Prof. Dr.-Ing.

Schuh, Günther

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  • Publication
    Mit digitalen Technologien zur nachhaltigen Wertschöpfung
    ( 2024)
    Schuh, Günther
    ;
    Palm, Daniel
    Zur Erreichung der geforderten Klimaneutralität kommt dem Industriesektor als einer der fünf emissionsintensivsten Sektoren eine große Bedeutung zu. Die zentrale Aufgabe ist es, Wirtschaftlichkeit und Ressourcenminimierung zu vereinen und Fabriken zum Ort nachhaltiger Wertschöpfung zu entwickeln. Zugleich fördern moderne Technologien die Entwicklung neuer Produkte und innovativer Geschäftsmodelle, wodurch Fabriken an wandelnde Anforderungen anpassbar gestaltet werden müssen. Zukünftig wird sich dieser Trend intensivieren und folglich die Themen der industriellen Agenda bestimmen. Bereits heute strukturieren führende Produktionsunternehmen ihre Wertschöpfungsnetzwerke daher aus einer ganzheitlichen Betrachtungsweise heraus: Das Supply Chain Management, die Fabrikplanung sowie die Produktionsplanung und -steuerung (PPS) werden dabei nicht als isolierte Disziplinen verstanden, sondern als eng verzahnte Elemente, die sich gegenseitig beeinflussen und verstärken sollten. Eine derart integrierte Optimierung der Wertschöpfungssysteme führt dabei nicht nur zum ökonomischen Vorteil, sondern ermöglicht genauso, die ökologische Bilanz signifikant zu verbessern.
  • Publication
    Pricing Models for Industrial Data
    ( 2023-05-10)
    Cassel, Leonard
    ;
    Schauss, Marc
    ;
    Bruhns, Lukas
    ;
    Schuh, Günther
    The increasing importance of digital technologies and connectivity in products, processes, and organizations leads to a growing amount of generated data. Manufacturing companies can use these data to control and monitor production processes as well as offer data-driven services in addition to their existing product portfolio. In the business to consumer (B2C) sector, data-driven services are already well established. Manufacturing companies on the other hand tend to struggle to effectively leverage and monetize data-driven service offerings, as the value creation and utility potential often is not apparent. However, to increase their competitive positioning, companies need to establish a sound understanding of the value creation of their products or services and the resulting pricing potential. Even though success stories exist in the manufacturing industry, most companies face challenges in effectively pricing their data-driven offerings. This is, in part, due to the many facets of pricing such offerings. Because of their diversity and complexity, these may seem intimidating to consider at first glance. Figure 1 shows four challenges of pricing data-driven service offerings that should be considered. The potential customer purchases a benefit and/or functionality, which, contrary to a physical product, is not tangible. Hence, the quantification of the customer’s benefit is based on the value created by the generated data and service offering. For manufacturing companies, the effort required to determine suitable price points may be higher, as the quantification depends on the benefit of the individual customer. From a customer’s perspective, estimation efforts regarding the return on invest may arise. Thus, both the customer and provider of data-driven service offerings need to have a sound understanding of the long-term value creation and utility potential that is generated. Manufacturing companies notably face obstacles in selecting and designing a suitable pricing model to sell their data-driven service offerings. In addition to being more difficult to quantify, data-driven service offerings require more extensive analysis of the value creation and utility potential. This is due to the previously outlined intangible customer benefit. This study aims to answer the question how to develop pricing models for industrial data. To do so, current industry standards and practices regarding data-driven service offerings were identified, along with an overview of relevant trends in this field. This knowledge lays the theoretical foundation of the pricing of data-driven service offerings. In a second step, three case studies were conducted, in which various types of data-driven service offerings were implemented. Each process was thoroughly analyzed to infer data about pricing strategies that can realistically be implemented in manufacturing companies. Finally, the previously acquired information about the process of pricing data-driven service offerings was abstracted to identify key performance indicators. These form the basis of general recommendations for pricing strategies that apply to all manufacturing industries.
  • Publication
    Derivation of Requirements for the Formation of Collective Target Systems for Technology-Based Cooperation Between Manufacturing Corporates and Startups
    ( 2023-03-04)
    Schuh, Günther
    ;
    Studerus, Bastian
    Disruptive innovations are putting incumbent manufacturing companies under increasing pressure to defend their competitive position in globalized markets. To withstand this pressure, they can form cooperation agreements with startups aiming for the creation of technical innovations and, thus, ensuring access to technologies and growth. Due to organizational differences and an insufficient explication of cooperation objectives, these cooperation pose a major challenge for both partners. In this paper, the authors discuss the status-quo in the formation of entrepreneurial target systems and, thereby, systematically derive corporate as well as startup-specific cooperation deficits. Based on the analysis of creating individual target systems, a first attempt is taken to elaborate requirements for the development of a model to form collective target system for the cooperation between corporates and startups. Subsequently, model characteristics for the derivation of joint targets and requirements are discussed to enable a comparison between corporates and startups. The development of a concept for a requirements comparison based on a collective cooperation target system supports corporates and startups to ensure the fulfilling of the competitive advantage.
  • Publication
    Design for Circularity - Identification of Fields of Action for Ecodesign for the Circular Economy
    ( 2023)
    Riesener, Michael
    ;
    Kuhn, Maximillian
    ;
    Hellwig, Frederike
    ;
    Ays, Johanna L.
    ;
    Schuh, Günther
    The prevailing production and consumption patterns mostly follow a linear logic: produce, consume and dispose. This orientation of the economic system is highly resource-intensive and exceeds planetary boundaries. In order to decouple the economic growth from resource consumption circular economy is an important enabler by creating and closing resource loops. The goal of a circular economy is the sustainable return of resources into the value chain. Thereby, the design of the products plays a decisive role. Ecodesign is a systematic design approach for products in order to reduce environmental impacts over the entire life cycle by defining design principles. Therefore, ecodesign has the potential to allow the implementation of a circular economy by identifying design principles that enable critical resources to be preserved in cycles. For this reason, a framework with nine fields of action for the realization of circular economy by ecodesign was derived. The fields of action are based on the circular economy strategies "Closing the Loop" and "Slowing the Loop" as well as the product lifecycle phases addressed by ecodesign "production", "use" and "recycling". Practical use cases deal with the impact of the various design principles at the material, component and product levels of analysis.
  • Publication
    Application of a Reinforcement Learning-based Automated Order Release in Production
    ( 2023)
    Schuh, Günther
    ;
    Schmitz, Seth
    ;
    Maetschke, Jan
    ;
    Janke, Tim
    ;
    Eisbein, Hendrik
    The importance of job shop production is increasing in order to meet the customer-driven greater demand for products with a larger number of variants in small quantities. However, it also leads to higher requirements for the production planning and control. In order to meet logistical target values and customer needs, one approach is the focus on dynamic planning systems, which can reduce ad-hoc control interventions in the running production. In particular, the release of orders at the beginning of the production process has a high influence on the planning quality. Previous approaches used advanced methods such as combinations of reinforcement learning (RL) and simulation to improve specific production environments, which are sometimes highly simplified and not practical enough. This paper presents a practice-based application of an automated order release procedure based on RL using the example of real-world production scenarios. Both, the training environment, and the data processing method are introduced. Primarily, three aspects to achieve a higher practical orientation are addressed: A more realistic problem size compared to previous approaches, a higher customer orientation by means of an objective regarding adherence to delivery date and a control application for development and performance evaluation of the considered algorithms against known order release strategies. Follow-up research will refine the objective function, continue to scale-up the problem size and evaluate the algorithm’s scheduling results in case of changes in the system.
  • Publication
    Intuitive Analyse komplexer Materialflüsse
    ( 2023)
    Schuh, Günther
    ;
    Schmitz, Seth
    ;
    Maetschke, Jan
    ;
    Janke, Tim
    ;
    Eisbein, Hendrik
    Produktionen mit komplexen Materialströmen lassen sich ohne Materialflusssimulation - im Folgenden Simulation genannt - nicht im Detail analysieren oder verbessern. Der für Simulationen typischerweise hohe zeitliche Aufwand hindert Unternehmen am profitablen Einsatz. Das Tool "Miori Production Control" unterstützt die Analyse komplexer Produktionszusammenhänge durch den Vergleich von unterschiedlichen, simulierten Zielszenarien. Die automatische Modellgenerierung und Fehlererkennung sowie KI-basierte Entscheidungsunterstützung erlauben einen niedrigschwelligen Einstieg
  • Publication
    Concept for Effective Identification and Initiation of Startup Investments for the Digital Transformation of Manufacturing Companies
    ( 2023)
    Schuh, Günther
    ;
    Schenk, Leonard P.
    The digital transformation is fundamentally disrupting established business models and existing value chains at an accelerating pace. Faced with multidimensional and inevitable changes, many incumbent companies lack necessary competences, processes, and structures to actively transform their business with sufficient speed and extent. While successful tech-companies therefore consequently rely on startup acquisitions, investments or cooperations, most incumbent manufacturing companies are not successful in systematically leveraging external corporate venturing as a catalyst for their digital transformation. Several reasons may already be found in the early phases of an investment. This applies especially to the identification of suitable startups, the determination of the potential value contribution of an investment, and the effective initiation of startup investments. Against this background, this paper presents a concept for effectively identifying and initiating external corporate venturing initiatives for the digital transformation of manufacturing companies. Thus, existing approaches in literature are discussed and analyzed to derive the requirements for developing a concept, which enables effectively identifying suitable startups for digital transformation objectives and subsequently initiating an investment. Based on these findings, the methodology and its sub-models are derived.
  • Publication
    Concept for a Function-oriented Ecology Analysis in Machinery and Plant Engineering
    ( 2023)
    Riesener, Michael
    ;
    Kuhn, Maximillian
    ;
    Ruschitzka, Christina
    ;
    Schuh, Günther
    Sustainability is becoming increasingly important, especially for the manufacturing industry due to its high resource consumption and CO2 emissions. Transparency about the environmental impact of products along their entire life cycle is a necessity for creating a shift towards sustainability. However, the possibilities for influencing sustainability are not directly apparent using existing methods such as the life cycle assessment. Within those methods, the use phase is only insufficiently analysed and a differentiated consideration of lifetimes and recovery processes of individual product scopes is not possible. Thus, there is no overview of the possible levers for improving the environmental sustainability performance, especially when dealing with complex products in industries such as machinery and plant engineering. Therefore, this paper introduces a sustainability assessment for machines and plants by focusing on life cycles of individual product scopes in order to enable a realistic analysis of the environmental impact along the entire product life cycle. Based on an assignment of the measured ecological criteria to the product structure and functional structure, fields of action for the development of ecologically improved products can be identified.
  • Publication
    Concept for Data-Based Sales and Resource Planning for Re-Assembly in the Automotive Industry
    ( 2023)
    Schuh, Günther
    ;
    Schmitz, Seth
    ;
    Schopen, Marco
    ;
    Neumann, Henning
    In linear economy, the growing wealth in the world is linked to a growing resource consumption and greenhouse gas emission. This results in a shortage of primary resources, environmental destruction through resource extraction, and global warming. A high productivity of the manufacturing industry, overcapacities and a decrease in the value of existing products intensify this situation. Circular economy offers resource-efficient value addition by multiple utilization of resources. One challenge in this form of value creation is the duration of reconditioning processes and the lack of product innovation in reconditioned products. To bring products back to the market as quickly as possible, the method of Re-Assembly is introduced and focused in this paper. Re-Assembly can be defined as reconditioning old products into new or higher-valued products, by assembling new or remanufactured parts and components after disassembly. However, manufacturing companies face difficulties in industrialization of such methods. In practice, one of the biggest challenges is the mid-and long-term planning of the reconditioning process. Due to uncertainties in the quality and quantity of the returning end-of-life products the resulting reconditioning process is challenging to predict in terms of process time and production costs. To encounter this, this paper presents a concept for sales and resource planning in the context of Re-Assembly. In the first step the uncertainties for the long term production planning and the resulting data requirements are identified. Based on this, a concept for sales and resource planning is presented. The approach is based on the Internet of Production reference framework and includes data from the whole product lifecycle. As the area of application, the automotive industry is chosen as it is the largest manufacturing industry in Germany and already leading in the recording of usage data of their products.
  • Publication
    Procedure for Hybrid Process Analysis and Design
    ( 2023)
    Schuh, Günther
    ;
    Schmitz, Seth
    ;
    Schopen, Marco
    Performing business processes are a critical asset for manufacturing companies operating on highly competitive markets. Conventional approaches to business process improvement, however, are vulnerable to subjectivity and high manual efforts in their execution. These challenges can be overcome with recent databased approaches that semi-automate process analysis and design. Those approaches formalize methodical knowledge on weakness detection, measure derivation and performance evaluation for business processes into a performance-related decision support. By enabling the databased automation of these tasks this formalization helps to reduce efforts and subjectivity in process analysis and design. However, practice lacks a procedure for applying this decision support in operative business process improvement. Moreover, this decision support only formalises methodological knowledge. Operative business process improvement in practice additionally requires the consideration of experts’ contextual knowledge about the company and the business process itself. This paper presents a hybrid approach for the analysis and design of business processes using a databased decision support. First, existing phase models for business process improvement are consolidated into a reference model. Second, an expert-based assessment is conducted on how decision support extends, modifies or eliminates the conventional tasks of process analysis and design. In the third step, a hybrid phase model for process analysis and design is developed that integrates the formalised methodological knowledge of the decision support and contextual knowledge of experts.