Approximate product life cycle costing method for the conceptual product design

Abstract

Although the product life cycle cost (LCC) is mainly committed by early design stages, designers do not consider the costs caused in subsequent phases of life cycle. The estimating method for the product life cycle cost in early design processes has been required because of both the lack of detailed information and time for a detailed LCC for a various range of design concepts. This paper suggests an approximate LCC method that allows the designer to make comparative LCC estimation between the different product concepts. The product attributes at the conceptual design phase and LCC factors are introduced and the significant product attributes are determined by statistical analysis. Neural network algorithms are applied to estimate LCC by considering the identified product attributes as inputs and the LCC as output. Trained learning algorithms for the known characteristics of existing products will quickly give the estimation of LCC for new product concepts. The estimation for maintenance and energy costs of electronic appliances is shown as an example. The proposed method provides the good estimation for the LCC and gives the guidelines leading to cost-effective design decision-making at the early design stage.