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 Gilan Province, examining the extent of Industry 4.0 technologies' implementation ...
Read More
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 Gilan 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 Gilan 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.
uncertainty
Iman Ebrahimi; Hadi Mokhtari; Mohammad Taghi Rezvan
Abstract
The cryptocurrency market, known for being one of the most volatile financial markets, has recently attracted significant attention. While some investors avoid entering this market due to fear of losses, others are eager to pursue substantial profits. This research employs scientific analysis and advanced ...
Read More
The cryptocurrency market, known for being one of the most volatile financial markets, has recently attracted significant attention. While some investors avoid entering this market due to fear of losses, others are eager to pursue substantial profits. This research employs scientific analysis and advanced models to examine the characteristics of this market. In the first phase, the focus is on portfolio formation in the cryptocurrency market based on relevant indicators. After evaluating the created portfolio, Markowitz, Sharpe and Sortino models are utilized for portfolio optimization. This approach allows for the creation of a portfolio that aligns with individual risk preferences, and passive portfolio management is also considered. In the second phase of the research, equal-weighted portfolios were formed alongside a market portfolio, and nine other selected portfolios were chosen based on criteria such as highest market value and best risk-return ratio. A particle swam optimization algorithm was employed to assess the performance of these portfolios. The results indicate that portfolios containing cryptocurrencies with the highest market value exhibit lower risk, and the Sharpe optimization model outperforms other models. Additionally, selecting a portfolio based on the standard deviation of return-to-risk ratio yields more favorable outcomes. This study also presents an innovative method for analyzing the coefficient of variation, leading to a better understanding of the relationship between return and risk. Ultimately, the findings emphasize that utilizing scientific trading strategies can facilitate risk management and enhance returns.
multiple-criteria decision-making
Ali Memarpour Ghiaci; morteza abbasi; Jafar Gheidar-Kheljani
Abstract
Suppliers 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 ...
Read More
Suppliers 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.
project management
Mohammad Amin Dorosti; Farhad Saiedi; Saied Yousefi
Abstract
Qualified contractors are the main pillar of sustainable construction projects and the technical arm of project implementation, that play a significant role in achieving sustainable development (economic, social, and environmental) goals. Evaluating competence and selecting the best contractor are among ...
Read More
Qualified contractors are the main pillar of sustainable construction projects and the technical arm of project implementation, that play 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 of competence and select contractors for sustainable construction projects from the perspective of employer companies. This study is applied in terms of purpose and qualitative research based on data type. In this study, the construction industry in Kish Island is studied, and an expert panel including project managers and experts from employer companies is formed, whose sampling was carried out in a non-random 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 for evaluating candidate contractors in tender meetings, carrying out this process in sustainable construction projects requires a comprehensive approach from the decision-makers of the employer companies and their simultaneous attention to 12 key criteria that are categorized in the three economic, social and environmental dimensions.
modeling and simulation
Faezeh baliagha; Zeinolabedin Sadeghi; Sayyed Abdolmajid Jalaee
Abstract
In recent decades, the decline in total factor productivity (TFP) has become an economic challenge in many countries.One of the main drivers of this decline is the misallocation of resources among firms, which has a significant impact on economic performance. This research aims to investigate the extent ...
Read More
In recent decades, the decline in total factor productivity (TFP) has become an economic challenge in many countries.One of the main drivers of this decline is the misallocation of resources among firms, which has a significant impact on economic performance. This research aims to investigate the extent of profit or loss resulting from the misallocation of production factors, using data from the Statistical Center of Iran for workshops with ten or more employees from 2011 to 2020. The research methodology is based on the analysis of microeconomic data and, by calculating physical productivity and value added, the status of various industries has been evaluated. The findings show that the chemical, non-metallic mineral, and petroleum products industries have had the highest productivity and value added, while the leather, clothing, and machinery repair industries have recorded the lowest. Also, the elimination of production and capital deviations has created the most improvement in high-yield industries and has shown the need for resource redistribution. In this regard, supporting low-yield industries, investing in research and development, improving energy and transportation infrastructure, and providing tax incentives are proposed. These measures can increase the productivity and competitiveness of industries and contribute to sustainable economic growth
modeling and simulation
Nahid Foroughi; Mahmoud Moradi; Keikhosro Yakideh; MohammadRahim Ramazanian
Abstract
This study investigated the impact of improvement strategies on key performance indicators in the Water and Wastewater industry using a System Dynamics approach. The main objective was to develop models for simulating the complex relationships between strategies and performance variables to enhance sustainability ...
Read More
This study investigated the impact of improvement strategies on key performance indicators in the Water and Wastewater industry using a System Dynamics approach. The main objective was to develop models for simulating the complex relationships between strategies and performance variables to enhance sustainability and performance management. Performance indicators were divided into two categories: input indicators (including the accounts receivable period, per capita debt of subscribers, non-revenue water, and the share of labor costs to sales) and output indicators (such as asset turnover ratio, per capita subscriber coverage, market growth, and employee professionalism). Based on the existing literature, policy documents, reports from Water and Wastewater companies, and expert opinions, 14 improvement strategies were identified and defined to enhance performance. Subsequently, two system dynamics models were designed to analyze these indicators, with mathematical functions defined for each variable. The results showed that the application of these strategies led to a reduction in the accounts receivable period and the share of labor costs to sales, as well as improvements in the asset turnover ratio, per capita subscriber coverage, market growth, and employee professional qualifications. This research proved that system dynamics modeling is a valuable tool for strategic decision-making and performance enhancement in the Water and Wastewater sector.
supply chain management
sima zinalpour; Saeed Yaghoubi; Tahmomoures Sohrabi
Abstract
The high accuracy difference of nonlinear Bayesian averaging models compared to 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 ...
Read More
The high accuracy difference of nonlinear Bayesian averaging models compared to 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 has been used to model the risk of the automotive industry. The time period of the present study was from 2011to2023, and in this study, information from 57 companies active in the automotive industry on the Tehran Stock Exchange was used. 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 to 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 unsystematic risk, the stability of the economic and business environment, good governance, and political environment should be on the agenda over 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 to these companies.
