Details
| Original language | English |
|---|---|
| Pages (from-to) | 385-397 |
| Number of pages | 13 |
| Journal | Business and Information Systems Engineering |
| Volume | 61 |
| Issue number | 4 |
| Early online date | 23 Feb 2018 |
| Publication status | Published - 1 Aug 2019 |
Abstract
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 underestimation of future residual values can incur large potential losses in resale value or, respectively, competitive disadvantages. For the purpose of facilitating residual value related management decisions, an operative decision support system is introduced with emphasis on its forecasting capabilities. In the paper, the use of artificial neural networks 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 implications 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.
Keywords
- Artificial neural networks, Business intelligence, Car leasing, Decision support systems, Residual value forecasts
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
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In: Business and Information Systems Engineering, Vol. 61, No. 4, 01.08.2019, p. 385-397.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Decision Support for the Automotive Industry
T2 - Forecasting Residual Values Using Artificial Neural Networks
AU - Gleue, Christoph
AU - Eilers, Dennis
AU - von Mettenheim, Hans-Jörg
AU - Breitner, Michael
PY - 2019/8/1
Y1 - 2019/8/1
N2 - 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 underestimation of future residual values can incur large potential losses in resale value or, respectively, competitive disadvantages. For the purpose of facilitating residual value related management decisions, an operative decision support system is introduced with emphasis on its forecasting capabilities. In the paper, the use of artificial neural networks 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 implications 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.
AB - 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 underestimation of future residual values can incur large potential losses in resale value or, respectively, competitive disadvantages. For the purpose of facilitating residual value related management decisions, an operative decision support system is introduced with emphasis on its forecasting capabilities. In the paper, the use of artificial neural networks 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 implications 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.
KW - Artificial neural networks
KW - Business intelligence
KW - Car leasing
KW - Decision support systems
KW - Residual value forecasts
UR - http://www.scopus.com/inward/record.url?scp=85069455326&partnerID=8YFLogxK
U2 - 10.1007/s12599-018-0527-3
DO - 10.1007/s12599-018-0527-3
M3 - Article
AN - SCOPUS:85069455326
VL - 61
SP - 385
EP - 397
JO - Business and Information Systems Engineering
JF - Business and Information Systems Engineering
SN - 0937-6429
IS - 4
ER -