Big data and machine learning in cost estimation: An automotive case study

This paper presents a case study on the applicability of machine learning and big data technology for product cost estimation, using data on the material cost of passenger cars. The study provides contributions to six research aspects. First, the authors show which machine learning algorithms are appropriate when dealing with product cost estimation for highly complex products with more than 2000 parts and hundreds of cost drivers. Second, our case study provides a novel approach to increasing the predictive accuracy of cost estimates for subsequent product generations. Third, the authors show that the accuracy is up to 3.5 times higher when using big data compared to an intermediate size of data. Fourth, machine learning can outperform cost estimates from cost experts during the early stage of new product development, even when dealing with highly complex products. Then, the authors evaluate the use cases, issues, and benefits of machine learning and big data from the perspective of cost experts. Specifically, the case study shows that machine learning can reliably select the most important cost drivers (fifth aspect) and calculate the average cost of cost drivers over thousands of product configurations (sixth aspect). However, cost experts must be knowledgeable about the product and remain careful when interpreting machine learning outcomes, as they can yield misleading outcomes in some exceptional cases. In conclusion, machine learning and big data empirically proved to be able to generate additional value in many aspects for managing costs during the early phase of new product development.

• Record URL:
  • https://doi.org/10.1016/j.ijpe.2023.109137
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0925527323003699
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09255273
• Supplemental Notes:
  • © 2024 Published by Elsevier B.V. Abstract reprinted with permission of Elsevier.
• Authors:
  • Hammann, Dominik
  • 0000-0002-4244-5169
• Publication Date: 2024-3

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 109137
• Serial:
  • International Journal of Production Economics
  • Volume: 269
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0925-5273
  • Serial URL: http://www.sciencedirect.com/science/journal/09255273

Subject/Index Terms

• TRT Terms: Algorithms; Cost estimating; Machine learning; Motor vehicle industry; Product development
• Geographic Terms: Germany
• Subject Areas: Data and Information Technology; Economics; Finance; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01905703
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 26 2024 10:02AM