Methodology for the analysis and design of an algorithm for a more robust traceability and improved confidence of action in the organizational structures of the value chain of companies, using customer complaints from use phase [AlGeWert]
Final Report Abstract
Increasing product and process complexity poses numerous challenges for manufacturing companies. In particular, if defects occur during the product use phase that could lead to legal claims on the part of customers, it is essential to quickly and accurately determine any liability risks. To counter this problem, the DFG project AlGeWert developed an algorithm that uses natural language processing (NLP) to test complaints automatically for criteria for liability bases based on German law and generate specific instructions for employees. The project focuses on the basic legal cases under German warranty law, producer liability under the German Civil Code and the Product Liability Act. The aim is to differentiate between these and to extract the relevant criteria from written customer complaints with the help of NLP. In the process, correlations between the parties involved in the value chain and responsibilities in the event of liability cases under German law were identified and linked.
Publications
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Identification and Assessment of Various Liability Cases Based on Written Customer Complaints. 2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 0254-0258. IEEE.
Lemke, I. & Schlüter, N.
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NLP-gestützte Reklamationsanalyse zur effizienten Ermittlung von Haftungsverantwortlichkeiten, GQW Tagung 2024
Lemke, I. & Schlüter, N.
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Towards an Algorithm-Based Automatic Differentiation of Liability Cases by Analyzing Complaint Texts. Proceedings of the 26th International Conference on Enterprise Information Systems, 603-611. SCITEPRESS - Science and Technology Publications.
Lemke, Insa & Schlüter, Nadine
