Industrial management
elham aghazadeh; Akbar Alem Tabriz
Abstract
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 ...
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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. IntroductionToday, in various industries, the productivity and efficiency of equipment contribute to the advancement of production and the profitability of production units. Beyond repair costs, equipment breakdowns also result in the expense of lost opportunities for the production unit. Without a solution to prevent these costs, bankruptcy for production units becomes a real possibility. Therefore, consideration should be given to a solution for the optimal monitoring of equipment. Clearly, swift action is crucial when any equipment is damaged, and such rapid response is unattainable through human effort alone. Despite significant technological advances in inspection and monitoring, this task can be delegated to smart tools and the Internet of Things (IoT). The IoT is regarded as one of the most crucial factors for the prosperity and progress of today's and future industrial businesses. Modernizing equipment is a priority for today's industries to quickly adapt to the evolving market changes and harness existing technologies. Businesses incorporating IoT into their infrastructure experience substantial growth in areas such as security, productivity, and profitability. As the use of industrial IoT increases, productivity levels in industries are naturally expected to rise. The IoT can accumulate massive amounts of information and data, enabling factories and companies to optimize their systems and equipment without being hindered by technological and economic limitations. However, a challenge arises from the substantial volume of data generated by the IoT, which is sent to cloud computing centers for processing. Centralized (cloud) processing results in high communication delays and lowers the data transfer rate between IoT devices and potential users, creating operational challenges in the network. To address this issue, the concept of edge computing has been proposed. Edge computing allows IoT services to process data near their own data sources and data sinks instead of relying on the cloud environment. This approach leads to reduced communication delays and more efficient utilization of computing, storage, and network resources. It also minimizes execution time and energy consumption, proving to be highly beneficial for IoT applications. Consequently, with the advent of "edge computing" technology, many researchers have embraced edge computing-based designs due to its numerous advantages.Materials and Methods In this research, a combined model of the Internet of Things and civilian drones was presented for the intelligent monitoring of industrial equipment, utilizing an edge computing approach. The model was investigated through a case study involving wind turbines. The performance of UAVs for intelligent monitoring of wind turbines was examined in three stages: 1) Detection process, 2) UAV computational evacuation process, and 3) UAV local computing process. Given the dual purpose of the final model, which involved a combination of the aforementioned three steps, the model was addressed using genetic methods with sparse sorting and the enhanced epsilon constraint method employing random numbers. The genetic method with sparse sorting outperformed the enhanced epsilon limit method, particularly in problems with large dimensions. The complexity of the problem made it challenging for the enhanced epsilon constraint method to provide timely responses in such cases.ResultsThe findings of this research offer valuable insights for the effective and accurate management and monitoring of industrial equipment across various industrial units, aiming to optimize costs, quality, and inspection time. Additionally, this research can provide guidance in considering regulatory restrictions in equipment placement before constructing an industrial unit. During the equipment arrangement phase, the model presented in this research can be utilized for optimal energy consumption and time management. As the combined model of the Internet of Things and civilian drones for intelligent monitoring of industrial equipment is a novel concept in the literature, there exist numerous opportunities for further development in this field. This may include the application of the model in additional case studies, such as enhancing the intelligent monitoring of power supply systems, fire services, etc. Moreover, there is potential for refining the mentioned model under conditions where drones operate simultaneously without a specific sequence.ConclusionFailure to monitor industrial equipment properly can result in substantial financial losses for factories and production units. The improper operation of equipment may lead to complete failure, necessitating the need for replacement. Additionally, increased equipment downtime, quality issues, reduced production speed, safety hazards, and environmental pollution can be consequences of equipment failure, ultimately diminishing the profitability of the production unit. Considering factors such as embargoes, emphasis on domestic production, and self-sufficiency, accurate supervision becomes economically crucial for factories.Effective management of the proper operation of industrial equipment is a fundamental requirement for every production unit, given that industrial equipment represents a significant investment for the unit. If device maintenance is limited to repairs only after breakdowns occur, production devices will consistently face unexpected halts, preventing production productivity from reaching its predetermined goals. Therefore, designing a framework for the "intelligent monitoring of the performance of all relevant industrial equipment" stands as one of the most crucial actions for any production unit. Depending on the type of equipment, monitoring the performance of industrial equipment may encompass periodic inspections, maintenance and repair planning, and scheduling the optimal operational time for the equipment
supply chain management
Mehrdad kiani; davood andalib ardakani; Habib Zare Ahmadabadi; Seid Heydar Mirfakhraddini
Abstract
Circular economy and Industry 4.0 are concepts that have garnered significant attention from businesses and universities in recent years. They are currently being promoted by many governments worldwide. The synergy between these two concepts offers the potential to move towards a more sustainable society ...
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Circular economy and Industry 4.0 are concepts that have garnered significant attention from businesses and universities in recent years. They are currently being promoted by many governments worldwide. The synergy between these two concepts offers the potential to move towards a more sustainable society and address the environmental and economic challenges related to managing organizational operations. This research aims to analyze the factors enabling the implementation of circular economy and Industry 4.0 in the supply chain of Yazd glass factories. In the initial phase of the research, a review of various articles was conducted using the meta-synthesis method to identify and categorize relevant enablers. This process resulted in the identification of 15 enablers categorized into four dimensions: economic, human resources, organizational management, and infrastructure. In the subsequent step, the Fuzzy DEMATEL technique was employed to examine the cause-and-effect relationships. The findings indicate that, within the economic dimension, the most influential enablers are "budget allocation for the implementation of circular economy and Industry 4.0" and "stimulation of demand for circular products." In the human resources dimension, "training and development of employees" and "organizational culture" play crucial roles. In the organizational-management dimension, "support and commitment of senior management" and "cooperation and networking with supply chain partners (industrial coexistence)" are highly significant. Lastly, within the infrastructure dimension, "development of information technology standards and infrastructures" and "security and protection of intellectual property rights" are considered the most effective enablers for the implementation of circular economy and Industry 4.0 in the Yazd glass factories. The results indicate that the Ardakan glass factories of Yazd should prioritize attention to economic and infrastructural enablers when implementing circular economy and Industry 4.0.
Introduction
The concept of the circular economy can be regarded as a solution to reduce production costs within a sustainable supply chain. In this context, the integration of cyber-physical systems, big data, data mining, data analytics, the Internet of Things, and novel business models can offer significant opportunities for the creation of sustainable industrial value, value capture, and the promotion of the circular economy (Antikainen et al., 2018). Industry 4.0, often referred to as the future of supply chains, can have numerous implications for sustainability, including the optimal utilization of resources and technology (Quezada et al., 2017). Based on the sustainability axis, the concept of Industry 4.0 aids industrial managers in encompassing not only environmental protection and control initiatives but also aspects of process safety, such as resource efficiency, human resource and societal well-being, and the development of smarter and more flexible supply chain processes (Luthra & Mangla, 2018). Numerous studies have explored the factors that impact the implementation of circular economy and Industry 4.0, and these factors have been broadly classified into categories such as barriers, challenges, drivers, and enablers (Fedotkina et al., 2019). Identifying the enablers that are effective in implementation is a crucial step in enhancing the performance of a circular and intelligent supply chain. Until these enablers are identified, it is impossible to determine their relative importance. Following their identification, industry practitioners and policymakers can develop appropriate strategies for their implementation. As such, this current research aims to identify, categorize, and analyze the effective enablers for implementing circular economy and Industry 4.0 at the Ardakan Glass Factory in Yazd, which is the largest glass factory in West Asia. To achieve this, both a qualitative method for enabler identification and the technical Dimtel method using fuzzy logic for establishing cause-and-effect relationships between enablers are employed. What sets this research apart from others is its focus on identifying the combined enablers for implementing the circular economy and Industry 4.0 at the Ardakan Glass Factory Group of Yazd, as well as the network approach that examines the relationships and interactions between these enablers. Given these key elements, this research aims to address the following questions:
-What are the effective enablers for implementing the circular economy and Industry 4.0 at the Ardakan glass factories in Yazd?
-What is the effectiveness and influence, including cause-and-effect relationships, of these enablers?
Materials and Methods
This research is categorized as applied-developmental research in terms of its purpose and is classified as a field-library study in terms of its methodology. Its objective is to formulate a novel scientific model of enablers for implementing circular economy and Industry 4.0 within organizational supply chains. Given the significant number of qualitative articles that have explored the enablers of Industry 4.0 and the circular economy across various industries and the need to establish a shared understanding of these enablers, the first stage of this research involved identifying effective enablers using the meta-synthesis qualitative method. Their validity was assessed through content validity, which involved obtaining opinions from 15 organizational experts. In the second phase of the research, the researchers evaluated the effectiveness and impact of these enablers using the Fuzzy DEMATEL method. The statistical population for the first stage of the research comprised all studies published in the Scopus database, the largest text database, related to the enablers that influence the implementation of circular economy and Industry 4.0 within organizational supply chains up until the commencement of this research. In the second stage of the research, the statistical population included all professors and managers with expertise in sustainability, familiar with circular economy, and knowledgeable about Industry 4.0 technologies at Ardakan Glass Factories in Yazd. For this phase, a purposeful sampling method was used to select ten participants.
Discussion and Results
The purpose of the current research is to analyze the enablers that are effective in implementing the circular economy and Industry 4.0 within the supply chain of Ardakan Glass Factories in Yazd. In the first stage of the research, various articles were reviewed, and the meta-combination method was employed to identify and categorize relevant enablers. This process led to the identification of 15 enablers across four dimensions: economic, human resources, organizational management, and infrastructure. In the second stage, the Fuzzy DEMATEL technique was utilized to investigate the cause-and-effect relationships between these enablers. The research results revealed that the economic and infrastructural enablers are considered influential dimensions that affect human resources and organizational management dimensions. Within the economic dimension, "budget allocation for the implementation of the circular economy and Industry 4.0" and "stimulation of demand for circular products" emerged as the most effective enablers. Additionally, in the infrastructure dimension, "development of IT standards and infrastructure" was identified as the most influential enabler for the implementation of the circular economy and Industry 4.0 within the supply chain. In the organizational management dimension, "the support and commitment of senior management" was recognized as the most influential enabler.
Conclusion
While the enablers mentioned are considered among the most effective ones in implementing circular economy and Industry 4.0 in the Ardakan Glass Factories of Yazd, it's crucial for the glass industry to prioritize the most important enablers. It's essential to pay adequate attention to all identified enablers. Using specific guidelines and a checklist of effective enablers during decision-making can facilitate the decision-making process and enhance decision-making capabilities. Therefore, based on the identified enablers and their importance in this research, it's recommended to develop and provide guidelines and checklists for executive managers. Among the significant limitations of this research is the reliance on a single scientific database, Scopus, for sourcing research. It's advisable to supplement this by utilizing other databases such as Google Scholar and Web of Science. Additionally, the classification of enablers was conducted using a qualitative approach. Researchers are encouraged to name and categorize enablers using survey and quantitative methods, such as cluster analysis, to expand their research scope. Another limitation pertains to the research's statistical population, which was restricted to Ardakan Glass Factories in Yazd due to time and cost constraints. To generalize the research results, it's advisable to investigate the same research topic in other glass factories across the country. Future researchers could employ methods like fuzzy cognitive mapping and systems dynamics to examine relationships and interactions between enablers. Moreover, the enablers identified and analyzed in this research were primarily based on international studies. To adapt these enablers to the specific conditions of Iran's industries, it's suggested that in-depth interviews be conducted with industry owners. This way, certain enablers that may be unique to Iran's circumstances or require different interpretations can be revised.
