The robust-reliable decision for selecting benchmark automotive platforms based on the combination of humane judgment simulation and KDD techniques

Abstract

In this study, we address robust-reliable decision-making approach to select benchmark platforms to develop the automotive family. Activities studied included selecting the appropriate decision-making method, simulating possible scenarios to determine the relative importance of attributes by experts and stakeholders, determining the decision space and valuing attributes based on databases and Knowledge discovery in databases (KDD) techniques, statistical analysis and sensitivity assessment. The robust-reliable decision is made using the Simple Additive Weighting (SAW) method for 6223 unique cases of expert judgments and stakeholder expectations. The database used to determine decision space and attributes values included 546 automobiles designed in 11 different segments based on 34 platforms. This has led to the reliable selection of five benchmark platforms with the highest level of desirability in terms of defined attributes and the greatest robustness to uncertainties in the expert judgments and the level of expectations of stakeholders.