Industry 4.0 is a set of technologies that companies require to promote innovation strategies and obtain a rapid response in dynamic markets. It focuses mainly on interconnectivity, digital technology, predictive analytics and machine learning to revolutionize the way companies operate and develop. Therefore, this article proposes and motivates the implementation of Industry 4.0 in organizations. Studying the state of the art and reviewing the current situation of business intelligence (BI) technology, the way it has positively impacted organizations at the economic and business level in terms of decision-making and some success stories implemented in different business, academic, social and governmental environments. Moreover, it addresses the future expected for Industry 4.0 primarily in BI and how companies should face this revolution. This article provides knowledge contribution about the current state and positive consequences of Industry 4.0, and high development in technology when implemented in the organization and the harmonization between production and intelligent digital technology.

Recent research has shown that there is a correlation between the circular economy (CE) and Industry 4.0 (I4.0). In addition, other research papers have analyzed the way that CE uses the different I4.0 technologies to transfer from the existing linear economy to CE; however, there are still gaps in the literature regarding the challenges and impacts that society and individuals must face to be ready for the transition from a linear to a circular economy. These challenges seek to guarantee the sustainability and sustainable development of the different business models that mobilize products and services through supply chains. Here, we conducted a review and compilation of the latest bibliography of circular economy and Industry 4.0 theory. The objective of this work is to present the evolutionary relationship between CE and I4.0, as well as its multi-step model of analysis. This research is relevant because its topics are timely and pertinent, especially for academics. Further, at the time that this research was performed, none of the countries were concerned about the impact that technological changes have on the human being and on society, and up to now we do not currently have studies that show how people are being trained to face the transition from the linear economy, which is common in most societies, toward a CE.

This chapter focuses on the vision of one of the biggest changes that will mark our way to live: Industries 4.0. Our intention is to discuss its basics, the automation and improvements on the processes, the way to transform an small and medium enterprises (SMEs) into an Industry 4.0, some of the economic and educational aspects, return of the investment, etc. This topic is very extensive; nevertheless, our purpose is to give a current vision of the industry and also show the Industry 4.0’s future trends. Furthermore, this chapter presents the process to migrate to Industry 4.0 until reaching to Society 5.0 and, in turn, includes a future version of Industry 5.0 that is expected to begin in 2020.

Cyberphysical systems will have a great development with the digital transformation known as industry 4.0. Cyberphysical Systems systems are devices that integrate capabilities to control and interact with a physical process. Among these are the Cobots, robots that perform tasks directly in conjunction with humans within a shared or nearby space. Safety is a fundamental issue when talking about Cobots, because there are requirements in terms of materials and design, kinetic limitations, and the implementation of sensors and algorithms that guarantee a safe workspace. Therefore, the potential risks of Cobot applications within the boom of industry 4.0 in cyberphysical systems are presented. Defining the fields of: Safety inspections; Routes and algorithms to avoid obstacles; Human-machine interfaces; Humans and Industry 4.0; Cyber security. Framed within the human-machine standards and protocols, within the safety functions and performance of a Cobot (ISO 10218-1 and ISO 10218-2 and ISO TS 15066). In this chapter, we discuss the different problems that are in the application of Cobots, in conjunction with different proposals for improvement and aspects to consider.

The present work demonstrates the design and implementation of a human-safe, portable, noninvasive device capable of predicting type 2 diabetes, using electrical bioimpedance and biometric features to train an artificial learning machine using an active learning algorithm based on population selection. In addition, there is an API with a graphical interface that allows the prediction and storage of data when the characteristics of the person are sent. The results obtained show an accuracy higher than 90% with statistical significance ( < 0.05). The Kappa coefficient values were higher than 0.9, showing that the device has a good predictive capacity which would allow the screening process of type 2 diabetes. This development contributes to preventive medicine and makes it possible to determine at a low cost, comfortably, without medical preparation, and in less than 2 minutes whether a person has type 2 diabetes.