Oliver Hartkopp

Automatic Defect Detection by Classifying Aggregated Vehicular Behavior

Detecting defects is a major task for all complex products, as automobiles. Current symptoms are the failure codes a vehicle produces and the complaints of a customer. An important part on the defect detection is the vehicular behavior. This paper highlights the analysis of vehicular data as a new symptom in the customer service process. The proposed concept combines the necessary preprocessing of vehicular data, especially the feature-based aggregation of this data, with the analysis on different sets of features for detecting a defect. In the modeling part a Support Vector Machine classifier is trained on single observed situations in the vehicular behavior and a Decision Tree is used to abstract the model output to a trip decision. The evaluation states a detection quality of 0.9418 as the F1-score.

Supporting Product Optimization by Customer Data Analysis

This paper introduces a concept for product optimization support based on the integration of customer data sources. The motivation is a common misunderstanding gap between the manufacturer and the customer. While the customer has certain needs, the manufacturer aims at embedding them into the product design. However, due to imprecise understanding of the needs and subsequent development mistakes, the product can vary from what the customer actually requires. The concept combines two different data sources in order to reveal the gap between the product and the customer needs. The first source is represented by a customer-product interaction log file. The second source is social media delivering customer feedback regarding the product.