This first chapter is based on Industry 4.0 in its entirety; origin, definition of the Fourth Industrial Revolution, its main characteristics, current transformation segments, challenges and opportunities, the presence of companies in worldwide network Industry 4.0, the disruptive technologies with a significant potential to drive economic impact and disruption, implications and challenges for business leaders in this new technological and business landscape.
1 The Industrial Internet (Industry 4.0): Concept and Context
Acccording to the findings of an analytical study on ‘Industry 4.0’ carried out by Policy Department of the European Parliament, the concept of Industry 4.0 (German Federal Ministry of Education and Research, Project of the Future: Industry 4.0) regards it as a series of disruptive innovations in production and leaps in industrial processes resulting in significantly higher productivity. It is viewed as the fourth time such a disruption took place following
- 1.The First industrial revolution when steam power combined with mechanical production led to the industrialisation of production in the late 1700s.
- 2.The Second industrial revolution when electricity and assembly lines resulted in mass production from the mid-1800s onwards.
- 3.The Third industrial revolution when electronics and IT combined with globalisation greatly accelerated industrialisation since the 1970s.
According to this logic, the fourth industrial revolution links intelligent factories with every part of the production chain and next generation automation that has started to occur since about 2010 (Werner and Shead 2013).
In their view, it is somewhat of an oversimplification to characterise the first and subsequent industrial revolutions in this way, and economic historians will differ as to whether this would be a continuation of the third or the beginning of a fourth industrial revolution. Also, this model does not point out that with each “revolution”, national industrial leadership has changed—from England, to Germany and the Continent of Europe, and then the United States. But two key questions to be answered are about the extent to which this would be a “disruptive” technology that changes the rules of the game and leads to a leap in productivity (rather than incremental change), and if so, the extent to which such change can be generalised throughout the economies of Member States (all, some, which, how, etc.) and sectors that can be affected (and to what extent, etc.). Nevertheless, the argument does fit in with the observed evolution of industrial systems away from the Taylorist and Fordist1 approach that has increasingly characterised production systems since the 1970s.
Although it may be that Artificial Intelligence is the disruptive technology with the greater applicability in the near future.
This is corroborated by the The One Hundred Year Study on Artificial Intelligence, launched in the fall of 2014 produced by Stanford University.
AI is a science and a set of computational technologies that are inspired by—but typically operate quite differently from—the ways people use their nervous systems and bodies to sense, learn, reason, and take action. While the rate of progress in AI has been patchy and unpredictable, there have been significant advances since the field’s inception 60 years ago. Once a mostly academic area of study, twenty-first century AI enables a constellation of mainstream technologies that are having a substantial impact on everyday lives.
Nilsson (2010) has provided a useful definition of AI:
Artificial intelligence is that activity devoted to making machines intelligent, and intelligence is that quality that enables an entity to function appropriately and with foresight in its environment.
These trends drive the currently “hot” areas of AI research into both fundamental methods and application areas:
- Large-scale machine learning concerns the design of learning algorithms, as well as scaling existing algorithms, to work with extremely large data sets.Deep learning, a class of learning procedures, has facilitated object recognition in images, video labeling, and activity recognition, and is making significant inroads into other areas of perception, such as audio, speech, and natural language processing.Reinforcement learning is a framework that shifts the focus of machine learning from pattern recognition to experience-driven sequential decision-making. It promises to carry AI applications forward toward taking actions in the real world. While largely confined to academia over the past several decades, it is now seeing some practical, real-world successes.
- Robotics is currently concerned with how to train a robot to interact with the world around it in generalizable and predictable ways, how to facilitate manipulation of objects in interactive environments, and how to interact with people. Advances in robotics will rely on commensurate advances to improve the reliability and generality of computer vision and other forms of machine perception.
- Computer vision is currently the most prominent form of machine perception. It has been the sub-area of AI most transformed by the rise of deep learning. For the first time, computers are able to perform some vision tasks better than people. Much current research is focused on automatic image and video captioning.
- Natural Language Processing , often coupled with automatic speech recognition, is quickly becoming a commodity for widely spoken languages with large data sets.
- Research is now shifting to develop refined and capable systems that are able to interact with people through dialog, not just react to stylized requests. Great strides have also been made in machine translation among different languages, with more real-time person-to-person exchanges on the near horizon.
- Collaborative systems research investigates models and algorithms to help develop autonomous systems that can work collaboratively with other systems and with humans.
- Crowdsourcing and human computation research investigates methods to augment computer systems by making automated calls to human expertise to solve problems that computers alone cannot solve well.
- Algorithmic game theory and computational social choice draw attention to the economic and social computing dimensions of AI, such as how systems can handle potentially misaligned incentives, including self-interested human participants or firms and the automated AI-based agents representing them.
- IoT research is devoted to the idea that a wide array of devices, including appliances, vehicles, buildings, and cameras, can be interconnected to collect and share their abundant sensory information to use for intelligent purposes.
- Neuromorphic computing is a set of technologies that seek to mimic biological neural networks to improve the hardware efficiency and robustness of computing systems, often replacing an older emphasis on separate modules for input/output, instruction-processing, and memory.
So that, the Gartner Top 10 Strategic Technology Trends for 2018 elaborated by (Panetta 2017), are based on the following statement.
Artificial Intelligence, immersive experiences, digital twins, event thinking and continuous adaptive security create a foundation for the next generation of digital business models and ecosystems.
“The continuing digital business evolution exploits new digital models to align more closely the physical and digital worlds for employees, partners and customers,” says David Cearley, vice president and Gartner Fellow, at Gartner 2017 Symposium/ITxpo in Orlando, Florida. “Technology will be embedded in everything in the digital business of the future”.
The evolution of intelligent things, such as collective thinking car swarms, is one of 10 strategic trends with broad industry impact and significant potential for disruption.
As follows, the strategic trends mentioned above in detail:
The Intelligent Digital Mesh
Gartner calls the entwining of people, devices, and content and services the intelligent digital mesh. It’s enabled by digital models, business platforms and a rich, intelligent set of services to support digital business.
Intelligent: How AI is seeping into virtually every technology and with a defined, well-scoped focus can allow more dynamic, flexible and potentially autonomous systems.
Digital: Blending the virtual and real worlds to create an immersive digitally enhanced and connected environment.
Mesh: The connections between an expanding set of people, business, devices, content and services to deliver digital outcomes.
Intelligent
Trend No. 1: AI Foundation
The ability to use AI to enhance decision-making, reinvent business models and ecosystems, and remake the customer experience will drive the payoff for digital initiatives through 2025.
Given the steady increase in inquiry calls, it’s clear that interest is growing. A recent Gartner survey showed that 59% of organizations are still gathering information to build their AI strategies, while the remainder have already made progress in piloting or adopting AI solutions.
Although using AI correctly will result in a big digital business payoff, the promise (and pitfalls) of general AI where systems magically perform any intellectual task that a human can do and dynamically learn much as humans do is speculative at best. Narrow AI, consisting of highly scoped machine-learning solutions that target a specific task (such as understanding language or driving a vehicle in a controlled environment) with algorithms chosen that are optimized for that task, is where the action is today. “Enterprises should focus on business results enabled by applications that exploit narrow AI technologies and leave general AI to the researchers and science fiction writers”, says Cearley.
Trend No. 2: Intelligent Apps and Analytics
Over the next few years every app, application and service will incorporate AI at some level. AI will run unobtrusively in the background of many familiar application categories while giving rise to entirely new ones. AI has become the next major battleground in a wide range of software and service markets, including aspects of ERP. “Challenge your packaged software and service providers to outline how they’ll be using AI to add business value in new versions in the form of advanced analytics, intelligent processes and advanced user experiences”, notes Cearley.
Intelligent apps also create a new intelligent intermediary layer between people and systems and have the potential to transform the nature of work and the structure of the workplace, as seen in virtual customer assistants and enterprise advisors and assistants.
“Explore intelligent apps as a way of augmenting human activity, and not simply as a way of replacing people”, says Cearley. Augmented analytics is a particularly strategic growing area that uses machine learning for automating data preparation, insight discovery and insight sharing for a broad range of business users, operational workers and citizen data scientists.
Trend No. 3: Intelligent Things
Intelligent things use AI and machine learning to interact in a more intelligent way with people and surroundings. Some intelligent things wouldn’t exist without AI, but others are existing things (i.e. a camera) that AI makes intelligent (...