Abstract
Due to the digitalization of life and the fiercely competitive global market, the fourth industrial revolution was inevitable. Industry 4.0 utilizes several interconnected technologies such as artificial intelligence (AI), machine learning (ML), big data (BD) to provide new solutions. The aim of this article is to provide an overview of the vital role that these technologies play in the realization and adoption of Industry 4.0, the numerous merits they can yield, and the multitude of contemporary solutions, applications, and services they can provide. Therefore, this article presents the concept of Industry 4.0 as well as those of AI, ML, BD, and big data analytics (BDA) technologies. Moreover, it goes over the potentials that these technologies could offer and the merits they could yield when applied within the context of Industry 4.0. Finally, it presents the summary of the main findings, open research issues and challenges, draws conclusions, and provides directions for future research.
TopBackground
The plethora of smart devices and the everyday life digitalization have led to a rapid increase of a wide variety of data sources, digital content as well as data structures and types (Gahi et al., 2016). Consequently, an exponentially increasing volume of heterogeneous data, which is called BD, is created and is differentiated from traditional data based on its volume, variety, veracity, velocity and value (McAfee et al., 2012).
BD can be processed using advanced analytical tools, called Big Data Analytics (BDA), which utilize analytic and parallel techniques in order to retrieve, process, examine, analyze and manage vast amounts of diverse digital data, information and statistics (Parwez et al., 2017). The BD era offers uncountable potentials for innovation in addition to numerous other advantages, all the more so as BDA offer prescriptive and predictive insight and enable intelligence gleaning, retrieval of crucial data and enhanced decision-making (LaValle et al., 2011). Therefore, there is no doubt that both BD and BDA are essential parts for the implementation of Industry 4.0.
The exponential increase of digital data has given rise not only to new requirements and challenges but also to new opportunities and potentials. ML is a novel scientific field which capitalizes on this vast amount of data as well as the increase in computational power in order to offer improved services and new solutions (Jordan & Mitchell, 2015). Intelligent and highly flexible models that learn through examples, meaning that they simulate the human way of learning, are being developed through ML (Mohri et al., 2018). Hence, ML can be used in various domains, such as computer vision, object recognition, natural language processing, intelligent decision-making systems, recommender systems etc. It is worth noting that as the volume of input and processed data increases so does its efficiency.
Key Terms in this Chapter
Industry 4.0: The fourth industrial revolution which aims at enhancing traditional industries by transforming them into intelligent ones.
Big Data: An exponentially increasing volume of heterogeneous data which is differentiated from traditional data based on its volume, variety, veracity, velocity and value.
Deep Learning: A specialized form (sub-field) of machine learning with a multitude of layers through which the data is transformed.
Smart Manufacturing: Also referred to as intelligent manufacturing, is a technology-driven global industrial method that utilizes interconnected devices to provide computer-integrated manufacturing.
Artificial Intelligence: The ability of digital computers or computer-controlled robots to autonomously perform tasks which are usually associated with intelligent beings.
Machine Learning: A sub-field of artificial intelligence that utilizes data and algorithms in order to imitate the manner in which humans perceive things and learn that is, learning through experience.
Big Data Analytics: Advanced analytical tools which utilize analytic and parallel techniques in order to retrieve, process, examine, analyze and manage vast amounts of diverse digital data, information and statistics.