# Reducing the Total Product Cost at the Product Design Stage
## 5 Conclusions  
The presented approach supports NPD decision-makers in evaluating the total cost of a new product using analogical and parametric estimation models. Parametric cost estimation is based on artificial neural networks that identify relationships between input and output variables. Input variables refer to a product and its manufacturing process, whereas output variables to costs, including NPD, material, labor, and warranty repairs. The identified relationships are used not only to cost estimation but also towards improving the process of designing product and manufacturing. Dependencies between the material density, material consumption or prototype tests can influence a number of defective products, affecting not only the NPD cost, but also the cost of manufacturing and after-sales service. The relationships identified by computational intelligence techniques were used not only for cost estimation, but also to search for the possibility of changes in product design, by which the cost was reduced, or the target cost was reached. If the estimated total cost of a new product has an unacceptable level for decision-makers, then they can be supported by simulating feasible changes in product design that reduce the cost of production and warranty repairs. The proposed model encompasses the product specification and company's resources, and it is formulated in terms of a constrain satisfaction problem that includes decision variables, their domains, and constraints. Problem solution requires searching for the values of variables (e.g., material properties and prototype tests), by which the total product cost is reduced. The use of the CSP paradigm serves as a well-suited framework for developing a decision support system. An additional advantage of the proposed approach also refers to the possibility of specifying other problems in terms of a CSP. For instance, a project selection problem can be formulated as a CSP, by adding appropriate variables and constraints. If there are considered for developing a few NPD projects in more than one product line, then decision-makers may be supported in project selection using multicriteria decision analysis, including identification of weighting parameters for evaluation criteria. The selection of an optimal project portfolio of new products can depend not only on sales- and cost-related \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & **Unit** & **Change** & **Total NPV** & **Change in** & **Change in** \\  & **Production Cost** & **in Cost** & **(Thousand)** & **Total NPV** & **Total NPV (\%)** \\ \hline Optimistic & 38.4 & \(-\)4.27 & 439.8 & 19.8 & 4.71\% \\ Basic & 42.7 & 0 & 420.0 & 0 & 0\% \\ Pessimistic & 47.0 & 4.27 & 400.2 & \(-\)19.8 & \(-\)4.71\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Scenario proposal for changes in the unit production cost. Figure 5: Tornado chart for changes in NPV. factors but also on the strategic management of a business. For example, a costly and time-consuming NPD project of creating innovation can be more advantageous for a company from the strategic perspective than less expensive product customization. However, it is worth noting that the majority of companies modify their existing products instead of developing them anew. The use of parametric modeling requires collecting enough amounts of data related to similar products what can be seen as a limitation of the proposed approach. Moreover, the specification of several parameters to build and learn artificial neural networks, as well as the implementation of constraint programming techniques can be counted as other limitations. Future research directions refer to identification of the impact of a number of decision variables, their granularity, and constraints on the time needed to obtain solutions, and the effectiveness of using constraint programming techniques. Furthermore, future research could focus on adding non-financial criteria for evaluating the potential of a new product. Conceptualization, M.R. ; methodology, M.R.