Review of Industry 4.0 from the Perspective of Automation and Supervision Systems: Definitions, Architectures and Recent Trends

Abstract

Industry 4.0 is a new paradigm that is transforming the industrial scenario. It has generated a large amount of scientific studies, commercial equipment and, above all, high expectations. Nevertheless, there is no single definition or general agreement on its implications, specifically in the field of automation and supervision systems. In this paper, a review of the Industry 4.0 concept, with equivalent terms, enabling technologies and reference architectures for its implementation, is presented. It will be shown that this paradigm results from the confluence and integration of both existing and disruptive technologies. Furthermore, the most relevant trends in industrial automation and supervision systems are covered, highlighting the convergence of traditional equipment and those characterized by the Internet of Things (IoT). This paper is intended to serve as a reference document as well as a guide for the design and deployment of automation and supervision systems framed in Industry 4.0.

1. Introduction

Automation and supervision systems are essential in any type of productive process. Indeed, these systems control vital infrastructure to ensure a well-functioning society, such as transportation systems, health systems, water, energy, economy and national security [1].

Sensors and actuators capture information from the process and manipulate its behavior, respectively, exchanging data with automation and control units, commonly industrial Programmable Logic Controllers (PLCs). In addition, supervisory and monitoring (Supervisory Control And Data Acquisition, SCADA) systems allow for the visualization of the most relevant magnitudes in real time to track the evolution of the process, providing graphical and numerical information, as well as alarm generation [2,3,4]. Moreover, digital communication networks are used to transmit data among the aforementioned equipment. Since their inception in the 1970s, both hardware and software have been evolving through the introduction of advancements in technologies, like electronics, computation, communications and control algorithms [3]. The third industrial revolution was mainly caused precisely by the massive introduction of automation technologies in industrial processes. Regarding communication technologies, they are used to monitor, exchange and collect data in real time to promote productivity, efficiency, traceability, reliability and security, with reduced costs to support the so-called smart factory [5].

Nowadays, these systems are being increasingly deployed in processes, factories and facilities that try to adopt the principles and technologies of Industry 4.0 and the Industrial Internet of Things (IIoT) paradigms. Indeed, automation and SCADA systems are evolving towards Industry 4.0 and IIoT concepts [1].

Namely, Industry 4.0, also called the fourth industrial revolution, is taking place and involves a large number of new technologies, among which IoT, Industrial Cyber-Physical Systems (ICPS), Artificial Intelligence (AI) and Cloud computing are found, just to name a few.

The Industry 4.0 denomination was coined within a strategic program of the German Government called Digital Agenda that started in 2009. Known as Industrie 4.0 in Germany, it was presented at the Hanover Fair in 2011 and started a new wave of developments for the digital industry. This paradigm has in-depth implications in a lot of aspects in industries related to efficiency, energy, sustainability, work conditions, human resources and production management, maintenance planning, etc. And, of course, the design and deployment of automation and supervision systems are also directly impacted by this new scenario.

In addition, the impact of this merging concept also affects non-industrial processes, such as smart grids, smart cities, etc. [6]. There are even terms that are accompanied by 4.0 in order to emphasize its advanced or innovative character, or that are framed in the Industry 4.0 arena, such as Energy 4.0, Operator 4.0, Engineer 4.0 or Education 4.0, among others.

Indeed, the literature shows an increasing amount of publications dealing with new technological developments oriented towards sensing, data acquisition, visualization, data storage and analytics, where PLC and SCADA systems are not being left behind. In fact, their presence and role in Industry 4.0-compliant facilities are still essential and needed [1,3,7,8].

Furthermore, more and more technologies outside the realm of pure automation and supervision are being incorporated into factories. For example, remote monitoring, web-based interfaces, cloud data storage and computing, cyber-security, IoT-enabled devices and Digital Twins (DTs) are achieving a progressively increasing presence in industrial systems. This way, equipment like PLC and SCADA systems includes advancements and functionalities to support some of the aforementioned technologies in order to be part of the industry of the future, e.g., Industry 4.0.

Consequently, the labor market related to the industry is progressively demanding profiles of engineers who know and handle Industry 4.0-associated technologies. For the Industry 4.0 scenario, diverse job profiles are required, such as informatics specialists, robot programmer, software engineer, data analyst, cyber security specialist and PLC programmers, the latter being an important job in this context [9]. In [10], 100 new professional profiles are identified for future factories adapted to Industry 4.0. The authors include PLC programmer, named as Industry 4.0 PLC programmer, and designers of industrial user interfaces, industrial UI designer [10].

In a similar sense, at the educational level, the number of degrees, master’s degrees and training courses related to industry 4.0 is growing each day and proves interest in these topics. In fact, higher education must respond to the challenges and opportunities that Industry 4.0 poses [11]. Regarding the next generation of industrial engineers, training is a constant challenge for academia, specifically when dealing with Industry 4.0 [12]. The Industry 4.0 vision needs significant preparation and training of engineering students so that they have the ability to solve problems and to face the challenges of this industrial revolution [11]. This way, the engineer must be trained in crucial technologies for Industry 4.0, highlighting automation equipment, communications and supervisory interfaces [13].

On the other hand, in current industry, there is a coexistence of both traditional legacy equipment as well as modern systems already designed following the Industry 4.0 and IIoT principles. Consequently, engineers and practitioners must be capable of solving the challenges and issues that both types of scenarios can pose.

This paper presents a review about Industry 4.0 regarding its concept, functional architecture and recent trends, from the point of view of automation and supervision systems. Namely, a journey is made from the (non-standardized) concept of Industry 4.0 to the most recent trends in hardware and software equipment, going through the evolution from the automation pyramid towards decentralized architectures oriented to Industry 4.0 and IIoT. The objective of the present paper is threefold. The first is to provide a panoramic view of the concepts and trends involved in the merging paradigms of Industry 4.0 and IIoT. Secondly, we will expound how the equipment (hardware and software) for industrial automation and supervision is being affected by such paradigms. Thirdly, we will elaborate a comprehensive reference document that could be useful for practitioners, engineers and researchers whose activities are related to automation and supervision.

The structure of the rest of the paper is as follows. Section 2 contextualizes Industry 4.0 as the fourth industrial revolution and provides different definitions and associated technologies. The Section 3 deals with the evolution from the hierarchical architecture of the automation pyramid towards decentralized and functional architecture for Industry 4.0 and IIoT. Section 4 expounds new features and trends in the development of automation and supervision systems to be integrated in Industry 4.0-enabled infrastructure. To conclude, the final remarks of the work are provided.

2. Industry 4.0 Concept

This section presents several extant definitions of Industry 4.0, along with considerations on the associated concepts and technologies. The aim is to provide a comprehensive outlook, given the current absence of a singular and universally accepted definition. Furthermore, prior to expounding upon these definitions, a concise historical perspective on preceding industrial revolutions is provided, as Industry 4.0 is commonly aligned with the fourth revolution. Additionally, a series of related public and private programs and initiatives are listed, which underscore the generated interest.

2.1. Industrial Revolutions

Industry 4.0 is considered the Fourth Industrial Revolution, making it pertinent to provide a historical perspective before delving into its definitions. Conventionally, four, or even five, industrial revolutions are identified, as described below.

The First Industrial Revolution emerged in the late 18th century, thanks to the steam engine invented by James Watt. This invention facilitated the introduction of mechanical equipment driven by steam power into various industries. In addition to the technological implications, profound social and economic changes ensued. This era is commonly referred to as Industry 1.0.

The Second Industrial Revolution (Industry 2.0) commenced in the late 19th century and extended until the mid-20th century. Its key technological advancements included the utilization of electricity as a source of energy and the implementation of the assembly line or mass production system.

The Third Industrial Revolution (Industry 3.0), also sometimes referred to as the Digital Revolution, began in the mid-20th century and is characterized by the automation of production, particularly with the introduction of industrial Programmable Logic Controllers (PLCs), invented in 1969. Additionally, industrial plants integrated advancements in robotics, electronics, information technology and telecommunications. Thus, the digitization of factories commenced with the incorporation of PLCs to automate certain processes and gather or share data [14].

The Fourth Industrial Revolution (Industry 4.0) has been advancing since the beginning of the 21st century [15], heralding the convergence of the digital, physical and virtual realms through the interplay of emerging technologies, such as Artificial Intelligence, blockchain, robotics, IoT, nanotechnology, bioinformatics, advanced materials, quantum computing and 3D printing, among others [16]. This could be described as a revolutionary transformation driven by a diversity of recent technologies [15]. Unlike previous revolutions, these emerging technologies and innovations are spreading much faster and more extensively [17]. One of the anticipated effects of Industry 4.0 is complete factory automation, enabled by the extensive use of these new technologies, which allows for highly advanced configurations of automated production [18].

Such is the relevance of this revolution that, in fact, the recent COVID-19 pandemic and the ensuing lockdowns highlighted the necessity of updating and modernizing systems to address tasks remotely [19], as well as achieving greater flexibility, agility and resilience through digital transformation [20], which is directly related to Industry 4.0 and its associated technologies.

The definition and implications of this fourth revolution are discussed in greater detail in the following subsection. Meanwhile, Figure 1 visually and schematically represents the aforementioned revolutions, as well as the fifth revolution, which is described following the said figure.

Finally, the fifth industrial revolution, also known as Industry 5.0, is currently being defined, even before its actual commencement. In fact, discussions about this revolution have been ongoing since 2017 [21]. However, this concept has gained greater prominence since the publication, in 2021, of a document titled “Industry 5.0: Towards a Sustainable, Human-centric, and Resilient European Industry” by the European Commission [22]. This document presents three main objectives that define Industry 5.0: a focus on the well-being of people (investors, workers, consumers), the resilience of the industry and the sustainability of the planet, going far beyond the mere production of goods and services for economic gain [23] While Industry 4.0 is primarily centered on digitization and technologies to enhance production efficiency and flexibility, Industry 5.0 acknowledges the industry’s long-term potential to serve humanity within the limits of the planet [21]. The concept of Industry 5.0, introduced by the European Commission, expands upon its predecessor (4.0) by ensuring that technological research and development not only consider industry competitiveness but also its contribution to society and the environment [23]. In this sense, Industry 5.0 does not seek to replace Industry 4.0 but rather appears as a complement to further progress achieved by various technologies and to strengthen the positive relationship between humans and machines [24]. Interesting review papers can be found in the literature to delve deeper into Industry 5.0 [25,26].

Considering the discussed revolutions, it could be argued that there is a shift in protagonism across the most recent ones. Up until the third revolution, the focus was primarily on machines; in the fourth, there was a pivot towards data; and, finally, in the fifth revolution, a human-centric approach will take center stage.

2.2. Definitions and Associated Terms

Defining Industry 4.0 is not an easy task due to the numerous and diverse existing interpretations. Moreover, there is often an overlap of emerging concepts that are so similar that they are used interchangeably. As stated by the Spanish Committee of Automatic Control (CEA) [27], the general concept of Industry 4.0 is not yet fully established and is still in a developmental phase. Given this circumstance, this subsection aims to collect some representative definitions and similar or equivalent terms.

To begin with, the diversity of available definitions, both in the scientific literature and the market, and, thus, the lack of a general consensus, can be considered a negative factor from a scientific perspective [28]. However, it also provides flexibility in motivating developments and advancements that contribute to the implementation of Industry 4.0 for practically any modern technology. Some authors even argue that each company should define what Industry 4.0 means for their specific case [28]. In this regard, maturity levels or indexes have been defined to assess the adoption of Industry 4.0 in companies, aiming to evaluate their progress and facilitate further advancements [29].

There are indeed very broad and generic definitions, such as the one provided by the Industrie 4.0 Platform of the German Government, which states that Industry 4.0 refers to the intelligent interconnection of machines and processes in the industry with the help of Information and Communication Technologies (ICT) [30]. Similarly, according to [31], Industry 4.0 involves the integration of various technologies, particularly IoT technologies, into existing technologies used in the production and industrial manufacturing sectors. This integration opens up new possibilities in terms of manufacturing capabilities, industry productivity and efficiency.

Furthermore, different labels or terms are used interchangeably in this context. For instance, the Digital Age, Internet of Everything (IoE), and Industrial Internet are considered equivalent to Industry 4.0 [15]. Digital manufacturing is also occasionally used [20], and even the term “smartization” is increasingly found in this context.

Additionally, terms like Digital Transformation, Industrial Digitalization, or simply Digitalization are also closely associated with Industry 4.0, yet they lack a clear and unequivocal definition. In different contexts, these terms can have distinct connotations as they may not hold the same meaning for an industrial manufacturing process as they do for a service-providing company. In [32], various definitions from the literature are reviewed, and an attempt is made to combine them into a comprehensive one: “Digitalization is the phenomenon of transforming analog data into digital language, which, in turn, can enhance the business relationships between customers and companies, adding value to the entire economy and society”.

However, for the context of this work, the previously mentioned formulation may not be satisfactory or complete. Therefore, it is proposed to consider digitalization in the industrial context as the application and integration of digital devices, software and digital communication networks, including the Internet, that enable the acquisition, transmission, storage, visualization/monitoring and analysis of data from machines and the production process.

In parallel, terms like Smart Manufacturing or Smart Factory are also used to refer to the new manufacturing facilities. While Industry 4.0 is more commonly used in Europe, Smart Manufacturing is more prevalent in the United States of America.

In this regard, it is worth noting that there is indeed a standardized definition for the term Smart Manufacturing. A joint committee (JWG21), called the Smart Manufacturing Coordinating Committee (SMCC), was established between the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) to establish standards for various aspects involving these concepts [33]. Similarly, in 2019, a committee was formed within the Institute of Electrical and Electronics Engineers (IEEE) for the management and development of standards related to Smart Manufacturing, known as the IEEE Computer Society Smart Manufacturing Standards Committee (IEEE C/SM SC) [34].

Although the SMCC favors the term Smart Manufacturing, it is used synonymously with Industry 4.0. Thus, the definition of Smart Manufacturing provided by this committee in mid-2021 is as follows: “Manufacturing that improves its performance aspects through the integrated and intelligent use of processes and resources in the cyber, physical, and human spheres to create and deliver products and services, which also collaborate with other domains within a company’s value chains. Performance aspects include agility, efficiency, safety, sustainability, or any other performance indicator identified by the company. In addition to manufacturing, other business domains may include engineering, logistics, marketing, procurement, sales, or any other domain identified by the company.” This definition, along with other related matters, can be found in the White Book on Smart Manufacturing published in August 2021 [18].

As mentioned in the previous subsection, Industry 4.0 is considered the fourth industrial revolution. However, the arrival of new technologies for Industry 4.0 does not necessarily mean that existing industries must discard the technologies that gave rise to the previous industrial revolution [24]. For example, at the level of communication networks, it involves an evolution through the utilization of existing protocols and standards [35].

Indeed, this new revolution represents the convergence of various technologies that have been evolving for many years but have recently come together. Numerous definitions conceive Industry 4.0 as an improvement in multiple aspects of the current industry, supported or driven by one or several of the so-called enabling technologies.

In this regard, many definitions of Industry 4.0 conceive it as the application and integration of a series of emerging or disruptive technologies, often referred to as enabling technologies. At times, more general terms like technological pillars or digital technologies are used [20]. Similar to the discussion on the concept and definition of Industry 4.0, there is no unanimity regarding which technologies are considered enabling, differing both in their number and denominations. However, the following are some of the most commonly considered relevant enabling technologies.

Commonly, nine pillars of Industry 4.0 are taken into account, namely: IIoT, Cybersecurity, Vertical and Horizontal System Integration, collaborative robotics, Big Data and Artificial Intelligence, Virtual/Augmented Reality, Additive Manufacturing, Simulation and Cloud Computing.

In [36], only five technologies are highlighted: IoT, Cyber-Physical Systems (CPS), Cloud Computing, Big Data Analytics and ICT. In [37], almost twice the number of technologies are identified, specifically: IoT, CPS, Cloud Computing, Big Data Analytics, Adaptive Robotics, Artificial Intelligence, Additive Manufacturing and Virtual/Augmented Reality.

In [20], the following technologies are considered: IIoT, Cybersecurity, Vertical and Horizontal System Integration, Big Data Analytics, Artificial Intelligence, Augmented Reality, Collaborative Robotics, Additive Manufacturing and 5G Connectivity. However, in other sources, wireless connectivity is mentioned instead of specifically referring to 5G.

On the other hand, the White Book on Smart Manufacturing [18] selects the following 12 enabling technologies: Additive Manufacturing or 3D Printing, Sensor and Measurement Technologies, IoT, Virtual and Augmented Reality, Collaborative Robotics, Simulation, Artificial Intelligence, Wireless Connectivity, Cloud Computing, Industrial Cybersecurity, Blockchain and Big Data Analytics.

Furthermore, some of these technologies are also considered as enabling technologies for the Industry 5.0 paradigm [26,38].

There are some technologies that have not been mentioned until this point but can also be considered as enabling technologies, such as Digital Twin and Edge Computing. Sometimes, they may not be explicitly listed because they are included within the broader categories of Simulation and Cloud Computing, respectively. Figure 2 schematically illustrates the main enabling technologies discussed. On the view of the surveyed literature, these are considered the key technologies from the viewpoint of the authors.

Regarding Vertical and Horizontal System Integration, it is pertinent to mention that it is directly related to automation and supervisory systems. Firstly, the use of open communication protocols stands out as a means to facilitate interoperability, enabling data sharing among diverse and heterogeneous hardware and software equipment, regardless of their manufacturers. Secondly, vertical integration is also based on interoperability, but it specifically focuses on exchanging information between Operational Technology (OT) and Information Technology (IT). OT encompasses data from the production process, originated from sensors, programmable logic controllers and SCADA systems, while IT refers to information from Manufacturing Execution Systems (MESs), Enterprise Resource Planning (ERP) and overall business management systems. This connectivity, commonly known as OT/IT convergence, is one of the key factors to advance towards Industry 4.0. To pave the way for innovations, new products and services, and higher levels of automation, any information must be easily accessible from any location, regardless of whether it belongs to IT or OT [19]. In other words, it is crucial to reduce, or even eliminate, the boundaries between IT and OT subsystems [19].

Below are some examples of definitions that incorporate various enabling technologies. According to [39], from the digital perspective of Industry 4.0, the premise of creating an intelligent and autonomous factory was established, enabling machines to communicate with each other using technologies, such as the IoT, Big Data, Digital Twins and Simulation, Additive Manufacturing, Autonomous Robots, CPS, Augmented Reality, Cloud Computing and Artificial Intelligence.

The convergence of the physical and digital worlds in industrial systems has given rise to the framework of Industry 4.0, which envisions future factories as intelligent environments where machines, sensors and actuators are interconnected, enabling collaboration, monitoring and control [40].

Indeed, the Smart Factory itself is sometimes included as a technology or pillar of Industry 4.0 [28]. In [28], Industry 4.0 is described as a shift in industrial organization towards increasing decentralization facilitated by concepts and technologies, such as IoT, CPS, Internet of Services (IoS), Cloud Computing, Additive Manufacturing and Smart Factories. These advancements help companies meet future production requirements and drive the transformation towards more flexible, agile and efficient manufacturing processes.

Emphasizing the importance of this pilar, a definition derived from a bibliometric analysis is provided in [37]: “Industry 4.0 is the implementation of CPS for creating Smart Factories through the utilization of IoT, Big Data, Cloud Computing, Artificial Intelligence, and Real-Time Communication Technologies for the Information and Communication across the Value Chain”.

Once the most representative enabling technologies and some definitions based on their combination have been presented, it is necessary to comment that, in the literature, there is again a diversity of attributions to these technologies regarding their role as the primary driver or lever of Industry 4.0. This is particularly noticeable concerning three technologies: CPS, IIoT and Smart Factory. In some instances, they are considered as just one of the many enabling technologies, while in other sources, they are portrayed as equivalent to Industry 4.0 itself.

CPSs are described as any entity composed of physical and cyber components that interact autonomously with each other, with or without human supervision [11]. In the industry, they are commonly referred to as Industrial CPSs (ICPSs), and there are multiple examples, such as robots, manufacturing cells, PLCs, data acquisition systems or SCADA systems [11]. Hence, ICPS is presented as an equivalent concept in [41], the backbone of Industry 4.0 [11] or the technical identity of the Fourth Industrial Revolution (4IR) [15].

IIoT is one of the most relevant enabling technologies. At the end of 2012, General Electric used the term IIoT to refer to the extension of the Internet of Things (IoT) concept to industrial applications, and since then, it has been extensively employed in the context of Industry 4.0. In fact, in numerous sources, it is pointed out that this technology is the primary driver of Industry 4.0, and sometimes Industry 4.0 and IIoT are used interchangeably [42]. Industry 4.0 is in its initial phase of development, with IIoT integration playing a significant role in bringing further evolution to industries and addressing stringent industrial requirements compared to IoT [35].

Regarding its main role, the IIoT is applied to connect machines and devices in industrial environments, focusing on Machine-to-Machine (M2M) communication, standing out for a greater amount of data collected compared to the IoT [4].

Furthermore, the overlapping of concepts is such that some propose that the 4IR is equivalent to the evolution of the IoT [15]. Under this premise, all aspects envisioned in terms of the 4IR are based on the technologies required for the manufacturing and implementation of the IoT evolution. In other words, if the technologies related to the evolution of IoT are not developed and implemented, all the expectations and possibilities associated with the 4IR cannot be realized [15].

A different and interesting perspective is found in [19], where it is asserted that initiatives such as Industry 4.0, ICPS and IIoT have emerged in response to the expected effects of integrating new technologies into automation systems.

Alongside the enthusiastic discourse and high expectations surrounding this revolution, there are also opposing views rarely mentioned in the literature. For instance, viewing Industry 4.0 as merely a collection of technologies can indicate a certain level of skepticism, as some authors argue that this paradigm is simply a collective term for technologies and concepts that have been known and applied for quite some time [28]. Similarly, the lack of a standardized and concrete definition may lead to the concept becoming a passing trend in business management [28]. Moreover, some authors question the novelty of the concept and even the term “industrial revolution” itself [43].

As demonstrated in this section, there is no standardized or universally accepted definition of Industry 4.0. In fact, the diversity of existing definitions does not contribute to clarifying the concept; on the contrary, it can be more confusing for someone starting to learn about this challenging yet fascinating paradigm.

In summary and as a conclusion to the preceding discussion, it can be asserted that the concept of Industry 4.0 does not represent a new technology per se but rather the integrated and combined utilization of various technologies, both emerging and traditional, to enhance industrial processes and businesses in several aspects, such as increased efficiency, flexibility, sustainability, etc.

2.3. Public and Private Initiatives

As mentioned above, Industry 4.0 has generated great interest since its conception, giving rise to numerous similar programs and initiatives in the public and private spheres. For this reason, this section shows some of them.

2.3.1. Public Programs

In the context of public administration, apart from the German program that gave birth to Industry 4.0, different strategic plans and initiatives have been established all over the world. To illustrate this, some of them are summarized in

Table 1. Public programs devoted to Industry 4.0 in different countries.

Program Title	Country	Reference
Industrie 4.0	Germany	[30]
Industria Conectada 4.0	Spain	[44]
Indústria 4.0—Estratégia Nacional para a Digitalização da Economia	Portugal	[45]
Industrie du Futur	France	[46]
Made Smarter	United Kingdom	[47]
Piano Nazionale Impresa 4.0	Italy	[48]
Produktion 2030	Sweden	[49]
Canada Digital Adoption Program (CDAP)	Canada	[50]
Society 5.0	Japan	[51]
Smart Manufacturing Coalition Leadership	United States of America	[52]
China Manufacturing 2025	People’s Republic of China	[53]

.

2.3.2. Private Initiatives

Regarding initiatives and programs by private companies, some are listed as a demonstration of the interest generated by Industry 4.0, which, in turn, contributes to making it a reality.

Siemens has a suite called Digital Enterprise and the cloud-based open operating system MindSphere [54]; Rockwell Automation offers its Connected Enterprise strategy [55]; Bosch has its Connected Industry division for Industry 4.0 [56]; Schneider Electric provides the IIoT platform EcoStruxure [57]; General Electric has its IIoT platform called Predix [58].

In addition, companies that have not traditionally been in the industrial sector have seen an opportunity in Industry 4.0 and the associated technologies, developing new products and services. Illustrative examples include major technology companies that have diversified their portfolio from the field of computing and expanded into Industry 4.0 and IIoT. For instance, Microsoft offers a version of its cloud, Azure, for IIoT, called Microsoft Azure IoT Connected Factory [59]; Cisco markets, among other products, provides sensors for IIoT [60]; IBM provides cloud and edge computing solutions [14].

3. Automation Pyramid and Architecture for Industry 4.0 and IIoT

This section explores a current trend related to the automation pyramid, which is being replaced by architecture aligned with Industry 4.0 and IIoT. These architectures offer the necessary decentralization and flexibility for these paradigms. The first subsection provides a description of the automation pyramid, its constituent levels and the limitations it imposes on the current and future scenarios. Following that, various architectures developed to orchestrate production processes within the framework of Industry 4.0 and IIoT principles will be presented.

3.1. Automation Pyramid

The automation pyramid is an architecture established in 1990 by the International Society of Automation (ISA)-95 standard [61], forming the basis of the IEC 62264 standard. It entails an Enterprise–Control System Integration standard, which proposes hierarchical levels ranging from the industrial process itself to accounting and business management systems. It was designed to be applicable across various industries and processes, enabling the representation of all components involved in process automation.

In other words, the automation pyramid serves as a conceptual reference framework, a theoretical–visual example in two dimensions to illustrate the five levels involved in an automation process and how all participating technologies are integrated (Figure 3).

The pyramid entails a hierarchical communication, of a horizontal nature among the components or subsystems at the same level, and vertical communication with those of the immediately superior and inferior layers. On occasion, the automation pyramid is represented with varying numbers of levels, ranging from four to six.

The following is a description of the elements and functionalities considering five levels:

• Level 1: Field. At this level, process data are acquired through sensors located within the process, and interventions on the process are performed using actuators. It is worth noting that this level also encompasses the actual production process. A larger number of devices are typically found at this level compared to the higher levels, hence the representation as a pyramid.
• Level 2: Control. This level includes logical control systems or devices such as PLCs or specific control computers, which execute control algorithms using input information provided by the sensors (Level 1). Based on the results obtained, they send appropriate commands to the actuators (Level 1). Some examples of devices at this level are PLCs, robot controllers, industrial PCs, variable-frequency drives, PID controllers, etc.
• Level 3: Supervision. At this level, data acquisition and recording, as well as supervision of all processes performed in the lower levels, take place. The process data are acquired by a control unit (Level 2) and visualized through a supervisory and monitoring software environment that presents this data in graphical and numerical formats.
• Level 4: Production. At this level, the entire monitoring and control of the production processes in a plant are managed. It includes not only the manufacturing part of the plant but also maintenance, goods reception, transportation, quality control and more. The systems used at this level are the MES, which are production-oriented software that monitors and documents plant management. These systems encompass information related to production operations, logistics, maintenance, quality and safety.
• Level 5: Corporative Management. This level encompasses the information systems that integrate and manage all the businesses or plants of a company. ERP systems are used at this level for resource planning. It includes information about customers, suppliers, offers, contracts, assets, consolidated information from multiple production plants, accounting, costs, project management and more. In some organizations, MES and ERP are combined into a single system.

Limitations of the Automation Pyramid

This architecture has been widely adapted and implemented in the last three decades, accompanied by hierarchical and diverse communication structures, often tailored to specific cases or domains [19]. Many times, ad hoc solutions are applied for integrating different systems, even if they may be inspired by the automation pyramid [29]. This implies that with the traditional architecture based on the automation pyramid, possibilities are limited, and replacing or modifying existing automation systems and communication networks with the latest technologies becomes challenging [19]. The levels of the pyramid are not fully connected and integrated, resulting in a lack of efficiency and poor decisions [4]. The main drawback of the pyramid is that data are exchanged between adjacent levels, and the integration of multiple vendors is not supported [4].

In other words, the automation pyramid is rigid, primarily due to the hierarchical nature of communications, which poses a limitation when making modifications driven by the incorporation of new technologies such as the enabling technologies described for Industry 4.0, where data exchanges are required between elements at different levels. For instance, incorporating new high-level functionalities that require (new) information from the production process presents a challenge within this rigid and hierarchical framework [19].

An example of this issue can be illustrated with smart sensors, which incorporate processing capability and data transfer, forming an interconnected network of sensors. Typically, they utilize wireless communication, forming what is known as Wireless Sensor Networks (WSNs). Unlike traditional sensors that deliver analog measurements connected to a PLC or data acquisition cards, for instance, these sensors can directly send digitized information to monitoring/supervisory software applications and higher-level systems such as MES or ERP [31]. These sensor networks can be combined with IoT and cloud technologies to enhance their communication, storage and processing capabilities [62], and they contribute to data acquisition and logging in Industry 4.0 [63]. An application scenario could involve sensors measuring non-critical parameters for control but essential for monitoring the process or production progress, or environmental and power consumption metrics, which do not necessarily need to consume bandwidth in intermediate communication networks or involve programming in PLC or data acquisition cards. The data transfer would occur without passing through Level 2; instead, it would be a direct exchange between the sensors and the software applications using them, a situation not considered in the automation pyramid. Furthermore, if the data were sent to a cloud-hosted database for subsequent querying and analysis by software applications, this exchange of information would also fall outside the scope of the pyramid.

Another type of constraint related to the pyramid is that an increasing number of devices implement functionalities that can be situated in more than one level. As a result, the structure of the pyramid is not suitable for including cases where PLCs incorporate a web server that allows one to monitor the evolution of variables. PLC models such as Siemens S7-1200 and S7-1500 or ABB AC500 provide a web server to visualize the data acquired by these devices. In such a scenario, the PLC as an automation element would be located at Level 2, but the monitoring function corresponds to Level 3.

In this sense, this increasing communication capability of PLC to access TCP/IP network causes a disruption in the SCADA network within the automation pyramid [3].

These challenges are expected to be addressed by new decentralized architectures [29]. Due to the limitations mentioned and the expansion of Industry 4.0 and IIoT, numerous architectures have emerged characterized by decentralization and data exchange. In decentralized architectures, the location of services or functions no longer depends on the specific hardware executing them, but rather, it is abstracted from that hardware to achieve greater generality and independence.

3.2. Reference Architectures for Industry 4.0

Having outlined the automation pyramid and its limitations in the previous section, this section briefly outlines reference architectures that attempt to overcome these limitations and promote the adoption of Industry 4.0 and IIoT. However, these architectures also have disadvantages, as will be discussed below. Of the various existing proposals, the Reference Architecture Model Industrie 4.0 (RAMI 4.0) is the one most extensively discussed.

Below, in the first place, the concept of a reference architecture is explained in order to understand its significance and why it receives attention at both public–private and academic levels.

A reference model, in general, is a model that can be used in many different cases and can serve as the basis for other specific models. In engineering, there are numerous examples, with perhaps the most well known being the Open System Interconnection (OSI) model by ISO, which outlines seven layers or levels and is used as a reference model for network protocols. Another reference model is precisely the ISA-95 standard, the automation pyramid. The advantage of using such models is a shared understanding of the function of each layer or element and the interfaces or connections defined between the different layers [64]. On the other hand, an architecture is defined, according to the ISO/IEC/IEEE 42010 standard of 2011, as the organizational structure of a system or component, its relationships, principles and guidelines that govern its design and evolution over time.

Therefore, a reference architecture model provides a common structure and language to describe and specify system architectures, and, thus, they are beneficial in promoting a shared understanding and interoperability of systems [65].

In other words, it constitutes a conceptual framework that serves as a guide for developing systems by following a structure and relationships between components, usually established through various layers or levels where such components or functionalities are situated. Essentially, it involves breaking down complex processes into more manageable and understandable parts. Moreover, these models are independent of specific technologies or solutions to be applied for their implementation, providing them with a high degree of abstraction.

The first reference architecture emerged in the 1980s, and over the decades that have passed, numerous proposals have been developed, many of them oriented towards organizational management [66]. Particularly, in the context of Industry 4.0, these reference models provide a framework for standardizing relevant technical systems, from development to operation, including integration [65].

A testament to the importance of these models is evident in the committees mentioned in Section 2.2. The joint ISO/IEC JWG21 committee aims to create a reference model that consolidates the existing ones [33]. Similarly, the IEEE C/SM SC committee also includes architectures and models within its scope of action [34].

3.2.1. Architecture RAMI 4.0

The reference architecture known as RAMI 4.0 was developed in 2015 by the German Electrical and Electronic Manufacturers’ Association (ZVEI) in collaboration with the German government’s Industrie 4.0 initiative, and it is described in the DIN SPEC 91345 standard. This architecture is based on the Smart Grids Architecture Model (SGAM), which consists of five layers and is oriented towards communications in Smart Grids.

RAMI 4.0 is illustrated through a three-dimensional map that demonstrates a structured approach to deploying Industry 4.0. It is a multi-layered and three-dimensional (cubic) architecture, as depicted in Figure 4. The model also offers a common terminology for all those involved in the Industry 4.0 ecosystem.

The three axes that define RAMI 4.0 provide a structured description of the main elements of an object or asset. They allow for tracking and describing the asset throughout its entire lifecycle and can be assigned to technical or organizational hierarchies. Complex interrelationships can be broken down into smaller, manageable sections by combining the three axes at each point in the asset’s life to represent every relevant aspect [64]. The three axes are as follows:

• Layers axis, divided into six layers representing information relevant to the asset’s function.
• Life cycle and value chain axis, representing the useful life of an asset and its value chain, based on the IEC 62890 standard.
• Hierarchy levels axis, for assigning functional models to specific levels, according to DIN EN 62264 and DIN EN 61512.

As seen, the last axis mentioned is based on the organizational proposals of ISA-95 and ISA-88, which are the standards IEC 62264 and IEC 61512, respectively. In this way, the traditional scheme of the automation pyramid, which defines Industry 3.0, is complemented by the advancements in Industry 4.0, both at the bottom with technological improvements in devices and products and at the top through global connectivity (Connected World) [66]. This means that the upper level represents the connection to the IoT [67].

In the descriptive document of the RAMI architecture, available on the German Industrie 4.0 platform website [68], Industry 3.0 is referred as “the old world” and described by the classic 5-level pyramid, which is characterized by the following:

• Hardware defines the structure.
• Functions are linked to the hardware.
• Communication takes place from one level to another.
• Product is insulated.

In contrast, the decentralized structure illustrated by Industry 4.0 (“the new world”) has the following characteristics:

• Flexible systems and machines; functions are distributed over the network.
• Network can overcome business constraints.
• Participants interact across hierarchical levels.
• All participants can communicate with each other.
• Products are part of the network.

From the perspective of automation and supervisory systems, it is worth mentioning where these systems’ equipment is located within this architecture. Specifically, in the Hierarchy Levels axis, PLCs correspond to the Control Device element, while SCADA systems are situated at the Station level. Additionally, the Integration layer represents the transition from the physical world to the world of information, and its content includes, among others, fieldbuses and also Human–Machine Interfaces (HMIs).

Furthermore, it is necessary to mention that the RAMI 4.0 architecture is also considered as a reference architecture for IIoT systems [35] or for ICPS [29], highlighting the overlap between the concepts mentioned in Section 2.

It could be asserted that the RAMI 4.0 architecture is the most relevant among those mentioned, as it is the proposal of the creators of the Industry 4.0 concept, and it also demonstrates a higher level of maturity compared to others [65]. However, there are few cases of practical application of this architecture in the literature, and those that have adopted it required significant efforts to achieve practical implementation [69].

3.2.2. Other Reference Architectures

The Industrial Internet Reference Architecture (IIRA) was introduced in 2015 by the Industrial Internet Consortium (IIC) of the United States, which was founded by companies, such as General Electric, IBM, Intel, AT&T and Cisco, and currently comprises more than 250 member organizations.

Similar to RAMI 4.0, the description and representation of the IIRA are generic, providing a high degree of abstraction to support the broad applicability required in the industry [66].

The IIRA consists of a layered model that considers four different perspectives: business, usage, functional and application (Figure 5). Recently, in December 2022, version 1.10 of the IIRA was published to address the current challenges of IIoT and industry trends, such as the convergence of IT and OT or digital twins, among others.

Other architectures are proposed within the framework of public programs or initiatives for Industry 4.0, such as the Industrial Value Chain Reference Architecture (IVRA) developed in Japan or the Intelligent Manufacturing System Architecture (IMSA), proposed in China. Moreover, alignments or correspondences between these proposals have been established due to their similarities and to strive for a unified reference architecture. For instance, in [70], the correspondence between RAMI 4.0 and IMSA is studied, while in [71], similarities and differences between RAMI 4.0 and IIRA are analyzed. Additionally, the scientific literature contains numerous papers that review and compare different reference architectures and propose new ones based on them [35,42,65].

Nevertheless, these architectures present the serious drawbacks of being very complex and abstract. Being neutral regarding technologies or manufacturers is a positive feature; however, given the size and heterogeneity of the technological solutions in the market, it is very difficult to apply such architectures in real industrial practice.

For example, there are only a few case studies in the literature that follow the RAMI 4.0 architecture and require important efforts in different aspects to reach the level of practical implementation [65]. Abstract architecture models do not address the integration of things from the industrial environment, mainly communication fieldbuses and heterogeneous components [72]. As asserted in [42], a complete understanding of existing architectures does not exist, and there is still an urgent need for establishing reference architectures as a vehicle to drive the development and evolution of Industry 4.0 systems.

Hence, the design and deployment of a new automation and supervision system under these architectures can be a very complex task, even more in the industrial arena, where a pragmatic focus is commonly applied.

3.3. IoT Architectures

Once the excessive level of abstraction of reference architectures has been expounded, this subsection presents architectures for the IIoT, which could be considered as a middle ground between the complex reference architectures and the classical automation pyramid. These architectures may be easier to understand, handle and apply when designing systems that include automation and supervision. Therefore, IoT architectures are considered crucial for the implementation of the Industry 4.0 framework.

Firstly, the orchestration of hardware and software nodes in IoT and IIoT systems is carried out following layered architectures. In fact, different architectures have been defined, ranging from the most basic and generic three-layered one to some proposals with eight levels. Therefore, there is no single reference architecture, and creating one is challenging due to the inherent fragmentation of the various possible applications, each of which often depends on different variables and design specifications [73]. Particularly in the industrial scenario, there are many heterogeneous components, such as PLCs, sensors, actuators, HMI panels, etc., that must collaborate within the IIoT system [72]. Depending on the level of granularity of the installation, one of the existing architectures may be chosen, or even a custom one with the necessary number of levels, to accommodate the different equipment and functions required.

The most basic and generic architecture is the three-level architecture, which consists of: Perception, Network and Application layers. Figure 6 shows architectures with three, four and five layers. Their main characteristics are described below.

• Perception/Sensing layer

Sometimes also known as the Object or Things layer, this is the physical layer where different objects that interact with the physical world are located, such as the sensors responsible for collecting information from the environment. In industrial installations, this layer consists of sensors and actuators placed in the process, as well as other data acquisition and automation devices [72].

• Network/Transport layer

It is the layer responsible for the transmission of information, which is why it is also sometimes referred to as the Transmission or Connectivity Layer. This layer includes all the technologies and protocols that enable data exchange between the other levels. Examples of elements in this layer are protocols, such as TCP/IP, MQTT, Wi-Fi, PROFINET, Modbus TCP or network devices (switches, routers, gateways) and protocol converters.

• Application layer

This is the top level, sometimes called the Services layer, and it provides applications and services for the end-user. This layer utilizes the data from the lower layers and includes the software to make those data available and useful for specific purposes such as analysis for business management, report generation, etc. In the industrial setting, it is at this layer where software applications for monitoring and controlling industrial devices are located, using graphical interfaces and supervisory systems [72].

This three-layer architecture is quite simple and easy to handle conceptually. The data collected from the process are communicated to other devices or network elements to be processed in the software applications for the end-user. However, when tackling the development of real applications, a higher level of detail and function division is usually required, meaning architectures with more layers. A step in this direction involves incorporating an intermediate layer between the Network and Application layers, commonly referred to as Middleware or Processing layer.

• Middleware/Processing layer

This layer performs critical functions, such as storage, filtering, analysis and processing of data, from the first layer so that it can be used by the applications in the upper layer. The data come from heterogeneous devices using different protocols, and the middleware hides the details of these various technologies, providing a layer of abstraction between the Network/Transport Layer and the applications using the data, thus contributing to interoperability between connected devices. For data accumulation, databases are commonly used, which can be accessed and queried by different applications.

Sometimes, additional layers are included beyond the ones mentioned. For instance, a Business or Enterprise layer that is dedicated to analyzing information and making decisions based on data from the perspective of business management and administration (ERP, business models, etc.). Another layer that can be added is the Security layer, which addresses aspects related to the protection of the entire architecture, such as cybersecurity, typically positioned above the Business layer. Additionally, there might be a Computing at the Edge layer, encompassing devices that operate at the edge for data collection and processing before connecting to the cloud.

Due to the relevance of the Middleware/Processing Layer, some aspects of middleware are developed in more detail.

As its name suggests, middleware acts as an intermediary between software applications, although it can also facilitate data exchange between hardware and software. It is a technology not commonly used in traditional automation and supervisory systems but is increasingly employed and necessary due to the aforementioned heterogeneity of devices and protocols used in Industry 4.0 and IIoT installations. Middleware offers advantages, such as the ability to run on different operating systems, support for standard protocols and interaction of services between heterogeneous devices, networks and applications [73].

For instance, an open-source middleware in the IoT domain is Node-RED [74], which is increasingly finding applications in both the literature and industry. Node-RED manages multiple communication protocols and facilitates data exchange with devices of different natures, such as PLCs using industrial protocols, like OPC, Modbus TCP, PROFINET, MQTT, among others. Data read from these devices can be logged into one or more databases and visualized through supervisory and monitoring software. Figure 7 schematically illustrates these possibilities, considering multiple PLCs as data sources within the context of this document.

As an example of introducing this middleware in an industrial setting, Siemens has prepared a document describing how to establish a connection between an S7-1500 PLC and Node-RED using OPC-UA [75].

4. Automation and Supervisory Systems in Industry 4.0 and IIoT

This section is dedicated to highlighting the role of PLCs and SCADA systems in the new paradigms as well as the innovations and trends they are incorporating. To this end, firstly, aspects are presented on how SCADA systems and PLCs are positioned in the Industry 4.0 and IIoT scenarios. Subsequently, the new developments in automation equipment towards the new paradigms are addressed. Finally, the generations of SCADA systems are described along with trends in their design and implementation aligned with the new concepts.

4.1. Aspects of Automation and Supervisory Systems in New Paradigms

As mentioned in Section 2.2, the Industrial Revolution represented by Industry 4.0 is driven by a combination of various technologies, without displacing the already established technologies in the industry. However, there is a certain bias detected when considering the continuity of traditional solutions compared to emerging ones in the scenarios of Industry 4.0 and IIoT. On one hand, manufacturers and developers of classic systems such as PLCs and SCADA argue that their products should prevail due to their long-standing track record, proven robustness and reliability, while incorporating new functionalities driven by developments in new technologies and tools. On the other hand, from an almost opposing perspective, manufacturers and developers in the IoT domain tend to assert that traditional solutions are falling behind, becoming practically obsolete, and, thus, they will be replaced by new devices and systems.

SCADA systems are not easily applicable to the IoT because those systems have always considered devices with very specific standards, a restricted range of manufacturers and lack mechanisms to ensure information security and confidentiality [76]. For instance, in [77], it is asserted that the cost, complexity and greater manpower of SCADA systems imply that these systems have begun to leave their place to monitoring systems based on IoT.

Furthermore, the lack of a standardized definition of Industry 4.0 has an adverse effect on the people in charge of the design, implementation and operation of automation and supervision systems. Engineers and researchers familiar with traditional systems find this absence of homogenization a source of uncertainty and difficulty in understanding, even leading to a rejection of this new paradigm. For students in this field, they encounter difficulties in learning these new challenges due to the lack of a concrete educational methodology, oriented towards the knowledge of existing commercial equipment and under the open-source perspective. From a commercial point of view, manufacturers relate these difficulties to possible delays in the implementation of new technologies and their associated benefits for companies.

4.1.1. Relevance in the Scientific Literature

In addition to the improvements and trends, for a broader overview, it is interesting to mention that there are scientific and technological publications that highlight the fundamental role of automation and supervisory systems in Industry 4.0 and IIoT. In fact, Industry 4.0 will become one of the most extensive areas of research in the next decade [41], with PLCs and SCADA systems being embedded within this paradigm.

While the revolution in the industrial sector is underway, promoting devices, tools, networks and software with broader purposes, the use of PLCs is not yet likely to disappear [78]. PLC-based systems will continue to be required and essential in the majority of solutions within Industry 4.0 [7,79]. PLCs play an essential role in Smart Manufacturing systems by providing control and interaction with sensors and actuators [80]. Thus, the role of PLCs is evolving from traditional controller tasks to decentralized and integrative functions [78]. Moreover, PLCs are considered IIoT devices, along with communication gateways, enabling connectivity to local networks or the internet for process monitoring and interaction [81]. In IIoT for factory usage, the data generated by the industrial sensors are generally input to PLC [4]. In the same vein, a SCADA system is an example of an IIoT-based system, incorporating sensors and actuators controlled by a PLC according to [82].

As indicated in Section 2, both PLCs and supervision systems are considered key elements of ICPS [11,29]. Therefore, they are an integral part of Industry 4.0 and are not going to become obsolete or be replaced. Process monitoring (in real time) is a key component for the implementation of Industry 4.0 as it enables the detection and resolution of inefficiencies and bottlenecks, as well as the determination of metrics to evaluate the operations performed [39]. Similarly, SCADA and monitoring systems are considered tools for maintenance management in Industry 4.0 [83], sometimes referred to as Predictive Maintenance 4.0 or Maintenance 4.0. In this context, the data collected from the process are used for predictive maintenance planning through Artificial Intelligence, and, on the other hand, user interfaces assist operators in making decisions about the process [83]. Furthermore, supervision systems act as a data source for MES, which are also employed in Industry 4.0 [29]. As stated in [3], the digital transformation under the Industry 4.0 paradigm has unleashed the full potential of SCADA systems.

4.1.2. Aspects about Reliability, Robustness and Long Lifetime

The lifespan of the automation system is expected to match the life of the industrial equipment [3]. On the one hand, PLCs are reliable and robust devices, specifically designed for industrial environments, capable of operating continuously for decades. Due to their robust and reliable characteristics and their extensive lifespan, PLCs are present in nearly all automated production processes. Consequently, a company seeking to incorporate IoT technologies into its infrastructure will not replace the functioning PLCs with devices that are still in the testing phase, as it would entail significant economic and operational risks. Apart from production downtime losses, it is essential to note that both PLCs and SCADA systems have high costs [4,84]. In the case of software, apart from the initial acquisition, there are additional expenses associated with licenses, which may need to be renewed periodically depending on the type of license. Moreover, software updates can also involve substantial expenditure. Similarly, new equipment, even if at a lower cost, would require training for the personnel responsible for its use and maintenance, expenses that would not be necessary if the existing infrastructure is maintained.

In addition to the aforementioned considerations, one must take into account the resistance to change that often characterizes many workers, including both managers and engineers. Engineers and technical leaders may resist these changes, particularly when it comes to replacing PLCs that they have been handling and operating for decades. Consequently, they might prefer to incorporate network connectivity solutions for Industry 4.0 rather than getting rid of their existing PLCs [80].

In fact, legacy equipment and technology pose a significant barrier to the widespread deployment of Industry 4.0 [85]. While Industry 4.0 is expected to enable interconnection and digitization in traditional industries [86], the current state of production processes still lacks the necessary interoperability for information exchange, especially concerning legacy equipment [87].

Indeed, older equipment presents compatibility issues with modern communication protocols such as Open Platform Communications-Unified Architecture (OPC-UA) or Message Queue Telemetry Transport (MQTT). Consequently, it becomes necessary to incorporate gateways and middleware to manage the heterogeneity of devices [35]. This heterogeneity leads to a lack of communication between the cloud and field-level devices in IIoT systems [35].

One way to implement IIoT in existing infrastructures is by overlaying a new network and associated equipment onto the existing installation to modernize the original system [88], for example, deploying new equipment for sensing and monitoring [88]. In this manner, it is possible to retain the systems that are functioning correctly, such as PLCs and SCADA systems, while enhancing their functionalities with the new IIoT network. An example of this approach could involve keeping an obsolete automation system operational and using middleware like Node-RED to collect the data via a communication protocol available in the system. Once the data are read, they can be sent to a local or remote database for visualization through a web-based open-source suite like Grafana. This configuration corresponds to the one outlined in Figure 7 of Section 3.3.

Regarding supervision systems, they are designed to provide stable functioning during the continuous operation that characterizes industrial activities. Nevertheless, their lifespan is relatively shorter and subject to updates, including both the SCADA software itself and the underlying operating system and hardware on which it runs [3].

Regarding software and hardware lifespan, SCADA systems are expected to go through multiple modifications, improvements and technological upgrades over the years [3]. The dependency on the support life cycle for SCADA components from vendors (commonly limited to a few years from release date), the functional or compatibility problems derived from software upgrades and the production stoppage to test the modifications under safety procedures are practical reasons that slow down the transition of existing SCADA systems towards the Industry 4.0 approach [3].

4.2. Trends and Novelties

In this section, the main innovations and trends for both PLC and supervision/monitoring systems are presented. These advancements stem from developments in the market and the latest scientific and technological breakthroughs.

It is important to note that reviewing all recent trends in the development of supervision and automation systems is a practically unattainable task. However, several trends have been identified that are illustrative of the progress being made towards Industry 4.0 and IIoT.

4.2.1. Trends in Automation Systems and PLC

Manufacturers of PLCs are continually incorporating improvements and new features into their latest models. These enhancements and updates in PLC technology are essential in meeting the requirements of the new era of manufacturing and production [89]. For example, older automation systems can be replaced with modern PLC models that feature open connectivity through the use of Industrial Ethernet standards [89].

• New Features

Traditionally, information from the industrial equipment mostly depends on the specific communication protocol between the control system and the device supplier, with limitations of interoperability and expandability [5]. Therefore, a significant improvement towards these advanced scenarios is the inclusion of support for modern protocols, such as MQTT and OPC-UA. MQTT is widely used in the IoT landscape, making it easier to integrate PLCs into IoT ecosystems using a common language. Similarly, the OPC-UA interface is identified as the communication standard for Industry 4.0 in the RAMI 4.0 architecture. Thus, it becomes essential for newly developed PLCs in the market to have support for this protocol. In fact, there are already PLCs available in the market that come with an embedded OPC-UA server, like Siemens’ S7-1500 [90] or Schenider Electric’s Modicon M262 [91].

Regarding MQTT communication, Siemens provides support for such a protocol in its S7-1200 and S7-1500 PLC series [92]. Another example can be found with ABB and its AC500 PLC series, which also supports MQTT and OPC-UA communication [93]. Figure 8 illustrates the capability of this set of PLCs to perform a direct communication with an MQTT broker, with the PLC itself acting as an MQTT client. This allows the exchange of information between devices via this protocol, integrating PLCs into the IoT ecosystem and Industry 4.0 applications. Furthermore, the different communication protocols supported, such as Ethernet or OPC-UA, are also represented.

The Ethernet connectivity in PLCs has been incorporated over the last decade, so it is not currently a trend or novelty. However, it is worth noting that thanks to this connectivity, support for protocols, such as MQTT, OPC-UA, Modbus TCP and PROFINET, is possible. In addition, the latter one is not so modern but also receives research efforts and is applied in the Industry 4.0 and IIoT arenas [1,8,94,95].

Functionality aligned with IIoT also includes online visualization of PLC data through a web interface, as mentioned in the section related to the automation pyramid. Some manufacturers have models that integrate a web server into their PLCs, such as the S7-1200 and S7-1500 series from Siemens, or the AC500 from ABB. This web server provides a webpage for visualizing the PLC’s status, and by editing its HyperText Markup Language (HTML) code, it is possible to monitor and control process signal values. Figure 9 illustrates the interaction among the physical facility or process, the PLC as the data reading and process control element and the web server with the integrated interface, serving as an embedded system for monitoring and control.

It must be remarked that a PLC featured with modern open communication protocols (MQTT, OPC-UA, TCP) and web access can be seen as an illustrative example of the convergence and integration of existing and disruptive technologies, which portrays the essence of Industry 4.0, as indicated in Section 2.2.

Another illustrative case of tools for Industry 4.0 involves the availability of Structured Query Language (SQL) libraries for direct data exchange, both read and write, between a Microsoft SQL database and a PLC [96].

An advanced functionality that is gradually being incorporated into PLCs is Artificial Intelligence. For instance, in 2018, Siemens introduced a technological module for the S7-1500 PLC and the decentralized peripheral ET200MP called the Neural Processing Unit (NPU). This NPU enables the processing of information, such as video images, using neural networks [97].

• Coexistence with IoT equipment

A notable novelty in recent years concerning automation devices is related to the application of IoT devices for this purpose. The technologies aligned with IoT enable easy configuration, multiple connectivity options (including wireless) and cost-effectiveness for the acquisition, transmission and logging of various types of information. As a result, they promote the objectives and characteristics of IIoT and Industry 4.0. In fact, the literature on these paradigms largely focuses on open-source and low-cost devices, such as RaspberryPi, BeagleBone, Arduino or ESP32 [84,98]. The importance of adopting open-source technologies to develop IIoT platforms for information integration is emphasized in [5]. The low-cost nature can contribute to the migration towards Industry 4.0 features in companies, since traditional systems for automation, data acquisition and supervision involve high costs [4,84,98].

Examples of coexistence between PLCs and IoT devices can be found both in the market and in the scientific literature. For instance, in [84], there is a proposal to integrate a PLC and an Arduino in a SCADA system based on LabVIEW using the OPC interface. The automation proposal in [94] involves PLCs based on Arduino, supervised by a Raspberry Pi, and an operator panel KTP600 from Siemens communicating via Modbus TCP. Further, in [99], the authors present a system of distributed I/Os based on Raspberry Pi for industrial automation. In [78], voice recognition programmed in the open-source language Python is combined with a Siemens S7-1200 PLC to implement an emergency stop. A conceptual framework for IIoT systems oriented towards smart manufacturing is proposed in [88], which includes PLCs in the network layer, highlighting the coexistence of such devices alongside other IoT technologies. In [1], cybersecurity issues are studied in a facility composed by an industrial SCADA alongside a set of Arduino-based automation units.

The use of these devices under the harsh conditions that characterize the industrial environment (temperature, humidity, dirt, etc.) raises concerns among professionals, who sometimes perceive them as toys lacking the necessary robustness and reliability [98]. Furthermore, their long-term continuous operation is still subject to study [84]. Other drawbacks include the lack of standardized signal range handling in the industry, the absence of warranties and user support and compliance with regulations and certifications [84,98].

However, there are companies that develop boards and enclosures specifically designed for the industrial environment. For instance, the Arduino Portenta H7 is designed for industrial use [100]. Revolution Pi [101] from Kunbus is a range of devices based on Raspberry Pi and adapted for industrial use, capable of acting as gateways, for example. Moreover, there are already PLCs based on Raspberry Pi available on the market, such as the PLC Raspberry Pi from Industrial Shields [102], and PLCs based on Arduino from the same manufacturer or those distributed by Controllino [103]. These solutions cater to the industrial setting and address some of the concerns related to robustness and reliability, providing more viable options for integrating IoT devices into industrial automation systems.

A similar case involves the inclusion of devices under the open-source and IoT philosophy but geared towards the industrial environment by well-established manufacturers in the automation field. For instance, Siemens sells an IoT gateway, the IOT2050 [104], based on ARM processors, resembling a Raspberry Pi but designed for industrial environments. It operates on a Linux-based operating system and comes equipped with integrated digital and analog inputs. This device is focused on data acquisition, processing and transmission to the cloud. For example, it can be connected to data sources like PLCs, perform necessary processing (calculating average values, statistics, etc.) and then send the information to a database or cloud platform such as Mindsphere (Siemens cloud). Another case is the PFC200 series of PLCs from Wago, which features a Linux-based operating system and MQTT connectivity with the cloud [105].

• Cybersecurity aspects

The cybersecurity measures to be considered in Industry 4.0 also apply to the equipment and systems involved in automation, supervision and industrial communication networks. As such, it is essential to address these aspects comprehensively as they impact all three subsystems mentioned.

Both PLCs and SCADA systems are not immune to vulnerabilities and potential malicious attacks [80,81,106]. Some aspects of cybersecurity in PLCs, supervisory systems and the communication networks in which they are integrated are discussed below.

In the case of older PLCs, they are connected to the network and exchange safety-critical information without being prepared for security threats [80]. The most notorious attack against PLC was the Stuxnet worm, and these devices are also susceptible to Denial of Service (DoS) attacks [107]. PLC manufacturers are including measures to protect against malicious intrusions such as password-protected access and Transport Layer Security (TLS) encryption of communications with HMI panels and configuration stations, among others [108].

However, despite the various countermeasures, new threats are constantly emerging. For example, in 2016, the PLC-Blaster worm [109] was developed that runs directly on the PLC, specifically on the Siemens S7-1200, without requiring a computer, as well as propagating itself via the communications network. Recently, a type of cyber-attack called Evil PLC [110] has been described that uses the PLC as a tool rather than a target, in other words, as a predator, the prey being engineering computers. Closer in time, in January 2023, Siemens reported a vulnerability in the CPU boot of S7-1500-series PLCs [111].

Regarding supervisory systems, the most recent SCADA design packages are incorporating additional components, such as user security, interfaces and communication protocols for greater security [106]. Current trends in improving cybersecurity in SCADA systems include the use of blockchain technology [112] and Artificial Intelligence [82] vulnerabilities that occur due to the operating system of the computer on which it is running must also be considered, although they are not strictly related to the monitoring system.

Likewise, the interconnection and accessibility through industrial communication networks not only provide multiple advantages but also greater vulnerability to cyber-attacks [95]. These networks are no longer physically isolated; they are now integrated into corporate intranets and the Internet, and due to Industry 4.0, this change is taking place abruptly [95]. This is more pronounced when using open protocols, such as TCP/IP, as their specifications are publicly known and detailed, reducing security by concealment. For example, Modbus TCP presents vulnerabilities to unauthorized access [95] and for OPC-UA, more than 30 threats have been identified recently [113]. In addition, the increasing use of wireless connections increases the exposure of data to security threats. Currently, the lack of available and proper security solutions in communication protocols leads the industry to struggle to achieve important Industry 4.0 objectives [1].

Communications by smart sensors can also be threatened by malicious reconfigurations as most of these elements are designed with security as a secondary priority [31]. In the case of WSN, their wireless nature also makes them vulnerable to cyber-attacks [63].

4.2.2. Trends in SCADA Systems

Before commenting on the current trends in monitoring systems, it is interesting to note that there are considered to be four generations of these systems [2,3,4,114]. The first corresponds to the monolithic SCADA systems of the 1970s, whose architecture consisted of a single Master Terminal Unit (MTU), i.e., a computer running the software and exchanging data with the Remote Terminal Units (RTUs) connected to the sensors and actuators of the process. This was an isolated installation, where the communication network used proprietary protocols that only allowed data transfer between RTU and MTU from the same manufacturer.

The second-generation distributed SCADA systems emerged from the development of Local Area Networks (LANs) and advances in computers (downsizing and improved capabilities) during the 1980s and 1990s. In these systems, information and control operations are shared among multiple intercommunicating stations. These distributed stations could be RTU, HMI, communication processors, data servers, etc. The protocol used in the LAN was still proprietary, which offered high transmission speed and traffic optimization but prevented communication with other devices using different protocols. Therefore, these are distributed systems capable of communicating with each other but only using the protocols, hardware, software and peripherals established by the manufacturers.

In the late 1990s and 2000s, the third generation, networked SCADA systems, was developed, in which the manufacturer no longer imposes the architecture and resources used. In these systems, open architectures are implemented and standardized communication protocols are used, implying better communication capabilities. Geographical distances are extended, the reach goes beyond the LAN and these systems are distributed in Wide Area Networks (WANs). The use of Ethernet and TCP/IP-based networks has contributed to these improvements.

The next generation of SCADA systems derives precisely from the adoption of IoT technologies [2,3,114,115,116,117]. The so-called Internet of Things SCADA systems (IoT SCADA systems) are the fourth generation and incorporate IoT and cloud computing technologies. This generation of SCADA systems is based on the IoT, providing enhanced functionalities, cost reduction and easier maintenance by leveraging cloud computing [117]. Thus, they allow real-time process information to be accessible ubiquitously using different operating systems and platforms. Cloud computing environments allow for the implementation of advanced monitoring algorithms, models and statistical analysis. These monitoring systems must not only be easier to maintain and integrate but also provide scalability, efficiency and cost reduction [118]. This fourth generation is currently under development.

• Complementarity SCADA—IIoT

SCADA systems and IIoT share some characteristics, such as data acquisition, processing and visualization. However, they are not mutually exclusive; on the contrary, they are complementary technologies [99,117,119].

The monitoring of information provided by different and numerous sensors is an important aspect for operations, maintenance and optimal planning in Industry 4.0 [36]. The increasing complexity of industrial applications results in more and more sensor data to be acquired, communicated and evaluated. In order to carry out adequate and efficient monitoring, an evolution must take place. Thus, the fourth generation of SCADA systems emerges as an evolution in which IoT enhances rather than replaces SCADA systems [116]. In this sense, IoT expands the vision of SCADA systems [117], providing the ability to collect and transfer data through a multitude of different protocols [119]. The deployment and use of SCADA systems are facilitated by the IIoT due to its ability to connect devices and processes [106]. The integration of IoT and SCADA systems can improve the interoperability of industrial applications [115]. The IIoT uses SCADA systems as data sources, so supervisory systems focus on monitoring and control, while the IIoT seeks connectivity to collect and analyze data to improve decision making and increase productivity [99]. In this sense, IIoT devices can be integrated into the SCADA system as part of the OT or IT component and can independently exchange data through multiple layers [3]. According to [4], the relationship between SCADA and IIoT is that IIoT-enabled systems are an evolution of SCADA and incorporate advanced technologies to improve the monitoring and control of industrial processes.

As remarked in the discussion of legacy systems, industries with a functional SCADA system can still use it and implement, in parallel, an IIoT platform to unify, analyze and share OT data [120]. An architecture that integrates SCADA systems together with an IoT platform enables a diversified amalgamation of network protocols to achieve high production quality [119]. SCADA systems are not prepared for advanced processing of large amounts of data, just as IoT platforms are not prepared for real-time monitoring and automation; therefore, these two types of technologies must coexist [121]. Moreover, thanks to the enhanced connectivity, remote monitoring is promoted by the IIoT, making the SCADA information accessible from anywhere and at any time [4].

Ultimately, the IIoT and associated low-cost, open-source equipment can help to expand data acquisition and transmission capabilities in the industrial environment. However, process monitoring, involving data visualization, interpretation and commands to PLCs and actuators, requires reliable equipment for medium- and long-term industrial operation. This aspect is especially critical in processes that may impact on the safety and integrity of plant personnel.

• User interface

Despite the central role of the various technologies mentioned above, the human factor and the interaction with them and with the process itself are not neglected in the Industry 4.0 and IIoT scenarios. As before the establishment of the new paradigms, the interfaces for human–machine interaction via HMI panels, supervisory/monitoring systems or Graphical User Interfaces (GUIs) must be user-friendly, intuitive and informative, considering visual and ergonomic aspects. These interfaces play a major role in any human-operated system, which is especially true for complex systems, such as those used in industry [29]. A clear interface is essential for system integration, both vertically and horizontally, in Industry 4.0 [29]. From this perspective, the design of HMI/user interfaces is a relevant research field in the context of Industry 4.0 and even in Industry 5.0 [122]. Regarding this topic, and beyond the limits of software, a universal pilar in the design of user interfaces is to provide a satisfactory user experience [123].

Ergonomic design of interfaces facilitates human–machine interaction [11]. In addition, they should be designed for intuitive and informative interaction, so that the personnel using them have an adequate understanding of the process being monitored [124]. There are guidelines and standards with recommendations for interface design, such as ISA101 HMI that establishes standards, recommended practices and technical reports related to HMI in manufacturing and process applications. Accessibility and universal design principles must also be taken into consideration so that interfaces can be used by everyone.

Moreover, the ISA112 SCADA Systems standards committee is developing a series of standards and technical reports about the design, implementation, operation and maintenance of SCADA systems [125].

Furthermore, in recent years, more attention is being paid to the user of automation and monitoring equipment, shifting towards a more user-centric approach [126]. Thus, apart from the user interface, the so-called User eXperience (UX) refers to considering more aspects of user perception, such as usability, functionality, evaluation, accessibility and even emotions, when using a product or service [126]. UX is mainly addressed in web and application design and has hardly been studied in HMI and monitoring systems [126], but it is receiving more and more attention [124].

Similarly, several professional profiles have been identified for Industry 4.0 that are directly related to supervision/monitoring and human–machine interaction, such as Industrial User Interface designer (UI designer), Industrial User eXperience designer (UX designer) or augmented and virtual reality developers [10].

• Augmented reality

On the other hand, developments related to augmented reality, virtual reality and other technologies are also being introduced, or are expected to be introduced. Human–machine interaction technologies can assist the operator through virtual or augmented reality to facilitate assembly or maintenance operations, or even in cases of remote diagnostics [127]. Augmented reality can assist the operator in assembling complex objects as well as in quality checking; it can also provide the worker with information, such as technical data, manuals or maintenance history, and it can also improve efficiency by providing relevant information in a timely manner, as well as geographically locating it in the appropriate place [128]. Virtual assistance involves support for the human operator in maintenance through appropriate visualization of data and information, providing an overview and better understanding of ongoing activities and processes [124]. Similarly, augmented reality can be incorporated into monitoring in the form of annotations or warnings close to real-time sensor measurements [129].

• Remote Access

Touch interaction has been a reality for many years thanks to HMI operator panels that incorporate a resistive touch screen, and there are many models on the market from different manufacturers with various sizes and features. However, in the 4.0 scenario, this interaction does not only take place on site in the production process via such panels but also via different options for remote access to the supervision/monitoring systems. The first supervision systems were designed to monitor the process locally and as advances in telecommunications and better features have been incorporated in the new generations, the use has been extended to the current state, where remote access to a SCADA system seems to be an implicit, almost basic functionality.

One of the advantages that IIoT provides is enabling remote machine monitoring through real-time data that can be accessed from anywhere and at any time to emphasize industrial automation [4]. IIoT-enabled SCADA systems can store and manage data in the cloud, making it easier to access and visualize data with web technologies over the Internet [4].

In this sense, the use of smartphone apps and web browsers to access the monitoring system interface is a clear current trend. More and more manufacturers of SCADA system design suites (WinCC OA from Siemens, Ignition from Inductive Automation, ProSoft, iFIX from General Electric, etc.) provide apps for Android and iOS for remote and ubiquitous connection. Depending on the features, the remote user can only view the process data or can also make changes by sending commands or modifying passwords, with the corresponding user identification.

• IoT open-source software to design supervisory systems

Analogous to IoT hardware devices with respect to PLCs, several software suites are available for designing supervision and monitoring systems. Most of them share features, such as web interface, lightweight and cross-platform (Windows and Linux). In addition, they are often freely distributable, i.e., free of charge, which is a significant advantage over traditional packages subject to high licensing costs for editing and operation. Likewise, there is a wealth of information on the Internet thanks to the community of users who contribute via forums or repositories, such as GitHub on configurations, application cases, troubleshooting, code for advanced options, etc. In contrast to the above advantages, these suites also have some limitations or drawbacks such as lack of user support or warranty or unverified long-term operational stability in some cases. Examples of SCADA software under the open-source and IoT philosophy are ScadaBR, Grafana, Tango Controls, OpenSCADA, RapidSCADA, SCADA-LTS and PyScada. Recent examples of using IoT open source for monitoring processes under the Industry 4.0/IIoT framework can be found in [8,62,77].

Similar to what is happening with new IoT devices recently introduced to the market by traditional PLC manufacturers, the same applies to software for supervision. For example, Siemens includes, in its catalogue, the environment for SCADA system design called WinCC Open Architecture (OA). It has features aligned with the new paradigms, such as compatibility with Windows, Linux, Android and iOS OS; support for multiple protocols (PROFINET, MQTT, OPC-UA, DNP3, etc.); object-oriented editor; client/server model; integration of Node-RED middleware, among others [130]. For example, it can be installed on a Raspberry Pi.

• Other trends

In the same sense as described for automation systems, the support for modern and open communication protocols such as OPC-UA or MQTT is also a trend in supervisory and monitoring systems [3,4,119]. Other open protocols that are also being integrated in these systems are Advanced Messaging Queuing Protocol (AMQP) and Constrained Application Protocol (CoAP) [4].

The use of middleware for data exchange between monitoring systems and measurement and control devices (smart sensors, PLCs, DAQs, etc.) is also a growing trend. Some IoT devices, such as the IoT2050 from Siemens, are already pre-installed with Node-RED middleware.

A trend in monitoring systems in terms of data accumulation is the use of SQL-based databases [119], this possibility being supported by a growing number of traditional suites, such as WinCC, Ignition or Indusoft. The aforementioned IoT open-source software also provides native support for databases. This trend has become more relevant precisely because of Industry 4.0 and IIoT, where the amount of data to be managed is very high [4,31]. The databases used can be local (within the factory itself) or remote, i.e., hosted in the cloud.

The penetration of Raspberry Pi deserves its own mention as a trend, both as a PLC and for supervision and monitoring systems. Its processing power, low cost and multiple available software environments make it a device with a growing presence in data collection, automation and information visualization tasks [2,4,94,131,132].

Sending alarms or information about the monitored process via text messages (SMS) or emails has been an option available for years in monitoring systems. The next step is the use of instant messaging applications, which allows the relevant operator or supervisor to be informed immediately of an event of interest. This functionality is available for some IoT-type environments, such as sending messages via the Telegram app from RapidSCADA or Grafana software [133].

Recent studies have examined the use of blockchain technology in SCADA systems for sensor authentication [112] and for energy demand management [116]. The latter source affirms that the use of blockchain technology in SCADA systems is an evolutionary proposal that contributes to the transition towards IIoT [116].

In a similar sense, there are examples of DT that are fed with data provided by supervision systems, for instance, to represent industrial energy facilities [134]. In fact, a very interesting application of DT, still underexploited in the literature, is derived from the visualization of data of the physical facility. In other words, the DT can be used to monitor the physical process or facility [11,134,135,136,137]. On the one hand, the operator must be informed about the current state of both the physical and digital systems in order to interact with the DT [138]. Therefore, simple and efficient interaction must be achieved through the HMI of the DT [134,139]. On the other hand, a step further can be performed since the DT provides the ability to monitor the physical counterpart due to the fact that DT is fed with real information via a communication linkage [11]. The DT can use, as input data, those provided in real time by the IoT, so the use of IoT and DT for monitoring is promising since it allows for predictive maintenance and provides more information about the physical asset that cannot be collected by sensors [140]. In factory scenarios, this technology facilitates the collection and analysis of equipment data in real time and makes them available to users anywhere in the world through dashboard visualization implemented in web pages or mobile applications [140]. Examples of DT-based monitoring systems can be found in recent studies applied to industrial work stations [141] or to visualize the real-time data of wind turbines [142].

Regarding the enabling technology, Artificial Intelligence, it is being increasingly applied to data provided by SCADA systems for tasks such as diagnostics and predictive maintenance [83], estimating energy production in photovoltaic or wind power systems or implementing cybersecurity measures [82].

Figure 10 schematically depicts the trends identified and presented for automation and monitoring systems. Among the trends illustrated in the figure, the following are highlighted for their relevance to the convergence of traditional equipment and IoT devices: the support of open communication protocols, the use of open-source IoT devices and software (such as gateways, middleware, etc.) and web-based access to monitoring systems.

5. Conclusions

PLC and SCADA systems are the prevalent technology for industrial automation and supervision for decades. In the present Industrial 4.0 era, these systems are facing challenges as well as adopting new advanced architectures and functionalities.

For these reasons, the present work reviewed the fundamentals of Industry 4.0, including reference functional architectures, and focused on the interplay of PLC and SCADA within them. New features and trends in the development of automation and supervision systems have been expounded to highlight their critical role in the Industrial 4.0 scenario.

In a summarized manner, the main conclusions of the paper are now listed:

• This paper aims to provide an overview of Industry 4.0 and IIoT by revisiting the essential concepts and aspects, and it is mainly oriented towards engineers and researchers in automation and monitoring.
• In view of the literature consulted, there are different definitions of Industry 4.0 and the associated concepts and terms. Regardless of the definition, it is clear that automation and monitoring systems play an essential role.
• Engineers and specialists moving in a scenario characterized by Industry 4.0 must be continuously learning about new technologies, market trends, potential threats, etc. Furthermore, the training of these personnel must cover not only aspects of automation, industrial communications and supervision but also aspects closer to the IT field, such as databases, IT communications and Artificial Intelligence, just to name a few. Nonetheless, it must be remarked that this comprehensive training of engineers and researchers are not easy tasks and require significant efforts, mainly in higher education.
• The most relevant functional reference architectures for Industry 4.0 have been presented as an evolution of the automation pyramid, but they also present limitations and drawbacks. Fundamentally, a high degree of abstraction makes it difficult to apply them in real industrial environments. IoT-oriented architectures are more manageable and easier to implement in industrial practice.
• The use of open IoT technologies, including hardware, software, middleware and communication protocols, favors the exchange of data and, therefore, the implementation of Industry 4.0. Therefore, these technologies must coexist with the traditional technologies of automation and supervision.
• Traditional industrial automation and monitoring solutions are not being replaced by new devices but are being upgraded in terms of performance, and new technologies are complementing their functionalities under new architectures.
• Both PLC and SCADA benefit from the connectivity provided by the (I)IoT and are part of the ecosystem to make Industry 4.0 a reality.

From a practical point of view, the manuscript depicts commercial equipment for IoT and automation and supervision purposes, which could be chosen when designing Industry 4.0-compliant facilities. Moreover, updated information about non-commercial developments under the open-source philosophy is also provided, for instance, about Raspberry Pi, Node-RED, etc. In spite of the fact that this paper does not cover cyber-security issues in depth, it is expected that this work contributes by taking into account these aspects in the automation and supervision fields.

In a self-criticism exercise, given the large amount of publications dealing with Industry 4.0 and IIoT, the authors would like to apologize for those contributions and trends that were left unnoticed.

A future work will deal with reviewing the implications and trends of automation and supervision aspects in the Industry 5.0 paradigm.