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Günther, Robin [Author]; Wende, Martin [Author]; Baumann, Sebastian [Author]; Bartels, Felix [Author]; Beckschulte, Sebastian [Author]; Korn, Goy Hinrich [Author]; Schmitt, Robert H. [Author]; Herberger, David [Author]; Hübner, Marco [Author]; Stich, Volker [Author]

Data Enabled Failure Management Process (DEFMP) across the Product Value Chain - [published Version]

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  • Media type: E-Article; Text
  • Title: Data Enabled Failure Management Process (DEFMP) across the Product Value Chain
  • Contributor: Günther, Robin [Author]; Wende, Martin [Author]; Baumann, Sebastian [Author]; Bartels, Felix [Author]; Beckschulte, Sebastian [Author]; Korn, Goy Hinrich [Author]; Schmitt, Robert H. [Author]; Herberger, David [Author]; Hübner, Marco [Author]; Stich, Volker [Author]
  • imprint: Hannover : publish-Ing., 2023
  • Published in: Proceedings of the Conference on Production Systems and Logistics: CPSL 2023 - 1 ; 10.15488/13418
  • Issue: published Version
  • Language: English
  • DOI: https://doi.org/10.15488/13464; https://doi.org/10.15488/13418
  • Keywords: Decision Support ; Data Analytics ; Data Management ; Production ; Data Enabled Failure Management Process ; Value Chain ; Konferenzschrift
  • Origination:
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  • Description: The continuously increasing amount of production data and the advancing development of digitization solutions promote advanced data analytics as a promising approach for failure management. Beyond the consideration of single units, examining the end-to-end value chain, including development, production, and usage, offers potential for failure in management-related investigations. Nonetheless, challenges regarding data integration from different entities along the value creation process, data volume and formats handling, effective analytics, and decision support arise. The CRISP-DM approach has become a widely established reference as a conceptual framework for data-driven solutions. However, the linkage between existing failure management procedures and the subsequent development of data-driven solutions needs to be specified. Accordingly, this paper presents a cross-value chain Data Enabled Failure Management Process (DEFMP). The central element is a process model to implement a cross-value chain data-enabled failure management, considering established quality management and data analytics approaches. Based on available failure, product, and process knowledge along the value chain, a path towards developing a comprehensive decision support system is shown. DEFMP combines a reactive failure process with a data-driven approach to incorporate data analytics for proactive improvements. Using DEFMP, the failure management process of a commercial vehicle manufacturer is adapted. With this, partial automation of failure management is made possible. In addition, the potential for improvements is identified and prioritized.
  • Access State: Open Access
  • Rights information: Attribution (CC BY)