Digitalization changes the way people work to a considerable extent. It alters business models and process organizations of whole industries. The ensuing market dynamics and faster innovation cycles cause an increase in complexity. In this article, the interconnection of digitalization and complexity in work systems is analyzed. For this purpose, a framework for comparing relevant complexity definitions is developed. Moreover, complexity drivers in digitalized labor systems in six different organizational dimensions (process organization, organizational structure, technology, working conditions, product and personnel) are explored. 23 experts from the academic and industrial sector were interviewed using semi-structured interviews. The results of a qualitative content analysis show that the consideration of complexity and digitalization has extensive impact what becomes evident in interdependent relations amongst the organizational dimensions. Furthermore, complexity drivers in digitalized work systems are determined as a result of the analysis procedure. Finally, the implications of the expert interviews for cooperative forms of work are discussed. The concept of a "task complexity mountain range" is presented to explain the effect of task complexity on performance and motivation in the context of work groups.

The environment of industrial enterprises is characterized not only by increasing volatility and uncertainty, but also by growing complexity and ambiguity (Ködding and Dumitrescu, 2022; Wonsak et al., 2021). For manufacturing process planning and CAM systems in use (CAM: Computer Aided Manufacturing), new requirements arise as a result of growing complexity and individualization of components, tools, and machines (Suhl and Isenberg, 2019; Jayasekara et al., 2019). In order to overcome these requirements, CAx system providers and researchers are currently developing approaches in technology-driven projects on how conventional support systems can be further developed, e.g., by integrating artificial intelligence (AI) (cf. Dripke et al., 2017). In technology development projects, however, the classic fields of work organization as well as other dimensions at the enterprise and individual level should be taken into account in addition to technology (Mütze-Niewöhner et al., 2022). In order to remain competitive, future developments must be considered by enterprises and systematically addressed in their strategic decisions (Ködding and Dumitrescu, 2022; Fink et al., 2005).This paper presents the application of scenario planning in the context of a technology-driven innovation project. Aim of this project with the acronym CAM2030 is to create a new generation of CAM systems by integrating innovative technologies, such as AI, evolutionary algorithms, and cloud computing in order to enable employees to perform manufacturing process planning for the production of complex products quickly, efficiently, and adeptly (cf. Burgert et al., 2022). To anticipate future developments of the work of CAM users, the scenario planning method by Fink and Siebe (2016) was used. Scenarios take into account that the future of work is open and uncertain, but will take place within a limited range of development possibilities (Burmeister et al., 2019). The intention within CAM2030 was to ensure that estimations of future support needs of CAM users are considered in the technology development activities. Since the scenario planning was tested for the first time in the context of the technology-driven project, the focus of this paper is on the application and discussion of the method. First, it provides a brief introduction to the applied scenario planning method according to Fink and Siebe (2016). Second, the procedure is described and methodological findings are discussed, e.g., to what extent the concept helped to successively guide the participants through scenario planning process. Challenges included, e.g., the involvement of multiple stakeholders, time demands on all participants, and enabling participants to focus on the open and uncertain development of CAM planning work within the technology-driven project. Third, as an outlook, it is reflected to what extent the applied method may support a future strategic decision-making process.

ZusammenfassungIm Verbundvorhaben „FlexDeMo – Flexible und demografierobuste Montageorganisationsformen partizipativ planen, simulieren und gestalten“ wurde durch das interdisziplinäre Zusammenwirken von Anwendungs-, Entwicklungs- und Forschungspartnern eine Methoden- und Werkzeugsammlung zur digitalen Unterstützung für eine partizipative und simulationsbasierte Montageplanung in KMU entwickelt. Diese ist in Form der im Internet frei zugänglichen FlexDeMo-Plattform (https://toolbox.flexdemo.eu) verfügbar. Das webbasierte Planungssystem – die FlexDeMo-Toolbox – stellt Methoden und digitale Werkzeuge bereit, die KMU bei der Gestaltung und Optimierung ihrer Montageorganisation von der Projektinitiierung bis hin zur Umsetzung begleiten und unterstützen. Entwicklung und Anwendung des Planungssystems sind eingebettet in ein partizipatives Vorgehen, das den Fokus auf die aktive Mitwirkung der betroffenen Beschäftigten richtet. Diese wurden im Laufe des Projektes zu bestimmenden Treibern und eigenverantwortlichen Gestaltern ihres Arbeitssystems.

Computer-Aided Manufacturing systems are common means to increase the flexibility and efficiency of production planning in manufacturing companies. Digital transformation processes increase innovation cycles and product individualization [1] and, by doing so, the complexity of production planning and CAM systems use [2]. In the R&D project CAM2030 a new generation of CAM systems is developed by integrating innovative technologies (AI, cloud computing, evolutionary algorithms). The highly innovative process requires new methods. This paper presents an integrated methodological approach that enriches co-creation methods [3] by integrating visualization methods of process modeling [4]. The methodology was developed in three steps: concept development, concept realization, and concept evaluation. Concept development: Due to the Covid-19 pandemic, a remote co-creation workshop was designed based on two assumptions: (1) Co-creation at an early stage of the innovation process benefits from integrating users’ perspective and need information [5]. (2) Modeling and visualizing CAM planning processes allows to build up a shared understanding of the status quo. Human-centered work design experts compiled, modeled, and visualized the project-specific CAM planning process with the C3 modeling method [4]. Technical communication experts focused on methods and tools to gather need information (requirements for intelligent CAM systems) remotely. The workshop comprises three parts: warm-up challenge to identify no-go design features of CAM systems, discussion of the CAM planning process and model, and derivation of design requirements and automation potential. Each part uses different practices, e.g., teams working in separate breakout sessions, documenting their results in a shared document on Google Docs in real-time. Concept realization: The workshop was conducted in February 2021 via Zoom. The participants (CAM users, software developers, researchers) (n=21) were acquired within the project consortium. The workshop was audio- and video-recorded. The participants’ notes were stored in Google Docs. The transcribed audio data were enriched with additional information, e.g., participants’ notes. After the workshop, the data were used to integrate, categorize, and prioritize need information and to revise the process model. Concept evaluation: The concept was evaluated by the workshop participants and the workshop leader team guided by two research questions: Is this methodological approach suitable for innovation processes? What are the potentials and challenges of the approach? The approach proved to be highly productive. The integration of co-creation and process modeling seems to be a promising approach to involve diverse perspectives in the design of intelligent CAM systems. The process model supported the workshop participants in creating a shared understanding of the CAM planning process and identifying potentials for optimization and automation. The collaboration in heterogeneous groups yielded a structured catalog of requirements that will go into the further innovation process of CAM systems. Shortcomings concern the live adaptation of the process model as well as bringing together partial results from different groups and cluster ideas. Under pandemic conditions, the approach is practical to a limited extent. Future research will focus on how the nexus of co-creation and process modeling can be advanced to enrich the design of innovative software systems.