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Impact of Big Data Analytics in Industry 4.0

Written by S. Oikonomidi

Paper category

Master Thesis

Subject

Computer Science

Year

2021

Abstract

Thesis Big data: Big data term refers to a large amount of data collected from various sources. After proper analysis and interpretation, it can provide valuable insights for business matters and support decision-making (Campos et al., 2017). Discovering unknown patterns and accurately predicting future opportunities is an important competitive advantage for companies (Campos et al., 2017). Therefore, in order to survive and develop in the market, big data investment is the top priority of today's strategic transformation. Although big data analysis can provide valuable information and positive impact, limitations and challenges should also be considered when implementing such projects (Gao et al., 2015). Since most companies are still in the early stages of adoption analysis, success is uncertain. Therefore, in order to reduce uncertainty and incentivize organizations to use big data opportunities, in addition to existing, continuous research on this topic is required. Forecasts and insights (Camposet al., 2017). The structure of the data is different; it can be unstructured or semi-structured, and their transformation is important in analysis. According to the Gartner glossary, "Big data is a large-capacity, high-speed, and/or diverse information asset." This characteristic is called 3V. Volume is the massive amount of data collected; Variety is the extraction of data from multiple sources with possibly different types of data structures, and Velocity is the continuous addition and change of data over time (Jukicet al., 2015). Many organizations try to add further descriptions, such as variability, because big data analysis can be understood in different ways, and value represents the benefits and practical help of the analysis to the organization (Jukicet al., 2015). It must be emphasized that appropriate technologies should be used to meet the challenges of 3V (Camposet al., 2017). In addition to the challenges posed by the nature of big data, organizations should also pay attention to other restrictions and considerations. Generally speaking, the complexity of the organization and its environment obscures the success of big data projects (Ylijoki and Porras, 2018). The selection of high-quality data is very important to ensure that decisions made on them do not negatively affect the organization (Gao et al., 2015). A common ethical issue is that the security, confidentiality, and legal framework of the data used should be understood by the organization and ensured by appropriate measurements. 2.1.2 Industry 4.0 Industry 4.0 is the fourth revolution in industrial production. Industry 1.0 introduced steam power, Industry 2.0 introduced electricity, Industry 3.0 introduced automation and information technology, and Industry 4.0 was designed to promote the development of business toward smart manufacturing (Weking et al., 2018). The strategic initiative of Industry 4.0 aims to improve the performance of the German public sector through high-tech solutions. Inspired by Industry 4.0 in the United States, France, Brazil and other countries, the concept was subsequently promoted to local organizations (Dalenogare et al., 2018). The new era is the result of the increasing influence of digitalization in the production process (Kagermann, 2015; Schumacher et al., 2016, quoted from Dalenogare et al., 2018). Industry 4.0 views organizations as chains that can be remotely operated and monitored through high-tech instruments (Rajpurohit and Arvid, 2016). Employees will be able to perform tasks and control processes remotely (Weking et al., 2018). The goal is to have a continuously operating smart factory that connects resources to each other and creates added value for customers and the company itself (Shafiq et al., 2015). Due to process optimization and customer-centricity, the expected benefits are multifaceted. The solution is the core part of the concept. The execution of the task will involve participants from different parts of the production chain, and these participants will actually benefit each other (Kagermann et al., 2013). Robotics or other evolutionary advances will enhance interaction with machines (Shafiq et al., 2015). Therefore, the ultimate goal is to optimize the IT environment and systems that are already in operation, aiming to influence all parts of the organization, and more importantly, to influence decision-making (Kagermann et al., 2013). Due to the increased transparency of instant information, flexible management of all manufacturing processes can be achieved, which can be achieved (Kagermann et al., 2013). In addition, by combining predictability and real-time control, decision-making will be further enhanced and can create advantages such as high customer and supplier satisfaction (Muller and Voigt, 2018). For example, customers can have personalized solutions that cover all their specified requirements (Kagermann et al., 2013), because they can easily change production and adapt to different customizations in the product (Shafiq et al., 2015). Considering that the development of new services may positively affect work-life balance, economic competitive advantage, and demographic changes (Garcıa and Garcıa, 2019), the advantages may also be social. Despite the opportunities created, organizations also face numerous challenges that will have to respond to implementation. Read Less