Abstract

Life cycle concerns have been considered as a major issue of increasing importance. Recently, companies have realized that both environmental and economic aspects have become a more competitive issue. This environmental and economic performance of a product includes all life cycle phases, from raw material extraction to end-of-life treatment. Life-cycle cost (LCC) has been considered to enable a comprehensive cost analysis to be carried out to improve economic performance, but this method is usually time and cost consuming. Therefore, there is a need for easy-to-use and approximate methods to support cost-effective decision making in early product development. In order to incorporate LCC into early product development, an approximate method for the estimation of maintenance cost as one component of LCC is proposed. This method allows the designer to make a comparative estimation of maintenance cost for design alternatives by considering high-level product attributes and the maintenance cost during the life cycle of products. To estimate the maintenance cost, the identified product attributes are used as inputs and the calculated maintenance cost is used as outputs in a learning algorithm based on artificial neural networks. The proposed approach does not replace the detailed cost estimation but it would give some cost-effective guidelines of products during the life cycle and create possibly new maintenance concepts.

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