Business & Information Systems Engineering
Decision Support for the Automotive Industry - Forecasting Residual Values Using Artificial Neural Networks
In the automotive industry, it is very common for new vehicles to be leased rather than sold. This implies forecasting an accurate residual value for the vehicles, which is a major factor for determining monthly leasing rates. Either a systematic overestimation or underestima- tion of future residual values can incur large potential losses in resale value or, respectively, competitive disad- vantages. For the purpose of facilitating residual value related management decisions, an operative decision sup- port system is introduced with emphasis on its forecasting capabilities. In the paper, the use of artificial neural net- works for this application is demonstrated in a case study based on more than 250,000 data sets of leasing contracts from a major German car manufacturer, completed between 2011 and 2017. The importance of determining price factors and the effect of different time horizons on forecasting accuracy are investigated and practical impli- cations are discussed. In addition, the authors neither found a significant explanatory nor predictive power of external economic factors, which underlines the importance of collecting and taking advantage of vehicle-specific data or, in more general terms, the exclusive data of corporations, which is often only available internally.
Gleue, Christoph; Eilers, Dennis; Mettenheim, Hans-Jörg von; and Breitner, Michael H.
"Decision Support for the Automotive Industry - Forecasting Residual Values Using Artificial Neural Networks,"
Business & Information Systems Engineering:
Vol. 61: Iss. 4, 385-397.
Available at: https://aisel.aisnet.org/bise/vol61/iss4/2