In terms of ecological change, sustainable use of resources, socio-ecological change and decarbonization are among the topics addressed. The developments that both concepts have undergone and the lines of research behind them are listed below.<\/p>\n\n\n\n
The transformation of work in connection with digital technologies has been a well-established subject of research for several years now. A wide range of that research provides a great deal of details on the state of digitalization in the working world and looks at different sectors, occupational groups, activities and forms of work organization.<\/p>\n\n\n\n
The production sector plays a key role in the study of the digitalization of work and the economy under the banner of Industry 4.0. In recent years, it has also become clear that digitalization has triggered far-reaching changes in work and the economy, which may not always be disruptive and may also correspond to longer-term pathways, but are so fundamental that the labor market is now significantly different from the “pre-digitalization era” as digital change processes continue.<\/p>\n\n\n\n
Just a few years ago, doubts about the “megatrend” of digitalization were repeatedly voiced. In particular, questions were raised about the disruptiveness attributed to the fourth industrial revolution and the actual spread of so-called Industry 4.0 technologies in companies initially appeared to be low. The perception of Industry 4.0 as a political project driven by vested interests contributed to these doubts [7].<\/p>\n\n\n\n
Research in this field has now been able to show that although the introduction of Industry 4.0 elements is less disruptive than initially assumed, it has nevertheless arrived in companies. However, a comparison shows different levels of digitization. For example, large companies are investing in these technologies first and the spread varies depending on the sector [8<\/a>]. The degree of distinction also is not the same everywhere regarding the technologies consistently in further development.<\/p>\n\n\n\n For example, while the use of robotics in industry is already widespread [9<\/a>], the opposite is still reported with regard to the use of AI technologies [10<\/a>]. However, even if the spread of individual technologies varies from sector to sector, there is now a broad consensus that digital changes are already shaping both the division of labor between man and machine and the organization of work (e.g. in the form of increased mobile working and working from home, through the spread of platform work, etc.) [6].<\/p>\n\n\n\n (Social science) research on Industry 4.0 examines the effects of this digitization and asks about the social consequences of technological change. Repeated reference has been made to a polarization between highly qualified profiteers and low-skilled losers of these developments [11]. Such assessments are largely compatible with the findings of work and industrial sociology regarding to the polarization of work due to technological changes in industry [12, 13]. The criticism of the technology-centric nature of the Industry 4.0 debate is equally compatible with earlier research findings [14]. A preliminary conclusion with regard to digitalization research is that digitalization has established itself as a central topic in the academic study of the transformation of work [15<\/a>].<\/p>\n\n\n\n Another important finding is that it is no longer assumed that digital technologies are technologically deterministic, but rather that they are socially embedded. Work policy negotiation processes are therefore formative for the use and implementation of digitalization in companies [16<\/a>].<\/p>\n\n\n\n Questions about the actual empirical relevance of digitalization remain relevant when it comes to individual technologies, but is rarely described as exaggerated hype in relation to the overall concept of digitalization. Rather, current articles highlight the points of contact and overlaps between digital developments and other transformation trends (including demographic change), with reference already being made to the associated “ecological opportunities” in the initial thought processes surrounding the topic of Industry 4.0 [17].<\/p>\n\n\n\n In addition to digitalization, ecological challenges are also described as “transformation drivers” [18<\/a>]. New products and production processes are emerging that will fundamentally change industrial production and call into question not only forms and quality of work, but also “the scope of German industrial production” [18<\/a>]. Ecological sustainability is not only seen as a socio-political issue. The term socio-ecological transformation is also frequently in use here, which already indicates that all areas of society are involved in adapting to the sustainable use of natural resources and that this triggers changes in social, political and economic structures.<\/p>\n\n\n\n The ecological transformation thus relates to processes of technology use and social behavior, but in concrete terms it is primarily shaped in companies: questions of resource consumption, product creation, logistics, site and occupational safety as well as the design of work content and conditions determine whether and how forms of production are established and what type they ultimately are.<\/p>\n\n\n\n Even though these issues have received particular attention in recent years, the need for a socio-ecological economic model has already been emphasized by the Enquete Commission [19<\/a>]. The ecological adaptation efforts of industry are often referred to here as ‘decarbonization’, i.e. the process of reducing carbon emissions, particularly from fossil fuels. This can be achieved through the transition to renewable energies and efficiency improvements, among other things, but requires a comprehensive restructuring of the economy and energy infrastructure.<\/p>\n\n\n\n The double transformation can mean completely different things in different industries. Ecological adjustments often go hand in hand with the use of digital technologies. For example, Pfeiffer describes the ecological transformation of the automotive industry as characterized by the development of drive technologies (battery, hydrogen, hybrid, etc.), autonomous driving technologies (within the car product), charging technologies or charging infrastructure and finally new business models and services (mobility services, connected car, etc.) that are associated with this [1].<\/p>\n\n\n\n In general, it can be stated that the maximum potential for companies can only be achieved through both components, i.e. through the synergetic interaction of digitalization and sustainability [20<\/a>]. At the same time, it should be noted that digitalization as a transformation will progress even without sustainability, while ecological sustainability in companies would be difficult to implement without digitalization, which means an imbalance of the double transformation [21<\/a>]. This leads to the hypothesis that digitalization in companies is a prerequisite and thus an enabler of environmental sustainability.<\/p>\n\n\n\n Although not formulated in detail, this connection has already been pointed out in past publications at the beginning of Industry 4.0. [22] and [23] have shown that resource consumption can be optimized with the help of additionally generated data in companies and the integration of digital simulations. With the help of cyber-physical systems (CPS), resource consumption became transparent and the waste in resource flows also became visible [22].<\/p>\n\n\n\n However, the form that such fundamental changes can be implemented by companies and their business areas is very specifically shaped in the companies themselves. This is where it is decided whether and to what extent sustainable practices are established in terms of resource consumption, product creation, logistics, site and occupational safety or the design of work content and conditions.<\/p>\n\n\n\n As with other major social and company-related challenges, trade unions and works councils are an active part of the negotiations in companies alongside company management and employees in such adaptation and design processes. Using the example of IG Metall’s “Work 2020” project, [8<\/a>] was able to show that these social partners can create important orientation knowledge in digitalization processes that are often incremental and decentralized, which can also be used to take into account the needs of employees during change. This seems necessary, because even if employees may in principle recognize the need for a double transformation, many also associate these issues with fears regarding their economic consequences [6, 24, 25<\/a>].<\/p>\n\n\n\n Based on [26<\/a>], which identified transformation factors for the design of adaptable productions, these were also identified for the double transformation process (Fig. 1). The interaction between the two transformation processes was also outlined. As explained above, it can be assumed that the triggers for the two processes are different. While digitalization was predominantly triggered by entrepreneurial factors, it is primarily socio-political triggers that lead to the definition of the goal of ecological sustainability.<\/p>\n\n\n\n The main enablers of digitalization include cyber-physical systems (CPS). However, the enabler of sustainability is digitalization itself, as it encompasses more than just CPS, such as artificial intelligence, business models, big data and much more. [27<\/a>]. As with the triggers, the drivers of digitalization primarily include entrepreneurial factors such as increased productivity and thus also the market position factor, while socio-political factors such as CO neutrality “labels” and a green initiative can be identified as drivers of ecological sustainability.<\/p>\n\n\n\n At the same time, however, the hypothesis can also be formulated that ecological sustainability is also a driver for digitalization, as a lot of data is required for implementation and its processing plays a major role. The main obstacles to change in the double transformation are costs and a lack of expertise. It can be concluded from this that if companies already lack the expertise to implement the respective transformation process, they will have to overcome even greater hurdles and require methodical support to successfully manage this transformation process.<\/p>\n\n\n\n The simultaneous occurrence of digital and ecological change processes represents an extraordinary challenge that cannot be overcome through minor adjustments. Thus, the hypothesis can be formulated that company parties must sometimes negotiate a reorganization of structures, e.g. via model company agreements [28<\/a>].<\/p>\n\n\n\n Research into the conditions under which a socio-ecological transformation of industrial production can succeed already has a long tradition. A study of practical examples from companies at the joint RUB\/IGM research center showed that in the past, such projects often focused on topics such as energy and material efficiency, but also on the development of new ecological product lines [29]. Alternative production methods have so far been tested to a lesser extent; such discussions have often remained political and had little equivalent at company level.<\/p>\n\n\n\n There is currently no generally accepted definition of the term digital transformation in the literature [30]. This article refers to Schallmo’s description, which in turn is based on several other definitions and states that digital transformation can take place for companies, business models, processes, relationships, products, etc. [31, 32] in order to increase the performance and reach of a company [33]. Based on the use of new technologies, companies must acquire the ability to capture data, make it available in the value network and analyze it in order to ultimately obtain information that enables optimization and decision-making [30].<\/p>\n\n\n\n It is clear from this definition that digital transformation involves a large number of elements that affect companies. Individual facets of these elements have already been examined in past research projects. Solutions have been developed in technical and methodological form and existing challenges have also been identified [34]. Based on this, Figure 2 shows an allocation of research content to the four core elements of digital transformation.<\/p>\n\n\n\n Worker assistance systems play an important role in the digital transformation. In the context of companies, systems and processes are becoming increasingly complex, more data can be generated and used, and there are fewer skilled workers on the labor market. Assistance systems can provide support for employees at all levels of the company. The implementation guide for digital assistance systems developed by [35<\/a>] focuses specifically on worker assistance systems on the store floor.<\/p>\n\n\n\n However, it can also be applied to other areas of the company. In particular, the participatory social partnership design for the introduction of such technologies has already been taken into account here in order to provide companies with a negotiating framework. A similar approach has also been developed for the introduction of human-robot collaboration systems [28<\/a>].<\/p>\n\n\n\n This guideline also contains perspectives from company stakeholders to counter organizational obstacles to the introduction of cobots to support employees. [36] and [37] have shown that production planning and control can be carried out more efficiently and reliably with the help of simulation. However, this requires the use of additional sensor technology to capture live data from production.<\/p>\n\n\n\n A human-centered approach was the focus of [38<\/a>], which takes into account the personal stresses and strains of employees [38<\/a>] in personnel planning to make ad hoc scheduling more flexible. [40] were able to show that resource efficiency can be increased for PPS by using simulations based on live production data. In this case, it was shown that digitalization can be used to increase ecological sustainability.<\/p>\n\n\n\nEcological transformation<\/h2>\n\n\n\n
Digital-ecological transformation in the operational context<\/h2>\n\n\n\n
Designing dual transformation processes<\/h2>\n\n\n\n
![\"Transformation](\"https:\/\/industry-science.com\/wp-content\/uploads\/2024\/10\/Hoose_I4S-EN-24-5_Figure-1-1024x408.jpg\")
Findings from application-oriented research<\/h2>\n\n\n\n
![\"Process](\"https:\/\/industry-science.com\/wp-content\/uploads\/2024\/10\/Hoose_I4S-EN-24-5_Figure-2-1024x699.jpg\")