Explainable AI for a No-Teardown Vehicle Component Cost Estimation: A Top-Down Approach

TL;DR

This paper introduces a data-driven, explainable AI approach for vehicle component cost estimation that bypasses traditional teardown and survey methods, providing accurate, fair pricing insights at the customer level.

Contribution

It presents a novel top-down, machine learning and game theory-based method for vehicle price modeling that improves accuracy and reduces biases compared to traditional approaches.

Findings

Identifies large pricing gaps across manufacturers and vehicle segments.

Highlights interactions between technology prices and vehicle specifications.

Demonstrates the method's accuracy using extensive vehicle database.

Abstract

The broader ambition of this article is to popularize an approach for the fair distribution of the quantity of a system's output to its subsystems, while allowing for underlying complex subsystem level interactions. Particularly, we present a data-driven approach to vehicle price modeling and its component price estimation by leveraging a combination of concepts from machine learning and game theory. We show an alternative to common teardown methodologies and surveying approaches for component and vehicle price estimation at the manufacturer's suggested retail price (MSRP) level that has the advantage of bypassing the uncertainties involved in 1) the gathering of teardown data, 2) the need to perform expensive and biased surveying, and 3) the need to perform retail price equivalent (RPE) or indirect cost multiplier (ICM) adjustments to mark up direct manufacturing costs to MSRP. This novel exercise not only provides accurate pricing of the technologies at the customer level, but also shows the, a priori known, large gaps in pricing strategies between manufacturers, vehicle sizes, classes, market segments, and other factors. There is also clear synergism or interaction between the price of certain technologies and other specifications present in the same vehicle. Those (unsurprising) results are indication that old methods of manufacturer-level component costing, aggregation, and the application of a flat and rigid RPE or ICM adjustment factor should be carefully examined. The findings are based on an extensive database, developed by Argonne National Laboratory, that includes more than 64,000 vehicles covering MY1990 to MY2020 over hundreds of vehicle specs.