# Reducing the Total Product Cost at the Product Design Stage
## 3 A Data-Driven Approach to Reducing the Total Product Cost
### A Method of Developing a Decision Support System  
A proposed method includes four stages: (1) data collection from the previous NPD projects that are similar to a new project, (2) determination of estimation models, (3) use of these models to cost estimation, and (4) simulation towards finding changes in product design that allow to reduce the total product cost. Figure 3 illustrates a framework for the proposed decision support system that uses analogical and parametric models to estimate the cost and constraint programming to reduce the search space and verify the possibility of reducing the total product cost. Data regarding products, their design, NPD project performance, sales, costs, etc., were stored in enterprise databases related to enterprise resource planning (ERP) system, computer-aided design (CAD), computer-aided engineering (CAE), computer-aided manufacturing or quality system. In the first stage, databases were considered as a potential source of useful information to evaluate the potential of a new product. Of course, a new product should be similar to past or existing products to obtain reasonable evaluations. Only these products were selected for analyis that were in the same product line as a new product, and that were the most similar products to a new product using the similarity value. The identification of the most similar product was especially useful in the case of finding a solution to the problem appeared in the phase of NPD. Case-based reasoning is widely used in product design, for example, in reducing time needed to NPD project completion [53]. The most similar products to a new product were retrieved from an enterprise database using the similarity function and similarity value that was calculated as the arithmetic average of similarity functions for all features. The similarity function was determined as follows:  
\[sim\left(f_{i}^{P},f_{i}^{R}\right)=1-\frac{\left|f_{i}^{P}-f_{i}^{R}\right|}{ \max(f_{i})} \tag{14}\]  
where \(sim\left(f_{i}^{P},f_{i}^{R}\right)\)--the similarity function of the \(i\)-th feature between the value of the new product \(f_{i}^{P}\) and the value of the retrieved product \(f_{i}^{R}\). In the next stage, dependencies were determined and used to cost estimation within product development, production, and after-sales service. The dataset was divided into learning and testing sets to verify the quality of estimation models. This study considered the use of parametric techniques, in which the cost was estimated using parameters that significantly affect the cost and they can be controlled by a company (e.g., the number of project team members, product components, and prototype tests). Relationships between variable are determined using back-propagation artificial neural networks (ANNs). ANNs are a powerful technique for solving complex approximation and classification problems. Their properties included high generalization capability, robust design, and ability to handle the incomplete data and to train and tune their parameters depending on a dataset and learning algorithm [5]. The use of ANNs offers advantages over ability to learn and identification of complex nonlinear relationships. The quality of the obtained results was compared using the mean absolute percentage errors. The cost was estimated using a more suitable model: analogical or parametric depending on the less error in the testing set. Figure 3: A framework for the proposed decision support system. The last stage of the proposed method refers to the simulation and searching for changes in product design towards reducing the total product cost. The size of the search space depends on the number of decision variables, their domains, and constraints that link variables and limit a set of admissible solutions. An exhaustive search always finds a solution if one exists, but its performance is proportional to the number of admissible solutions. Therefore, an exhaustive search tends to grow very quickly as the size of the problem increases, which limits its usage in solving many practical problems.