Industrial management
Maghsoud Amiri; HamidReza Talaie; Shahab Bayatzadeh
Abstract
In an era of intensifying global competition and unprecedented environmental, economic, and technological changes, organizations require a high level of readiness to adopt Industry 5.0-based technologies and business models. This study aims to investigate the impact of Industry 5.0 readiness on sustainable ...
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In an era of intensifying global competition and unprecedented environmental, economic, and technological changes, organizations require a high level of readiness to adopt Industry 5.0-based technologies and business models. This study aims to investigate the impact of Industry 5.0 readiness on sustainable business growth, considering the mediating roles of efficiency, responsiveness, and competitive advantage, with a case study of Mobarakeh Steel Company in Iran. Despite the growing body of research on Industry 5.0, a literature review indicates that the impact of Industry 5.0 readiness on efficiency, responsiveness, competitive advantage, and sustainable business growth has not yet been examined within an integrated framework. The research is applied in nature and follows a descriptive-correlational design. Data were collected using a standardized questionnaire from a purposive sample of 105 employees. The validity of the instrument, including content, convergent, and discriminant validity, as well as its reliability, was confirmed. The data were analyzed using structural equation modeling (SEM). The results revealed that Industry 5.0 readiness significantly enhances organizational efficiency and responsiveness. These two factors, in turn, strengthen competitive advantage and ultimately lead to sustainable business growth. Moreover, efficiency and responsiveness were found to mediate the relationship between Industry 5.0 readiness and competitive advantage. These findings offer practical guidance for industrial managers aiming to strategically transition toward Industry 5.0 and effectively leverage emerging technologies through a human-centric approach to achieve sustainable growth.
Industrial management
Bahareh Deljoo; Rohollah Ghasemi; Mohsen Moradi moghadam; Ali Mohaghar
Abstract
The food industry has been transformed by the Fourth Industrial Revolution, particularly through the application of the Internet of Things (IoT). This technology enhances efficiency by connecting various components of the factory—both wired and wirelessly—and paves the way for smart factories ...
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The food industry has been transformed by the Fourth Industrial Revolution, particularly through the application of the Internet of Things (IoT). This technology enhances efficiency by connecting various components of the factory—both wired and wirelessly—and paves the way for smart factories aligned with sustainability goals. The aim of this research is to analyze the capability–attractiveness of IoT applications in the food industry based on sustainability indicators and the readiness of selected companies in the food industry of Tehran province to implement these technologies. First, a systematic literature review was conducted to identify relevant IoT applications in the food industry, along with sustainability-based attractiveness indicators and capability criteria. The case study are selected companies in the food industry of Tehran province and its subsidiaries, which are currently deploying IoT technologies across various areas. Using the Best-Worst Method (BWM), the weights of the indicators were determined. Then, decision matrices were developed separately for evaluating the applications based on attractiveness (sustainability) and capability indicators, and each application was scored accordingly. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was then used to obtain final rankings. Based on the capability–attractiveness matrix, the most promising IoT applications identified for implementation in the company include “real-time data collection,” “inventory management and shelf replenishment,” “energy consumption management,” and “smart fire detection systems.” The findings offer valuable insights for identifying and adopting IoT applications in the food industry, considering the capacities and infrastructure of companies.
multiple-criteria decision-making
Mojtaba Farrokh
Abstract
Nowadays, despite the growing awareness of evaluating suppliers based on sustainability aspects, there are still limitations in selecting suppliers according to sustainable performance due to the lack of a comprehensive list of sustainability criteria and well-developed methods for assessing them. Given ...
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Nowadays, despite the growing awareness of evaluating suppliers based on sustainability aspects, there are still limitations in selecting suppliers according to sustainable performance due to the lack of a comprehensive list of sustainability criteria and well-developed methods for assessing them. Given the complexity and uncertainty surrounding these criteria and the supplier evaluation process, along with the need for high precision and sensitivity, this study aims to apply a rough-fuzzy DEMATEL-TOPSIS approach to rank meat and livestock suppliers. This hybrid method is designed to manage both internal and external uncertainties as well as the complexity of sustainability criteria. In the first phase, the rough-fuzzy DEMATEL method is used to determine the weights and interrelationships among the criteria. In the second phase, the rough-fuzzy TOPSIS method is employed to rank the suppliers. The applicability of this approach is examined through a case study in the meat supply chain. The results reveal that five criteria—cost, livestock health and meat freshness, impact on the local community, delivery reliability, and technological capability—are the most influential factors in selecting sustainable suppliers.
uncertainty
Faramarz Nouri; Alireza Fazlzadeh; Seyedsamad Hosseini; Samad Rahimiaghdam
Abstract
This research aims to design a model for the internationalization of companies operating under uncertainty, seeking to address the shortcomings of existing one-dimensional approaches that ignore the dynamic and behavioral complexities of stakeholders. This research is applied and developmental in terms ...
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This research aims to design a model for the internationalization of companies operating under uncertainty, seeking to address the shortcomings of existing one-dimensional approaches that ignore the dynamic and behavioral complexities of stakeholders. This research is applied and developmental in terms of its purpose, and mixed and exploratory in terms of its type. Data were collected through interviews and questionnaires from managers and experts involved in the internationalization process of small and medium-sized companies, who were selected through purposive sampling. The final model was developed using fuzzy soft systems methodology and fuzzy structural interpretive modeling. The final model identifies key dimensions for the internationalization of companies under uncertainty. These dimensions include: monitoring macro trends, developing a technology ecosystem, financial and advisory support, developing standards, managing stakeholder relationships, developing specialized training centers, advanced intelligent systems for forecasting, developing flexible entry strategies, networking and strategic alliances, optimizing product quality and processes, and managing supply chain complexity. The presented model serves as an analytical tool for a deeper understanding of the complexities of internationalization in small and medium-sized enterprises and for more effective policymaking in the face of uncertainties in the business environment.
multiple-criteria decision-making
mahdi mashhadikhani; alireza poorebrahimi; mostafa moballeghi
Abstract
Artificial intelligence, through process optimization, productivity enhancement, and cost reduction, has created a significant transformation in production, management, and innovation. Accordingly, the present study aims to examine the mutual effects of parameters influencing the adoption of AI-based ...
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Artificial intelligence, through process optimization, productivity enhancement, and cost reduction, has created a significant transformation in production, management, and innovation. Accordingly, the present study aims to examine the mutual effects of parameters influencing the adoption of AI-based technologies: A case study of Kerman Motor Company. The research method is applied in terms of purpose and survey-based in terms of data collection. Data were gathered through the distribution of 130 questionnaires among the employees of Kerman Motor Automotive Company, selected by simple random sampling using Cochran’s formula. The measurement instruments were the technology adoption questionnaires developed by Chatterjee et al. (2021) and Shon & Vawn (2020). For data analysis, structural equation modeling (SEM) was employed. The findings indicated that employees’ subjective norms have a positive and significant effect on perceived usefulness and perceived ease of use of AI-based technologies in the automotive company. Perceived usefulness positively and significantly affects employees’ behavioral intention and attitudes toward the use of AI-based technologies. Perceived ease of use positively and significantly influences employees’ attitudes toward the use of AI-based technologies, and attitudes positively and significantly influence behavioral intention. Finally, behavioral intention to use AI-based technologies in the automotive company has a positive and significant effect on actual use. Overall, the results revealed that the effect of all research variables was positive and significant, and all research hypotheses were supported.
project management
Roya Soltani; Ali Nobakhti
Abstract
The performance of a project is influenced by multiple factors, the variations of which can lead to delays in execution, cost overruns, and reduced quality. The competence of the project manager in applying both soft and hard skills plays a crucial role in the successful completion of projects in today’s ...
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The performance of a project is influenced by multiple factors, the variations of which can lead to delays in execution, cost overruns, and reduced quality. The competence of the project manager in applying both soft and hard skills plays a crucial role in the successful completion of projects in today’s volatile, uncertain, complex, and ambiguous (VUCA) environment. Therefore, the design and development of a data-driven recommender system that can replace subjective judgments in the process of selecting qualified project managers, and accurately propose and recommend suitable candidates, appears to be both essential and inevitable. The recommender system proposed in this paper is based on multilayer feedforward neural networks trained with the Levenberg–Marquardt backpropagation algorithm, while employing a genetic algorithm to optimize the structure and weights of the neural network. The results demonstrate that the optimized neural network–based recommender system can identify and recommend qualified project managers with high accuracy. Compared with the non-optimized neural network, it achieves improved performance by reducing the MSE (from 0.351 to 0.094) and RMSE (from 0.593 to 0.307). This reduction in error indicates that the genetic algorithm effectively fine-tunes the neural network parameters, thereby enhancing system performance and enabling more accurate identification of competent project managers.
supply chain management
Maryam Hosinie; davood andalib ardakani; Alireza Naser Sadrabadi; Seyed Mojtaba Hosseini Bamkan
Abstract
Despite the fact that organizations equipped with Industry 4.0 technologies hold great potential for enhancing supply chain resilience and expanding organizational performance—enabling them to better cope with crises and disruptions—the systematic integration of these two domains has faced ...
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Despite the fact that organizations equipped with Industry 4.0 technologies hold great potential for enhancing supply chain resilience and expanding organizational performance—enabling them to better cope with crises and disruptions—the systematic integration of these two domains has faced delays and represents a research gap. The present study aims to identify and analyze the causal relationships among factors influencing the implementation of Industry 4.0 in resilient supply chain management within the Yazd steel industry. In the first phase of the study, factors were identified and categorized using the qualitative meta-synthesis method, resulting in the identification of 21 factors across five dimensions. In the second phase, the fuzzy DEMATEL method was employed to determine the causal relationships among the identified factors. The results revealed that the managerial-institutional dimension is the most influential, while the operational dimension is the most influenced in this process. Furthermore, among the factors, "commitment and support of top management for the implementation of Industry 4.0 technologies" in the managerial-institutional dimension; "investment in Industry 4.0 technologies" in the technological-structural dimension; "effective collaboration and communication among supply chain partners and stakeholders" in the organizational dimension; "development of speed capability for a resilient supply chain" in the operational dimension; and "government support for the adoption of Industry 4.0 technologies" in the cultural dimension were identified as the most influential factors for implementing Industry 4.0 in resilient supply chain management in the Yazd steel industry.This study provides a framework for evaluating and developing the link between resilient supply chain and Industry 4.0.
