Regression models for estimating product life cycle cost

Product life cycle cost (LCC) is defined as the cost that is incurred in all stages of the life cycle of a product, including product creation, use and disposal. In recent years, LCC has become as crucial as product quality and functionality in deciding the success of a product in the market. In order to estimate LCC of new products, researchers have employed several (parametric) regression analysis models and artificial neural networks (ANN) on historical life cycle data with known costs. In this article, we conduct an empirical study on performance of five popular non-parametric regression models for estimating LCC under different simulated environments. These environments are set by varying the number of cost drivers (independent variables), the size of sample data, the noise degree of sample data, and the bias degree of sample data. Statistical analysis of the results recommend best LCC estimation models for variable environments in stages of the product life cycle. These findings are validated with real-world data from previous work.