A learning algorithm based estimation method for maintenance cost of product concepts

Life cycle concerns have become a major issue of increasing importance. Life cycle cost as an analytical method has been developed to enable comprehensive cost analysis to improve economic performance of products during their life cycle. This paper presents a learning algorithm based estimation method for maintenance cost as life cycle cost of product concepts. In order to develop the proposed method, we identify some attributes that represent corrective maintainability of product concepts and add them to the product attributes used to make a selection amongst product concepts. From the list of all the product attributes, 24 product attributes strongly correlated with maintenance cost are chosen. To estimate maintenance costs of product concepts, the selected product attributes are used as inputs and maintenance cost are used as outputs in a learning model based on based on artificial neural networks. The proposed approach does not replace the detailed cost estimation but it would aid cost-effective decision making for product concepts.