دانلود کتاب مدیریت دانش مشارکتی از طریق چرخه عمر محصول

Over the past three decades, the manufacturing industry has developed very fast. On the one hand, the complexity of modern products has been increasing in terms of their structure, function, and technological requirements. These products, from the small ones such as smart phones and integrated circuits to the big ones like electric cars and high-performance aero-engines, have brought dramatic change to people’s life. On the other hand, the severe competition across the global supply chain has raised demanding requirements for manufacturing enterprises not only to reduce cost and lead time but also to improve their capabilities of reacting to challenges through the whole product lifecycle.
The diversified application of information technology (IT), without doubt, is a major element in the innovation of the manufacturing industry. Since 1980s, there have been great efforts, from both industry and academia, on developing effective and efficient IT systems under the umbrella of computer-integrated manufacturing to improve the management of data, technologies, and processes. Recently, as the development of artificial intelligence (AI), there has been great interest in intelli-gent manufacturing and the global intelligent manufacturing network has been a new paradigm. The addressing of issues relative to the integrated and collaborative nature of modern product development as well as the sustainable and intelligent requirements has become a new trend of innovation in manufacturing. Specifically, the integrated nature emphasizes the integration of multidisciplinary resources in a distributed environment. The collaborative perspective concerns the collaborative work of team members of multiple roles as well as the adaptation of models and the interoperability between systems and platforms. The sustainability perspective involves achieving the balance between the efficiency of manufacturing processes and the constraints imposed on these processes in terms of sustainable concern. Last but not the least, the intelligent perspective focuses on the development of intelligent methods to effectively utilize information and knowledge in the product design and manufacturing process so as to achieve swift and resilient response market needs.
In this context, we need to reconsider the development of effective IT and intelli-gent methods to meet the new challenges mentioned above. In particular, there is a great need of effective support for the management of product design and develop-ment knowledge through the lifecycle. The previous methods for this support have some drawbacks: (1) focusing on data without enough attention to model; (2) valuing information but missing context; (3) valuing process but overlooking users; and (4) focusing on query but overlooking interaction. This book precisely aims to provide some insights into these drawbacks. Different from the previous ones on enterprise knowledge management (KM) which are more focused on operation and innova-tion management, this book has a specific focus on the collaborative nature of KM from a particular computational perspective, i.e., addressing the interface between IT and KM. The main reason for this unique perspective is that, as the development of state-of-the-art technologies such as big data, industrial Internet of things (IoT) and cloud computing, a huge amount of data has been generated. In this sense, the effective support of KM throughout the product design and manufacturing process should heavily rely upon this data, and thus there is an urgent need of research on the computing power and algorithms useful for this purpose. Additionally, discovery of knowledge from both the product data and the complex process, whereby this data is generated, is also of highly importance for the next-generation KM systems. As such, this book precisely covers case studies from the design and manufacturing process of complex products and focuses on the computational issues such as knowledge modeling, knowledge storage and retrieval, and knowledge reasoning and reuse.