Big data applications to take up major challenges across manufacturing industries: A brief review

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Abstract

Growing population and depleting resources are forcing the manufacturers to become more competent and sustainable. Customers are becoming more and more informed about the product they consume, and therefore, manufacturers need to be flexible and open to new changes taking place. Big Data Analytics (BDA) is one such technology that has transformed the manufacturing sector. Organizations have now become more flexible with regards to their processes and, at the same time, have also improved their product quality. Here in this paper, we have analyzed various advantages of Big Data that manufacturing organizations can exploit for their betterment. We have also discussed examples of certain leading manufacturers and how they can lead to big data-enabled manufacturing. Lastly, we discuss certain shortcomings that need to be addressed to make big data analytics viable for organizations of all sorts and not just for top manufacturers.

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.

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