Authors:
Oliver Böhme
and
Tobias Meisen
Affiliation:
Chair for Technologies and Management of Digital Transformation, Bergische Universität Wuppertal, Rainer-Gruenter-Str. 21, Wuppertal, Germany
Keyword(s):
Machine Learning, Classification, Prediction, Deep Neural Networks, MLP, LSTM, Multivariate, Automotive, R&D, Projects Progressions, Project Life Cycle, Comparative Analysis.
Abstract:
The increasing complexity in automotive product development is forcing traditional manufacturers to fundamentally rethink. As a result, many companies are already investing in the development of methods to increase the controllability of their development processes. The use of data-driven approaches is a promising way to provide an early prediction of potential problems in the course of a project by learning from the past. In vehicle development, projects can be divided into two basic categories: new vehicle launches and model enhancement projects. The course of projects according to the above-mentioned categories can be based on different influencing factors. To verify this hypothesis and to determine the extent of the differences in the data, we carry out a data-driven classification of the project category. In contrast to the recognition of other time-dependent data (e.g., univariate sensor data courses), we use multivariate project information from the automotive industry. With t
his paper, which is of an application nature, we prove that a multivariate classification of automotive projects can be realized based on the underlying project’s progression.
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