Leeway of Lean Concept to Optimize Big Data in Manufacturing Industry: An Exploratory Review

2018 ◽  
pp. 189-199
Author(s):  
Hardik Majiwala ◽  
Dilay Parmar ◽  
Pankaj Gandhi
Keyword(s):  
Big Data ◽  
Manufacturing Industry

CBI4.0: A Cross-layer Approach for Big Data Gathering for Active Monitoring and Maintenance in the Manufacturing Industry 4.0

2021 ◽  
pp. 100236
Author(s):  
Muhammad Faheem ◽  
Rizwan Aslam Butt ◽  
Rashid Ali ◽  
Basit Raza ◽  
Md. Asri Ngadi ◽  
...  
Keyword(s):  
Big Data ◽  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Data Gathering ◽  
Cross Layer ◽  
Active Monitoring

scholarly journals Information Fusion for Multi-Source Material Data: Progress and Challenges

2019 ◽  
Vol 9 (17) ◽  
pp. 3473 ◽  
Author(s):  
Zhou ◽  
Hong ◽  
Jin
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Fusion ◽  
Manufacturing Industry ◽  
Data Representation ◽  
Source Material ◽  
Future Research ◽  
Unified Framework ◽  
Open Problems ◽  
Material Data

The development of material science in the manufacturing industry has resulted in a huge amount of material data, which are often from different sources and vary in data format and semantics. The integration and fusion of material data can offer a unified framework for material data representation, processing, storage and mining, which can further help to accomplish many tasks, including material data disambiguation, material feature extraction, material-manufacturing parameters setting, and material knowledge extraction. On the other side, the rapid advance of information technologies like artificial intelligence and big data, brings new opportunities for material data fusion. To the best of our knowledge, the community is currently lacking a comprehensive review of the state-of-the-art techniques on material data fusion. This review first analyzes the special properties of material data and discusses the motivations of multi-source material data fusion. Then, we particularly focus on the recent achievements of multi-source material data fusion. This review has a few unique features compared to previous studies. First, we present a systematic categorization and comparison framework for material data fusion according to the processing flow of material data. Second, we discuss the applications and impact of recent hot technologies in material data fusion, including artificial intelligence algorithms and big data technologies. Finally, we present some open problems and future research directions for multi-source material data fusion.

scholarly journals The Manufacturing Industry and the Big Data Challenges: The case-study of the Global Tech Company

2021 ◽  
Vol 8 (2) ◽  
pp. 232-243
Author(s):  
Cezar Honorato ◽  
Francisco Cristóvão Lourenço de Melo ◽  
Reinaldo Cesar de Morais
Keyword(s):  
Big Data ◽  
Manufacturing Industry

scholarly journals Big Data

2013 ◽  
Vol 135 (10) ◽  
pp. 32-37 ◽  
Author(s):  
Ahmed Noor
Keyword(s):  
Big Data ◽  
Manufacturing Industry ◽  
Smart Manufacturing ◽  
New Wave ◽  
New Paradigm ◽  
Management Tools ◽  
Heterogeneous Information ◽  
Large Sets ◽  
Controlled Environments ◽  
And Control

This article reviews the benefits of Big Data in the manufacturing industry as more sophisticated and automated data analytics technologies are being developed. The challenge of Big Data is that it requires management tools to make sense of large sets of heterogeneous information. A new wave of inexpensive electronic sensors, microprocessors, and other components enables more automation in factories, and vast amounts of data to be collected along the way. In automated manufacturing, Big Data can help reduce defects and control costs of products. Smart manufacturing is expected to evolve into the new paradigm of cognitive manufacturing, in which machining and measurements are merged to form more flexible and controlled environments. The article also suggests that the emerging tools being developed to process and manage the Big Data generated by myriads of sensors and other devices can lead to the next scientific, technological, and management revolutions. The revolutions will enable an interconnected, efficient global industrial ecosystem that will fundamentally change how products are invented, manufactured, shipped, and serviced.

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