# Reducing the Total Product Cost at the Product Design Stage
###### Abstract  
Currently used decision support systems allow decision-makers to evaluate the product performance, including a net present value analysis, in order to enable them to make a decision regarding whether or not to carry out a new product development project. However, these solutions are inadequate to provide simulations for verifying a possibility of reducing the total product cost through changes in the product design phase. The proposed approach provides a framework for identifying possible variants of changes in product design that can reduce the cost related to the production and after-sales phase. This paper is concerned with using business analytics to cost estimation and simulation regarding changes in product design. The cost of a new product is estimated using analogical and parametric models that base on artificial neural networks. Relationships identified by computational intelligence are used to prepare cost estimation and simulations. A model of product development, production process, and admissible resources is described in terms of a constraint satisfaction problem that is effectively solved using constraint programming techniques. The proposed method enables the selection of a more appropriate technique to cost estimation, the identification of a set of possible changes in product design towards reducing the total product cost, and it is the framework for developing a decision support system. In this aspect, it outperforms current methods dedicated for evaluating the potential of a new product. business analytics; cost estimation; new product development; project management; production planning; decision support systems; predictive and prescriptive analytics; systems modeling and simulation 202  
production cost is mainly related to materials, labor, and energy consumption. Nevertheless, the number of prototype tests affects product reliability, and finally, the costs of defective products in manufacturing and after-sales service. Poor reliability of a product can reduce customer satisfaction and enlarge the cost of warranty repairs. Therefore, the trade-off should be found between the NPD cost and costs related to manufacturing and after-sales service. These costs also depend on the quality of material used in manufacturing, including material density and strength. Consequently, the relationships between materials used, prototype tests, defective products, and costs are sought. In the product design stage, analogical, and parametric techniques are mainly used to cost estimation [3]. Dependencies between variables can be recognized using parametric models that base on multiple regression analysis and computational intelligence techniques. These techniques include, among others, artificial neural networks and neuro-fuzzy systems that are successfully used in the field of production, marketing, distribution [4; 5; 6]. The use of these techniques in the context of NPD have been presented to forecasting new product success and cost estimation [7]. The contribution of this study is to present the use of business analytics, including computational intelligence techniques, to identify dependencies within materials parameters, number of prototype tests, defective products, and repairs in the warranty period. These dependencies affect costs in the whole product life cycle, from product design, through production process, to after-sales service. Dependencies identified by computational intelligence techniques are used not only to cost estimation of a new product, but also to search for feasible variants of changes at the product design stage. These changes aim to reduce the total cost of a new product. Recognition of feasible changes requires the specification of variables, their domains and constraints, including the above-mentioned dependencies. The considered problem can be specified in terms of a constraint satisfaction problem (CSP) that can be solved using constraint programming (CP) techniques.