Data-driven customer requirements discernment in the product lifecycle management via intuitionistic fuzzy sets and elec
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Data-driven customer requirements discernment in the product lifecycle management via intuitionistic fuzzy sets and electroencephalogram Shanhe Lou1,2 · Yixiong Feng1,2
· Hao Zheng1,2 · Yicong Gao1,2 · Jianrong Tan1,2
Received: 12 October 2017 / Accepted: 24 January 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018
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
Introduction The increasing pressure from the global economy on the short product life cycle and diverse customer requirements calls for low cost, short lead-time and high-quality products (Ming et al. 2007). Benefit from the advanced manufacturing techniques and the new generation of information technologies, the product lifecycle management (PLM) is realized to provide an efficient platform to create, manage and disseminate product-related information in an integrated way, which can significantly reduce the development cycle and increase yields (Lee et al. 2008). There are three sequential periods included in the PLM (Li et al. 2015a): the begin-
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Yixiong Feng [email protected]
1
State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
2
Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, Zhejiang University, Hangzhou 310027, China
ning of life (BOL) consists of product conceptual design and physical realization. The middle of life (MOL) concentrates on logistics, operations and maintenance. And the end of life (EOL) includes disassembly, recycle and disposition. With the popularization of embedded sensing devices and industrial Internet, large amount of structured, semi-structured and unstructured data are generated during the
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