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Decision Support for the Automotive Industry

dc.contributor.authorGleue, Christoph
dc.contributor.authorEilers, Dennis
dc.contributor.authorMettenheim, Hans-Jörg
dc.contributor.authorBreitner, Michael H.
dc.description.abstractIn 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.de
dc.relation.ispartofBusiness & Information Systems Engineering: Vol. 61, No. 4
dc.relation.ispartofseriesBusiness & Information Systems Engineering
dc.subjectArtificial neural networks
dc.subjectBusiness intelligence
dc.subjectCar leasing
dc.subjectDecision support systems
dc.subjectResidual value forecasts
dc.titleDecision Support for the Automotive Industryde
dc.typeText/Journal Article