فصلنامه مطالعات مدیریت صنعتی
پیاپی 71 (زمستان 1402)

In today's industrial units, operators monitor equipment performance, and the challenging coordination between units in vast operating environments with high volumes of equipment can lead to irreparable damage. Despite considerable technological advancements in inspection and surveillance, this responsibility can be effectively delegated to smart devices and the Internet of Things (IoT). Furthermore, the emergence of "edge computing" technology has prompted researchers to explore edge-based computing designs due to their numerous benefits. This study presents a combined model of IoT and civilian drones for intelligent monitoring of industrial equipment performance, employing an edge computing approach. The model is specifically investigated through a case study involving wind turbines. The model evaluates the performance of drones for intelligent monitoring of wind turbines in three stages: 1) Detection process, 2) UAV computational evacuation process, and 3) UAV local computation process. Given the dual purpose of the final model, which involves a combination of the aforementioned three steps, a genetic method was employed for problem-solving with negligible sorting. The amplified epsilon restriction method, utilizing random numbers, was also considered, but the combination of genetic and negligible sorting methods outperformed it, particularly in large problems where the enhanced epsilon restriction method struggled to provide timely responses due to the inherent complexity of the problem.

The purpose of this article is to design a model for improving the sustainable performance of small and medium food companies in Guilan Province. The aim is to provide managers and decision-makers with insights into the factors, challenges, and consequences associated with enhancing the sustainable performance of these companies in the current business environment. The article falls under the category of applied research in terms of its purpose, exploratory-descriptive research in nature, and field studies based on data collection. It is classified as qualitative research. The Grounded Theory method was employed to create a paradigm model, progressing through three stages of open, axial, and selective coding. The results of data analysis using Grounded Theory led to the identification of 13 main floors, 29 main categories, and 248 concepts. The paradigm model, developed through theoretical coding, encompasses six main classes: pivotal phenomenon (sustainable performance improvement), causal conditions (organizational survival, social and environmental obligation, gaining competitive advantage), contextual conditions (environmental factors, organizational factors), interfering conditions (environmental factors, organizational factors), and strategies (macro level, organizational level). Finally, the economic, social, and environmental consequences of achieving sustainable performance improvement were derived through the implementation of these strategies.

In this study, a comprehensive classification for supply chain risks in the pharmaceutical industry is presented using the Bailey’s classical strategy method and the four-stage Collier method. Initially, through the examination of texts related to the main hazard groups, supply chain elements, considering resources and functions, and categorizing upstream supply chain organizations, primary industry, and downstream supply chain organizations within the industrial and market environment, infrastructural environment, and external macro environment were modeled. In the next stage, criteria related to the security and safety of the supply chain were identified. In the final stage, a two-dimensional matrix classification for the identification of supply chain risk factors was proposed through the cross-tabulation of supply chain elements with security and safety criteria. Based on this classification and utilizing the exemplification method through a synthetic framework, a detailed list of risk factors was compiled. The aim of this study is to propose a comprehensive risk classification for pharmaceutical industries.

Bailey’s classical strategy method has been used to develop a comprehensive classification of supply chain risks in pharmaceutical industries. In order to review the existing knowledge about supply chain risk groups, a systematic review of literature was performed. In the first stage, to find articles related to supply chain risks in the pharmaceutical industry, different combinations of related keywords have been used to search for articles in relevant databases. The selected articles were examined in three stages: extracting and classifying the main risk groups of the supply chain (the first dimension of the conceptual framework of classification), extracting and classifying criteria for a low-risk supply chain (the second dimension of the conceptual framework of classification), and applying the two-dimensional framework of classification to identify and classify risk factors of the supply chain.

A total of 77 articles were selected for review. Based on the analysis of these articles, 83 risk groups were identified. These risk groups were arranged into a model including upstream supply organizations, the main industry, and downstream supply organizations, considering the relationships between supply chain’s resources, functions, and outcomes in the industry and market environment, infrastructural environment, and external macro environment. In the next step, 30 criteria for a safe and secure supply chain were identified. These criteria are divided into two general categories: criteria for the security of the internal supply chain environment (criteria of resistant supply chain resources and criteria of resilient supply chain functions) and criteria for the safety of the external supply chain environment (criteria of safety of market and industry, criteria of safety of infrastructural environment, and criteria of safety of external macro environment). In the last stage, through cross-tabulation of resource groups with resource resistance criteria, function groups with function resilience criteria, and peripheral environment elements with peripheral environment safety criteria, a model for identifying risk factors in the industrial environment was proposed. Based on this model, 372 risk factors of the supply chain of the pharmaceutical industry were identified.

In this study, a new classification for supply chain risks of the pharmaceutical industry has been presented. The proposed classification is highly comprehensive, and the number of risk groups counted in this study is more than all the studies that have been done in this field so far. Most existing risk taxonomies are incomplete and do not follow a specific theoretical model. The classification of risk groups identified in this study has been done based on a model that considers the relationship between assets, functions, and outcomes of the supply chain. The risk groups identified in this study cover from the upstream of the supply chain to the main industry and the downstream of the supply chain. Many risk taxonomies focus on the pharmaceutical industry and do not cover the entire supply chain from raw material production to customers. In this study, cross-tabulation of resource groups with resource resistance criteria, function groups with function resilience criteria, and peripheral environment elements with peripheral safety criteria create an ideal model for identifying risk factors in the industrial environment. The classification proposed in this study can be used to evaluate the resistance and resilience of the supply chain. This model can also provide a suitable basis for identifying and evaluating risks in the supply chain environment. In addition, results of this study provide a very practical guide for choosing supply chain risk management strategies.

The current study aimed to cluster the application of digital technologies from Industry 4.0 in the agricultural food distribution network. To achieve this goal, a bibliometric technique was employed to identify prominent trends and themes in this field through the analysis of articles, authors, countries, and co-citations of authors and bibliographic pairs. Through an extensive search in the Scopus scientific database, bibliographic information for 331 valid and relevant scientific articles was acquired. This information was inputted into the bibliometric package in R software, and the most influential journal, author, university, country, and most cited authors were determined. To visualize the information, Vosviewer software was utilized for co-citation analysis of authors, cited references, and bibliographic pairs. The findings from the network analysis revealed that the studies on the application of digital technologies in the agricultural food distribution network can be categorized into five main clusters.

This paper presents a multi-objective mathematical model for the reverse supply chain of hospital waste management in Iran during the COVID-19 pandemic, incorporating dimensions of sustainability. The objectives of the model are as follows: 1) Minimizing the costs associated with building facilities and waste treatment centers, vehicle fuel costs, and environmental costs due to pollutant emissions; 2) Maximizing the energy generated from the waste combustion process; 3) Minimizing the risk of virus transmission resulting from inadequate waste management; and 4) Maximizing the number of job opportunities in the established centers. It is important to note that existing uncertainties are addressed through the application of fuzzy set theory. Given the multi-objective nature of the model, two multi-objective algorithms, namely the Pareto archive-based Krill Herd Algorithm and Non-dominated Sorting Genetic Algorithm II (NSGA-II), are employed to solve the defined problem. The results indicate that the proposed Krill Herd Algorithm converges to a solution with higher quality and dispersion compared to NSGA-II. Additionally, through a comparison of the spacing index and running time of the two algorithms, it is observed that NSGA-II explores the solution space with higher uniformity and solves the model in less time.

Projects, during their execution, face various risks that can impact the achievement of project objectives. Therefore, the need for extensive project risk management is widely recognized. In a systematic risk management process, after risk evaluation, risk analysts are confronted with the risk response phase, where they decide on the actions to be taken regarding identified risks. Hence, designing and implementing a structured approach to manage and mitigate risks will yield beneficial outcomes for successful completion within the desired budget, time, and quality. In conducted studies, a comprehensive approach that integrates the time and cost implications of risks and response strategies has been lacking. In this article, an optimization model of zero-one programming has been employed to select the most suitable risk response strategies for the project. In the developed framework, the modeling of the impact of risks on the time and cost of activities, as well as the effect of implementing risk response strategies on reducing the undesirable time and cost implications of risks, has been utilized to select optimal strategies. Finally, to evaluate the efficiency of the model, an industrial case study was utilized, which confirmed the favorable performance of this framework.

To explain a framework for managing the risk of transportation in the food industry supply chain, the initial step involves identifying 12 risks that can potentially lead to transportation disruptions, based on the research background. Utilizing the Delphi method and gathering opinions from 15 academic and industrial experts across 3 stages, the risks were ultimately defined. Furthermore, expert opinions were sought to determine solutions to address the identified risks. The grey DEMATEL method was employed to investigate the interaction of risks. The findings revealed that weather problems, natural disasters, insufficient skilled labor/labor strikes, infrastructure capacity, and inflation and exchange rate changes are among the risks that exert a more significant influence on other risks than they are influenced by them. Subsequently, using the grey COPRAS method, the prioritization of solutions to mitigate the identified risks, based on expert opinions, was undertaken. The results indicated that the top-ranked solution is the definition of key performance indicators. Therefore, it is recommended to managers that, in order to establish a robust supply chain and proactively manage risks, they should identify stakeholders and critical processes. Afterward, an agreement on the financial flow in each situation should be obtained, and a value flow map drawn. This approach enables the implementation of preventive measures to reduce supply chain risk and facilitates the preparation of an emergency plan for unforeseen conditions, thereby enhancing resilience.