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Technology forecasting using network data envelopment analysis use case: Electrical vehicles

: Adjogble, Franck K.; Leyendecker, Nadja; Warschat, Joachim; Fischer, Thomas; Ardilio, Antonino

Emrouznejad, Ali (Ed.):
Data envelopment analysis and performance measurement: Recent developments : Proceedings of the DEA 2018, 40th International Conference of Data Envelopment Analysis; April 2018, Aston Business School, Aston Univesrity, Birmingham, United Kingdom
Birmingham: Aston Business School, 2018
ISBN: 978-1-85449-438-2
International Conference of Data Envelopment Analysis (DEA) <40, 2018, Birmingham>
Fraunhofer IAO ()

The future is not predictable, is it? While of course no one can exactly foresee all events, we can learn from the past and extrapolate historic developments into the future, especially in the area of technology forecasting. Based on Technology Forecasting with Data Envelopment Analysis (TFDEA by Inman (2004)) and Network Data Envelopment Analysis (NDEA by Cook et al. (2010) describe the approach of Technology Forecasting with Network Data Envelopment Analysis (TFNDEA). While classical DEA only analyses a system’s efficiency as a whole by considering in- and outputs, the Network DEA based approach provides the possibility to look inside a system and to determine the efficiency and development of the interdependent subcomponents. The TFNDEA approach is applied by this work to the use case of electric vehicles, a technology with extensive development in the last years and high expectations for the future. Based on datasets from Tudorie (2012) a test prediction is done for the year 2017, which can be verified with current data. Then the approach is used to calculate predictions for the year 2020, to provide a forecast of the development of electric vehicles.