نشریه مدیریت صنعتی
سال شانزدهم شماره 53 (تابستان 1403)

Given the dependencies of key economic sectors, the global steel demand is expected to increase. Therefore, pursuing multiple strategies and aligning with global market developments is essential to enhance and achieve competitiveness in this industry. One of the development approaches in the steel industry, like other technology-based industries adapting to advancements in new technologies, is using a technology transfer approach. This industry is one of the largest and most important industries in the world and plays a very important role in many economic sectors. It can be significantly recognized in global markets as one of the main and powerful players by improving its competitiveness. The main objective of this research is to evaluate the competitiveness model in the steel industry with a technology transfer approach.

This research is applied considering its objective and follows a descriptive survey methodology for data collection. The statistical population of this research includes managers and employees of companies active in the country's steel industry. The data collection tool in this research was a questionnaire distributed randomly among the population. Ultimately, 384 questionnaires suitable for data analysis were collected. The gathered data were analyzed through the structural equation approach and with the help of Smart-PLS software. The model validation in this research was formed comprehensively with an interpretive-structural modeling approach.

The results showed that among the influencing factors, internal factors have the highest impact on competitiveness, while among the contextual factors, political and legal factors have the most significant effect. Additionally, the strategy of investment and cooperation was identified as the most effective for improving competitiveness, potentially yielding important economic, social, and organizational benefits for companies in the steel sector. Consequently, it is suggested that these companies enhance the selection and purchasing power of domestic and foreign customers by diversifying their product offerings, including related and complementary products, to cater to a wide range of needs within the industry.

The petrochemical industry is not in a favorable condition in this regard, and the volume and lines of products and derivatives in the industry are limited despite the available potential. Support from official, governmental, and legislative institutions for stakeholders and investors in the steel sector is crucial. By providing appropriate facilities and subsidies, these institutions can encourage and motivate active and potential investors to expand their production activities. Additionally, it is essential to reduce cumbersome and discouraging laws related to investment and exports. One of the most important drivers for increasing competitiveness among companies in the steel industry is the presence of a supportive atmosphere and attitude from senior company managers. The industry needs to step towards attracting, transferring, and developing existing technologies. Therefore, the presence of expert and capable managers with an entrepreneurial orientation in new technologies can be a crucial factor in developing the steel industry.

Investors typically seek to strike the optimal balance between potential returns and associated risks in their trades. Various models have been presented to choose the optimal portfolio using different approaches. one of these methods is based on the statistical distribution of asset return. In these methods, the type of distribution of returns is first identified, and a suitable portfolio selection method is then applied based on this identified distribution type. This study compares the effectiveness of the mean-absolute deviation-entropy model utilizing both Skew-Normal Distribution and Skew-Laplace-Normal Distribution for constructing an optimal portfolio in the Tehran Stock Exchange over 36 months from April 2018 to March 2020.

The data used in this study comprises the monthly returns of 181 companies listed on the Tehran Stock Exchange. These returns were gathered from a statistical population of 338 members utilizing Morgan's table and Cochran’s formula. After fitting density functions for Skew-Normal and Skew-Laplace-Normal distributions to the returns, maximum likelihood estimates were obtained using the Stats package and the optim Function in R software. The reliability of these estimates was then checked using bootstrap sampling with 1,000 repetitions. Subsequently, relationships corresponding to the mathematical expectation of return distribution and the objective function representing the risk of absolute deviation were estimated using numerical methods. Therefore, this paper aimed to propose a multi-objective optimization model, namely a mean-absolute deviation-entropy model for portfolio optimization by using a goal-programming approach based on Skew-Normal Distribution and Skew-Laplace-Normal Distribution. The objective functions of the model were to maximize the mean return, minimize the absolute deviation, and maximize the entropy of the portfolio.

It can be inferred from the observed values ​​of the descriptive statistics of the monthly stock returns corresponding to the stock exchange symbols that some stocks have different skewness and kurtosis values ​​compared to the normal distribution. For example, The symbol "Shepna" exhibits negative skewness, indicating a left-skewed distribution. Similarly, the distribution of the "Basama" symbol exceeds the normal distribution. These instances suggest that the normal distribution is inadequate for describing monthly return distributions. Instead, distributions with parameters should be employed to account for skewness and kurtosis. According to the obtained results, the model utilizing the Skew- Laplace- Normal distribution has a higher performance ratio than the model based on the Skew-Normal distribution.

The reason for this superiority, where the model utilizing the Skew-Laplace-Normal distribution outperforms the model based on the Skew-Normal distribution, is the incorporation of both skewness and kurtosis criteria within the former. Additionally, upon analyzing the descriptive statistics of the symbols, it's evident that the kurtosis of most stock symbols is substantial. Therefore, integrating a combination of higher-order moments (skewness and kurtosis) along with entropy leads to enhanced performance.

The availability of energy is a vital aspect of a nation’s economic and social development, with energy consumption serving as a telling metric of the level of prosperity that can be achieved. However, the conventional systems of electricity production that rely on large, centralized power plants have become inadequate in recent years due to the high expenses of production, air pollution, and poor energy quality. In response to these challenges, smart grids have emerged and offer several advantages. Effective management of electricity demand is critical in the context of smart grids, and the implementation of demand-response techniques plays an instrumental role in achieving this objective. These programs enhance energy consumption patterns during peak load times, resulting in appropriate pricing and grid reliability. There are two distinct categories of load response programs: price-oriented and incentive-oriented. In the scope of this research, we focused on the former, which relies on real-time pricing. Our objective was to develop a multi-objective mathematical model that considers load response programs for smart energy grids.

The study employed a scenario-based approach and classified the parameters into two distinct categories: deterministic and non-deterministic. Wind speed, solar radiation, energy demand, and local electricity prices were marked as non-deterministic due to their nature. As each non-deterministic parameter adheres to a specific probability distribution, a scenario was created for each parameter based on its corresponding distribution. Subsequently, a mathematical multi-objective model was developed that aimed to minimize operating costs, reduce pollution emissions, and minimize peak load, along with the related constraints. After collecting the required data, the model was run using the GAMS programming language. In addition, the study evaluated the impact of load response programs on enhancing objective functions.

The study findings demonstrate that the implementation of smart grids, accompanied by active consumer participation in load response programs, can result in a significant reduction in operating costs, pollution emissions, and peak load. Additionally, the study indicates that a higher level of consumer participation in load response programs can enhance the overall effectiveness of the programs. Specifically, the study shows that a 20% increase in consumer participation resulted in a 15%, 17%, and 13% improvement in operating costs, pollution emissions, and peak load reduction, respectively.

Smart grids represent a modern digital solution that streamlines the transfer of electricity between suppliers and consumers in the realm of energy transmission. This advanced system enables the regulation of home appliances, promoting energy conservation and cost-effectiveness, while simultaneously enhancing the reliability of the energy transmission network. Governments may opt to implement smart grids as a strategic solution to address complex issues such as energy independence, global warming, and pollution emissions.

Project delays are a prevalent issue in construction projects, particularly in Iran. The project schedule is established and approved after signing the contract and designing the plans, however, most of the project managers cannot complete the project on time and projects often overrun their scheduled completion dates. Generally speaking, the project is "delayed". Various factors contribute to these delays, and extensive research has been conducted to explore their causes. Disagreements between contractors and employers over the causes of delays often result in disputes and negotiations regarding costs and accountability. Consequently, delays are generally a costly problem. In some cases, it has been observed that delays may not be costly for all parties involved and can create conflicts of interest. In addition, project delays have many varied effects that increase the complexity of the problem. When explaining the cause-and-effect relationships of delay impacts, the System Dynamics model illustrates the outcomes of management decisions related to these delays. This paper aims to examine the consequences of construction projects’ delays and the relationships between them from the perspectives of various stakeholders.

This research utilized a combination of qualitative and quantitative methods, as well as nested methods. First, delay effects were gathered from various sources and compared with expert opinions during the initial interview. After categorizing these delay effects, a conceptual model was developed. The case study centered on a construction project in the central Iranian city of Qom. Due to the limitations of the case study, the number of project delay effects was reduced. Relevant variables were defined and incorporated into the System Dynamics model for simulation. Additionally, the perspectives of project actors from a second interview were considered. These viewpoints informed the development of policies and conditions, which were then, analyzed using the System Dynamics mode.

The results of this study revealed that, in inflationary conditions, costs increased exponentially over time. However, employing contract termination could prevent the imposition of more costs in such a situation. Nonetheless, if managers prioritize reputation over cost, they would increase the project’s budget allocation to avoid additional expenses and expedite its completion. Moreover, the simulation of project financing during price fluctuations indicated that the purchase profit at a lower price could compensate for the cost of delays of up to two months. In a speculative scenario, up to 10% of the project budget could be invested in other medium-term projects, with the resulting revenues supporting the primary project.

This paper investigated the assertion that delays are costly in all construction projects. After setting up a list of delays and categorizing them, this paper simulated a System Dynamics model. This study confirmed that delays are almost always costly. However, their impact depends on the managers’ mindset and project conditions. This paper proved that project managers could strategically introduce temporal delays. Specifically, under the two analyzed scenarios, managers could arrange delays such that the benefits from the delay would outweigh its costs.

The geographical positioning of Automated Teller Machines (ATMs) is a pivotal data point that significantly aids in the analytical process and decision-making for a multitude of critical banking and economic determinations. Given the constraints imposed by the insular viewpoint prevalent in the nation’s banking ecosystem, maintaining a consolidated perspective of all ATMs’ geographical locations is not feasible. In this study, we utilized the ATM Location Prediction (ATMLP) algorithm to determine these machines’ geographical coordinates. This data is indispensable and plays a cardinal role in the implementation of a multitude of artificial intelligence algorithms.

The ATMLP algorithm comprises three primary stages. The first stage involves constructing a bipartite user-location graph. The relationship between users is derived from transactional interactions, while the relationship between geographical locations is established using devices with known locations. The second stage involves the computation of two crucial indices: spatial similarity and neighborhood similarity, within the ATM network using the bipartite graph. This stage also includes a time-space distance finding module, which has two steps in its procedure: finding co-located ATMs and then clustering them. Distance-based features are assigned to edges because they reflect the similarity level between the pair of ATMs, nodes connected by edges. The third stage of the algorithm fine-tunes the results for better accuracy. In this process, low-confidence edges are filtered out by leveraging similarity metrics from the previous stage and cosine similarity between pairs of ATMs. In the end, the algorithm reports the geographical latitude and longitude for each ATM, plus the probability score indicating how correct it is.

By leveraging 2100 ATM locations (a portion of the data available in Datis Arian Qeshm Company) and examining 562609790 transactions in four months from the start of April 2022 to the end of July 2022, we identified the location of 4000 existing ATMs across the country belonging to 12 banks. The results obtained indicate a high credibility of the algorithm (80.95%).

In this study, we applied a developed method in banking to predict edges in location-based social networks, and using it, we accurately estimated the geographical coordinates - latitude and longitude - of ATMs on a national scale. Location-based social networks, due to data integration at multiple levels, enable problem-solving that was previously impossible. The use of these methods has less processing cost and higher speed due to the use of algorithms and graph-based databases, and they provide more accurate results. This study has significant implications for future research in banking technology, particularly about location prediction for ATMs.

In recent years, with the expansion of the concept of sustainable development in the life cycle of construction projects and the dynamic and eventful nature of these projects, the issue of sustainable risk management has garnered increasing attention from researchers. However, the lack of systematic reviews and bibliometric analyses of published research in this field has obscured the future perspectives and evolution of this area of study. This study aims to evaluate and synthesize the existing body of literature to identify potential research gaps and delineate the boundaries of knowledge in the risk management of sustainable construction projects (SCPs).

This study is descriptive-analytical and is classified as a systematic review in terms of implementation method. The PRISMA guidelines were adopted for the systematic review of the literature, including descriptive and content analysis. In this process, 1,630 articles published in the Scopus and Web of Science databases from 2015 to 2023 were retrieved and screened, resulting in 113 eligible articles for analysis. To complete the systematic literature review, a bibliometric analysis encompassing co-authorship and keyword co-occurrence analysis was conducted using VOSviewer software for a more comprehensive evaluation of the field of risk management in sustainable construction projects (SCPs).

Regarding the descriptive analysis of the retrieved papers based on the year of publication and their frequency in various journals, it was found that research on risk management in sustainable construction projects (SCPs) has increased in recent years. The content analysis of the articles, based on research themes, indicated that the articles were classified into five main themes: general risks of sustainability, sustainable procurement, supply chain, technology adoption, and human resources. A deeper exploration of the papers revealed that SCPs' risks were primarily categorized into eight dimensions: financial and economic; social; executive, managerial, and organizational; knowledge and skills; governmental and legal; stakeholders/employers; environmental; and technology, equipment, and resources. According to the research methods, experimental and applied research contributions in SCPs' risk management were less prevalent compared to descriptive and review research. This highlights opportunities for conducting applied research that addresses various problems and assumptions. In terms of strategy, a significant number of papers employed the survey method, with some based on case studies. Given that the accuracy and generalizability of survey research results are typically higher than those of case studies, there is a noticeable preference for the survey strategy among researchers. Additionally, researchers often used statistical analysis methods to assess SCPs' risks. According to the bibliometric analysis and the keyword co-occurrence results in the field of SCPs' risk management, it is anticipated that future researchers will focus more on concepts, approaches, and fields such as the circular economy, recycling, risk assessment, factor analysis, leadership in energy and environment, developing countries, and questionnaire surveys.

According to the findings, future articles are expected to focus on completing the puzzle of sustainability risk management systems in sustainable construction projects (SCPs), specifically addressing identification, evaluation, control, and response to risk. This focus should be particularly prominent in developing countries and should be approached through experimental and applied studies utilizing mixed methods and hybrid approaches under uncertainty. Various studies have examined barriers and risks in construction projects from different sustainability perspectives, such as economic, environmental, and social. However, the economic dimension has received more attention from researchers than others, which does not provide a comprehensive assessment of the risks associated with sustainable projects. Such comprehensive studies are crucial for the efficient allocation of resources. The primary contribution of this research to the bibliometric analysis of SCPs' risk management lies in its unique approach to determining the direction of future research.