Big Data Analytics in Smart Manufacturing: Principles and Practices

The edited book explores the challenges and limitations of integrating smart manufacturing and big data analytics, highlighting the potential benefits of data-driven business modelling processes in the manufacturing industry. It discusses interdisciplinary theoretical concepts, definitions, and models, provides real-world scenarios and applications, and is accessible to a wide interdisciplinary audience.

Format: Hardback
Length: 192 pages
Publication date: 14 December 2022
Publisher: Taylor & Francis Ltd

The significant objective of this edited book is to bridge the gap between smart manufacturing and big data by exploring the challenges and limitations. Companies employ big data technology in the manufacturing field to acquire data about the products. Manufacturing companies could gain a deep business insight by tracking customer details, monitoring fuel consumption, detecting product defects, and supply chain management. Moreover, the convergence of smart manufacturing and big data analytics currently suffers due to data privacy concern, short of qualified personnel, inadequate investment, long-term storage management of high-quality data. The technological advancement makes the data storage more accessible, cheaper, and the convergence of these technologies seems to be more promising in the recent era. This book identified the innovative challenges in the industrial domains by integrating heterogeneous data sources such as structured data, semi-structured data, geo-spatial data, textual information, multimedia data, social networking data, etc. It promotes data-driven business modelling processes by adopting big data technologies in the manufacturing industry. Big data analytics is emerging as a promising discipline in the manufacturing industry to build the rigid industrial data platforms. Moreover, big data facilitates process automation in the complete lifecycle of product design and tracking. This book is an essential guide and reference since it synthesizes interdisciplinary theoretical concepts, definitions, and models, involved in the smart manufacturing domain. It also provides real-world scenarios and applications, making it accessible to a wider interdisciplinary audience.

The significant objective of this edited book is to bridge the gap between smart manufacturing and big data by exploring the challenges and limitations.

Companies employ big data technology in the manufacturing field to acquire data about the products. Manufacturing companies could gain a deep business insight by tracking customer details, monitoring fuel consumption, detecting product defects, and supply chain management. Moreover, the convergence of smart manufacturing and big data analytics currently suffers due to data privacy concern, short of qualified personnel, inadequate investment, long-term storage management of high-quality data.

The technological advancement makes the data storage more accessible, cheaper, and the convergence of these technologies seems to be more promising in the recent era. This book identified the innovative challenges in the industrial domains by integrating heterogeneous data sources such as structured data, semi-structured data, geo-spatial data, textual information, multimedia data, social networking data, etc. It promotes data-driven business modelling processes by adopting big data technologies in the manufacturing industry.

Big data analytics is emerging as a promising discipline in the manufacturing industry to build the rigid industrial data platforms. Moreover, big data facilitates process automation in the complete lifecycle of product design and tracking. This book is an essential guide and reference since it synthesizes interdisciplinary theoretical concepts, definitions, and models, involved in the smart manufacturing domain. It also provides real-world scenarios and applications, making it accessible to a wider interdisciplinary audience.

Weight: 524g
Dimension: 261 x 185 x 19 (mm)
ISBN-13: 9781032065519