Hellwig, Michael
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Modern research in the field of Smart Manufacturing often focuses on the big data aspect, where the goal is to obtain actionable insights from the data. In this paper, the focus is shifted back to the Smart Shop Floor and how to efficiently derive information with the big data tasks that follow as simple as possible. A condensed literature review of the existing architectures and frameworks for Smart Manufacturing is combined with the experience of practitioners to assess the requirements for a Smart Shop Floor Information System Architecture. On this basis, an architecture is proposed that consists of eight modular building blocks. After a detailed description of the roles and functionalities of these building blocks, a reference implementation using readily available, open-source tools and technologies is laid out. This reference implementation intends to strike the right balance
between generality and specificity. It provides the reader with a tangible starting point for implementing and adapting the proposed architecture to their own needs.
The Flexible Job Shop Problem (FJSSP) represents an extension of the Job Shop Scheduling Problem (JSSP), in which the sequence of operations on assigned machines has to be optimized. In the FJSSP, operations can be processed by different machines, with different processing times on each machine type. The paper proposes a Genetic Algorithm (GA) variant that uses an encoding capable to be applied to as general FJSSP instances as possible and variation principles designed to maintain a population of feasible candidate solutions over the whole search process. Additionally, the GA parameter setting for each FJSSP instance is derived from problem-specific attributes, such as the production environments flexibility and the variety of duration values , and a restart scheme that accounts for the dissimilarity of the initial population is introduced to avoid premature convergence. The GA results are compared to bestknown solutions in literature as well as to the solutions of the commercial solver GUROBI being applied to a Mixed-Integer Linear Programming (MILP) representation of the FJSSP. In addition to demonstrating the decent results of the developed GA on 402 recognized FJSSP benchmark instances, the paper investigates the influence of different aspects of problem complexity on the relative GA performance to assert the GAs viability for more complex FJSSP variants including uncertainties. Regardless of the machine flexibility of a FJSSP instance, the experimental results suggest that both approaches tie on smaller and medium sized problem instances. With increasing complexity in problem characteristics like problem size, flexibility, and duration variety, the GA gains advantage over the MILP/GUROBI approach. The observation substantiates the expectation that the proposed GA is well suited for practical (more realistic) scheduling problems with additional requirements like worker flexibility or uncertainty effects.
The integration of Internet of Things (IoT) devices in buildings holds immense potential for enhancing performance with respect to building efficiency or building sustainability. This paper systematically explores the literature in the intersection of IoT solutions, emerging digital technologies, sustainability, and Building Information Modelling. In this way, it sheds light on the state-of-the-art and the transformative potential of IoT and digital technologies in creating more sustainable buildings. While IoT systems are often used to improve various aspects of sustainability, the environmental sustainability of IoT systems themselves is rarely investigated. We therefore analyze the literature regarding the environmentally sustainable management of IoT devices employed. The analysis confirms a gap in the research in that IoT solutions are seen as a means to an end but are not the focus of sustainability considerations. Hence, it identifies the need for a sustainability-related Asset Management of IoT systems in buildings. It leads to the proposal of a corresponding implementation concept that bundles all the necessary IoT-related information of a building environment inside a Digital Twin. This establishes a comprehensive overview of the activities and properties of the IoT devices with applications in different use case scenarios, such as monitoring of operational states or insight into recycling potentials.
In this paper, we consider the question of data aggregation using the practical example of emissions data for economic activities for the sustainability assessment of regional bank clients. Given the current scarcity of company-specific emission data, an approximation relies on using available public data. These data are reported in different standards in different sources. To determine a mapping between the different standards, an adaptation to the Covariance Matrix Self-Adaptation Evolution Strategy is proposed. The obtained results show that high-quality mappings are found. Nevertheless, our approach is transferable to other data compatibility problems. These can be found in the merging of emissions data for other countries, or in bridging the gap between completely different data sets.
The usage of data gathered for Industry 4.0 and smart factory scenarios continues to be a problem for companies of all sizes. This is often the case because they aim to start with complicated and time-intensive Machine Learning scenarios. This work evaluates the Process Capability Analysis (PCA) as a pragmatic, easy and quick way of leveraging the gathered machine data from the production process. The area of application considered is injection molding. After describing all the required domain knowledge, the paper presents an approach for a continuous analysis of all parts produced. Applying PCA results in multiple key performance indicators that allow for fast and comprehensible process monitoring. The corresponding visualizations provide the quality department with a tool to efficiently choose where and when quality checks need to be performed. The presented case study indicates the benefit of analyzing whole process data instead of considering only selected production samples. The use of machine data enables additional insights to be drawn about process stability and the associated product quality.
Im vorliegenden Paper wird ein Vergleich zwischen Produktions-und Simulationsdaten präsentiert welches im Rahmen einer größeren Initiative zur Verwendung von Shopfloor Daten bei einem Projektpartner in der Automobilindustrie umgesetzt wurde. In diesem Projekt wurden die Daten die während der Füllbildsimulation entstehen mit den Daten aus der finalen Werkzeugabnahme verglichen um zu analysieren, wie genau diese miteinander über einstimmen. Je besser die Simulation ist, desto schneller kann der gesamte Werkzeugentwicklungsprozess abgewickelt werden, welcher als Kernprozess massives Einsparungspotenzial und damit Wettbewerbsvorteil mit sich bringt.
Creating a schedule to perform certain actions in a realworld environment typically involves multiple types of uncertainties. To create a plan which is robust towards uncertainties, it must stay flexible while attempting to be reliable and as close to optimal as possible. A plan is reliable if an adjustment to accommodate for a new requirement causes only a few disruptions. The system needs to be able to adapt to the schedule if unforeseen circumstances make planned actions impossible, or if an unlikely event would enable the system to follow a better path. To handle uncertainties, the used methods need to be dynamic and adaptive. The planning algorithms must be able to re-schedule planned actions and need to adapt the previously created plan to accommodate new requirements without causing critical disruptions to other required actions.
Through mandatory ESG (environmental, social, governance) reporting large companies must disclose their ESG activities showing how sustainability risks are incorporated in their decision-making and production processes. This disclosure obligation, however, does not apply to small and medium-sized enterprises (SME), creating a gap in the ESG dataset. Banks are therefore required to collect sustainability data of their SME customers independently to ensure complete ESG integration in the risk analysis process for loans. In this paper, we examine ESG risk analysis through a smart science approach laying the focus on possible value outcomes of sustainable smart services for banks as well as for their (SME) customers. The paper describes ESG factors, how services can be derived from them, targeted metrics of ESG and an ESG Service Creation Framework (business ecosystem building, process model, and value creation). The description of an exemplary use case highlighting the necessary ecosystem for service creation as well as the created value concludes the paper.
Smart services disrupt business models and have the potential to stimulate the circular economy transition of regions, enabling an environmentally friendly atmosphere for sustainable and innovation-driven growth of regions. Although smart services are powerful means for deploying circular economy goals in industrial practices, there is little systematic guidance on how the adoption of smart services could improve resource efficiency and stimulate smart regional innovation-driven growth, enabled through circular design. Implemented in the scope of Vorarlberg’s smart specialization strategy, this paper contributes to the literature on the circular economy and regional innovation-driven growth by assessing critical factors of the value creation and value capture implemented within the scope of the quadruple helix system. By identifying the main challenges and opportunities of collaborative value creation and value capture in setting-up smart circular economy strategies and by assessing the role of innovation actors within the quadruple helix innovation system, the study provides recommendations and set of guidelines for managers and public authorities in managing circular transition. Finally, based on the analysis of the role of actors in creating shared value and scaling-up smart circular economy practices in the quadruple helix innovation systems, the paper investigates the role of banks as enablers of circular economy innovation-driven regional growth and smart value creation.
