Abstract
The widespread deployment of inexpensive sensors, processors, embedded systems, etc., as well as the latest advances in data storage, analytics, cloud, etc. triggered significant changes in industrial engineering. This chapter aims to examine the adoption of the internet of things (IoT) in industry and the challenges for higher education. In this respect, the authors tried to explain the relationship between major concepts: IoT and industrial internet of things (IIoT). At the same time, they focused on the presentation of some IIoT enablers that could be viewed as building blocks for IIoT higher education curricula. In order to ensure the required qualifications and to develop the necessary skills for current employees, management staff, and students, academic engineering programs must undergo important changes. Some of these changes are also discussed in this chapter. Nevertheless, since there are so many uncertainties that lie between today and the future, higher education programs need to keep up with the latest technological tendencies.
TopIntroduction
In recent years, the rapid evolution of information and communications technologies involved many changes in various fields, including industrial engineering. Thus, the widespread deployment of inexpensive processors, embedded systems, smart sensors, wireless sensor networks, but also the advances in data storage, analytics, cloud infrastructure, etc., enabled the rise of a new digital industrial wave, referred as the fourth industrial revolution (IR 4.0). And the biggest technological initiative for implementing this revolution is the Industrial Internet of Things (IIoT), considered at the heart of the 4th industrial revolution, according to various worldwide surveys. The adoption of IIoT could have a huge impact. Thus, Accenture estimates the Industrial Internet of Things (IIoT) could add $14.2 trillion to the global economy by 2030 (Daugherty & Berthon, 2015). According to a McKinsey report (Manyika et al., 2015), this revolution is well under way, anticipating that by 2025, the percentage of factories adopting IIoT will reach 65%-90% in advanced economies and 50%-70% in developing economies. Several scientific papers and studies have focused their attention on this new paradigm, examining, among others, the multiple promised benefits, but also the demands and challenges encountered in developing it. In addition to the technological barriers, the uptake of the Internet of Things paradigm in industrial field is delayed by the so-called skills gap. Thus, one key to success would be the qualification and human resource development in the near future. Researchers estimate that, as former industrial revolutions, IR 4.0 will not only influence the industry itself, but also the labor market, by creating new jobs, and also causing the displacement of some existing ones. For example, the World Economic Forum, considering 15 important developed and emerging economies, anticipated the production of 2.1 million new jobs, offset by the elimination of 7.1 million jobs (WEF, 2016). According to an analysis from Deloitte (Giffi et al., 2015), over the next decade, there will be 3.5 million job openings in manufacturing, but only enough skilled labor to fill less than half of them. In order to reduce the skills gap, education is essential. Over the years, throughout the world, education in engineering has undergone important changes. But, in order to ensure the required qualifications and to develop the necessary skills for workforce, management and students as future workforce in the fourth industrial revolution, the education system must also evolve from Education 3.0 to Education 4.0. Also, in the future, companies will have to pay more attention to developing the skills of their employees, by re-training or further training on new technologies.
Key Terms in this Chapter
Fast Data: This concept, which appeared soon after “big data,” refers to the fast and efficient use of data to provide instant results and responses. This type of data is used when speed is important.
Big Data: The term is used for massive amounts of increasing data, structured or unstructured data, which can be processed and analyzed through various techniques/algorithms. As a concept, big data is defined around the five V’s: volume (scale of data), velocity (analysis of streaming data), variety (different forms of data), veracity (uncertainty of data), and visibility (accessed from the disparate geographic location). But in the last years, another V is added: value.
Industrial Internet of Things (IIoT): IIoT applies IoT concepts and technologies to the industry. The result will allow industrial things (devices, sensors, etc.) to communicate with other things, whether human beings or other devices.
Smart Data: This term refers to data, often obtained from big data and IoT that has value. Currently, there is an increasing focus on smart data instead of big data, big data being turned into smart data.
Real-Time Data: The term refers to the data acquired from the physical world and analyzed in a timely way.
Internet of Things (IoT): The concept depicts a world where different things, living and non-living entities, are connected to a single common network.