The technologies used in industry 4.0 or intelligent Industry constitute the pillars to support the new Intelligent Industry model. A concept born within what the founder of the International Monetary Fund, Klaus Schwab, named the fourth industrial revolution. Big Data, autonomous robots, cloud computing, the Internet of Things (IoT) and Artificial Intelligence (AI) are just a small sample of all these disruptive technologies, destined to transform the future of global industry at scale over the next few decades.
We are witnessing a paradigm shift in which, through the convergence of Industry 4.0 technologies, the boundaries between physical, digital and biological are gradually blurring. A new era marked by hyperconnectivity and interoperability is the basis of this process of technological convergence, which will lead to the digitalization of the economy and society at all levels.
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Types of Industry 4.0 Technologies
Some of the technologies that constitute the basis of industry 4.0 or intelligent industry have been used for a long time, in the era known as industry 3.0. Therefore, one might ask, where the difference between the two types of industries lies? Well, the move toward the intelligent industry is mainly driven as a result of three factors:
- Technological convergence: ensures the exchange of data and information between machines, devices, systems, and services, in a standardized and secure way.
- A new collaboration ecosystem: enables the business environment to transcend into an ecosystem based on open innovation.
- Systemic remodeling: affects the production, consumption, and logistics systems.
For all these reasons, and given the unstoppable advance of this industrial model, we will now analyze the technologies in industry 4.0, to try to understand how their implementation will affect organizations of all sizes and in all economic sectors.
1. Big Data: Although there is indeed no unanimity when it comes to establishing an exact definition of Big Data, there is no doubt about the potential that structured, semi-structured and unstructured Big Data can have at an enterprise level if its capture, storage and analysis is carried out accurately. Based on this idea, we could differentiate between those who use the concept of Big Data to refer to huge amounts of data that cannot be processed through traditional methods; versus those that go further and define it as a new generation of technologies, architectures, and strategies designed to capture, store and analyze growing volumes of data of heterogeneous origin and at high speed. We see this disparity of opinions on the concept of Big Data reflected in, for example, the definition published by the analyst firm Gartner, which ensures that it is “Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation”. Also, according to McKinsey “Big data refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze”. A definition that the consultancy firm echoes in its report Big Data: The next frontier for innovation, competition, and productivity by the McKinsey Global Institute, indicating that “we assume that, as technology advances over time, the size of datasets that qualify as big data will also increase”. On the other hand, Forrester defines big data as “The practices, processes, and technologies that close the gap between the data available and the ability to turn it into business insights”.
As we can see, there is no consensus on the exact definition of Big Data. However, there is no doubt about the great potential for industry 4.0 technologies, especially when it comes to monetizing data and better understanding customers. According to a recent study by HFS Research, entitled Bad Data is Killing Your Business Transformation Efforts, only 5% of CEOs show a high degree of trust in the data that their company uses to make decisions. According to this report, “the biggest impediment to becoming a data-driven organization is not technology, but governance and the necessary cultural changes within organizational behaviors”. In addition, the document underlines that “data integration (42%), process automation (42%) and data visualization (41%) are currently the top three investment areas related to data management”. Looking ahead to 2023, “companies are expected to invest more in security and data protection (50%), support for artificial intelligence and automated machine learning (41%) and advanced analytics and predictive modeling (40%)”. All these investments show a clear need to adopt solid data management strategies, together with an underlying data infrastructure, that offer a value-added activity to improve business results.
2. Advanced robotics: According to data published by the International Federation of Robotics (IFR), the sale of industrial robots in Europe, Asia, and America has increased compared to 2021. In total, during the first half of 2022, sales have reached 486,800 units, which is equivalent to 27% more than the previous year. This increase in sales of the number of industrial robots responds to a clear trend toward automation and innovation by different economic sectors, which require flexible production and manufacturing systems that adapt quickly to changes in demand; and, also, allow product customization. However, it is worth highlighting a particularity that differentiates the industry 4.0 robots compared to, for example, those of the previous era. While the new bots have intelligent sensors and are autonomous, their older versions require an operator or human intervention to control them.
On the other hand, collaborative robotics is another industry 4.0 technology that is gaining ground and in which robots help humans to perform more complex or repetitive tasks. Articulated robotic arms are a clear example of this type of collaborative bot, also known as cobots.
3. Simulation: Simulation tools or technologies within the industry 4.0 framework are intended to optimize business processes in all kinds of industries. They enable the recreation of virtual processes or technical systems in the real world, within a controlled and repeatable environment. Thanks to simulation, it is possible to identify bottlenecks and optimize production, analyze critical points in industrial processes (design, production, logistics, maintenance), reduce process implementation times, minimize costs and anticipate all kinds of scenarios that may emerge, thereby enhancing business resilience. There are multiple types of economic processes in which simulation is being used, such as inventory management, controlling production to improve the level of service and adapting to customer needs; or optimizing the quality levels related to the product delivery service, ensuring efficient use of resources and a reduction in delivery times.
4. Integration: Traditionally, industry management and control systems have been divided into analysis, management and execution systems (industrial computing); and control, supervision and communication equipment, i.e., all the connections that occur between the machines and the sensors. The evolution of these two worlds enhanced by the automation, standardization and integration of equipment has made it possible to integrate data generated by the different equipment, suppliers, and computer applications used in a company. While horizontal integration refers to the integration of the various IT systems used in the different stages of business manufacturing processes, vertical integration also focuses on the integration of IT systems, but at different hierarchical levels, to provide an end-to-end solution.
The integration of data, both vertically and horizontally, allows interconnection and total integration between systems and equipment. In such a way you can access and store all the data that is generated at all levels of a company, which can later be transformed into value-added information. The benefits of this integration range from reduced costs to the improved ability to cost-effectively manufacture small custom batches while maintaining the highest quality standards.
5. Internet of Things: This concept was first coined by Kevin Ashton, co-founder of the Auto-ID Center at the Massachusetts Institute of Technology (MIT). The term refers to technology based on the connection of everyday objects to the Internet through the integration of sensors. Thus, objects can exchange, aggregate and process information about their physical environment, to provide added value to end users. The application of this technology in industrial environments is known as the IIoT (Industrial Internet of Things) and refers to a set of sensors, instruments and autonomous devices connected, through the Internet, to industrial applications. This entire network allows the collection, exchange, and analysis of data, aimed at optimizing production, increasing efficiency and safety, and reducing operating costs. The main difference between the Internet of Things (IoT) and its industrial version (IIoT) is that while the IoT is focused on small consumers, the IIoT focuses on increasing the safety and efficiency of industrial and manufacturing processes.
6. AI: AI is one of the industry 4.0 technologies used to revolutionize business models and management. We could define AI as the ability of machines to use algorithms, learn from data and use what they learn for decision-making, as a human would. AI has multiple applications ranging from image recognition, classification and labeling; to protection against cybersecurity threats, such as those used in banks and online payment systems; predictive maintenance, in the manufacturing industry, which allows data to be collected in real time from different manufacturing sources (machines, sensors, etc.), to anticipate possible failures before they happen; and its use within quality control in industrial production, facilitating the analysis of large volumes of data linked to, for example, the additive manufacturing of a product or the subtractive manufacturing such as the cutting or molding of a part.
7. Cybersecurity: Digitization brings many benefits, but also certain risks and one of them is the increased level of exposure to cyberattacks and the repercussions that an event of these characteristics can have. From the theft of data or sensitive information to the deterioration of the corporate image and economic losses caused by a halt in activity. Faced with this situation, company investments in cybersecurity have increased in recent years. To this we must add all the regulations enforced by the EU, such as the approval of the NIS2 directive, which seeks to improve resilience and response capacity to cyberattacks, in public and private sectors; and the PIC Law (Protection of Critical Infrastructures) that ensures the protection of all installations, networks, systems, physical equipment, and information technology used to operate essential services (Administration, water, power, energy, space, chemical industry, nuclear industry, research facilities, health, financial and tax system, information technology, communications, and transportation).
8. Cloud computing: Cloud computing allows remote access to software, file storage and data processing, via the Internet. According to the study The NIST Definition of Cloud Computing published by the National Institute of Standards and Technology (NIST), Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. It is worth mentioning the three service models available for cloud computing: Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). All of these cloud computing solutions provide the flow of data to users over the internet, from cloud service providers’ systems to front-end clients and vice versa. However, the difference lies in the offerings. In the case of IaaS, the infrastructure is rented, and the user accesses it with an API or a panel. While the user manages the operating system, applications, and middleware; the vendors take care of hardware systems, networks, hard drives, data storage, and servers. In the case of PaaS, the third-party cloud service provider provides and manages the hardware and an application software platform, but the user is responsible for the applications that run on it and the data on which these applications are based. SaaS offers users a software application in the form of a service, i.e., the user can hire or use the software through the Internet, while the management is carried out by the cloud service provider.
9. Additive manufacturing: 3D printing is a type of additive technology that allows a three-dimensional reproduction of a design that has been previously created digitally. The application of this technology in industrial environments is what is known as additive manufacturing, i.e., a production system that allows complex and durable objects to be mass-produced, adding layers of material, such as plastic, ceramic, or metal, until the three-dimensional object is formed. The benefits of this technology applied to industry 4.0 include precision in product design, reduction of human errors in manufacturing, customization, reduced logistics and manufacturing costs, and great flexibility, agility, and adaptability for manufacturing lines.
10. Augmented Reality: Augmented Reality (AR) in the industrial world requires reliable and precise devices capable of visualizing, for example, 3D elements in high resolution, AI algorithms applied to image processing to detect the setting, and gesture recognition libraries, etc. The aim is to improve business processes including product design, using three-dimensional models visualized with layers of RA or mobile devices; manufacturing, using geolocated information that uses RA glasses to superimpose information and metadata in the real environment from automation and control systems; remote assistance, to carry out an activity under controlled supervision; work procedures that are displayed in augmented reality using animations to update the worker’s knowledge regarding a specific issue, etc.