Bayesian Dynamic Program for a New Product Development

This work concerns a new product development (NPD) network in the digital environment aiming to find attributes with more convergence for value-added purposes. Different views are effective in new product development. Here, the effective factors are categorized into customers, competitors, and the company's own past experiences. Also, various attributes are considered to develop a product. Thus, using digital data of attributes, the optimal set of attributes should be chosen to be included to the new product development. With respect to the multistage decision-making process of the customer, competitor, and company's own past experience, we develop a dynamic program being a useful tool for multistage decision making. To counteract the dynamism of digital data in different time periods, the Bayesian approach is employed to determine the loss function of moving through the stages of the proposed NPD digital network.

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