A biclustering-based heterogeneous customer requirement determination method from customer participation in product development

Xinghua Fang (), Jian Zhou (), Hongya Zhao () and Yizeng Chen ()
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Xinghua Fang: Shanghai University
Jian Zhou: Shanghai University
Hongya Zhao: Shenzhen Polytechnics
Yizeng Chen: Shenzhen Polytechnics

Annals of Operations Research, 2022, vol. 309, issue 2, No 14, 817-835

Abstract: Abstract Timely identification of heterogeneous customer requirements serves as a vital step for a company to formulate product strategies to meet the diverse and changing needs of its customers. By relaxing the search for global patterns in classical clustering, we propose a biclustering-based method, BiHCR, to identify heterogeneous customer requirements from the perspective of local patterns detection. Specifically, conforming to customers’ attitudes toward products derived from customer participation, we first transform the original data matrix with customers as rows and customer requirements as columns into a binary matrix. Then, by combining the two significant biclustering algorithms, Bimax and RepBimax, we design BiHCR to identify the biclusters embedded in the binary matrix to improve the detection results from the larger biclusters and their overlaps. Furthermore, the empirical case of smartphone development in a Chinese company verifies that BiHCR can identify homogeneous subgroups of customers with similar requirements without redundant noise compared with Bimax. Additionally, in contrast to RepBimax, our proposed BiHCR can also detect the intractable overlapping biclusters in the binary matrix used to describe the heterogeneity of customer requirements. Since the process of customer participation in product development gradually became a dominant approach to collecting customer requirements information for many industries, a conceptual framework of customer requirements identification is constructed and the detailed steps are clarified for manufacturers.

Keywords: Customer requirement; Biclustering; Bimax algorithm; Customer participation; Product development (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10479-020-03607-7

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