The rise of new information and communication technologies forms the cornerstone for the future development of work. The term Industry 4.0 refers to the vision of a fourth industrial revolution that is based on a network of autonomous, self-controlling, self-configuring, knowledge-based, sensor-based and spatially distributed production resources. All in all, different forms of the application of the Industry 4.0 concept can be observed, ranging from autonomous logistic transport systems drawn upon the idea of swarm intelligence to smart knowledge management systems. This paper aims to develop a theoretical framework to analyze different applications of Industry 4.0 on an organizing continuum. The general research questions are: What forms of organizing digitalized work lead to the reproduction of routines, and what forms foster innovation within Industry 4.0? The authors thus analyze the consequences of different forms of organizing work on workers’ perceptions and the results of the working process.
This paper provides case studies for different stages of the organizing continuum in the context of Industry 4.0. The cases and a further analysis of all 295 funded projects are based on the Platform Industry 4.0 Map, which is part of the Industry 4.0 initiative of the German Federal Ministry of Economic Affairs and Energy and the German Federal Ministry of Education and Research. The consequences for people acting in such organizational and digitally supported structures are discussed.
A variety of applications of Industry 4.0 can be found. These applications mainly vary in the dimensions of the degree of formalization, the location of control authority, the location of knowledge and the degree of professionalization. At the right side of the organizing continuum, the digitalization organizes a work environment that supports highly qualified humans. They have broad leeway and a high degree of autonomy to design and create innovative forms of digitalization for tomorrow. At the left side of the organizing continuum, Industry 4.0 structures a work environment with narrow leeway, a low degree of autonomy and a top-down structure of control authority predetermined by digital applications. In this case, employees fill the gaps the machines cannot handle.
As the paper focuses on Industry 4.0 developments in Germany, the comparability with regard to other countries is limited. Moreover, the methodological approach is explorative, and broader quantitative verification is required. Specifically, future research could include quantitative methods to investigate the employees’ perspective on Industry 4.0. A comparison of Industry 4.0 applications in different countries would be another interesting option for further research.
This paper shows that applications of Industry 4.0 are currently at a very early stage of development and momentarily organize more routines than innovations. From a practical point of view, professional vocational and academic training will be a key factor for the successful implementation of digitalization in future. A joint venture of industry and educational institutions could be a suitable way to meet the growing demand for qualified employees from the middle to the right-hand of the organizing continuum in the context of Industry 4.0.
Industry 4.0 is designed by men, and therefore, humans are responsible for whether the future work situation will be perceived as supportive or as an alienated routine. Therefore, designers of Industry 4.0, as well as politicians and scientists, absolutely must take the underlying outcomes of digitalized work into account and must jointly find socially acceptable solutions.
This paper provides a promising avenue for future research on Industry 4.0 by analyzing the underlying organizational structures of digital systems and their consequences for employees. Moreover, the paper shows how Industry 4.0 should be organized to simply reproduce routines or to support innovation.
Wilkesmann, M. and Wilkesmann, U. (2018), "Industry 4.0 – organizing routines or innovations?", VINE Journal of Information and Knowledge Management Systems, Vol. 48 No. 2, pp. 238-254. https://doi.org/10.1108/VJIKMS-04-2017-0019Download as .RIS
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Copyright © 2018, Emerald Publishing Limited
In recent discussions about the development of new information and communication technologies, we can observe a paradigm shift marking the transition of work from a real-physical world to a virtual world. In this vein, real products are linked to Web-based applications and are increasingly integrated into production processes. The rise of these new information and communication technologies forms the cornerstone for the future development of work. Since the beginning of industrialization, technological leaps have led to paradigm shifts and have had a strong impact on the functional areas of individual work systems; in retrospect, these changes can be defined as industrial revolutions. The term Industry 4.0 refers to the vision of a fourth industrial revolution, which is currently considered a future project (Lasi et al., 2014). The vision of Industry 4.0 can be understood as a comprehensive digitalization and linkage of production processes, starting from the customer’s order, through the creation of production processes, to downstream product services. In this regard, predominantly self-organized value-creation networks are expected to lead to profound changes in economic interactions. Therefore, considerations in the context of Industry 4.0 go beyond the mere optimization of IT-supported processes. According to Kagermann et al. (2013, p. 5):
Industry 4.0 enables continuous resource productivity and efficiency gains to be delivered across the entire value network. It allows work to be organised in a way that takes demographic change and social factors into account.
This phenomenon can be observed in various countries all over the world (for an overview see Gausemeier and Klocke, 2016, p. 14f.), yet discussed under different terms, such as the so-called “Second Machine Age” by Brynjolfsson and McAfee (2014) in the USA or “Made in China 2025” (Zhang et al., 2014). Nevertheless, the term Industry 4.0 is increasingly used to encompass these developments (Zhang et al., 2016).
On the one hand, particularly in the initial phase of a sociotechnical change, there are great expectations and visions related to the arising possibilities. On the other hand, there is a high degree of uncertainty about the social consequences of these new technologies. This discrepancy seems explicable given that different scientific disciplines are involved in the research and implementation of Industry 4.0. The understanding of Industry 4.0 is based on the disciplines of computer science, engineering, political science, sociology and economics , which impedes a uniform definition. A definition upon which representatives from different backgrounds (politics, business, science and trade unions) agree was created within the framework of the “Industry Platform 4.0”, initiated by industrial associations, companies, the Federal Ministry for Economic Affairs and Energy and the Federal Ministry of Education and Research in Germany in 2015 and reads as follows:
The term Industrial 4.0 stands for the fourth industrial revolution, a new stage in the organization and management of the entire value chain over the life cycle of products. This cycle addresses the increasingly individualized customer requirements and extends from the idea of the development and manufacturing, the delivery of a product to the customer up to the recycling, including associated services. The basis is the availability of all relevant information in real time through the networking of all entities involved in the value creation as well as the ability to derive from the data the optimal value flow at any time. By linking people, objects and systems, dynamic, real-time and self-organizing, cross-company value-added networks emerge, which can be optimized according to different criteria such as cost, availability and resource consumption (German Federal Ministry of Economics and Energy, 2015 translated by the authors).
Roblek et al. (2016) and Hermann et al. (2016) identified cyber-physical systems, the internet of things (IoT), the internet of services (IoS), and the concept of smart factories as the four key components of Industry 4.0. As Baheti and Gill (2011, p. 161) point out, cyber-physical systems “refer to a new generation of systems with integrated computational and physical capabilities that can interact with humans through many new modalities”. Big data analysis plays one of the most critical roles in IoT frameworks. Vermesan et al. (2009, p. 9) define the IoT “as a dynamic global network infrastructure with self-configuring capabilities based on standard and interoperable communication protocols where physical and virtual ‘things’ have identities, physical attributes, and virtual personalities and use intelligent interfaces and are seamlessly integrated into the information network” (for an overview of definitions, see Li and Li, 2016, p. 75). In contrast to that, the IoS:
[…] presents a paradigm in which everything is available as a service on the Internet. It can also be viewed as networked services or systems across the real and virtual worlds over the Internet. In the IoS, services – as encapsulated functional entities containing interaction processes by service providers and customers – are distributed, virtualized, and converged over the Internet to meet the requirements of and create value for customers” (Xu et al., 2015, p. 81).
Another major goal is the implementation of an intelligent and self-organizing factory, which is also referred to as a smart factory (Lucke et al., 2008). Within this intelligent factory, smart logistics, intelligent objects and mobile communication devices are produced in consideration of the efficient use of resources. The goal is to produce intelligent products that can be further developed even after delivery; the products hence become active elements of the production process themselves (Sendler, 2013). This vision describes the independent management and organization of individualized customer orders (batch size = 1) across the entire value-added chain by implementing a continuous horizontal and vertical network. Industry 4.0 encompasses different forms of information exchange: information exchange between humans, human–machine interaction (H2M) and, currently the most prominent, information exchange between machines (M2M). M2M communication and smart products build a single entity (Roblek et al., 2016). According to Dawid et al. (2017), smart products can also be characterized and improved along the dimensions of input (e.g. sensors and acquisition of relevant data), output (e.g. displays, including visual, auditory or haptic signals), human–computer interfaces (e.g. control and interaction), interoperability (e.g. exchange of information between artifacts), integration (e.g. meaningfulness or usefulness of a larger-scale system) and resource efficiency (e.g. obtaining, generating and saving energy to deliver their service). From a technical view, the lack of standardized interfaces causes most smart solutions to work in isolation, thus leading to a fragmentation of diverse systems. Fragmentation occurs “when a larger number of products co-exist in an environment without really cooperating” and “indicates missed opportunities for exploiting synergies” (Dawid et al., 2017, p. 207). In summary, we conclude that Industry 4.0 currently remains in a very early stage of implementation and can rather be considered a vision than a reality. Apart from these technical issues, Industry 4.0 may be an interesting opportunity for massive reorganization processes in companies by making production processes truly smart.
Industry 4.0, like all information technologies in general, inheres in its own types or models of organizing things. In a nutshell, so-called smart systems can, on the one hand, reproduce simple and monotonous routines in the form of digitized mass production. In this case, the cyber-physical systems of Industry 4.0 can be considered a reproduction or an analogy of real-world forms of organizing social behavior to reproduce routines. On the other hand, cyber-physical systems can rebuild flexible, innovative and totally new H2M interactions in a fluid network with very smart and innovative forms of cooperation and collaboration.
What type of Industry 4.0 will ultimately emerge strongly depends on such underlying concepts of organizing. Therefore, our research questions are as follows:
What forms of organizing digitalized work lead to the reproduction of routines?
What forms of organizing digitalized work allow the creation of innovations by Industry 4.0?
To analyze different forms of organizing digital architectures, we will elucidate some general assumptions regarding different forms of organizing from the perspective of social sciences and organizational studies. Then, we will discuss how these types of organizing are reproduced or rebuilt in digital structures and which underlying digital architecture or form of organizing leads to imitating and reproducing routines or to creating innovations. Finally, we will provide relevant empirical evidence by presenting different case studies on Industry 4.0.
The continuum of organizing digitalized work
The digital architecture of Industry 4.0 is not an end in itself but could support people working in and with those structures to make production or service processes smarter and more efficient. Therefore, we focus on the relationship between the underlying digital architecture and the work assumed by the underlying digital architecture (Figure 1). From an organizational perspective, we describe the forms and types of organizing as a continuum (Meyer and Rowan, 1977), with its two poles of routinized and innovative work tasks. The origin of our theoretical underpinning is Burns and Stalker’s (1961) differentiation between mechanistic and organic organizations. They characterize the continuum with 11 features that are overlapping and not clearly distinguished, yet according to Lam (2005, p. 118) can be summarized in four more general attributes:
degree of formalization;
distinctness of control authority;
location of knowledge; and
degree of professionalization.
Accordingly, we draw on these four broad categories and demonstrate their applicability with regard to the topic of Industry 4.0. In addition, we refer to other approaches that support the distinction between mechanistic and organic organizations.
As Figure 1 shows, mechanistic and organic types of organizations are opposite ends of an organizing continuum. Between the two poles, mixtures and coexisting forms in one organization (e.g. within different units) may occur (Lam, 2005, Lawrence and Lorsch, 1967). In a meta-analysis, Damanpour (1991) found empirical evidence for the mechanistic-organic model, especially regarding four characteristics we will discuss in more depth in the following sections.
Degree of formalization
On the one hand, mechanistic organizations follow a strict and rigid structure whilst the environment is assumed to be relatively stable and predictable (Burns and Stalker, 1961). Hence, the employees’ working tasks are characteristically highly standardized and can easily be broken down into functionally differentiated duties, making them suitable for a support by digitalized structure (Lam, 2005). Task requirements in a mechanistic organization are Taylor-made (Taylor, 2006/1911), e.g. the software or digital architecture produces reiterative, specialized, easy to handle and formalized duties, what we call a high degree of formalization. A high degree of formalization in a mechanistic-oriented organization increases the probability for the workforce to be replaced by other employees or by intelligent cyber-physical systems. This is true even for intelligent cyber-physical systems, like the cellular transport system discussed below, since such systems exclusively work in a predefined and programmed logistic environment and cannot develop a new environment or new strategic goals by themselves. The underlying process can be called digitization, meaning that a pure reproduction of analog signals into a digital form (Tilson et al., 2010). As of yet, there is no change in the former working tasks. In contrast to the technical process of digitization, digitalization includes a “sociotechnical process of applying digitizing techniques to broader social and institutional contexts that render digital technologies infrastructural” (Tilson et al., 2010, p. 749), which is especially relevant for organic organizations.
On the other hand, organic organizations are based on a more fluid set of work arrangements, which result from changing environmental conditions (Lam, 2005). Therefore, organic organizations show a low emphasis on setting strict working rules, which in turn facilitates innovations. Here, digitalization occurs as the underlying learning algorithms develop a self-governing and self-changing digital structure of human working tasks. A low degree of formalization (few formalized duties and broader working rules) permits openness, which encourages the growth of new ideas and behaviors, as many authors have already shown (Burns and Stalker, 1961; Damanpour, 1991; Pierce and Delbecq, 1977). The organic structure can be seen as a learning work environment, which creates the opportunity to constantly change working tasks. The low degree of formalization assumes that the workforce is more specialized and skilled to fill the missing knowledge gaps.
In this sense, we can differentiate a degree of formalization that can be reflected in the continuum from strong to fuzzy boundaries (e.g. communities of practice). The characteristics of the underlying digital architecture can differ in various ways, i.e. the architecture can range from strictly determining every employee’s single step to complete a working task to facilitating the exchange of innovative ideas in the form of a knowledge management system or e-learning system (Tsui, 2005; Lau and Tsui, 2009). Thus, we must take into account whether a digital device can lead a fixed, defined group of employees to fulfill a clearly structured and highly formalized work task (mechanistic) or whether in an organic organization caused by a low degree of formalization, it can facilitate an open and flexible exchange for different networks and people who can use it in many different ways (organic).
Distinctness of control authority
In mechanistic organizations, rights and obligations are attached to roles, and these are translated to functional positions and hierarchical structure (Lam, 2005; Burns and Stalker, 1961). The overall direction for information processing and decision implementation is top-down. A senior monitors and aligns the work of the subordinates top-down so that they co-produce the required good. Here, digitalization is rebuilding given organizational structures without any leeway for employees. They must follow the given digital structure alongside strict rules and constraints without any divergence and can be thus characterized as Taylor’s agents (Taylor, 2006/1911) in a digital environment. In mechanistic organizations, no means or ends are flexible, everything is default and no leeway for behavior or learning is possible. All employees must follow the predefined steps of the digital system.
In organic organizations, control authority is horizontal or organized in networks. Hence, peers must discuss how they coordinate themselves requiring digital devices to support interaction in a flexible and open way. A spread of communication beyond any technical definition exists (Lam, 2005). Means and ends of the working process are part of the decision-making process within the structure (Offe and Wiesenthal, 1980). Participation is incorporated into the analog and the digital structures of organizing, and employees are highly valued experts (Wilkesmann and Wilkesmann, 2011). They may select their own means to a prescribed end and jointly discuss the common goals in such network structures. The digital architecture enables and supports this type of autonomy. The perception of self-determination establishes fundamental differences in the perception of work, whether in digitalized or organizational structures (Ryan and Deci, 2000; Ryan and Deci, 2006; Wilkesmann and Schmid, 2014).
Location of knowledge
In a mechanistic organization, knowledge is located exclusively at the top (Lam, 2005; Burns and Stalker, 1961). Therefore, a strict hierarchy communicates information only top-down. In terms of digitalization, the software is the host of the knowledge that processes information in a strict hierarchical way to the work activities.
In an organic organization, relevant knowledge to solve organizational problems can be located anywhere, i.e. within the organization or in a broader network (Lam, 2005). As a result, specialization is required in ensuring a broader knowledge base and increasing the cross-fertilization of ideas (Damanpour, 1991). Moreover, expertise and personal knowledge are important to enforce boundary-spanning activities to acquire new knowledge and new ideas outside the organization (Pierce and Delbecq, 1977; O’Mahony and Bechky, 2008; Kinnie and Swart, 2012; Swart et al., 2014). Digitalization supports the knowledge transfer in networks with different experts within or outside organizational boundaries (Wilkesmann and Wilkesmann, 2011).
Degree of professionalization
In a mechanistic organizations, vertical communication between the superior and subordinates prevails (Burns and Stalker, 1961). The employees need no expertise to fulfill their work tasks: therefore, no professionalization is required. With regard to the implementation of structures of Industry 4.0, either low-qualified workers will fill the gaps, that the digital device cannot fully automate, or the digitalization will establish a fully standardized and automated routine. In organic organizations, importance and prestige are attached to affiliations, and expertise can also be also external to the firm (Lam, 2005). Employees are highly qualified and mostly academics, which frequently entails a high commitment to their profession rather than to their organization (Kinnie and Swart, 2012; Wilkesmann et al., 2009; Wilkesmann 2015). On the one hand, digitalization should both be based on and must foster professional networks to spread new ideas. On the other hand, those new ideas require protection against data loss and leakage.
Drawing on these four criteria, we will now characterize and distinguish the two poles of the organizing continuum within Industry 4.0, i.e. organizing routines in mechanistic organizations vs organizing innovations in organic structures.
Organizing routines by Industry 4.0
At the mechanistic end of the continuum, we find types of organizations or organizational units that mostly operate under strong behavior controls (Ouchi and Maguire, 1975), as in traditional production organizations, which Mintzberg (1979) describes as “machine bureaucracies”. Most theories of organizational studies more or less describe this type of organization (Eisenhardt, 1989). The core of these organizations or organizational units consists of blue-collar workers. The classical form of organizing work or information processes in a mechanistic organization is hierarchical, i.e. interests and information are enforced top-down via domination. A digital system, which is implemented in a mechanistic organization, constrains, monitors and steers people and coerces obedience. People cannot truly interact with the digital system and much less define the goals that must be fulfilled. The perceived self-determination is low, and the leeway for action is constrained. For people working in such an environment, individual and organizational goals are not congruent. Moreover, the scope of influence of the workers is very narrow. As Coleman (1990) emphasizes, this perception could lead to a perception of alienation among the people involved as the behavior of members and the actions of the organization or the digital structure are entirely unrelated. Recurring activities can be routinized very easily, which is the reason why such a Tayloristic organization produces, reproduces and improves routines that are indispensable in a mass production system. A stricter version of this understanding can be realized by taking humans completely out of the organizing process, permitting cyber-physical systems (M2M) to autonomously undertake classic routine operations. Our first two case studies (see below) will give examples that are located at the left-hand of the organizing continuum.
Organizing innovations by Industry 4.0
At the organic end of the continuum, we find more knowledge- and service-oriented organizations or organizational units, so-called “professional bureaucracies” or “adhocracies” (Mintzberg, 1979), where the expertise of white-collar or gold-collar workers (Kelley, 1985) is demanded. Burns and Stalker (1961) were the first to compare the characteristics of organizations that positively influence innovation. In their study, they found that the adoption of innovation is more likely when organizations have an organic rather than a mechanistic structure to respond to the environmental requirements. In this sense, decentralized, flat (organic) structures support the generation and the implementation of innovative ideas effectively throughout the organization, whereas this is not found to be the case for centralized, tall (mechanistic) organizations (Damanpour, 1991; Lam, 2005; Burns and Stalker, 1961).
Knowledge and new ideas can flow very easily in an organic organization, because all expertise is integrated in a horizontal decision-making or information process (Wilkesmann and Wilkesmann, 2011). All the power of this type of organizing originates from the network. The people involved determine the decision-making or information process by horizontal communication because they possess the relevant knowledge and need support from intelligent knowledge management systems or digital devices to enhance their skills and competencies. These forms are located from the middle to the right-hand side of the organizing continuum. The individual and organizational goals are congruent. People act in a structure of that kind because their goals and needs are congruent with the goals and needs of the organizational structure. Therefore, the members of the organization receive no selective incentives but rather invest their own resources in the form of time and engagement. Membership is clearly defined but overall weaker than at the other end of the organizing continuum; owing to their personal expertise, it is usually comparatively easy for the members to exit the organic organization (Hirschman, 1970). At the right-hand side of the organizing continuum (Figure 1), we find organizing forms with fuzzier boundaries, which have in recent years been called “collaborate communities” (Adler and Heckscher, 2006) or “communities of practice” (Wenger, 1998). This form enables highly self-determined actions, leading to a high degree of intrinsic motivation (Ryan and Deci, 2000, 2006). Members are engaged in those communities because they jointly pursue a common goal. A digital system is open to every potential user for different purposes. The employees’ scope of influence is very broad. High social homogeneity and a high degree of specialization are necessary because of the missing formal structure. Technical structures are flexible and open; they permit autonomy to the employees and help organize a structure with fuzzy boundaries. The likelihood that innovations will emerge in such an open and flexible organizing structure is high. Our last example will give some empirical illustration of this extreme end of the continuum. In the following, we will provide examples of all the different stages of the organizing continuum in the context of Industry 4.0. Moreover, we will discuss consequences for people acting in such organizational and digitally supported structures.
Except for the last case, which remains only a vision, the first four cases presented below are part of the so-called Platform Industry 4.0 Map, which belongs to the Industry 4.0 initiative of the German Federal Ministry of Economic Affairs and Energy and the German Federal Ministry of Education and Research. In Summer 2017, the Platform Industry 4.0 Map contained a total of 295 cases. The selected cases reflect the characteristics and problems that we have identified in the underlying theoretical framework. In this sense, our methodological approach refers to multiple-case studies design described by Yin (2014, p. 47): “Each case must be carefully selected so that it either predicts similar results (a literal replication) or produces contrasting results, but for predictable reasons (a theoretical replication)”. Therefore, five cases were selected to provide theoretical and practical evidence for different stages of the organizing continuum described above. After the presentation of the selected cases, we further analyze all 295 listed cases in terms of their distribution within the organizing continuum. The selected cases are in their own way quite innovative along the continuum; however, most of them are not creating innovations per se. In the following, we start at the mechanistic end and move on step by step toward the right-hand end of the organizing continuum.
The first case study is located in the field of logistics. In 2011, the research center for “Cellular Conveyor Technology” of the Fraunhofer Institute for Material Flow and Logistics (IML) was opened. The practical aim of this project is to make logistics supply chains more energy-efficient and to react more flexibly to unforeseen events. The starting point of the center is to transfer the concept of swarm intelligence to the subject of logistics. Based on the organizing of fish, bees or birds, which are more successful in swarms than by themselves, the Fraunhofer IML simulates a complete storage center in a 1,000-m2 research hall using this nature-related organizing principle.
Agent-based software enables the vehicles to work together autonomously with other components (e.g. lift) and coordinate with each other. Approximately 50 unmanned transport vehicles autonomously seek out their tasks and autonomously decide on the best method of transportation. The vehicles range freely within the research hall and move practically everywhere, i.e. from goods arrivals to picking and shipping. Hence, implemented into practice, this entire system would be able to adjust its capacities to seasonal or daily fluctuations, as well as to changed orders or customer demands. Moreover, it can randomly shift output between the storage and transport processes and the single sub-areas (Kamagaew et al., 2011).
Regarding the organizing type of the cellular transport system, we observe that humans have completely been replaced by the system. The system directs its goals autonomously and is no longer bound to rigid line management. Ultimately, the system seems to realize the old dream, conceived in the 1980s, of an automated factory running without humans. In contrast to that former vision, the developed cellular transport system now functions without digital hierarchies by following the swarm approach.
Wearables and augmented reality
The next case is also located in the field of logistics. In 2016, a German IT service provider began using augmented reality (AR) glasses at its headquarters’ logistics hub. The practical aim of this implementation is to allow hands-free picking in the warehouse with the help of smart glasses. The smart glasses communicate with the digital warehouse management system and the mobile AR warehouse picker app. A small loudspeaker is installed in the earpiece of the frame. At the right edge of the glass the word “Welcome” appears, while a voice greets the picker and indicates what time it is. As soon as the picker sits on the pallet truck, the glasses report, “There is an order for the customer 351, a delivery of 22 packages is to be put together”. “Confirmed”, says the picker, and the glasses navigate the person with colored arrows to the right shelf, until a colored rectangle in the field of view signals hit to the picker. The picker takes the marked package, fixes the glasses on it and the glasses scan the barcode. Immediately, the confirmation done appears in the visual field. Then, a new order pops up, and the picker navigates to the next order, following the commands of the smart glasses. The entire system also offers an integrated low stock check to ensure the adequate inventory of the product just picked. The possibility of having both hands free at work and no longer having to handle large-hand scanners and delivery notes can significantly accelerate the work. In particular, AR can optimize standardized working processes based on the decrease in mistakes and the reduction in paths and processing times.
This case shows that the formalization of the tasks to be fulfilled is quite high, and the control authority is vertically organized as the entire working process is managed top down by the applied digital device. Accordingly, the picker has no leeway to self-organize the working procedure because the knowledge is located at the top of the organization. As the pickers are not self-determined – the control authority of the digital system externally monitors them – their motivation is extrinsic because even the path the picker must follow is literally dictated by the AR system. The picker must stick to the digital rules and follow the commands, which are stored within the software system. In this sense, the employees’ degree of professionalization is low as the digital device completely determines their behavior.
Collaborative robots In 2015, a large supplier of industrial motors introduced a collaborative robot (Co-Bot) called YuMi®. The company developed a collaborative, dual-arm, small-parts-assembly robot solution that includes flexible hands, parts-feeding systems, camera-based part location and robot control. YuMi® comes from the words you and me. In this sense, the main field of application is smart-parts assembly, where humans and robots work side-by-side on the same tasks. The important aspect of safety is built into the functionality of the robot itself. For that reason, barriers to human–machine collaboration by erecting fencing and cages, as we know from automotive production, are no longer necessary. The acceptance by the production staff is most important. The use of Co-Bots in production must therefore be designed in such a way that the employees experience the new technology as supportive rather than as a rival. This implies that during the introductory phase, employees should be familiarized with the functions. The conceptualization of the Co-Bot allows to easily teach the Co-Bot new moves. In this sense, the workers obtain leeway to (re-)program the Co-Bot in a way that is supportive to their own tasks. In terms of hierarchy, it is the workers who apply their knowledge to the Co-Bot; hence, humans direct the digital system and not the other way around. The field of application is more flexible than in the case described above, because the Co-Bot can be used in different parts and for different purposes of small-parts assembly. In this sense, the boundaries of the underlying digital structure are fuzzier.
Intelligent-adaptive assistance systems
Intelligent-adaptive assistance systems form a new generation of mobile, context-sensitive systems for knowledge and management support in smart manufacturing. One of the main aims is to broaden the qualification of the staff by compensating for skills that may be lacking. APPsist is an app developed by researchers, which automatically adjusts the support requirements of employees working on the shop floor. The app takes into account the specific, existing skills of the workers. If a machine breaks down, for example, the worker usually contacts a colleague from maintenance. The app, installed on a device like a tablet, allows the worker to fix the problem on site. The worker is able to repair the machine by him- or herself because the app provides the relevant knowledge to address the specific problem. Thus, learning processes are built for a wide variety of requirements, e.g. commissioning, operation, troubleshooting, maintenance, repair and preventive maintenance of plants. In this Industry 4.0 case, the workers at the shop-floor level are supported by the system to expand their own knowledge. As a result, the former lower-qualified workers gain more competencies and, in return, more leeway within their workplace. Ultimately, the digital system facilitates work tasks and can be seen as a further development of knowledge management (KM) practices within an industry-related context. Therefore, typical problems of storing expert knowledge into the system can be anticipated. In particular, employees at the next higher level are expected to lose their professional expertise, as by storing their knowledge in the app, they make themselves superfluous. In turn, the location of their knowledge can be situated anywhere within the organization. Nevertheless, the degree of formalization can be considered medium because the tasks are neither reiterative nor highly complex. Additionally, this is also true for the distinctness of control authority, because the app gives no strict instructions but proposed solutions. This case can be interpreted as a practical application of a KM system. The only restriction is that workers on the shop floor solely apply existing and already-stored knowledge; they do not store their expertise in the digital system.
Knowledge management in the internet of things
Our last description is not an empirical case but rather a vision of the future of Industry 4.0. It can be considered a further development of KM in the direction of the so-called IoT. KM IoT refers to building a broader network among people, objects and systems (Roblek et al., 2016). In comparison to classical knowledge management systems, automatically collected data and information by Industry 4.0 applications (e.g. data collected by RFID-chips) allow access to previously non-digital centralized data. A KM IoT system like that continuously collects data (e.g. tracking transport goods, filling tanks and silos, measuring temperature of compressors and monitoring machines and systems) and combines the data with smart algorithms. In this way, organizations gain access to new sources of data and information that can be used for decision-making processes in a more comprehensive view. Such data systems may contribute to the detection of new patterns of behavior or machine interaction, in this vein promoting think tanks of new services, products or ideas. In other words, the access to those data enables managers at the strategic level or researchers in R&D units to create new innovations. The analysis of the acquired data fosters the development of new Industry 4.0 applications and novel business models, e.g. a change from pure production to customer-oriented and personalized services for special solutions is possible. The roll-out of those innovations is still linked to humans’ expertise; however, KM IoT may strongly support the underlying decisions.
Taking the other cases into account, the underlying knowledge work of those systems (e.g. APPsist, Wearables/AR and Co-bots) would be arranged at the right-hand side of the organizing continuum. As in this case, the leeway for creating innovations in general is broad, employees are assumed to be highly intrinsically motivated. In addition, involved persons, e.g. in communities of practice, can be part of networks (also beyond organizational boundaries). The formalization of the underlying digital system of a KM IoT is characteristically rather low, while the organization of activities proceeds in a bottom-up way. Ultimately, such a system fosters the growth of innovative ideas, in contrast to the strict replication of routines.
Distribution of Industry 4.0 cases
An interesting aspect is to further analyze how and to what extent the currently existing cases of Industry 4.0 are located and distributed within the organizing continuum. To this end, we conducted a document analysis of all 295 cases that are currently funded by the German Federal Ministry of Economic Affairs and Energy and the German Federal Ministry of Education and Research. The document analysis was based on their descriptions within the German Platform Industry 4.0 Map. The project descriptions were collected, jointly categorized and counted. The analysis of all the cases discloses some interesting and, to some extent, unexpected results: Almost one-third of the funded projects (n = 186) develop applications where the term Industry 4.0 seems barely justified. Most of them (n = 146) can be interpreted as digitization efforts, i.e. merely converting formerly analog processes into digital ones. The applications range from making IT infrastructures available to simple information management systems to connecting data exchange processes from one corporate division to another. The lack of innovation even applies to applications in the context of (intelligent) automation. Only two cases contain self-adaptive components, as we presented above, whereas the remaining 60 cases aim at simple digitized automation processes (Figure 2). Actually, efforts of digitalization play only a secondary role: ultimately, 15 cases focus on the implementation of wearables and/or AR within industry-related working processes. With a total number of nine, Co-Bots also play a secondary role. Cases that can be categorized under the term intelligent-adaptive assistant systems and/or classical KM applications constitute a number of 23 projects. What remain are 20 miscellaneous cases that range from virtual simulation environments or learning factories (which are primarily run by universities not companies) to special sensor applications and solely consulting activities to projects that focus primarily on data protection (n = 4). The latter are remarkable, as a lively public debate on lacking data protection is taking place in the context of Industry 4.0. Apart from that, only 41 of 295 cases take enterprise resource planning systems as a future common facilitator into account. Another result of our analysis reveals that in total, only nine projects focus on batch size of one, of which the majority (n = 6) focus on three-dimensional printing. To date, the original promise of Industry 4.0 has not been fulfilled.
In summary, the figure shows that most of the currently funded projects are located at the left-hand side of the organizing continuum, whereas only few cases can be found in the middle or at the right-hand end.
Discussion and conclusion
Starting from the definition of Industry 4.0, we must clarify that most fields of application are still in the development phase. Moreover, currently, most applications do not fit the initial definition of Industry 4.0 because they are single solutions and do not encompass all aspects in terms of self-organized value-creation networks. Nevertheless, we identify interesting practical solutions for digitalizing working processes. In this paper, we describe an organizing continuum for analyzing digitalized work in the context of Industry 4.0 (Figure 1), i.e. from reproducing and improving routines (exploitation) at one end to enabling inventions and innovations (exploration) at the other end – in the sense of March (1991). This framework constitutes a useful instrument to analyze the working conditions structured by digitalization.
At the right-hand side, the digitalization of work processes organizes a work environment that supports highly qualified humans with tools to enhance the flow of new ideas. Employees working at the right-hand side of the continuum are highly qualified people who probably program Industry 4.0 tools for the middle and left-hand side of the continuum. They have broad leeway and a high degree of autonomy to design digital applications for tomorrow. For that reason, these employees obtain rare skills which in turn leads to a high degree of bargaining power within the organization. Employees with high self-esteem cannot be effectively led by top-down decision structures. Highly qualified persons solve complex problems in an egalitarian structure with vertical or bottom-up participation. Often, commitment is given to a community or an innovative idea, not to a formal organization. Therefore, they prefer working in organic structures that have fuzzy boundaries given that boundary spanning is more probable. Most important, the form of digitalized work at the right-hand side of the continuum organizes a structure that meets their needs, as shown in the case of KM IoT.
Digitalization also organizes structures located at the left-hand side of the continuum, which can be considered as the applied knowledge from the right-hand side of the continuum. Knowledge workers from the right side program their vision of tomorrow’s production process. As our cases show, Industry 4.0 also structures a work environment with narrow leeway caused by a strict top-down digital control structure and a low degree of autonomy. The high degree of formalization in a rather mechanistic-oriented organization leads to a comparatively low skill level of the workforce, i.e. low-professionalized employees are easily replaceable by other employees or intelligent cyber physical systems. The fear of being replaced by machines or artificial intelligence is a predominant motif in the discussion of the societal consequences of Industry 4.0 and the future of employment against the background of computerization, as Frey and Osborne (2013) empirically but more or less hypothetically analyzed. If employees remain in the production processes, they must fill in the gaps the machines cannot handle. At the left side of the continuum, workers do not have to think; the knowledge is incorporated in the digital device, they thus have to perform in a given structure. Even if digital systems follow the idea of swarm intelligence as shown in the case of intelligent automation, the application itself momentarily remains at the stage of reproducing and automating routine work, as the applications are not able to create innovations themselves.
The application of Industry 4.0 at the left-hand side of the continuum strongly reminds us of the debate and visions of the so-called computer integrated manufacturing (CIM) of the 1980s (AWF, 1985) and basic principles of Taylorism (Taylor, 2006/1911). Industry 4.0 and CIM have in common that all production processes are computerized, leading to a deserted shop floor as humans have become superfluous. The difference between the two concepts is that Industry 4.0 allows for the fully automated production of a batch size of one, thus meeting the individualized needs of the customers. Industry 4.0 can also be considered comparable with Taylorism because the right-hand side of the continuum describes brainwork, whereas the left side refers to highly standardized manual labor. The only difference is that in Industry 4.0, the work environment is organized by digital structures and not by human hierarchies. In that sense, workers using wearables or AR systems in warehouses become a variation of Taylor’s vicarious agents, as the digital system specifies every movement of warehousemen. Moreover, the rise of digital Taylorism leads to an aggravated form of Tayloristic principles, as the introduced systems do not allow any conversation with co-workers, which in turn increases isolation and alienation in Marx’s sense among workers (Marx, 1981/1844). However, and there is always a however, digitalization also generates great opportunities: knowledge workers at the right-hand side of the continuum can program, organize and produce work environments with broad leeway and autonomy for employees at the middle of the continuum, which means that workers gain more autonomy and competencies by using the developed applications (e.g. APPsist). At the far left-hand side, humans are released from performing rather boring and meaningless human work. Apart from that, Industry 4.0 in this early stage of development causes new forms of uncertainty and threats (e.g. societal changes, data protection and data security) that must be taken more seriously into account (Magruk, 2016).
In summary, Industry 4.0 is designed by men, and therefore, humans are responsible for whether the future work situation will be perceived as supportive or as an alienated routine. Therefore, designers of Industry 4.0 applications, as well as politicians and scientists, absolutely must take the underlying outcomes of digitalized work into account and must jointly find socially acceptable solutions (e.g. unconditional basic income to absorb negative societal effects of unemployment caused by digitalization). Finally, we can conclude that applications of Industry 4.0 are currently at a very early stage of development, and they mostly focus on the left side of the organizing continuum, i.e. Industry 4.0 momentarily organizes more routines than innovations.
From a practical point of view, professional vocational and academic training will be a key factor for the successful implementation of digitalization in future. Training and further training measures have to be created which take up the theoretical aspects of Industry 4.0 and link them to the practical aspects of the increasing complexity of machines. A joint venture of industry and educational institutions could be a suitable way to meet the growing demand for qualified employees from the middle to the right-hand of the organizing continuum in the context of Industry 4.0.
The limitations of our paper are twofold: as we focus on the developments of Industry 4.0 in Germany, we cannot compare our results with the developments in different countries. Moreover, our methodological approach is explorative, and quantitative verification is necessary. Future research should include quantitative methods to investigate the employee perspective on Industry 4.0, for example. A comparison of Industry 4.0 applications in different countries would be another interesting option for further research.
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