Research articlesEngineering human-focused Industrial Cyber-Physical Systems in Industry 4.0 context
Abstract
The world is increasingly interconnected, and this can also be seen in industry, where an ecosystem of digitalized assets, and humans with appropriate digital interfaces, constantly interact with each other. Digital transformation efforts in the industry rely on Industrial Cyber-Physical Systems that are driven by service-based cooperation among humans and digitalized industrial assets. This implies a radical paradigm change in their engineering and operation, which is focused on the symbiosis of digitalized assets and humans that cohabit a collaboration-driven industrial ecosystem. This work discusses how a digital transformation can effectively be achieved in an industrial ecosystem via a digitalization process performed along the three dimensions of the Reference Architecture Model for Industry 4.0, facilitated by the specification, development and implementation of an Asset Administration Shell. The discussion focus is put on humans and how the digitally transformed industrial environments empower her/his capabilities and interactions. It is also critically pointed out how one should go beyond technology and consider additional aspects. Therefore, it is argued that human-centred efforts in Industry 4.0 (I4.0) should be seen in the larger context of sustainability and circular economy in order to properly consider the interplay of the involved socio-technical dimensions.
This article is part of the theme issue ‘Towards symbiotic autonomous systems’.
1. Digitalization and networking of the economy
As a consequence of technical but also social evolution processes initiated at the end of the eighteenth century with the first industrial revolution, a true new revolution became increasingly visible with the start of the new millennium. Its scientific, technical, social and cultural effects are affecting businesses, people and society. This is labelled as the fourth industrial revolution, conceptually and also technically associated with several efforts such as ‘Industry 4.0’, ‘Internet-of-Things’, ‘Internet-of-Services’, ‘Industrial Cyber-Physical Systems’, ‘Smart Manufacturing’, ‘Digital Transformation’ [1,2]. The digitalization and networking pursued give rise to large-scale collaborative ecosystems that have the potential to create value for all stakeholders involved and impact not only the industry but also the society at large [3].
Historically, the first industrial revolution introduced steam and water power into industrial production, while the last one is linked to Cyber-Physical Systems (CPS), and Internet-of-Services (IoS) [4], as extensively discussed in the scientific literature and public press. For this work, it is important to note the new added type of interaction between humans and the industrial ecosystem, via a ubiquitous and service-oriented manner. In the first revolution, humans made use of the performance of steam and water power-driven machinery. Beginning in the third revolution, humans started to become a pro-active but integrated part of the processes in the digitalized and networked industrial environment. The fourth revolution develops this trend to perfection.
When digitalized and networked things communicate with each other, they enable collaborations across enterprise systems and layers [5], thereby creating the potential to provide market innovations [6]. When huge amounts of data are exchanged, automatically analysed and respective decisions are automatically taken, e.g. by artificial intelligence (AI) algorithms, a careful check of the role of the human is needed. It needs to be understood that their capacity to interact with the involved systems is an economic necessity. This is opposed to the option to lay off as many people as possible. In particular, for the discussion in this paper, we think it possible to integrate the human in an interesting way. But this touches upon almost all aspects of our lives: how we live, how we work, how we operate and how we think and how we understand value creation in the future!
In a first step, we will show that it is possible to include the human in this fourth revolution conceptually in different ways, in line with other reported proposals [7]. The scope of this endeavour can be seen by addressing the following questions: Who is the digitalized and networked human that interacts with a digitalized and networked environment? What is her/his knowledge, capability, role and/or business [8–10]? Moreover, humans are involved in different roles in Industry 4.0 (I4.0). For instance, a role can be that of a machine-designer, a user or a service technician. Each role requires a different set of digitalized data, information and knowledge that must be available to the humans to fulfil that role and also to be understood by her/him in order to act accordingly. The model to be used needs also to cover the fact that the human can be linked to different phases of the life cycle of all digitalized and interconnected components of the industrial ecosystem.
A solution for this complex task is provided by the formalization of the digitalization and networking process for the industrial environment via the Reference Architecture for Industry 4.0 (RAMI 4.0) [11]. It includes the digitalization and networking of physical objects, which in industrial environments are referred to as Industrial Cyber-Physical Systems (ICPS) [4] or Industrial Internet-of-Things (IIoT), and are accompanied with the respective semantic technologies and architectural principles. This provides the base for an industrial ecosystem of services (Industrial Internet-of-Services (IIoS)) [12]. An indication of this structured context is shown in figure 1.
Figure 1. Industry 4.0 built on different domains empowered by CPS, IoT and IoS. (Online version in colour.)
The digitalized and networked world does not stop at the borders of industrial enterprises or stay within domain-specific collaborative networks [5,13]. It includes other domains, such as home, energy, mobility, health and so forth. Again, the amalgamation of CPS, IoT and IoS, and the associated engineering methods, tools and technologies, is extended to these fields of society. It entails the important questions raised above in relation to human’s integration and interactions. In the next paragraphs, RAMI 4.0 and its implications for the human in Industry 4.0 context will be discussed.
2. Digitalization of the industrial ecosystem with RAMI 4.0
For a successful digitalization in industrial settings, two major architectures backed up by prominent industrial stakeholders have emerged. The first was the RAMI 4.0 [11] as a result of the German-driven Plattform Industrie 4.0 activities, while the other that emerged later was the Industrial Internet Reference Architecture (IIRA) [14] stemming from the Industrial Internet consortium. As also shown in figure 2, in both approaches, the industrial digitization differs but also shares common ground [15]. While for this work RAMI 4.0 was selected as the basis for a systematic industrial digitalization approach, there is an implicit mapping to some aspects of IIRA that are out of the scope of this paper and should be detailed in future works.
Figure 2. Industrial digitalization: IIRA (a) and RAMI 4.0 (b) architectures [15]. (Online version in colour.)
The RAMI 4.0 [11], as shown in figure 2, is a three-dimensional model that allows formalizing the digitalization and the internet-based networking process for every object composing an industrial ecosystem. These objects are explicitly labelled as ‘assets’, which with the addition of an Asset Administration Shell (AAS), are transformed to I4.0 components. RAMI 4.0 represents the digitalized and networked components of the industrial ecosystem in a three-dimensional space. Each point of this space has the following coordinates for each dimension: in dimension 1, the assets are positioned within the control management hierarchy, following IEC62264 (ISA’95)/IEC61512 (ISA’88) standard enterprise reference architectures [16]; in dimension 2 are the value stream and life cycle of the asset, following IEC62890 [17]; in dimension 3 are the layers for digitalization and networking the asset along its life cycle and/or value stream.
The space of the first two dimensions (two-dimensional space) allows a one-to-one mapping of an asset of the hierarchy to the phases of the life cycle/value stream of that asset. The result is a tuple <asset, life cycle-phase-of-the-asset>. From a functional viewpoint, this means that each asset of the hierarchy dimension has too many elements in the value stream dimension as the number of phases of the life cycle of that asset, e.g. <asset, design>, <asset, development>, <asset, production/operation>, <asset, maintenance>, etc. The third dimension enables the definition, specification and implementation of six digitalization layers for each phase of the life cycle and value stream of an asset. The result is a tuple <asset, life cycle-phase-of-the-asset, layer>, e.g. <asset, maintenance, business>. This becomes obvious when looking at the different layers: asset, integration, communication, information, function and business, defined for each of the bi-dimensional compositions addressed above.
As an example, consider a robot in a production system positioned at the level station of the IEC 62264 in the hierarchy of the first dimension of RAMI 4.0. It can be specified in its design, its development and its implementation phases of its life cycle (classified as ‘type’ in the second dimension). But it can also be specified in its deployment, its operation, its maintenance, its recycling phases of its life cycle (classified as ‘Instance’ in the second dimension). The application of the third dimension to this asset means digitalizing and networking the robot-type and/or digitalizing the robot-instance, migrating it from an asset into an I4.0 component. More specifically, the digitalization of the robot allows us to expose its characteristics and capabilities for each phase of its life cycle in the IoS: (i) data and information concerning the robot-design, (ii) data and information concerning the robot development, (iii) data and information concerning the robot implementation, data and information concerning the robot deployment, (iv) data and information concerning the robot operation, (v) data and information concerning the robot maintenance, (vi) data and information concerning the robot recycling. An exemplary tuple for an instance of the robot would be <robot, production, point-to-point-operation function>.
A novel, business-oriented engineering approach, consistent with the RAMI 4.0 specifications as described above, will apply a digitalization and networking process navigating the layers of the third dimension in a top-down fashion. This means, only after the required IoS-based business application in layer six is defined, the process of defining and engineering the contents (and technologies) used by the layers below starts, and goes until layer 1 (Asset) is reached. Therefore, the decision to digitalize and to network one or more elements of the two-dimensional space [(IEC 62264 / IEC 61512), IEC 62890] depends exclusively on their contribution to performing a desired IoS-based business required in layer six.
It is important to note that the choice of which data and from which phase of the life cycle and value stream of a physical asset will be digitalized, depends on the contribution of the digitalized data to the respective business/application.
The proposed digitalization engineering approach contains six steps, as described and exemplified below. As the process is top-down, step 1 starts in layer 6.
Step 1 deals with the contents of layer 6 (business): they are answering the question: Which ‘business’ associated with which phase of the life cycle (IEC 62890) of which asset/s (IEC 62264 / IEC 61512) should be performed as IoS? For the example of the asset robot, let us consider ‘production (operation)’ as the IoS-based business application. This means that the assets associated with this business will be those able to provide digital data and information supporting the operation of the robot, i.e. the robot itself but also the manufacturing execution system (MES) and the production planning and control modules (PPC) in hardware and software, the SCADA and the robot operator.
Step 2 deals with the contents of layer 5 (function): they are answering the question: Which ‘function’ of the digitalized assets will have to be exposed as ‘service’ in the IoS network for supporting the business defined in layer 6? In the robot example, these functions would be (i) function monitoring of robot operative data, (ii) function control logic, (iii) function dispatching of the MES, (iii) function robot serial number and construction data, etc.
Step 3 deals with the contents of layer 4 (information): they are answering the question: Which is the digitalized data and information (modelled with adequate semantic) that are necessary for correctly representing the function selected in layer 5? For the robot example, this refers to the states of peripheries connected to the joints; characteristics of the objects to be handled; work plans associated with those objects; etc. All this information, when exposed as IoS, will help the robot operator to make a decision about robot operations proactively.
Step 4 deals with the contents of layer 3 (communication): they are answering the questions: Which Internet-based communication technology and protocols, which I4.0-compliant communication interface between the robot and the other digitalized assets of the network, such as SCADA, MES, PPC, PLCs, should be implemented for adequately communicating the data and information required in layer 4? As a reference for the example robot, one or more of a wide variety of industrial communication protocols or technologies would be applicable [18,19].
Step 5 deals with the contents of layer 2 (integration): they are answering the question: With which digital interfaces will be acquired ‘the digital data’ of the tuple <asset, life cycle-phase-of-the-asset> to be communicated on layer 3? In the example, this means the implementation of the digital interface for the asset robot, for the asset MES, for the asset PPC, etc.
Step 6 deals with the contents of layer 1 (asset): they are answering the question: which is the tuple <asset, life cycle-phase-of-the-asset> that will provide the necessary digital data specified in layer 2? As already addressed for Step 1 of the example, this includes the robot itself but also the MES and the PPC modules (in hardware and software), the SCADA and robot-operator, etc.
Once a duple <asset, life cycle-phase-of-the-asset> is positioned in the first two dimensions of the RAMI 4.0 cube, the digitalization process follows clear rules on all layers of the third dimension.
As a major result of the application of this RAMI 4.0-based digitalization approach to an industrial ecosystem, the last is now formalized as a three-dimensional vectorial/functional space that is completely populated with tuples <asset (according to IEC 62264 / IEC 6152), life cycle-phase-of-the-asset (according to IEC 62890), layer (one of the six layers of the RAMI 4.0 vertical dimension)>.
Coming back to the example of a robot, considering it as Instance performing, in operation phase, the function point-to-point transfer, the tuple will be <asset: Robot, life cycle-phase-of-the-asset: Operation, layer: Function (point-to-point-transfer)>. For the same robot, but now considering it as Type in its first life cycle phase (e.g. development), the tuple will be <asset: Robot, life cycle-phase-of-the-asset: Design, layer: Information (e.g. technical specifications)>.
3. Digitalizing with the asset administration shell
In the traditional definition of industrial systems, they are constituted by a set of components, included the human. As formally represented by the tuple, the Industry 4.0-compliant digitalization approach means a radical shifting from this traditional component-oriented to a capability-oriented infrastructure. It is a kind of servitization process, which shifts the focus from the all-in-one component model towards capabilities, e.g. functionalities offered via an IoS-based function-/business-oriented infrastructure, which also allows the composition/orchestration of distributed services that are dynamically put together.
As a matter of fact, any tuple <asset, life cycle-phase-of-the-asset, layer 6> is able to publish, offer data, information and functions as ‘services’, as well as subscribe, consume and compose ‘services’ offered by other tuples. Moreover, the individual digitalization layers can be opened up to other stakeholders, which can provide their highly customizable capabilities and sophisticated behaviours for being easily integrated into the digitalized and networked ecosystem.
With the use of the AAS [20] in the RAMI4.0-compliant digitalization, this capability-driven way is drastically reshaping how industrial systems are designed, developed and operated. Essentially, the form of specifying and implementing from layers 3 (communication) to 6 (business) of the vertical dimension, i.e. third element of the tuple, for any asset positioned within the RAMI 4.0 model, is creating a uniquely addressable digital (cyber) image, information that formally represents the characteristics, functions and behaviours for each type and instance of that asset (figure 3). This digital (cyber) representation has to guarantee a consistent data model during the whole life cycle (from Type to instance and vice versa). This allows the linkage of all stakeholders of the supply chain and value stream in business processes. In addition, as shown in figure 4, the human as a key stakeholder and her/his interactions can therefore be significantly eased in their integration in such kind of I4.0 systems.
Figure 3. The AAS for digitalizing assets within the Industry 4.0 Reference Architecture Model (RAMI4.0) [11]. (Online version in colour.) Figure 4. A capability-oriented digitalized and networked industrial ecosystem facilitated by the AAS. (Online version in colour.)
AAS [20] enables the digital (cyber) consistent value chains and acts as a concrete form of implementing different ‘digital twins’ [21] of the physical assets associated with different phases of the life cycle of the physical asset. It allows embedding the asset into I4.0-compliant communication, facilitating controlled access to potentially all information of all life cycle phases of the asset. Moreover, it can be addressed via the Internet and identifies the digitalized asset uniquely.
Being formalization of the networking and exchange of information between value creation partners associated with the assets, the AAS specifies how information should be prepared and structured in a package for the exchange among those partners. This is based on standardized syntax and semantics. Moreover, the structure of the AAS is able to handle access protection, visibility, identity and rights for managing confidentiality and integrity.
Each AAS has its own identifier and different sub-models that address different digital twin aspects, digital representation of different data and information from the life cycle phases of the physical asset with its sub-model elements.
Applying the engineering principles specified by the RAMI 4.0, it is possible to provide each asset within an industrial ecosystem with the structural, functional and behavioural capabilities to communicate and interact and collaborate with each other. This collaboration consists of the exchange of digitalized data and information and is aims to achieve proprietary or common IoS-based business or application goals along the life cycle for any of the assets.
The integration of humans [7] in I4.0 (ICPS) environments is not new, but how it can be realized is still a challenging undertaking. Using the AAS in the digitalization and networking process, all assets, including the human, have acquired the capabilities to expose and/or consume services that represent their characteristics, functionalities and capabilities. The implications of this digitalization process become visible by analysing an I4.0-conform industrial environment. With the digitalization and IoT networking methods and technologies formally provided by the RAMI 4.0 and the AAS, the humans provide the physical assets of the industrial ecosystem with capabilities to interact, via IoS-interfaces, with other I4.0-components (CPS) and humans applying a very human-oriented communication fashion. Thus it links directly to efforts towards the Industry 5.0 vision that complements Industry 4.0 paradigm with a focus on sustainable, human-centric and resilient industry [22].
Humans are linked to one or more roles in each of the phases of the life cycle for each I4.0 component (digitalized and networked asset in the industrial ecosystem). They have now the possibility to interact with any other I4.0 component through its AAS. This interaction is mainly done on the basis of service provision or service consumption, but also service-orchestration, service-choreography on layer 6 of the third dimension of RAMI 4.0. However, it is also possible that humans provide their know-how and capabilities within each of the other RAMI 4.0 coordinates. In order to participate in the community of I4.0 networked components, represented by the AAS, the human needs adequate interfaces for providing or consuming the services, e.g.augmented/virtual reality assistant systems, digital glasses, digital watch, etc.
Humans with very different and disjoint roles as well as knowledge background along the life cycle of a physical asset can now interact in the IoS network due to the common I4.0-language (data and information model) used in layer 4 and layer 5 of the addressed digitalized assets (I4.0 components).
By specifying the position of the human within the three-dimensional coordinate system formalized by the RAMI 4.0 and the AAS, as shown in figure 4, the spectrum of possibilities for not only positioning the human but for composing their capabilities with those capabilities offered by other digitalized assets of the industrial environment, is really huge. Moreover, applying the basic rules for functional composition within the vector spaces represented by the RAMI 4.0 allows identifying new forms of collaboration between humans and I4.0-Components that were never possible before. Since I4.0 components may have decision-making capabilities, possibly supported by AI, the human can now go in negotiation and decision-making process with those I4.0 components.
4. Humans in Industry 4.0
The German technology platform Industrie 4.0 initiated the process of revolutionizing production by fostering and pushing technologies, as discussed in this paper, stressed from the very beginning four dimensions in this process: vertical and horizontal integration in enterprises, new procedures for integrating engineering and human-centred efforts, as an important condition to keep the humans in the loop. However, human aspects have not been sufficiently addressed within the context of I4.0 [23], something that has also been recognized by industrial stakeholders, and efforts are initiated in this direction. The platform Industrie 4.0 published a charter for Work and Learning in I4.0 [24] that is based on three central pillars: people, organization and technology, aiming at the sustainable design of work and training. Therein, it is recognized that the participation of employees and their representatives contributes to more value creation, innovation and economic progress in companies.
(a) Human empowerment in future industrial environments
The changes due to rapid technological advances always have an impact on the traditional and established processes in industry, and, as such, they affect directly or indirectly humans interacting with them. The digitalization and especially the demonstrated practical applications of artificial intelligence (AI) in industry, has fuelled the fear of human obsolescence and replacement by intelligent machines. While aspects of job insecurity and replacement by robotic automation and AI-empowered services are real, these are often asymmetrically presented in the press. While digitalization via AI and CPS creates new challenges [25] to the status quo, it also gives rise to new [26].
In the context of I4.0, new skills are needed for humans [27,28]. These skills are increasingly multi-disciplinary and include both the developer side, i.e. how to develop technologies and processes in I4.0, but also the user side, i.e. how to make use of the capabilities offered to the employees in the new I4.0 working environment. Therefore, a stronger interplay of academia and industry is needed to develop such approaches and environments for learning and training for I4.0 skills [28–30], while modern methods, such as Massive Open Online Courses (MOOCs) in industry [31], can provide the needed scalability to educate and train the workforce.
In future collaborative industrial environments, humans are envisioned to work side-by-side with AI-empowered robots in various forms, where humans can easily interact with them and focus on higher-level general tasks that are not yet mastered by robots while delegating mechanical and repetitive tasks as well as hazardous ones to the machines. Overall, AI-enabled systems have the possibility to significantly enhance the human to machine interaction.
Up to now, machines were treated as ‘passive’ and ‘dumb’ devices with predefined static ways of interaction that the human had to learn to manage, something that created inefficiencies in processes and required significant efforts to integrate the interactions of humans and machines. In a new era of AI-powered machines, such gaps can be minimized, as intelligence embedded in machines and industrial systems can act as a powerful mediator between humans and machines, thereby effectively narrowing the gap among them. A typical example is the introduction of AI-driven chat-based systems that can interact via voice with the human employees on the shop-floor, an interface that is much more human-friendly and adjustable over time, as with AI, machines can learn and handle better situations that emerge.
Human-centred approaches are possible in the new I4.0 era [32] due to the new technologies that empower them. However, usage of technology by itself needs to be properly integrated into collaborative processes at all levels of industrial automation that provide new capabilities and empower humans to have a better experience than merely carrying tasks out of necessity. Advances in the domains such as ICPS [4], industrial agents [33], integrated into a holistic framework such as the RAMI 4.0 and AAS, have the potential to create the basis where such human-centred efforts can flourish from a technological and business point of view.
(b) Beyond humans as assets
I4.0 specifications define many different levels of communication upon which to base digitalized industrial processes. As these processes will be automated to a large extent, it will take a conscientious effort to include human-assets in the process of value creation and to make sure that their role is socially sustainable.
The I4.0 Charter for Work and Learning [24] stresses that the new technologies should be used in a socially, ecologically and economically sustainable manner. However, it is not clear how companies accomplish this link in practical terms. To understand the ambiguity in this discussion, the difference between economic decisions that often lead to the layoffs in the workforce and the proposed social sustainability, it is illumining to closely examine the term ‘human assets’.
As given in Webster’s online dictionary, the definition of the word asset highlights two important aspects. The word is sometimes used to generally indicate an advantage or a resource. In a more narrow sense, the term refers to either an entire property, e.g. of a corporation that is applicable to the payment of debts or items on a balance sheet showing the book value of property owned. The description of a technical system (e.g. a production system) of a company in terms of assets refers to the book value of the property owned and appears in the financial balance sheet of the respective enterprise. If one extends the term to humans involved in the company’s operation, it is important to clearly define what is meant.
If one opts for the rather vague meaning of a simple ‘advantage’ or a ‘resource’, one misses the fact that the human assets appear in a company’s balance sheet as cost factor only, pointing more into the direction of ‘book value’ or ‘property owned’. Then the ‘book value’ of a human is reduced to the financial value of her/his functioning correctly in the overall system. All the intangible values of the respective human being, e.g. the ability to think associatively and thus come to new solutions or to solve a difficult human situation at work, is not considered.
The discussion of any new technological system gravitates towards the technology, its advantages and disadvantages and its technical features. Usually, the link to the humans involved is left out or only vaguely addressed. Nevertheless, technology is a tool for humans to create value. Thus, it is important to understand whether this value creation is only for a small class of people or whether the tool will be used to share the created value in a wider community. The discussion of technology, and especially of radical new production technology, needs to move beyond the value-free aspects of highlighting special features. The reflection in this work of the aspects of ‘human assets’ indicates the poles one has to consider.
It is beyond the scope of this paper to discuss an economic advantage that can be generated by companies that include the workforce consciously in a socially sustainable way and that reflect this in their organization. However, efforts such as SoSmart [34] provided proof to this claim. Here, it should be reinforced the objective of identifying options based on the technology that can help humans to find new forms of value creation.
5. Innovations towards capability-oriented Industry 4.0 infrastructure
To realize a human-focused capability-oriented digitalization, several technical innovations have to be in place. A high degree of miniaturization and integration is required in a cross-layer fashion not only within enterprises and industrial domains but also at an ecosystem level. For building the respective systems, standardized interfaces (mechanical and electrical) have to be used. The system needs a software backbone that allows for autonomous operation and for self-ramp up of the assets into the system environment. The addition of software introduces the necessity for complementary standardized interfaces guaranteeing connectivity and interoperability [35,36]. The technical boundary conditions for application are associated with the reconfigurability of the systems, a plug and produce use, the possibility to change or introduce new modules, new building blocks without re-coding the governing control software and an unconditional non-proprietary performance.
One paradigm that exemplifies the shift towards a more intelligent and autonomous, capability-oriented infrastructure envisioned in I4.0 is that of software agents, especially when applied in industrial contexts [33,37]. The concept of the physical agent was introduced in the 1990s for specifying the association of a software agent to physical production components [38], explicitly where decision-making process with autonomy behaviour and proactive collaboration was involved. An equivalent concept was applied by the Holon in an industrial context, following the definition of Holonic Systems introduced by Köstler [39]. In all definitions and applications reported in the past, the software agent part of a physical agent was responsible for some control or monitoring action associated with that component, which increasingly was done via services [40].
The agents are seen as an interesting paradigm in the ICPS domain as they can help with the integration and intelligence challenges that emerge [33]. Industrial applications of software agents [37] over the years demonstrate their applicability; for instance, some early results show a one-to-one mapping of a machine-to-machine agent, of a transport module to a transport agent and of a product to a product agent [41,42].
In the sequence of reported innovation, the results of the European Union project IDEAS (2010–2013), driven by agent technology, proved that device changes in a running production line could be carried out within minutes as opposed to changes in a legacy line which comprises complicated mechanical adjustment and a tedious ramp-up process after the change is finished. [35].
Another set of examples were the innovations created in the European Union SOCRADES project (2006–2009) [43], where world-leader technology providers like Schneider Electric, SAP, Siemens, ABB, Microsoft, ARM, among others, co-innovated on the design, development and prototype implementation of service-oriented devices and systems for performing SoA-based management, control and automation functions in representative industrial domains. Together with its successor European Union project IMC-AESOP (2010–2013) [44], it was possible to show that the complete hierarchy standardized by the IEC 62264 / IEC 61512 can be migrated into a flat functional service-infrastructure, using industrial devices and systems as well introducing edge and cloud technology [45].
Results of these examples advanced the penetration of I4.0 methods, tools and technologies in manufacturing and process industries and demonstrated these in edge- and cloud-based infrastructures [44]. However, the technical aspects are only a subset of the overall factors that are linked to the acceptance of the digital transformation in industrial environments [46], while the social aspects also need to be considered as discussed in §4.. Moreover, even when the human aspects are considered, they are usually limited within a specific domain; here, a more inter-disciplinary approach is needed that also links technological progress with that of social sustainability and circular economy.
6. Sustainability and circular economy in Industry 4.0 context
I4.0 is complex and comprises of a large collection of technologies [2], and concepts that have socio-technical angles and impacts that pertain to technological, social and organizational aspects [47]. Efforts in I4.0 have moved well beyond the realm of research. Research has proven the power of I4.0 by providing evidence that production lines can be very quickly adapted to new products without difficult ramp-up procedures, by systems that can be realized by many different vendors without complications of proprietary software or interface definitions, that new ways of interacting with the humans in the emerging I4.0 industrial environments are beneficial and viable etc. I4.0 has been linked to the circular economy via different factors, and AI is one of them [48].
The RAMI 4.0 specification [11] is designed to improve the operation of multilevel international enterprises, introducing the level ‘connected world’ into the traditional IEC 62264/ IEC 61512 hierarchies. Globalization comes to mind. Nevertheless, it is clear that it aims at all companies, especially if they have their own production. Revolutions in a production environment need a long time. Legacy equipment is not easily dismantled as it still might be economically interesting to keep it for the manufacturing of actual products. The implementation of new powerful software systems promising organizational and economic benefit will also take time as the decision for the related investment may be postponed and these tools need to be applicable in a brownfield application case. In short, this change is ongoing, but it is also struggling with the implications of the worldwide COVID-19 crisis, which puts many of the supply chains under pressure. This will require a new orientation, also for the innovations efforts that are discussed throughout this work.
This paper has described the technical beauty of the related I4.0 installations that opens new ways for including humans in industrial production and development of future-looking sustainable approaches. I4.0 platform has recognized this via the creation of its own charter to the subject [24]. However, today most efforts investigating sustainability focus on organizational sustainability but not overall societal sustainability [49]. The aspect of social sustainability needs to be emphasized, reinforcing the fact that the world is witnessing some early efforts in this direction.
Via the critical discussion on RAMI 4.0, the AAS and its implications, it is evident that the role of the human can effectively be linked to its capabilities and the functions s/he performs in the digitalized system. This can be coined as an advantage for the respective enterprise. But this paper also showed that the ‘human asset’ could be easily reduced to a book value. Ongoing operations in large companies which lay off part of their workforce make it plain how difficult it is for some management to consciously recognize the advantage of the human for their operation. The necessity of such decisions is argued on an economic basis, which, however, focuses on narrow aspects and short-term profit optimization, and overlooks the larger picture, historical experience from past industrial revolutions, and social sustainability. In all countries, there exist examples that prove the economic advantage of supporting the workforce sufficiently, probably one of the most famous is that of Henry Ford in 1914. He effectively doubled the income of his workers while reducing the work hours at the same time [50]. This economic advantage caused by ensuring human involvement is one of the topics we encourage to research more. This is very promising, but it is not mainstream thinking or acting and takes perseverance and courage on the decision maker’s part. The technology behind I4.0 is capable of fostering such an advantage.
Figure 1 includes more than the mere industrial world. It indicates many other fields of application that require or provide communication with Internet-based devices. The definition of a path in the RAMI 4.0 for this communication also explains a rather disturbing context that is observed today. Individuals can retrieve their own information and thus build their own ‘reality’. This underlines the fact that one will have to make an effort to ensure that the information received by a party or by a technical asset is credible. Billions are spent on the development of the system under consideration here, and an equivalent effort is necessary to empower humans with the knowledge and the tools to be able to cope with it in a sustainable way, not only for their employees but also for their command of this technology.
As additional consideration for the capability-oriented I4.0-based digitalization, it is essential to stress that it includes the realistic option to build small systems in a plug and produce way without requiring sophisticated knowledge for the ramp-up. Also, a realization of smaller systems is possible at levels of investment that were not thinkable before. As industrial and especially Information and Communication Technologies evolve over the years [51] new capabilities will be made available at a mass scale. This again is a big chance for humans to use the technology to create their own products independent of large enterprises. This is not wishful thinking, but it is reaching reality already today in FabLab (fabrication laboratory or MakerSpace) environments.
Industry 4.0 has been proven to have social capital (e.g. functioning social groups, common understanding and norms, common values, trust, cooperation and reciprocity) as a prerequisite for information sharing and collaboration [52]. In addition, the impact of innovation policies at the country level across the world differ [53] and also affect the realization of Industry 4.0. Hence the path towards implementing Industry 4.0 is not expected to be the same along with the world nor with the same effects.
7. Conclusion
Positioned within the structural and functional specifications of the RAMI 4.0 and the concrete implementation of an AAS, this work introduced the significant implications of a radical paradigm change in the engineering and operation of industrial systems generated by worldwide ongoing industrial digital transformation efforts. The discussion focused on the positions and roles of humans, on the symbiosis of digitalized humans and assets that cohabit a collaborative-driven industrial ecosystem and how these digitally transformed industrial environments are empowering human capabilities and interactions. Moreover, it has been discoursed that human-focused efforts in Industry 4.0 should be seen in the larger context of sustainability and circular economy in order to properly consider the interplay of the involved socio-technical dimensions. The next step, beyond the innovation possibilities provided by the digitalization process, will be to actually create organizations able to reinforce the fact that the human-centred approach unleashes the human potential, drives new forms of value creation and realizes sustainability.
Data accessibility
Additional specific information and results supporting this paper can be found in [2,9,10,29].
Authors' contributions
The content presented is based on the professional experiences of all authors, and all three contributed to the development of the text equally.
Competing interests
We declare we have no competing interests.
Funding
We received no funding for this study.
