# Reducing the Total Product Cost at the Product Design Stage
## 2 Literature Review  
### The Product Design Stage  
Product design requires using specific methods to improve its efficiency, and consequently, reduce the cost of this stage. Conceptual design is the key factor in development of new products and redesign of existing products [13]. Generally, conceptual design refers to selecting an appropriate solution principle from various alternatives in order to link it to each product function component. Conceptual schemes are generated to satisfy design requirements towards combining solution principles for different function components without violating a set of constraints [14]. One of the most popular tools for conceptual design is morphological matrix. This tool involves many factors referring to task-related factors (e.g., complexity of product, and vagueness of initial engineering requirement) and decision-related factors (e.g., the expertise and diversity of design engineers, and the methods for aggregating judgments) [15]. Data and information for this phase come from experiences and subjective judgments of the design team and customer requirements regarding, for example, high reliability of a product.  
Product conceptual design can be supported by matrix-based methods (e.g., quality function deployment, design structure matrix) and computer-based methods (e.g., genetic algorithms) [13]. Quality function deployment makes a framework for improving product design, among others, in the aspect of reducing project and product cost and time, and better communication of cross-functional teamwork, increasing customer satisfaction and market share [16]. The design structure matrix captures existence and strength of an interaction between design tasks or parts of a product [17]. Computer-based methods use computational intelligence and multi-agent systems to solve problems with the large size of the domain and frequently changing structure [18]. Big data analytics improves deficiency of other methods through using real data instead of predicted or simulated data,and consequently, it quantifies external and internal uncertainties in the product design process [19]. The use of big data analytics is growing for the better data-driven decision making [20]. Data analytics provide a cost-effective way to obtain users' information of products that can be easily collected and used for product improvement [19]. This research develops predictive and prescriptive analytics for the product development process.  
The product design phase begins the product life cycle, and consequently it affects other phases, including the impact on the material and energy consumption, unit production cost, and product price. The placement of product design in the product life cycle from a perspective of sustainable manufacturing is illustrated in Figure 1.  
Sustainable manufacturing aims to obtain a trade-off between environmental, social, and economic dimensions to meet stakeholder requirements and achieve a competitive advantage [22]. An environmental dimension can be considered in terms of prevention of environmental damage, land conservation, emissions, and energy reduction. A social dimension concerns the relationship between stakeholders within and outside an enterprise. In turn, an economic dimension affects quality and productivity, delivery on time, innovation, and cost [23]. Assessment for the above-mentioned dimensions refers to environmental impact reduction, and it can be considered from an eco-efficiency perspective [24].