Materials Today: Proceedings
Big data applications to take up major challenges across manufacturing industries: A brief review
Introduction
Big data is one of the nine pillars of Industry 4.0. Though it has been around for quite some time now, it is in recent years that its importance and usage are being explored and analyzed to make it suitable for carrying out predictive analysis for businesses. Gartner in 2001 simply described big data as ‘high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making and process automation’ [1]. Some analysts consider big data as the electricity for the 21st century due to its importance in powering businesses to make executive decisions based on the data they have analyzed. Big data has three attributes (3Vs) used to understand the nature and characteristics of big data. They are namely: volume, variety and velocity. Volume is the most common attribute as big data is synonymous with a massive, unprecedented amount of data. According to a survey, approximately 2.5 quintillion bytes of data are being generated every day. In fact, in the last couple of years, more data has been generated than the total data generated since the beginning of time. Moreover, in 2020 there was approximately 1.7 MB of data generated per human per second. It is an unfathomable amount of data, and therefore nowadays, companies have data storage capacities of hundreds of petabytes which is to help them in future decision making. The first companies to make optimum use of this data are the tech giants-Facebook, Google. They have an unimaginable amount of data stored on their servers, and this data enables them to improve the system and thereby pave the way for complete automation and artificial intelligence.
Variety is the characteristic of big data that makes it quite different from the traditional data collection techniques for making excel sheets or CSV files. Today data and the old techniques have various varieties and there are images, sensor data, encrypted packages, tweets, posts, videos, etc. The data, thus being generated, is highly varied and unstructured; it is not specific data that could be transformed into an excel sheet. Therefore, to analyze this data and create a bigger picture, sophisticated tools like deep neural networks are required, analyzing the data's hidden pattern to help predict the results [2]. Velocity is another crucial aspect of big data. Velocity is not merely the speed at which the data is being generated, but also differentiates entities. For example, Facebook has to deal with the unlimited amount of real-time data (photos, videos, post) each day. It has over 250 billion photos stored in it has severs, and this number is growing exponentially. Similarly, in some other entity, the rate of data generation might be different, and even the rate at which it is processed is different, e.g., the data is being processed in batches. Therefore, the velocity of data generation determines the computing power of the system. Big data alone is of little use. Therefore, it is used with analytics and both terms to form a very efficient technique that can be used in many different ways to minimize costs, enhance sustainability, and maximize profits by forecasting trends and helping companies formulate their policies accordingly. Big data and other Industry 4.0 technologies are used during the COVID-19 pandemic. Their applications are successful for data storage, contact tracing and treatment of COVID-19 patients [3], [4], [5], [6], [7], [8], [9], [10], [11], [12].
Section snippets
Big data in manufacturing industry
Advanced manufacturing techniques are part of the launch of initiatives like Industry 4.0 (Germany), Made in China 2025 (China), Advanced Manufacturing Partnership (USA) and Society 5.0 (Japan). These programs were developed to make the productions process more streamlined thereby providing companies with an edge over their competitors by maximizing profits and improving product quality from ground level up. This new manufacturing technology is data-intensive and has been formed with the
Manufacturing data lifecycle
Data is the basis for implementing smart manufacturing. However, in its raw form, it is of little use, and therefore, it is converted into information with certain specifics and parameters that the user can understand, thereby enabling them to make crucial decisions. A multi-step process is used to extract useful information from raw data.
Big data use in smart manufacturing
Manufacturing is the activity of transforming raw material into a final product along with adding value to it. During this procedure, various processes working in synergy are used. The manufacturing industry has benefitted from the use of big data and its related services. In a 2016 report by NIST, three indexes of interest in smart manufacturing were declared. They were- product, business, and production. Most conventional manufacturing organizations and platforms can be quantified into any of
Big data use in manufacturing
With the advent of Industry 4.0, the manufacturing industry has transformed itself into a smart, cognitive entity. Factories have been implanted with a considerable number of sensors. These sensors provide information on the production facility's overall health and provide remedies if it finds any faults. Many companies are actively adopting this automation and artificial intelligence model to gain an additional edge over their competitors by making their production processes more robust and
Difficulties in implementing big data for manufacturing
Big data analytics has proved to be very fruitful for manufacturing. It has led to a rise in smart factory culture whereby factories have been provided with intelligence to minimize human interaction and maximize quality and quantity. However, big data is still not being integrated globally; there are just numerable organizations using it. The most striking feature for these organizations is resource availability as these organizations employ tens of thousands of people and has tens of billions
Conclusion
Various papers and blogs written for Big Data analytics (BDA) in the manufacturing industry were evaluated thoroughly to create a clear picture to capture the ongoing trends in the big data industry and industry 4.0 as a whole. Big data has turned out to be a boon to manufacturers. With the proper implementation of big data, the organization has gained substantial profits. Smart factories, the outcome of big data, are of utmost importance for their cutting edge technology. Big data has also
CRediT authorship contribution statement
Mohd Azeem: Conceptualization, Data curation, Writing - original draft. Abid Haleem: Conceptualization, Supervision, Validation. Shashi Bahl: Formal analysis, Investigation, Writing - review & editing. Mohd Javaid: Formal analysis, Investigation, Writing - review & editing. Rajiv Suman: Writing - review & editing. Devaki Nandan: Writing - review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
References (74)
- et al.
Redefining diabetic foot disease management service during COVID-19 pandemic
Diabetes Metabolic Syndrome Clinical Res. Rev.
(2020) - et al.
Letter to the editor in response to: Telemedicine for diabetes care in India during COVID19 pandemic and national lockdown period: Guidelines for physicians
Diabetes Metabolic Syndrome Clinical Res. Rev.
(2020) - et al.
Smart materials types, properties and applications: A review
Mater. Today Proc.
(2020) - et al.
Sustainability of coronavirus on different surfaces
J. Clinical Exper. Hepatology.
(2020) - et al.
Challenges and solutions in meeting up the urgent requirement of ventilators for COVID-19 patients
Diabetes Metabolic Syndrome Clinical Res. Rev.
(2020) - et al.
Industry 4.0 technologies and their applications in fighting COVID-19 pandemic
Diabetes Metabolic Syndrome: Clinical Res. Rev.
(2020) Numerical simulation of the debonding behavior of fiber reinforced metal matrix composites
Mater. Today Proc.
(2020)Axisymmetric finite element analysis of single fiber push-out test for stainless steel wire reinforced aluminum matrix composites
Mater. Today Proc.
(2020)From cloud computing to cloud manufacturing
Rob. Comput. Integr. Manuf.
(2012)- et al.
Data driven production modeling and simulation of complex automobile general assembly plant
Comput. Ind.
(2011)
Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives
Comput. Ind. Eng.
BlobSeer: Next-generation data management for large scale infrastructures
J. Parallel Distrib. Comput.
Data-driven smart manufacturing
J. Manuf. Syst.
A survey of big data management: Taxonomy and state-of-the-art
J. Netw. Comput. Appl.
Innovation: A data-driven approach
Int. J. Prod. Econ.
Big data analytics based fault prediction for shop floor scheduling
J. Manuf. Syst.
Big data oriented root cause identification approach based on Axiomatic domain mapping and weighted association rule mining for product infant failure
Comput. Ind. Eng.
A framework for Big Data driven product lifecycle management
J. Cleaner Prod.
Multi-agent system applications to fight COVID-19 pandemic
Apollo Med.
Biodegradation of plastics: A state of the art review
Mater. Today Proc.
Impact of the coronavirus pandemic on the supply chain in healthcare
British J. Healthcare Manage.
Biosensors applications in fighting COVID-19 pandemic
Apollo Med.
Finite element modeling and simulation of the fiber–matrix interface in fiber reinforced metal matrix composites
Mater. Today Proc.
Finite element analysis of VGCF/pp reinforced square representative volume element to predict its mechanical properties for different loadings
Mater. Today Proc.
Fiber reinforced metal matrix composites - a review
Mater. Today Proc.
An architecture design for smart manufacturing execution system
Comput.-Aided Des. Applic.
On architecting and composing engineering information services to enable smart manufacturing
J. Comput. Inf. Sci. Eng.
Big Data in product lifecycle management
Int. J. Adv. Manuf. Technol.
A unified framework and platform for designing of cloud-based machine health monitoring and manufacturing systems
J. Manufact. Sci. Eng. Trans. ASME.
Corona warriors under risk during COVID-19 pandemic
Current Med. Res. Practice.
Telemedicine technologies for confronting COVID-19 pandemic: A review
J. Industr. Integr. Manage.
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