Explainable AI for a No-Teardown Vehicle Component Cost Estimation: A Top-Down Approach. Moawad, A., Islam, E., Kim, N., Vijayagopal, R., Rousseau, A., & Wu, W. B. IEEE Transactions on Artificial Intelligence, 2(2):185–199, April, 2021. Conference Name: IEEE Transactions on Artificial Intelligence![Explainable AI for a No-Teardown Vehicle Component Cost Estimation: A Top-Down Approach [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 3 downloads 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 the use of this framework on a very specific (but generalizable) application, interesting for a more general AI audience. We detail 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 uncertainties involved in gathering teardown data, the need to perform expensive and biased surveying, and the need to perform retail price equivalent or indirect cost multiplier 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 classes, market segments, etc. There is also clear interaction between the price of technologies and other specifications present in vehicles. Those results are indication that old methods of manufacturer-level component costing, aggregation, and application of flat and rigid adjustment factors should be carefully examined. The findings are based on a database developed by Argonne, which includes over 64 000 vehicles covering MY1990 to MY2020 with hundreds of vehicle specs.
@article{moawad_explainable_2021,
title = {Explainable {AI} for a {No}-{Teardown} {Vehicle} {Component} {Cost} {Estimation}: {A} {Top}-{Down} {Approach}},
volume = {2},
issn = {2691-4581},
shorttitle = {Explainable {AI} for a {No}-{Teardown} {Vehicle} {Component} {Cost} {Estimation}},
url = {https://ieeexplore.ieee.org/abstract/document/9380163},
doi = {10.1109/TAI.2021.3065011},
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 the use of this framework on a very specific (but generalizable) application, interesting for a more general AI audience. We detail 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 uncertainties involved in gathering teardown data, the need to perform expensive and biased surveying, and the need to perform retail price equivalent or indirect cost multiplier 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 classes, market segments, etc. There is also clear interaction between the price of technologies and other specifications present in vehicles. Those results are indication that old methods of manufacturer-level component costing, aggregation, and application of flat and rigid adjustment factors should be carefully examined. The findings are based on a database developed by Argonne, which includes over 64 000 vehicles covering MY1990 to MY2020 with hundreds of vehicle specs.},
number = {2},
urldate = {2024-12-12},
journal = {IEEE Transactions on Artificial Intelligence},
author = {Moawad, Ayman and Islam, Ehsan and Kim, Namdoo and Vijayagopal, Ram and Rousseau, Aymeric and Wu, Wei Biao},
month = apr,
year = {2021},
note = {Conference Name: IEEE Transactions on Artificial Intelligence},
keywords = {Biological system modeling, Catboost, Data models, Estimation, Machine learning, Manufacturing, Predictive models, Pricing, component pricing, explainable AI, interpretable machine learning, shapley values, vehicle price (MSRP)},
pages = {185--199},
}
Downloads: 3
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