شناسایی مؤلفه ها و شاخص های پیشران در مدیریت زنجیره تأمین سبز مبتنی بر اینترنت اشیاء (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
هدف این پژوهش شناسایی مولفه ها و شاخص های پیشران در مدیریت زنجیره تأمین سبز مبتنی بر اینترنت اشیاء است. این پژوهش از نظر روش شناسی، پژوهشی آمیخته است که در دو مرحله انجام شد. ابتدا با مرور مبانی نظری و مطالعات پیشین از طریق روش تحلیل محتوای کیفی، شاخص های مربوط به پیشران های مدیریت زنجیره تأمین سبز مبتنی بر اینترنت اشیا شناسایی شدند؛ سپس برای تأیید و اعتبارسنجی شاخص های شناسایی شده، این شاخص ها در اختیار 22 نفر از خبرگان حوزه مدیریت و فناوری اطلاعات قرار گرفتند. نتایج پژوهش نشان دهنده ی آن است که مدل زنجیره تأمین سبز مبتنی بر اینترنت اشیاء دارای 9 مؤلفه و 66 شاخص است. مؤلفه های شناسایی شده عبارتند از: مدیریت هوشمند زنجیره تأمین، پایش لحظه ای وضعیت اشیاء در زنجیره تأمین، انتقال هوشمند اشیاء در طول زنجیره تأمین، مکان یابی هوشمند اشیاء در زنجیره تأمین، شفافیت اطلاعاتی در زنجیره تأمین و کاهش فساد، مدیریت کیفیت هوشمند در زنجیره تأمین، منبع یابی هوشمند در زنجیره تأمین، مدیریت توزیع هوشمند و مدیریت موجودی هوشمند. پیشران های گسترده ی مدل پیشنهادی حاکی از لزوم توجه به استفاده از اینترنت اشیاء در مدیریت زنجیره ی تأمین در جهت بهبود عملکرد کلی زنجیره تأمین و تمرکز بر ملاحظات زیست محیطی است.Identifying components and driving indicators in green supply chain management based on Internet of Things
This research aims to identify key components and indicators for managing green supply chains utilizing the Internet of Things (IoT). The methodology employed is a mixed approach consisting of two stages. First, through qualitative content analysis, this study reviews theoretical foundations and previous research to identify indicators associated with drivers for green supply chain management based on IoT. Subsequently, these indicators were presented to 22 experts in management and information technology to validate and verify them. The research findings reveal that the IoT-based green supply chain model encompasses nine components and 66 indicators. These components include intelligent supply chain management, real-time monitoring of object statuses in the supply chain, intelligent object transfer along the supply chain, intelligent object location in the supply chain, information transparency within the supply chain, corruption reduction, intelligent quality management within the supply chain, intelligent sourcing in the supply chain, intelligent distribution management, and intelligent inventory management. The comprehensive drivers in the proposed model emphasize the importance of incorporating IoT in supply chain management to enhance overall supply chain performance while addressing environmental concerns.IntroductionAs technology continues to advance rapidly across various industries, mankind has enjoyed an improved quality of life. However, the environmental toll of recent decades, such as global warming, water scarcity, polar ice melting, habitat destruction, and deforestation, has raised significant environmental concerns. Modern human activities have contributed to these environmental issues. Consequently, there is mounting pressure on companies to integrate environmentally responsible practices into their operations and supply chains. Recognizing the pivotal role of green supply chain management in sustainable job creation, environmental problem reduction, improved public health through safer food consumption, and enhanced agricultural land productivity, recent years have witnessed increased interest and research into the determinants of green supply chain management.MethodologyThis research adopts a mixed-method approach conducted in two stages. Firstly, qualitative content analysis is employed to review theoretical foundations and prior studies, facilitating the identification of indicators associated with drivers for green supply chain management using IoT. Subsequently, these identified indicators are validated and verified by 22 experts specializing in management and information technology.ResultsThe research findings indicate that green supply chain management, with an IoT approach, comprises nine components: intelligent supply chain management, real-time monitoring of object statuses, intelligent object transfer, intelligent object location, information transparency, corruption reduction, intelligent quality management, intelligent sourcing, intelligent distribution management, and intelligent inventory management.ConclusionsThis study highlights the presence of nine components and 66 indicators within the IoT-based green supply chain model. These components encompass various aspects of supply chain management, emphasizing the importance of incorporating IoT technology to enhance overall supply chain performance while addressing environmental considerations. Due to the growing concerns surrounding environmental issues and the emission of harmful substances by companies, it is highly recommended to incorporate the IoT into supply chain management. This integration serves to monitor and control the quantity of waste generated, and encourages the use of environmentally-friendly 3D printing for creating IoT sensors instead of traditional plastic materials. Furthermore, it is advisable to optimize waste collection schedules and routes for garbage trucks, as these measures can significantly reduce the time and resources spent on waste management. To facilitate this transition, managers should organize in-service training programs to educate employees about IoT technology and communication equipment, emphasizing the positive impact of these advancements on green supply chain management. Additionally, adopting state-of-the-art technologies like Radio-Frequency Identification (RFID) in supply chain systems can contribute to the development of a sustainable and environmentally-conscious supply chain. Legislative bodies should also play a crucial role in promoting green supply chain practices by identifying and addressing legal loopholes in existing supply chain-related laws. This can be achieved through the implementation of incentives, such as tax reductions for eco-friendly companies, or penalties, including tax hikes, financial fines, and even legal repercussions, to encourage the adoption of smoother and more environmentally responsible supply chain management practices. It's worth noting that this research has certain limitations. It primarily relied on articles within specific databases during a defined timeframe, excluding other valuable sources like foreign books and theses due to accessibility constraints. Furthermore, qualitative research inherently depends on the researcher's interpretation and perspective, potentially affecting the reliability of the results. Lastly, challenges related to the COVID-19 pandemic and respondent reluctance posed difficulties during the research process.
تبلیغات
