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

• URL: http://arxiv.org/abs/2006.08828v1
• Date: Mon, 15 Jun 2020 23:47:19 GMT
• Title: Explainable AI for a No-Teardown Vehicle Component Cost Estimation: A Top-Down Approach
• Authors: Ayman Moawad, Ehsan Islam, Namdoo Kim, Ram Vijayagopal, Aymeric Rousseau, and Wei Biao Wu
• Abstract summary: 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. 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.
• Score: 1.3700176775696422
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Machine Learning-Based Manufacturing Cost Prediction from 2D Engineering Drawings via Geometric Features [0.0]
  We present an integrated machine learning framework that transforms how manufacturing cost is estimated from 2D engineering drawings.<n>By coupling cost prediction with explainability tools such as SHAP, the framework identifies design drivers.<n>This end-to-end CAD-to-cost pipeline shortens quotation lead times, ensures consistent and transparent cost assessments across part families.
  arXiv  Detail & Related papers  (2025-08-17T17:16:38Z)
• A Data-Driven Pool Strategy for Price-Makers Under Imperfect Information [0.0]
  This paper studies the pool strategy for price-makers under imperfect information. Price-makers should estimate the market results with respect to their offer curves using available historical information.
  arXiv  Detail & Related papers  (2024-11-22T02:58:45Z)
• A Primal-Dual Online Learning Approach for Dynamic Pricing of Sequentially Displayed Complementary Items under Sale Constraints [54.46126953873298]
  We address the problem of dynamically pricing complementary items that are sequentially displayed to customers. Coherent pricing policies for complementary items are essential because optimizing the pricing of each item individually is ineffective. We empirically evaluate our approach using synthetic settings randomly generated from real-world data, and compare its performance in terms of constraints violation and regret.
  arXiv  Detail & Related papers  (2024-07-08T09:55:31Z)
• Multi-attribute Auction-based Resource Allocation for Twins Migration in Vehicular Metaverses: A GPT-based DRL Approach [85.65587846913793]
  Vehicular Metaverses are developed to enhance the modern automotive industry with an immersive and safe experience among connected vehicles. We propose an attribute-aware auction-based mechanism to optimize resource allocation during VTs migration. We train a DDA auctioneer using a generative pre-trained transformer (GPT)-based deep reinforcement learning (DRL) algorithm to adjust the auction clocks efficiently.
  arXiv  Detail & Related papers  (2024-06-08T09:41:38Z)
• Causal Forecasting for Pricing [7.077353694086149]
  This paper proposes a novel method for demand forecasting in a pricing context. Our methods bring together the Double Machine Learning methodology for causal inference and state-of-the-art transformer-based forecasting models.
  arXiv  Detail & Related papers  (2023-12-23T15:38:22Z)
• Power Hungry Processing: Watts Driving the Cost of AI Deployment? [74.19749699665216]
  generative, multi-purpose AI systems promise a unified approach to building machine learning (ML) models into technology. This ambition of generality'' comes at a steep cost to the environment, given the amount of energy these systems require and the amount of carbon that they emit. We measure deployment cost as the amount of energy and carbon required to perform 1,000 inferences on representative benchmark dataset using these models. We conclude with a discussion around the current trend of deploying multi-purpose generative ML systems, and caution that their utility should be more intentionally weighed against increased costs in terms of energy and emissions
  arXiv  Detail & Related papers  (2023-11-28T15:09:36Z)
• Refined Mechanism Design for Approximately Structured Priors via Active Regression [50.71772232237571]
  We consider the problem of a revenue-maximizing seller with a large number of items for sale to $n$ strategic bidders. It is well-known that optimal and even approximately-optimal mechanisms for this setting are notoriously difficult to characterize or compute.
  arXiv  Detail & Related papers  (2023-10-11T20:34:17Z)
• Benchmarking Automated Machine Learning Methods for Price Forecasting Applications [58.720142291102135]
  We show the possibility of substituting manually created ML pipelines with automated machine learning (AutoML) solutions. Based on the CRISP-DM process, we split the manual ML pipeline into a machine learning and non-machine learning part. We show in a case study for the industrial use case of price forecasting, that domain knowledge combined with AutoML can weaken the dependence on ML experts.
  arXiv  Detail & Related papers  (2023-04-28T10:27:38Z)
• FeDXL: Provable Federated Learning for Deep X-Risk Optimization [105.17383135458897]
  We tackle a novel federated learning (FL) problem for optimizing a family of X-risks, to which no existing algorithms are applicable. The challenges for designing an FL algorithm for X-risks lie in the non-decomability of the objective over multiple machines and the interdependency between different machines.
  arXiv  Detail & Related papers  (2022-10-26T00:23:36Z)
• A Fair Pricing Model via Adversarial Learning [3.983383967538961]
  At the core of insurance business lies classification between risky and non-risky insureds. The distinction between a fair actuarial classification and "discrimination" is subtle. We show that debiasing the predictor alone may be insufficient to maintain adequate accuracy.
  arXiv  Detail & Related papers  (2022-02-24T10:42:20Z)
• Model Distillation for Revenue Optimization: Interpretable Personalized Pricing [8.07517029746865]
  We present a customized, prescriptive tree-based algorithm that distills knowledge from a complex black-box machine learning algorithm. It segments customers with similar valuations and prescribes prices in such a way that maximizes revenue while maintaining interpretability.
  arXiv  Detail & Related papers  (2020-07-03T18:33:23Z)
• Studying Product Competition Using Representation Learning [7.01269741110576]
  We introduce Product2Vec, a method based on the representation learning algorithm Word2Vec to study product-level competition. The proposed model takes shopping baskets as inputs and, for every product, generates a low-dimensional embedding that preserves important product information. We show that, compared with state-of-the-art models, our approach is faster, and can produce more accurate demand forecasts and price elasticities.
  arXiv  Detail & Related papers  (2020-05-21T00:36:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.