Type:

Year:

Editor:

Author:

Rosemann, Daniel; Löffelholz, Tobias; Wurst, Johanna; Lachmayer, Roland

Series:

Institution:

Page(s):

DOI number:

ISSN:

Abstract:

Cost planning for Product-Service Systems faces rising complexity, making life-cycle cost estimates essential. This paper investigates how machine learning (ML) can be applied for life-cycle cost estimation in product development. A literature review was conducted to identify ML-based methods, classify them across life cycle phases, and compare them against traditional methods. Results show that traditional models remain transparent but limited in early stages, while ML methods achieve higher accuracy in data-rich phases. A clear research gap exists for hybrid models and end-of-life costing.

Keywords:

product lifecycle management (PLM), data-driven design, cost management, machine learning