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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 es‐
timation and simulation regarding changes in product design. The cost of a new product is esti‐
mated 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 tech‐
niques. The proposed method enables the selection of a more appropriate technique to cost estima‐
tion, 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.
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