Internet of Production IoP
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Circular economy (CE) is considered to be the business model of the future, since it enables decoupling of economic growth and resource consumption. Digitalization is an enabler for companies to accomplish the transition to circular business models, as it enables automated data sharing and usage, but it also poses an enormous challenge. The data required for the implementation of circular business models is generated during the entire life cycle of a product. Digital product passports (DPP) represent a solution for the exchange of product-related data across the entire life cycle and various stakeholders. So far, they have hardly been integrated into production planning and control (PPC) systems. This paper describes requirements, specific use cases and related data flows for an integration of DPP and PPC systems. Finally, a model is presented that enables event-driven creation and use of data for the bidirectional integration of DPP into PPC systems.
Die notwendige Transformation der Linear- zur Kreislaufwirtschaft ermöglicht die Entkopplung des Wirtschaftswachstums und Ressourcenverbrauchs. Das ökonomische und ökologische Potenzial der Kreislaufwirtschaft wird durch die zeitgleiche Umsetzung mehrere, simultaner Kreislaufstrategien gesteigert. Diese Umsetzungsform bedingt allerdings vielfältige und komplexe Entscheidungen für die operative Abwicklung. Das entwickelte kaskadierte Entscheidungsmodell differenziert diese Entscheidungen innerhalb verschiedener Ebenen des Wertschöpfungssystems.
Synthetic event data generated by an AnyLogic simulation for the evaluation of parameterized production planning scenarios with the consideration of both economic and sustainabiliy related KPIs.
The event data file is provided in the standard OCEL 2.0 SQLITE format (https://ocel-standard.org/). It can for example be opened with the following webapp: https://ocelot.pm/.
Additionally, there is a PDF file of a generated visualization included in this publication.
This dataset was produced as a side-product from the "Production Planning for Sustainability" app of CRD-B3.II within the Internet of Production (IoP) research project.
Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany ́ s Excellence Strategy – EXC-2023 Internet of Production – 390621612
Zusammenarbeit verschiedener Supply-Chain-Partner und ein überbetrieblicher Datenaustausch stellen wesentliche Erfolgsfaktoren für Unternehmen in komplexen Netzwerken dar. Mangelnder Datenaustausch und daraus resultierende Informationsdefizite führen dagegen zu Problemen in Lieferketten. In diesem Beitrag wird ein Assistenzsystem zur Bewertung und Gestaltung von Offenheit und Vertrauen zwischen Supply-Chain-Partnern vorgestellt.
The shop floor is a dynamic environment, where deviations to the production plan frequently occur. While there are many tools to support production planning, production control is left unsupported in handling disruptions. The production controller evaluates the deviations and selects the most suitable countermeasures based on his experience. The transparency should be increased in order to improve the decision quality of the production controller by providing meaningful information during his decision process. In this paper, we propose a framework in which an interactive production control system supports the controller in the identification of and reaction to disturbances on the shop floor. At the same time, the system is being improved and updated by the domain knowledge of the controller. The reference architecture consists of three main parts. The first part is the process mining platform, the second part is the machine learning subsystem that consists of a part for the classification of the disturbances and one part for recommending countermeasures to identified disturbances. The third part is the interactive user interface. Integrating the user’s feedback will enable an adaptation to the constantly changing constraints of production control. As an outlook for a technical realization, the design of the user interface and the way of interaction is presented. For the evaluation of our framework, we will use simulated event data of a sample production line. The implementation and test should result in higher production performance by reducing the downtime of the production and increase in its productivity.
In recent years, the complexity of the management of supply chains has increased significantly due to the growing individualization of products and dynamics of the market environment. To remain competitive, ensuring efficient and flexible processes and procedures along the entire supply chain are of particular importance for companies. Especially in the inter-company context, decisions must be made as quickly and correctly as possible. To enable good decision-making processes data must be processed and provided in a targeted manner. Currently, however, the necessary transparency is often lacking within the supply chains. In this article, a software-based assistance system for decision support on supply chain level is presented that aims to increase the transparency and efficiency of the decision-making process. A concept for decision support on supply chain level is presented. This paper focuses on the conceptual linkage of relevant decisions and data. Therefore, indicators are identified and linked with the relevant decisions. Moreover, a suitable way of visualizing the identified indicators for each decision in a user-friendly manner is defined. These results are then used to implement the software tool.
