Stefanie Berninger
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Institute
Circular Economy (CE) has a low implementation rate so far, despite its many environmental and economic benefits. The concept of Upgrade Circular Economy (UCE) aims to address the existing challenges by aiming for a more continuous value enhancement of circular products and an industrialization of the associated processes. Digital Product Passports (DPP) are a key component of the concept as they act as a data hub for the circular value network. However, their prevalence in industrial practice is low. The aim of this work is to extend a concept for the integration of DPPs into the existing system landscape of manufacturing companies to advance the implementation rate. The core aspects of the work are the derivation of new use cases and data requirements that arise in an UCE, the formulation of evaluation options and implementation recommendations for the DPP, as well as examples for the concrete implementation of event technology. The aim is to provide manufacturing companies with practical options for the use of DPPs as a basis for the implementation of UCE.
BPMN-2.0-Schulung
(2024)
The transition from linear to circular value-preserving models poses significant challenges, particularly in the context of Reverse Logistics. Variability in returned products in terms of time and quality and logistical complexities referring to decentralized and central factories hamper implementation. While digitalization offers the potential to optimize these processes, many companies lack a structured, scalable, and interoperable approach for integrating digital tools into Reverse Logistics. Moreover, the concept has largely been neglected and limited to managing returns from quality and warranty claims. Existing Enterprise Architecture Models frequently fall short in addressing challenges such as product condition and variability, independence from proprietary systems, and the integration of multiple Circular Economy (CE) strategies thereby constraining their practical applicability. The present paper aims to address these gaps by proposing a generic, vendor-independent, data-driven architecture model that enables companies to implement efficient, sustainable Reverse Logistics in alignment with CE goals. This model fulfills the pressing need for practical frameworks that support interoperability, reuse of existing IT infrastructure, and comprehensive process transparency. Consequently, organizations will be empowered to implement smart Reverse Logistics and to meet regulatory demands, resource efficiency targets, and consumer expectations for sustainable products. Furthermore, the model should serve to meet national and supranational environmental protection targets.
PRepChain: A versatile privacy-preserving reputation system for dynamic supply chain environments
(2025)
Despite their significant added value in the context of consumer-oriented e-commerce, reputation systems have seen limited adoption in other business settings and models these days. Yet, reliable reputation scores are essential in such settings for easing the establishment of new business relationships—an aspect that is particularly crucial in dynamic supply chain environments, where business partners change frequently.
Existing approaches, however, usually target other application domains and fall short in addressing the specific challenges of dynamic supply chains—especially with respect to reliability (incl. availability) and privacy preservation (incl. confidentiality). To close this research gap and to support novel directions in this important research area, we propose PRepChain, our highly-configurable approach that leverages fully homomorphic encryption and distributed competences to provide businesses with a versatile reputation-enriched ecosystem.
PRepChain is specifically designed to operate in dynamic environments by also offering a trade-off between data availability and confidentiality guarantees. We make contributions in four primary directions: (i) It offers performant privacy preservation even in large-scale settings, (ii) ensures availability of computed reputation scores, (iii) seamlessly integrates with existing supply chain information systems, and (iv) in addition to subjective reputation scores, it also supports reliably-calculated, i.e., objective, ones, thereby strengthening the reliability of third-party-sourced information. Our evaluation of PRepChain documents its performance—based on a real-world use case—, security, and privacy preservation, hence, its applicability. We conclude that it is indeed destined for practical deployments in modern supply networks.
Decision making in modern supply chain management relies heavily on data-driven decision support. Companies show a growing interest in building insights not only on data from within the company’s own boundaries, but also from collaborators and other actors in the market. While the topic of data and information sharing has been the focus of previous works, there has been a lack of studies focusing on practical implementations in the supply chain domain. Our aim is to conduct a technical feasibility study of data sharing in supply chain management. We analyze the requirements for cross-company data sharing in supply chains, and discuss existing technologies that enable such collaboration. We apply a distributed analytics framework that has already been implemented in the healthcare domain to a simulated use case of key performance indicator (KPI) exchange between supply chain actors. We find that the application is able to compute and exchange KPIs from the simulated companies’ datasets without requiring centralization of the databases. Furthermore, we find that the framework supports integration of data quality assessment and privacy preservation mechanisms. The application thus yields promising results with regard to technical feasibility. Factors that may facilitate scalability are discussed as directions for future research.
The establishment, expansion, and operation of reliable value-creation networks present an increasing challenge for manufacturing companies, given the growing volatility of the market environment in which they operate. For example, the development of new business areas, mass customization, or the disruption of supply chains frequently necessitates the establishment of partnerships with new suppliers, both short- and long-term. The utilization of supplier key performance indicators (KPIs) can facilitate the selection of new business partners, as they provide a quick and objective indication of their reliability. Nevertheless, access to potentially sensitive KPIs, such as a supplier's on-time delivery performance, is currently mainly limited to existing supplier relationships and not made available to other companies. This paper presents a coordinated approach for supplier rating systems, thereby enabling the privacy-aware exchange of supplier KPIs across organizations and exemplifies it using an application in the “Internet of Production”.
Specifically, we conduct interdisciplinary research by formulating the requirements from a business perspective (supply chain design, trust in data sharing, and business models) and evaluating promising solutions from a technical perspective (information security, data quality, data sovereignty, and collaboration). This approach enables the combination of state-of-the-art technology with the evolving requirements of stakeholders, thus creating new paths for exploiting inter-organizational supply chain rating.
Komplexe Anforderungen an das operative Supply-Chain-Management führen zu komplexen Entscheidungssituationen innerhalb des betrieblichen Alltags. Ein konzeptioniertes, modulares Simulationsmodell für das operative Supply-Chain-Management kann die Entscheidungsfindung auf Grundlage von Analysen und Daten unterstützen. Die Modularisierung ermöglicht eine flexible, effiziente und unternehmensspezifische Anwendung des Simulationsmodells. Die Modularität ist durch die individuelle Kombination geeigneter Module und Funktionen sowie der Abbildung dazugehöriger Informationsflüsse realisiert. Informationsflüsse werden dabei durch definierte Informationsobjekte, wie z. B. einen Fertigungsauftrag oder eine Maschine, spezifiziert. Die Module und Funktionen bilden die unterschiedlichen Prozessschritte ab.
In der neuen Folge unseres Podcasts „TuWAs-Talkline“ erläutern Marco Lohrey, Projektmanager am FIR an der RWTH Aachen, und Stefanie Berninger, Projektmanagerin im Bereich Produktionsmanagement am FIR an der RWTH Aachen, wieso erfolgreiches Supply-Chain Management für komplexe Produktion unabdinglich ist.
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.
Crises pose significant short and long-term threats to companies. The research project PAIRS aims to strengthen the resilience of actors in the supply-chain, en-ergy, and healthcare sectors in crisis situations. The basis for this is the newly created potential in data exchange, which is leveraged by combining internal with external (company-)data, e.g. in the GAIA-X network. AI is then the key to iden-tifying the time of the crisis and deriving appropriate actions to deal with it. Therefore, crisis scenarios are generated, and risks are assessed. In this paper, the project fundamentals are discussed. This includes the development of a project definition of the term "crisis", which is based on literature research of various scientific disciplines (e.g. economics or political science), as well as interviews with professional and academic experts from different fields. Moreover, a specif-ic example from the supply-chain domain is introduced to illustrate the process of requirement identification.
