Dynamic customer preference analysis for product portfolio identification using sequential pattern mining

In the initial stage of product design, product portfolio identification (PPI) aims to translate customer needs (CNs) into product specifications (PSs). This is an essential task, since understanding what customers really want is at the center of product design. However, design information is incomplete and design knowledge is minimal during this stage. Furthermore, PPI is often a confusing and frustrating task, especially when customer preferences are changing rapidly. To facilitate the task, the purpose of this paper is to capture the time-sensitive mapping relationship between CNs and PSs.

This paper proposes a design sequential pattern mining model to uncover implicit but valuable knowledge from chronological transaction records. First, CNs and PSs from these records are transformed and connected according to the transaction time. Second, procedures such as litemset generation, data transformation and pattern mining are conducted based on the AprioriAll algorithm. Third, the uncovered patterns are modified and applied by engineers.

Using the retrieved patterns, engineers can keep up with the dynamics of customer preferences with regard to different PSs.

Computational experiments on a case study of customization of desktop computers show that the proposed method is capable of extracting useful sequential patterns from a design database.

Considering the times tamps of the transactions, a sequential pattern mining-based method is proposed to extract valuable patterns. These patterns can help engineers identify market trends and the correlation among PSs.