Lukas Budde

There exists a wide range of optimization models in the Operations Management (OM) community to solve complex problems such as lot sizing. However, their practical performance is often criticized due to the complexity of implementation and insufficient applicability in real-world decision processes. These theory-driven approaches are either simple to compute, but only focus on single aspects of the decision without being able to capture the practical problem comprehensively, or are complex computational models with limited practicability. We apply a Design Science Research approach to resolve this issue and show how lot size decision-making models should be designed to thoroughly support managers. Our innovative model combines Discrete Event Simulation (DES) with OM methods and is developed and tested in a case study in the metal processing industry. Results reveal that the model is suitable to provide transparency about effects and a range of efficient solutions.

Industry 4.0 is a ubiquitous term throughout general newspapers, on company websites or in scientific journals. One of its key constructs is the smart factory, envisioned as a future state of a fully connected manufacturing system, mainly operating without human force by generating, transferring, receiving and processing necessary data to conduct all required tasks for producing all kinds of goods. Although the understanding of smart factory concepts has been sharpened in the last years, it is still difficult for industrial companies to establish a concrete strategy roadmap within the jungle of different terminologies, ideas and concepts. To generate further clarity and to consolidate the previous findings around smart factory for researchers as well as for practitioner, we conducted a systematic literature review. For this purpose, we chose a five steps approach: Scope definition, topic conceptualisation, literature search, literature analysis and synthesis, and synthesis of future research questions. During our review, we found that research within each perspective is fragmented and unequally advanced. Most publications treat single use cases with low generalizability, often rely upon machine data, typically have a technical nature and seldom incorporate impact estimations. In this paper, our key academic and practical contribution lies in the categorisation of the selected publications into eight thematic distinct perspectives within the sphere of smart factory: Decision making, cyber-physical systems, data handling, IT infrastructure, digital transformation, human machine interaction, IoT, and cloud manufacturing and services. These are further developed into the smart factory research model, stating a foundation for future research endeavors.

Industrie 4.0 still rules the discourse of industrial production in practice and academia. Under the variety of possible technologies and applications, especially small and medium enterprises have difficulties identifying the use cases relevant to them. This article presents a method that permits the identification of use cases according to the strategic priorities of a manufacturing company. The method is validated through the real case of a metal processing company.

Even though organizations may plan for long-term enterprise-wide ob-jectives, they are shaped by local decision-maker’s actions. The latter tend to have conflicting goals, such as short-term and immediate satis-faction of local business needs over organization-wide objectives. While local and diverse decision-making enables specialized products and services, ungoverned behavior may lead organizations that are hard to control and manage. Hence, the challenge is to harness, rather than eliminate decentral autonomy by reaping its benefits while limit-ing its downsides. Pursuing this purpose, this Design Science Research (DSR) study presents the creation and evaluation of a governance mechanism: a nudge-based label. It also contains a set of design fea-tures, which are evaluated quantitatively and qualitatively with expert surveys and discussions. The contributions include design knowledge about labels and the investigation of nudging as an intra-organizational governance mechanism.

Smart services become increasingly important among manufacturing companies. However, many companies struggle to offer smart services that are economically successful. Developing smart services is complex, challenging and includes manifold elements from business model innovation to software engineering. Therefore, it is essential to approach smart service development as part of a well-defined and efficient process. A dominant approach to align smart service development and related product development processes is neither found in academia nor in practice. By conducting a systematic literature review, we present the current status of servitization literature according to smart service innovation. We identified core dimensions and related activities of service innovation processes based on a vast variety of different designations. Additionally, we consolidated core dimensions and activities in a framework to build the foundation of a guiding process for smart service innovation. Finally, a flow chart proposes an appropriate sequence of smart service innovation activities.

Whilst focus is widely accepted as leading to superior performance, little is known about why factories exist that are not focused. This paper aims at improving our understanding of this phenomenon based on a single case study research design. Specifically, we investigate the motivation behind strategic decision making that leads to unfocused factories. The reasoning behind such decisions is concerned not only with site-level performance but also takes firm-level outcomes into account. Our research makes propositions about unfocused factories and thus contributes to the factory focus literature and might point into the direction of a new research path.