Supporting Complaint Management in the Medical Technology Industry by Means of Deep Learning

Philip Hake, Jana-Rebecca Rehse, Peter Fettke

In: 3rd International Workshop on Artificial Intelligence for Business Process Management. International Workshop on Artificial Intelligence for Business Process Management (AI4BPM-2019) befindet sich Business Process Management September 1-6 Vienna Austria Springer 2019.


Complaints about finished products are a major challenge for compa-nies. Particularly for manufacturers of medical technology, where product quality is directly related to public health, defective products can have a significant impact. As part of the increasing digitalization of manufacturing companies ("In-dustry 4.0"), more process-related data is collected and stored. In this paper, we show how this data can be used to support the complaint management process in the medical technology industry. Working together with a large manufacturer of medical products, we obtained a large dataset containing textual descriptions and assigned error sources for past complaints. We use this dataset to design, imple-ment, and evaluate a novel approach for automatically suggesting a likely error source for future complaints based on the customer-provided textual description. Our results show that deep learning technology holds an interesting potential for supporting complaint management processes, which can be leveraged in practice.

machine_learning_complaint_management_cr.pdf (pdf, 491 KB)

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence