مدیریت کیفیت برای نگهداری و تعمیرات پیشگویانه
ترجمه نشده

مدیریت کیفیت برای نگهداری و تعمیرات پیشگویانه

عنوان فارسی مقاله: اکوسیستم مدیریت کیفیت برای نگهداری و تعمیرات پیشگویانه در دوره صنعت 4/0
عنوان انگلیسی مقاله: The quality management ecosystem for predictive maintenance in the Industry 4.0 era
مجله/کنفرانس: مجله بین المللی نوآوری کیفیت - International Journal Of Quality Innovation
رشته های تحصیلی مرتبط: مهندسی صنایع، مدیریت
گرایش های تحصیلی مرتبط: مدیریت نوآوری و فناوری، مدیریت کیفیت و بهره وری، تکنولوژی صنعتی، بهینه سازی سیستم ها
کلمات کلیدی فارسی: نگهداری و تعمیرات پیشگویانه، مدیریت کیفیت، تجزیه و تحلیل داده های بزرگ، هوش مصنوعی (AI)، ساختمان پلتفرم، فناوری اطلاعات و ارتباطات (ICT)، زمان واقعی
کلمات کلیدی انگلیسی: Predictive maintenance، Quality management، Big data analytics، Artificial intelligence (AI)، Platform construction، Information and communication technology (ICT)، Real-time
نوع نگارش مقاله: مقاله نظری (Theoretical Paper)
نمایه: DOAJ
شناسه دیجیتال (DOI): https://doi.org/10.1186/s40887-019-0029-5
دانشگاه: College of Business, University of Nebraska-Lincoln, Lincoln, NE, USA
صفحات مقاله انگلیسی: 11
ناشر: اسپرینگر - Springer
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
شناسه ISSN: 2363-7021
فرمت مقاله انگلیسی: PDF
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E13257
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Abstract

Introduction

Review of relevant literature

Case description of predictive maintenance

Conclusions

References

بخشی از مقاله (انگلیسی)

Abstract

The Industry 4.0 era requires new quality management systems due to the ever increasing complexity of the global business environment and the advent of advanced digital technologies. This study presents new ideas for predictive quality management based on an extensive review of the literature on quality management and five realworld cases of predictive quality management based on new technologies. The results of the study indicate that advanced technology enabled predictive maintenance can be applied in various industries by leveraging big data analytics, smart sensors, artificial intelligence (AI), and platform construction. Such predictive quality management systems can become living ecosystems that can perform cause-effect analysis, big data monitoring and analytics, and effective decision-making in real time. This study proposes several practical implications for actual design and implementation of effective predictive quality management systems in the Industry 4.0 era. However, the living predictive quality management ecosystem should be the product of the organizational culture that nurtures collaborative efforts of all stakeholders, sharing of information, and co-creation of shared goals.

Introduction

In today’s competitive global environment, businesses need to be agile, flexible, resilient, and possess dynamic capabilities [1, 2]. The advent of advanced digital technologies makes it possible for firms to completely innovate the concept of quality management. A living ecosystem equipped with advanced digital technologies (e.g., smart sensors, machine learning, big data analytics, and artificial intelligence (AI)) can be developed to manage quality [2]. On August 14, 2018, a 200-m section of the Ponte Morandi Bridge (built in 1968) in Genoa, Italy, collapsed causing 41 deaths, 5 missing, and 15 injured. The main causes of bridge collapse were aging and lack of bridge management. Incidents such as this highlight the importance of bridge maintenance. Structural health monitoring (SHM), a new technique developed for structure maintenance, is an up-to-date technology-based system that analyzes weaknesses of existing systems, such as locating local and global damage structure and the significance of such damages. The speed and precision of decision-making for bridge repair and maintenance are facilitated by real-time monitoring of bridge conditions. Bansal et al. [3] proposed a real-time predictive maintenance system using neural network methods, while Shi and Zeng [4] suggested a condition-based maintenance strategy that considers economic factors for predictive maintenance in real time. Predictive maintenance, also known as condition-based maintenance, is possible today due to the advanced digital technologies [3–6].