سیستم پشتیبانی تصمیم گیری پویا برای پیش بینی ارزش مشتری برای توسعه محصول جدید

In recent years, firms have focused on how to enter markets and meet customer requirements by improving product attributes and processes to boost their market share and profits. Consequently, market-driven product design and development has become a popular topic in the literature. However, past research neither covers all of the major influencing factors that together drive customers to make purchase decisions, nor connects these various influencing factors to customer purchasing behavior. Past studies further fail to take the time value of money and customer value into consideration. This study proposes a decision support system to (a) predict customer purchasing behavior given certain product, customer, and marketing influencing factors, and (b) estimate the net customer lifetime value from customer purchasing behavior toward a specific product. This will not only enable decision-makers to compare alternatives and select competitive products to launch on the market, but will also improve the understanding of customer behavior toward particular products for the formulation of effective marketing strategies that increase customer loyalty and generate greater profits in the long term. Decision-makers can also make use of the system to build up confidence in new product development in terms of idea generation and product improvement. The application of the proposed system is illustrated and confirmed to be sensible and convincing through a case study.

Decisions on new product development are crucial but complex. New product development is regarded as a competitive weapon that helps firms to survive and succeed in dynamic markets. Lucrative new products play an important role not only in penetrating markets, but also building and retaining customer relationships and yielding profits. However, new product development, from idea creation to product introduction, requires inter-departmental communication among designers, engineers, and marketing personnel. Furthermore, to achieve a competitive edge in a market, sensible decisions must be made about various aspects of new product development, such as product attributes, customer segment, and promotion and marketing strategies. These decisions are inter-linked and will ultimately affect profitability. It is challenging to reach a consensus among the various parties involved in product development, who have different responsibilities and concerns. Decision aids such as a decision support system are thus of benefit in solving such decision problems. In recent years, many conventional and market-based decision support systems for product design have been developed [1], [22], [23], [29] and [66]. These highlight the key areas that ought to be considered in making decisions on new product development, including customer requirements, customer satisfaction, market demand, product quality, product design, and pricing. In particular, Gao et al.[20] stated that the timely response to market changes and customer needs becomes one of the competitive advantages. They proposed a novel process model for concurrent product design. Within feature-based part design and process planning, the dynamic change, model reduction, path search and time consumption of concurrent design process are analyzed, which helps improve the overall design process and shorten the product development cycle. However, no decision support system takes all of the key areas into account at the same time. Further, existing systems are insufficient and unconvincing in their ability to determine the most lucrative products among alternatives. Some disregard the influence of customer behavior and satisfaction, and most fail to take the time value of money into consideration. A new, comprehensive decision support system that overcomes these shortcomings is needed to help firms make more sensible and reliable decisions on new product development. In response to this need, this study proposes a decision support system for new product development that consists of two sub-models: a customer purchasing behavior (CPB) model and a net customer lifetime value (NCLV) estimation model. The system predicts customer purchasing behavior using a system dynamics approach based on three pieces of information: product attractiveness, customer preferences and satisfaction, and marketing strategy. It also estimates the long-term NCLV based on Markov analysis. This can help managers to determine which product will be most lucrative to launch and the kinds of marketing strategies that should be adopted for the new product. It also helps improve new product development in the future by collating up to date information on market and product attributes. This section has given the general background to the study. Section 2 discusses the literature on new product development and related decision support systems. The methodology for the development of the proposed decision support system is presented in Section 3. Section 4 introduces the proposed system and discusses its findings. Some concluding remarks are offered in Section 5.

A decision support system for new product development and relationship marketing is proposed that focuses on the modeling of customer purchasing behavior and the NCLV. The proposed system consists of a CPB model (sub-model 1) and an NCLV estimation model (sub-model 2). The structure and formulation of these two sub-models are described. The applicability of the system is verified by applying it to seven power tools products from the same company. The implications of using the proposed system are explored, and it is concluded that the system offers effective decision support by predicting the customer switching probability and determining the NCLV for products. The results show that it is convincing and accurate, and should help companies to develop competitive new products and relationship marketing strategies that increase business growth and sustainability. The system proposed in this study is not used to assess the individual impacts of the various influencing factors on product success or conduct a sensitivity analysis of product, customer, and marketing factors. However, it could certainly be extended in the future to these areas. We apply the system to the power tool industry to test its applicability, but it would be of interest to implement it in other industries. Future research could conduct case studies in a range of different industries to test the system's capability or make customizations if necessary.