks, enhances construction quality, and increases transparency and accountability. From an operational perspective, sustainability competency criteria contribute to the optimization of costs and resources, improvement of operational efficiency, utilization of standard equipment, and reduction of material wastage. Moreover, selecting contractors in accordance with these criteria ensures the implementation of sustainable construction projects, taking into account the specific geographical and environmental conditions of Kish Island, while fulfilling sustainability requirements such as carbon footprint reduction and ecosystem preservation. Overall, the integration of economic, social, and environmental dimensions provides a practical and actionable framework for project managers’ decision-making, enhancing the competitiveness of construction companies and promoting the sustainable performance of projects.
Conclusison
Based on the findings of the study, it can be concluded that contractors who demonstrate strong performance across the twelve identified criteria in the economic, social, and environmental dimensions have a greater capacity to achieve the sustainable development objectives of construction projects. Design-build contracts further enhance this capacity by fostering team integration, efficient communication, and innovative solutions, and by selecting contractors based on maximum value rather than the lowest price. The economic evaluation of contractors should focus on financial credibility, resource management, and cost optimization while maintaining service quality. The social evaluation should encompass human resource management, social capital, professional ethics, and stakeholder engagement. The environmental evaluation should assess contractors’ capability and experience in implementing sustainable processes, waste and energy management, and environmental training. Complementary measures include the development of multidimensional evaluation systems, creation of a contractor performance database, and interdisciplinary assessments. The study’s limitations include difficulties in accessing experts and the restricted generalizability of the results beyond Kish Island. Future research could develop the current study by applying advanced quantitative methods under uncertainty and establishing mechanisms for continuous monitoring and performance evaluation of contractors.
modeling and simulation
Nahid Foroughi; Mahmoud Moradi; Keikhosro Yakideh; MohammadRahim Ramazanian
Abstract
AbstractThis study investigated the impact of improvement strategies on key performance indicators in the water and wastewater sector using a system dynamics approach. The main objective was to develop models for simulating the complex interactions between strategies and performance variables to enhance ...
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AbstractThis study investigated the impact of improvement strategies on key performance indicators in the water and wastewater sector using a system dynamics approach. The main objective was to develop models for simulating the complex interactions between strategies and performance variables to enhance sustainability and performance management. Performance indicators were categorized as input indicators (including accounts receivable period, per-capita subscriber debt, non-revenue water, and labor cost share of sales) and output indicators (such as asset turnover ratio, per-capita subscriber coverage, market growth, and employee professionalism). Based on the literature, policy documents, company reports, and expert opinions, 14 improvement strategies were identified and incorporated into two system dynamics models. Results indicated that implementing these strategies reduced accounts receivable periods and labor costs while improving asset turnover, service coverage, market growth, and employee skills. These findings demonstrate that system dynamics modeling is an effective tool for strategic decision-making and performance improvement in the water and wastewater sector.Introductionith the increasing complexity and rapid changes in competitive environments, organizations require innovative approaches to enhance performance and achieve sustainable competitive advantage. Performance management is a fundamental approach that, in addition to evaluation, encompasses continuous feedback, goal setting, training, and incentive systems (Aguinis & Pierce, 2008). Prior research has emphasized that analyzing causal relationships and dynamic interactions within organizations, particularly under multi-factor conditions, can enhance decision-making (Tseng & Levy, 2019). In this context, systems thinking and simulation models have gained importance as tools for predicting policy impacts and designing improvement strategies (Shafiee et al., 2021; Eidin et al., 2024). Nevertheless, many studies have primarily focused on ranking and benchmarking performance rather than addressing operational interventions (Kameli et al., 2023). In the Iranian water and wastewater sector, limited resources and deteriorating infrastructure further highlight the necessity of adopting advanced analytical approaches (Hejazi et al., 2024). Accordingly, this study applies system dynamics modeling to examine how improvement strategies influence key input and output indicators in the water and wastewater sector, while providing a framework for enhancing sustainability and supporting strategic decision-making.Literature ReviewThe system dynamics approach, introduced by Forrester (1961), is a framework grounded in systems science and computer-based simulation that enables the analysis of complex system behavior and the prediction of long-term policy effects. Features such as feedback loops, stocks and flows, nonlinear relationships, and mutual interactions make it an effective tool for modeling organizational and infrastructural systems (Mustafee et al., 2010; Mielczarek, 2016). Numerous studies have demonstrated that system dynamics is an effective method for performance analysis and strategic decision-making, including in wastewater network asset management, automotive supply chains, urban and water resource management, and complex projects (Mohammadifardi et al., 2019; Norouzian-Maleki et al., 2022; Calderon-Tellez et al., 2024). However, most studies have focused only on partial analyses of performance indicators, and integrated evaluations of multiple strategies and both input and output indicators remain scarce. In the water and wastewater sector, the development of separate models for resource-related input indicators and performance-related output indicators, along with the simulation of their interactions, remains limited. The present study addresses this gap by introducing two distinct models and analyzing the combined effects of improvement strategies, thereby providing an innovative and context-specific framework for comprehensive performance assessment in this sector.MethodologyThis descriptive-analytical study employed a system dynamics approach to examine the long-term effects of 14 improvement strategies on key performance indicators in water and wastewater companies. Eight input indicators (accounts receivable period, per-capita subscriber debt, non-revenue water, and labor cost share) and output indicators (asset turnover ratio, per-capita subscriber coverage, market growth, and employee professionalism) were identified based on the literature and expert opinions. Two system dynamics models were developed, encompassing financial, operational, and human resource subsystems. Reinforcing loops represented the positive effects of network expansion, reduction of non-revenue water, and employee motivation, while balancing loops captured the moderating effects of accounts receivable management and constraints associated with sales growth. These models enabled the simulation of improvement scenarios and the identification of key leverage points affecting system performance.ResultsUsing Sterman’s five-step modeling process (2000), input and output system dynamics models were simulated for the selected indicators. Variable relationships were established based on financial data, industry standards, water and wastewater regulations, and the opinions of ten experts to evaluate the long-term effects of improvement strategies on key company performance metrics.Structural and behavioral tests confirmed the validity of the models, and sensitivity analysis showed that the provincial pricing coefficient had the greatest impact on net sales, with a ±20% change resulting in approximately a 20% change in sales. In contrast, other key parameters, such as the conversion rate of unauthorized connections and government funding allocation, had minimal effects (less than 0.1%) on the growth rate of service units. These results indicate that the models are stable with respect to input variations, with the primary sensitivity associated with pricing policies.Eight scenarios were developed: four aimed at improving operational efficiency and reducing financial constraints, and four targeting productivity enhancement and market growth. Simulation of the input model showed that the simultaneous implementation of incentive/punitive strategies, private sector involvement, and network expansion reduced accounts receivable issues, controlled resource losses, and optimized labor costs. The output model demonstrated that a combination of developmental actions, tariff adjustments, and human resource empowerment improved asset productivity and service coverage, stabilized market growth, and enhanced employee skills and expertise. Overall, the results indicate that the coordinated and targeted application of strategies creates an optimal balance between short-term efficiency, financial sustainability, and long-term development. The developed models provide water and wastewater managers with a systematic tool to support strategic decision-making and evaluate long-term impacts.DiscussionThis study demonstrated that, through system dynamics modeling, the simultaneous interaction of managerial, financial, and infrastructure strategies significantly affects the efficiency and sustainability of water and wastewater companies. Separating input and output indicators and designing distinct models enabled the analysis of the combined effects of 14 improvement strategies, showing that enhancements in accounts receivable management, reduction of resource losses, and optimization of labor costs were accompanied by increased asset productivity, network expansion, and improved employee skills. The results confirmed that a combination of incentive-based strategies, financing of smart technologies, and infrastructure development can effectively balance short-term objectives with long-term goals. Financial resource constraints and external factors, such as inflation and demand fluctuations, emphasize the importance of active management and complementary policies. Overall, the findings indicate that successful management in these companies requires an integrated and synergistic approach across human capital, financial structure, and infrastructure.ConclusionThis study confirmed the effectiveness of the system dynamics approach in analyzing and predicting the long-term effects of improvement strategies in water and wastewater companies. The developed models, by simulating the complex interactions among 14 strategies and key performance indicators, enable evidence-based decision-making with a systems perspective. The results showed that a combination of managerial actions, infrastructure development, and human capital empowerment not only enhances operational efficiency but also strengthens financial sustainability and service accessibility. This study recommends that companies focus on integrated strategies and continuous performance monitoring, while future research should consider the impact of external factors such as climate change and emerging technologies in future models. Overall, this study offers a practical and context-specific framework for comprehensive performance improvement in water and wastewater companies.
modeling and simulation
Faezeh baliagha; Zeinolabedin Sadeghi; Sayyed Abdolmajid Jalaee
Abstract
The decline in total factor productivity (TFP) poses a significant economic challenge globally, with resource misallocation among firms being a key driver. This study investigates the impact of resource misallocation on productivity in Iranian industrial workshops with ten or more employees, using data ...
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The decline in total factor productivity (TFP) poses a significant economic challenge globally, with resource misallocation among firms being a key driver. This study investigates the impact of resource misallocation on productivity in Iranian industrial workshops with ten or more employees, using data from the Statistical Center of Iran (2011–2020). Employing microeconomic data analysis, we assess physical productivity and value added across industries. Results indicate that the chemical, non-metallic mineral, and petroleum products industries exhibit the highest productivity and value added, while the leather, clothing, and machinery repair sectors perform the lowest. Eliminating production and capital distortions significantly improves high-yield industries, highlighting the need for resource redistribution. Policy recommendations include supporting low-yield industries, investing in research and development, enhancing energy and transportation infrastructure, and offering tax incentives to boost productivity and foster sustainable economic growth.
Introduction
Over recent decades, many countries, including Iran, have faced declining total factor productivity (TFP), contributing to slower macroeconomic growth. Recent studies emphasize the role of microeconomic factors, particularly the misallocation of resources among firms, in driving this decline (Gopinath et al., 2017). Resource misallocation reduces aggregate productivity by preventing the optimal allocation of production factors, such as labor and capital, across firms (Zhang et al., 2023). Efficient reallocation can enhance output without requiring additional inputs.
While existing research highlights the macroeconomic impacts of resource misallocation, there is a gap in understanding its effects on micro-level productivity, particularly in Iran’s industrial workshops. This study addresses this gap by analyzing the impact of suboptimal resource allocation on the productivity of industrial workshops with ten or more employees, using data from 2011 to 2020.
Research Question
How does suboptimal resource allocation affect the productivity growth of industrial workshops with ten or more employees in Iran?
Methodology
This study employs theoretical models, including Cobb-Douglas and CES production functions, to estimate total factor productivity (TFP) and assess the impact of resource misallocation. Key variables include labor wages, value added, physical capital, and industry-specific data (four-digit ISIC codes). Microeconomic data from the Statistical Center of Iran for industrial workshops with ten or more employees (2011–2020) were analyzed to evaluate productivity and efficiency. The analytical framework calculates physical productivity and compares actual output to efficient output to quantify the effects of distortions in resource allocation.
Results
The analysis reveals significant variation in total factor productivity (TFP) across industries after removing distortions. The chemical, non-metallic mineral, and petroleum products industries demonstrate the highest TFP, reflecting substantial misallocation in these sectors. The average physical productivity, absent distortions, is approximately 5 trillion IRR. Industries such as electrical equipment, unclassified machinery, and computer and electronics surpass this average, with productivity ranging from 60 to 110 trillion IRR. Conversely, industries like rubber and plastics, metal products, pharmaceuticals, textiles, tobacco, transportation, paper, wood, furniture, and beverages fall below the average, with productivity between 0.1 and 5 trillion IRR. The leather, clothing, and machinery repair sectors exhibit the lowest productivity, averaging 10 billion IRR, indicating a need for targeted interventions.
Production efficiency, measured as the ratio of actual to efficient output, peaked in 2013 at 76.82 million IRR, followed by 2018 (37.36 million IRR), 2019 (20.68 million IRR), and 2016 (17.02 million IRR). The lowest efficiency was recorded in 2011 (1.29 million IRR) and 2012 (9.51 million IRR), with an overall average of 22.06 million IRR, suggesting persistent inefficiencies throughout the study period.
Conclusion
This study examines total factor productivity (TFP) and the impact of resource misallocation in Iranian industrial workshops with ten or more employees from 2011 to 2020. Findings confirm that industries such as chemicals, non-metallic minerals, and petroleum products achieve the highest productivity when distortions are eliminated, while leather, clothing, and machinery repair industries lag significantly, with an average productivity of 10 billion IRR. The average physical productivity, free of distortions, is approximately 5 trillion IRR, though high-performing industries like electrical and computer equipment far exceed this (60–110 trillion IRR).
Production efficiency peaked in 2013 (76.82 million IRR) but remained below the period average (22.06 million IRR) in most years, with 2011 and 2012 showing the lowest performance. Resource misallocation, particularly in capital, consistently undermined industrial productivity, with 2013 exhibiting the greatest losses due to regulatory and investment inefficiencies. A notable TFP decline in 2014 (–6.91%) compared to countries like China and India underscores Iran’s structural challenges.
Eliminating investment-related distortions yields greater TFP improvements (e.g., 8% in 2016) than removing production distortions (3% in 2016). A strong correlation (r = 0.75) between these factors suggests that reforms in one area can enhance the other. To improve productivity, policies should prioritize efficient resource allocation, support high-TFP industries, reduce market entry barriers, promote small businesses, and curb monopolies. Investments in technology, workforce training, and innovation centers are critical, alongside modern management systems, improved energy and transportation infrastructure, and tax incentives for technology-driven industries. Continuous evaluation and stakeholder engagement are essential for effective implementation.
Future research could explore the impact of resource misallocation on productivity across Iran’s provinces, offering a regional perspective on industrial performance.
supply chain management
sima zinalpour; Saeed Yaghoubi; Tahmomoures Sohrabi
Abstract
The highly accurate difference between nonlinear Bayesian averaging models and classical models indicates the failure of classical models. Classical models do not have the ability to determine the optimal model and always follow a predetermined pattern. Accordingly, to improve this gap, a hybrid of nonlinear ...
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The highly accurate difference between nonlinear Bayesian averaging models and classical models indicates the failure of classical models. Classical models do not have the ability to determine the optimal model and always follow a predetermined pattern. Accordingly, to improve this gap, a hybrid of nonlinear Bayesian averaging models and panel time-varying parameters was used to model the risk of the automotive industry. The time period of the present study is from 2011 to 2023, and in this study, information from 57 companies active in the automotive industry on the Tehran Stock Exchange was analyzed. A total of 119 risks affecting the automotive industry supply chain were identified. Based on nonlinear Bayesian averaging approaches, 15 unsystematic risk indicators and 13 systematic risks were identified as the most important risks in the supply chain. After identifying the factors, an attempt was made to examine these factors over time in the automotive industry supply chain based on the TVP-PFAVR approach. Given that the significant proportion of systematic risk in the supply chain is higher than that of unsystematic risk, the stability of the economic and business environment, good governance, and political environment should be on the agenda for management stability. In fact, stabilization policies in the form of demand-side policies, including monetary-fiscal policies, should be included in the Central Bank's mandate to reduce the economic-financial risks of the automotive industry supply chain. Given the significance of financing constraint indicators in creating systematic risk, ranking automotive industry companies is strongly recommended to optimally allocate financial resources among these companies.
Introduction
Today, uncertainty is increasing and change is occurring rapidly; disruptions are imminent. All markets and industries may experience different types of disruption. Supply chain disruptions are unplanned events that may occur and affect the normal (or expected) flow of materials (Ghadir et al., 2022). These disruptions may occur at one level of a supply chain and quickly spread throughout the entire supply chain or even to other supply chains (Rezaei-Vandchali et al., 2020). The critical effects of disruptions on the performance of supply chains prompt researchers to focus on the management of supply chain disruptions and identify a wide range of risks (Sharma, 2021).
In this regard, supply chains have realized that in order to have a competitive advantage in the long term, they should improve their abilities to respond to and reduce a wide range of supply chain risks (Barianis et al., 2019). Therefore, the identification of supply chain risks increasingly attracts the attention of academics and professionals in industry, because identifying, evaluating, reducing, and monitoring possible disruptions in the supply chain leads to reducing the negative impact of risk events on supply chain operations (Munir, 2020; Yang et al., 2021).
The automobile industry is one of the important sectors of the national economy, and its proper performance can lead to sustainable economic development. In fact, among the country's industries, the automobile industry is known as a primary industry, and due to the issue of sanctions, its supply chain is facing crises and various risks as a result. Considering these issues, the need to design a supply chain risk management system that is in harmony with the characteristics of this industry is felt more than ever.
Methods
This research belongs to the category of analytical applied research. In this article, in order to determine the factors affecting the supply chain, systematic factors and non-systematic factors affecting the supply chain are obtained. A complete list of variables affecting systematic and non-systematic risks in the automotive industry on the Tehran Stock Exchange is provided for calculation in estimation models. In this research, the information of 57 companies in the automotive and parts manufacturing industry, active in this industry, has been used. Considering that the types of risks affecting the supply chain of the automobile industry affect the activities and financial ratios of the company, this article identifies the important economic risks. Unlike previous research that generally used survey tools, in the current paper, real information from these companies is used.
Results
Based on theoretical and empirical foundations, 119 risks are identified in the form of non-systematic risks and systematic risks. After identifying the factors, an attempt was made to examine these factors over time in the automotive industry supply chain based on the TVP-PFAVR approach. In this article, based on nonlinear Bayesian averaging approaches, 15 unsystematic risk indicators and 13 systematic risks were identified as the most important risks to the supply chain.
Conclusion
The purpose of the current research was to present a model for the supply chain risks of automotive industries listed on the Tehran Stock Exchange using approaches based on Bayesian averaging. The period of the current research was from 2011 to 2023. In this research, the information of 57 companies active in the field of the automobile industry on the Tehran Stock Exchange was used. In order to determine the optimal model, Bayesian averaging and weighted least squares were used. Out of 119 risks, based on nonlinear Bayesian averaging approaches, 15 unsystematic risk indicators and 13 systematic risks were identified as the most important risks to the supply chain.
