Now showing 1 - 4 of 4
  • Publication
    Offering Two-way Privacy for Evolved Purchase Inquiries
    ( 2023)
    Pennekamp, Jan
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    Dahlmanns, Markus
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    Fuhrmann, Fuhrmann
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    Heutmann, Timo
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    ; ; ;
    Schmitt, Robert H.
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    Wehrle, Klaus
    Dynamic and flexible business relationships are expected to become more important in the future to accommodate specialized change requests or small-batch production. Today, buyers and sellers must disclose sensitive information on products upfront before the actual manufacturing. However, without a trust relation, this situation is precarious for the involved companies as they fear for their competitiveness. Related work overlooks this issue so far: existing approaches protect the information of a single party only, hindering dynamic and on-demand business relationships. To account for the corresponding research gap of inadequately privacy-protected information and to deal with companies without an established trust relation, we pursue the direction of innovative privacy-preserving purchase inquiries that seamlessly integrate into today's established supplier management and procurement processes. Utilizing well-established building blocks from private computing, such as private set intersection and homomorphic encryption, we propose two designs with slightly different privacy and performance implications to securely realize purchase inquiries over the Internet. In particular, we allow buyers to consider more potential sellers without sharing sensitive information and relieve sellers of the burden of repeatedly preparing elaborate yet discarded offers. We demonstrate our approaches' scalability using two real-world use cases from the domain of production technology. Overall, we present deployable designs that offer two-way privacy for purchase inquiries and, in turn, fill a gap that currently hinders establishing dynamic and flexible business relationships. In the future, we expect significantly increasing research activity in this overlooked area to address the needs of an evolving production landscape.
  • Publication
    The Monetization of Technical Data: Innovations from Industry and Research
    (Springer, 2023)
    Trauth, Daniel
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    The monetization of data is a very young topic, for which there are only very few case studies. There is a lack of strategy or concept that shows decision-makers the way into the monetization of data, especially those who have discovered or are threatened by the digital transformation or Industry 4.0. Because machine data is usually unstructured and not usable without domain knowledge/metadata, the monetization of machine data has an as yet unquantifiable potential. In order to make this potential tangible, this work describes not only contributions from science, but also practical examples from industry. Based on different examples from various industries, the reader can already become part of a future data economy today. Values and benefits are described in detail. The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.
  • Publication
    Towards Ontology-based Lifecycle Management in Blisk Manufacturing
    Product Lifecycle Management (PLM) handles the typical stages of a product's lifespan, and is usually implemented via different methods. This paper addresses the stages product design, process design, process analysis and manufacturing in the product and process development chain of a blade-integrated disk (blisk). Domain ontologies are evolved and incorporated, as well as used with Uniform Resource Identifiers (URI) to implement a comprehensive PLM in the Internet of Production. An architecture based on the Resource Description Framework (RDF) that offers both ingestion and utilization of valuable information along the blisk lifecycle, and therefore enables PLM for all involved participants, is presented.
  • Publication
    FactDAG: Formalizing Data Interoperability in an Internet of Production
    ( 2020)
    Gleim, L.
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    Pennekamp, J.
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    Liebenberg, M.
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    Buchsbaum, M.
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    Niemietz, P.
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    Knape, S.
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    Epple, A.
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    Storms, S.
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    Trauth, D.
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    Bergs, T.
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    Brecher, C.
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    Decker, S.
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    Lakemeyer, G.
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    Wehrle, K.
    In the production industry, the volume, variety, and velocity of data as well as the number of deployed protocols increase exponentially due to the influences of the Internet-of-Things (IoT) advances. While hundreds of isolated solutions exist to utilize these data, e.g., optimizing processes or monitoring machine conditions, the lack of a unified data handling and exchange mechanism hinders the implementation of approaches to improve the quality of decisions and processes in such an interconnected environment. The vision of an Internet of Production promises the establishment of a Worldwide Lab, where data from every process in the network can be utilized, even interorganizational and across domains. While numerous existing approaches consider interoperability from an interface and communication system perspective, fundamental questions of data and information interoperability remain insufficiently addressed. In this article, we identify ten key issues, derived from three distinctive real-world use cases that hinder large-scale data interoperability for industrial processes. Based on these issues, we derive a set of five key requirements for future (IoT) data layers, building upon the FAIR data principles. We propose to address them by creating FactDAG, a conceptual data layer model for maintaining a provenance-based, directed acyclic graph of facts, inspired by successful distributed version-control and collaboration systems. Eventually, such a standardization should greatly shape the future of interoperability in an interconnected production industry.