Data-driven customer requirements discernment in the product lifecycle management via intuitionistic fuzzy sets and electroencephalogram

Shanhe Lou, Yixiong Feng (), Hao Zheng, Yicong Gao and Jianrong Tan
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Shanhe Lou: Zhejiang University
Yixiong Feng: Zhejiang University
Hao Zheng: Zhejiang University
Yicong Gao: Zhejiang University
Jianrong Tan: Zhejiang University

Journal of Intelligent Manufacturing, 2020, vol. 31, issue 7, No 10, 1736 pages

Abstract: Abstract Large amount of data are collected through the product lifecycle management, and the benefits of big data analytics permeate the entire manufacturing value chain. However, the existing methods pay little attention to the analysis of customer requirements data in the beginning of life period. Thus, a data-driven approach for customer requirements discernment is proposed in this paper. It not only manages the vagueness in the semantic expression level using the intuitionistic fuzzy sets, but also adopts the electroencephalogram data as endogenous neural indicators to handle the vagueness in the neurocognitive level. An experimental research integrated with the Kano model is developed to record the EEG data which inherently interpret customers’ psychological states. Benefit from the data mining method, the effect of customer requirements on psychological response can be investigated using the EEG data. Taking the data of initial requirement importance, performance realization levels and customers’ psychological states into consideration, three novel adjusting models are established to acquire the comprehensive importance of each requirement. A case study is conducted to illustrate the feasibility of the approach proposed in this paper.

Keywords: Big data; Product lifecycle management; Customer requirements discernment; Electroencephalogram; Intuitionistic fuzzy sets (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10845-018-1395-x

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