نوع مقاله : مقاله پژوهشی

نویسندگان

• محمد جواد ارشادی ¹
• امیر عزیزی ²
• مجید مهاجری ³

¹ دانشیار، پژوهشگاه علوم و فناوری اطلاعات ایران (ایرانداک)، تهران، ایران

² استادیار، دانشگاه آزاد اسلامی واحد علوم و تحقیقات، تهران، ایران

³ کارشناسی‌ارشد، دانشگاه آزاد اسلامی واحد علوم و تحقیقات، تهران، ایران

چکیده

یکی از چالش های مهم در صنایع خودرو سازی مواجه با ریسک های متفاوت است به ویژه زمانی که به علت پاسخگویی به نیاز مشتریان محصولات جدید ارایه می شود ﮐﻪ باعث ﻋﺪم ﺷﻨﺎﺳﺎﯾﯽ دﻗﯿﻖ و درست در ﺗﻐﯿﯿﺮ روش و طراحی، ﻣﺎﺷﯿﻦ آﻻت و ﻣﻮاد جدید، میزان تقاضا و ﺳﺮﻋﺖ ﺗﻮﻟﯿﺪ و موارد دیگر می شود. این موارد می تواند آسیب ها و خطرات جدی به بار آورد. ﺑﺮای ﺑﻪ ﺣﺪاﻗﻞ رﺳﺎﻧﺪن اﺛﺮات ﻧﺎﻣﻄﻠﻮب این ریسک ها ﺑﺮ روﻧﺪ ﺗﻮﻟﯿﺪ، باید به دنبال روش های درست برای شناسایی ریسک ها و اولویت بندی ریسک ها برای اعمال کنترل ریسک های پر اهمیت بود. از این رو در این مقاله ، پس از شناسایی حوزه های اصلی ریسک های شناسایی شده ریسک های خطوط تولید گرید بندی شدند و بر اساس آن رویکرد نقشه های شناختی فازی توسعه داده شد و 13 ریسک در سه گروه ریسک تکنیکی ، استراتژیکی و عملیاتی شناسایی شده مورد تجزیه و تحلیل قرار گرفتند. سپس با استفاده از رویکرد مدل سازی ساختاری تفسیری، همبستگی ریسک ها ارزیابی شدند و مهمترین ریسک ها با استفاده از فرآیند تحلیل شبکه ای مشخص شدند. در نهایت نتایج نشان می دهد که ریسک های خطا در طراحی، پایین بودن انگیزه ، کمبود منابع مالی، کمبود قطعات و بهره وری پایین جزو پنج ریسک اصلی در زنجیره تامین قطعات خودرو ایساکو می باشند.

کلیدواژه‌ها

• " ارزیابی ریسک"
• " رتبه بندی "
• " نقشه شناختی فازی"
• " مدلسازی ساختاری تفسیری "
• " فرآیند تحلیل شبکه ای "

عنوان مقاله [English]

Auto parts supply chain risk assessment and rating models using fuzzy cognitive map and Interpretive Structural Modeling

نویسندگان [English]

• Mohamad javad Ershadi ¹
• Amir Azizi ²
• Majid Mohajeri ³

¹ Associate Professor, Iranian Research Institute for Information Science and Technology (IranDoc), Tehran, Iran

² Assistant Professor, Science and Research Branch, Islamic Azad University, Tehran, Iran .

³ M.A., Science and Research Branch, Islamic Azad University, Tehran, Iran

چکیده [English]

One of the major challenges in the automotive industry is facing different risks, especially when introducing new products to meet customer needs. This often leads to difficulties in accurately identifying and adapting to changing methods, designs, new machinery and materials, demand fluctuations, production speed, and more. These factors can result in serious injuries and risks. In order to address these risks, it is crucial to employ effective risk identification methods and prioritize them to exert control over critical risks. Therefore, this paper focuses on identifying the main areas of risks in the automotive industry, specifically within the production line. The identified risks are then categorized and graded. Based on this assessment, a fuzzy cognitive maps approach is developed to analyze 13 risks, which are further divided into three groups: technical, strategic, and operational risks. Furthermore, an interpretive structural modeling approach is used to evaluate the interrelationships among these risks, allowing for a comprehensive understanding of their correlations. Through the network analysis process, the most significant risks are identified. The findings reveal that design errors, low motivation, lack of financial resources, lack of parts, and low productivity are among the top five risks in the ISACO auto parts supply chain.
 
Introduction
The increasing complexity of industrial systems and the incorporation of new technologies, processes, machinery, and materials have highlighted the importance of considering environmental and safety aspects in risk assessment. Evaluating the impact of failures and their effects is a critical task in industries, particularly in the automotive sector. Among the various risk assessment techniques, failure mode and effects analysis (FMEA) has been widely recognized as a reliable method. Despite the extensive application of FMEA, there are limitations associated with this approach. One of the significant drawbacks is that it considers the SOD factors independently without considering the interdependencies among failures. In reality, production stages are not executed simultaneously, and potential failures do not occur concurrently. Some failures are influenced by previous stage failures and, in turn, affect subsequent stages. On the other hand, interpretive structural modeling (ISM) allows for the comprehensive structuring of a set of interconnected factors in an organized model. By utilizing fundamental concepts of graph theory, ISM describes the intricate pattern of conceptual relationships among variables. In this way, it overcomes the limitations of independent consideration of failures in FMEA. Therefore, this paper employs ISM as an approach to assess the impact of failures. It provides a comprehensive and structured model that captures the interrelationships among various factors. By using this approach, the evaluation of failures becomes more accurate and reliable, considering the interdependencies among different stages and failures.
Materials and Methods
This research is categorized as applied research in terms of its objective and descriptive-qualitative in terms of its method. Field studies were conducted as the data collection tools for this research. The scoring method (utilizing experts) was used for data analysis, and a case study of the ISACO company was employed to test the model. The required data for this research, aimed at presenting a model for identifying production risks in the first stage, were collected through a literature review. Relevant English and Persian books, student theses, related websites, journal articles, conferences, and seminars focusing on the identification of multi-stage production risks were used to gather research literature. Existing documentation from various industries was also utilized in the field of risk assessment and identification. In the initial stage, the main risks of the automotive parts supplier company are identified. In this phase, risks identified in existing scientific research sources were finalized through interviews with experts. The extracted risks are evaluated and ranked based on the failure mode and effects analysis method in the second step. In the third step, the interactions among various risks are examined using the fuzzy cognitive map approach. The results obtained from the second step are utilized in this phase through normalization. In the fourth step, the final ranking of risks is determined based on the static analysis conducted in the third step. In the fifth step, an interpretive structural model is used to determine the interdependence and susceptibility of risks to each other.
Discussion and Results
Based on the research objectives, the risks in the production line domain were first identified using the FMEA (Failure Mode and Effects Analysis) approach. Then, the FCM (Fuzzy Cognitive Mapping) method was employed to design a fuzzy network, and ultimately, the ISM (Interpretive Structural Modeling) approach was used to analyze the penetration and interdependence of risks. The ranking of risks using the FMEA approach is as follows: lack of motivation, parts shortage, low productivity, rework in execution, and weak supervision are ranked from 1 to 5, respectively. After considering the interactions among risks in the dynamic analysis of FCM, the factor of lack of motivation descends from rank 1 to 7. Furthermore, the factors of low productivity and lack of financial resources rank first and second, respectively.
Conclusion
Decision-making in the field of risk management involves considering various factors that are subject to change over time. The dynamic nature of these factors can influence the effectiveness of risk management decisions, and their impact on the desired outcomes needs to be carefully assessed. Proper risk management requires a comprehensive understanding of potential failures and the ability to predict and mitigate their consequences. Analyzing risks, employing effective mitigation strategies, and conducting thorough evaluations are essential for ensuring the success of a project or business venture. Professional risk management involves identifying and addressing potential vulnerabilities, evaluating their impact on the desired objectives, and devising appropriate strategies to prevent or mitigate their occurrence. The use of risk assessment methodologies, such as Failure Mode and Effects Analysis (FMEA), allows for systematic identification and prediction of potential failures, while incorporating flexibility and adaptability in risk mitigation approaches. These methodologies offer advantages such as scalability, speed, high accuracy in predicting failures, enhanced understanding of complex systems, and facilitation of decision-making processes. By employing fuzzy cognitive mapping (FCM) in FMEA, the prioritization and prediction of potential risks can be effectively performed. This approach provides a more flexible and comprehensive understanding of risks, enabling easier decision-making and utilization of valuable feedback from domain experts. Following the identification of primary risk areas, the risks associated with production lines were classified, and a fuzzy cognitive mapping approach was developed based on this classification. Thirteen identified risks were then analyzed using interpretive structural modeling (ISM) to assess the interrelationships among the risks and provide further insights for decision-making.

کلیدواژه‌ها [English]

• " Risk analysis"
• " Ranking "
• "Fuzzy cognitive map "
• " Interpretive structural modeling "
• "Network analysis process "