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.
Highlights
The shorter product life cycles result in increasing product variety in contemporary businesses
If the total costs related to new product development (NPD), production, and after-sales service are unacceptable for decision-makers, they can prefer to acquire information of feasible changes in product design that could reduce the costs involved in the whole product life cycle
The artificial neural networks (ANNs) trained according to the Levenberg–Marquardt algorithm (LM) algorithm obtained the least mean absolute percentage errors (MAPEs) in the testing set for estimation models regarding the cost of material (V14 ), production (V15 ), and warranty (V18 ), whereas the ANNs trained according to the GDX algorithm for estimating the cost of NPD (V1 )
Summary
The shorter product life cycles result in increasing product variety in contemporary businesses. 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. The contribution of this study is an extension of a cost estimation model towards involving the variables related to product design (e.g., material properties, prototype tests), production process (e.g., the number of defective products), and after-sales stage (e.g., the number of repairs in the warranty period).
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