04 Fakultät Energie-, Verfahrens- und Biotechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/5
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Item Open Access Improvement of delivery reliability by an intelligent control loop between supply network and manufacturing(2021) Bauer, Dennis; Bauernhansl, Thomas; Sauer, AlexanderManufacturing companies operate in an environment characterized as increasingly volatile, uncertain, complex and ambiguous. At the same time, their customer orientation makes it increasingly important to ensure high delivery reliability. Manufacturing sites within a supply network must therefore be resilient against events from the supply network. This requires deeper integration between the supply network and manufacturing control. Therefore, this article presents a concept to connect supply network and manufacturing more closely by integrating events from the supply network into manufacturing control’s decisions. In addition to the requirements, the concept describes the structure of the system as a control loop, a reinforcement learning-based controlling element as the central decision-making component, and the integration into the existing production IT landscape of a company as well as with latest internet of things (IoT) devices and cyber-physical systems. The benefits of the concept were elaborated in expert workshops. In summary, this approach enables an effective and efficient response to events from the supply network through smarter manufacturing control, and thus more resilient manufacturing.Item Open Access A system thinking normative approach towards integrating the environment into value-added accounting : paving the way from carbon to environmental neutrality(2022) Miehe, Robert; Finkbeiner, Matthias; Sauer, Alexander; Bauernhansl, ThomasLife Cycle Assessment (LCA) is increasingly being applied in corporate accounting. Recently, especially carbon footprinting (CF) has been adopted as ‘LCA light’ in accordance with the Greenhouse Gas Protocol. According to the strategy ‘balance, reduce, substitute, compensate’, the approach is intended to provide the basis for optimization towards climate neutrality. However, two major problems arise: (1) due to the predominant focus on climate neutrality, other decisive life-cycle impact categories are often ignored, resulting in a misrecognition of potential trade-offs, and (2) LCA is not perceived as an equal method alongside cost and value-added accounting in everyday business, as it relies on a fundamentally different system understanding. In this paper, we present basic considerations for merging the business and life-cycle perspectives and introduce a novel accounting system that combines elements of traditional operational value-added accounting, process and material flow analysis as well as LCA. The method is based on an extended system thinking, a set of principles, a calculation system, and external cost factors for the impact categories climate change, stratospheric ozone depletion, air pollution, eutrophication and acidification. As a scientifically robust assessment method, the presented approach is intended to be applied in everyday operations in manufacturing companies, providing a foundation for a fundamental change in industrial thought patterns on the way to the total avoidance of negative environmental impacts (i.e., environmental neutrality). Therefore, this is validated in two application examples in the German special tools industry, proving its practicability and reproducibility as well as the suitability of specifically derived indicators for the selective optimization of production systems.Item Open Access Complex job shop simulation “CoJoSim” : a reference model for simulating semiconductor manufacturing(2023) Bauer, Dennis; Umgelter, Daniel; Schlereth, Andreas; Bauernhansl, Thomas; Sauer, AlexanderThe manufacturing industry is facing increasing volatility, uncertainty, complexity, and ambiguity, while still requiring high delivery reliability to meet customer demands. This is especially challenging for complex job shops in the semiconductor industry, where the manufacturing process is highly intricate, making it difficult to predict the consequences of changes. Although simulation has proven to be an effective tool for optimizing manufacturing processes, reference data sets and models often produce disparate and incomparable results. CoJoSim is introduced in this article as a reference model for semiconductor manufacturing, along with an associated reference implementation that accelerates the implementation and application of the reference model. CoJoSim can serve as a testbed and gold standard for other implementations. Using CoJoSim, different dispatching rules are evaluated to demonstrate an improvement of almost 15 percentage points in adherence to delivery dates compared to the reference. Findings emphasize the importance of optimizing setup time, particularly in products with high variance, as it significantly impacts adherence to delivery dates and throughput. Moving forward, future applications of CoJoSim will evaluate additional dispatching rules and use cases. Combining CoJoSim with dispatching methods that integrate manufacturing and supply networks to optimize production planning and control through reinforcement-learning-based agents is also planned. In conclusion, CoJoSim provides a reliable and effective tool for optimizing semiconductor manufacturing and can serve as a benchmark for future implementations.Item Open Access Increased resilience for manufacturing systems in supply networks through data-based turbulence mitigation(2021) Bauer, Dennis; Böhm, Markus; Bauernhansl, Thomas; Sauer, AlexanderIn manufacturing systems, a state of high resilience is always desirable. However, internal and external complexity has great influence on these systems. An approach is to increase manufacturing robustness and responsiveness-and thus resilience-by manufacturing control. In order to execute an effective control method, it is necessary to provide sufficient information of high value in terms of data format, quality and time of availability. Nowadays, raw data is available in large quantities. An obstacle to manufacturing control is the short-term handling of events induced by customers and suppliers. These events cause different kinds of turbulence in manufacturing systems. If such turbulences could be evaluated in advance, based on data processing, they could serve as aggregated input data for a control system. This paper presents an approach how to combine turbulence evaluation and the derivation of measures into a learning system for turbulence mitigation. Integrated in manufacturing control, turbulence mitigation increases manufacturing resilience and strengthens the supply network’s resilience.Item Open Access A conceptual framework for biointelligent production : calling for systemic life cycle thinking in cellular units(2021) Miehe, Robert; Buckreus, Lorena; Kiemel, Steffen; Sauer, Alexander; Bauernhansl, ThomasA sustainable design of production systems is essential for the future viability of the economy. In this context, biointelligent production systems (BIS) are currently considered one of the most innovative paths for a comprehensive reorientation of existing industrial patterns. BIS are intended to enable a highly localized on-demand production of personalized goods via stand-alone non-expert systems. Recent studies in this field have primarily adopted a technical perspective; this paper addresses the larger picture by discussing the essential issues of integrated production system design. Following a normative logic, we introduce the basic principle of systemic life cycle thinking in cellular units as the foundation of a management framework for BIS. Thereupon, we develop a coherent theoretical model of a future decentralized production system and derive perspectives for future research and development in key areas of management.