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

Author

Listed:
  • Shanhe Lou

    (Zhejiang University
    Zhejiang University)

  • Yixiong Feng

    (Zhejiang University
    Zhejiang University)

  • Hao Zheng

    (Zhejiang University
    Zhejiang University)

  • Yicong Gao

    (Zhejiang University
    Zhejiang University)

  • Jianrong Tan

    (Zhejiang University
    Zhejiang University)

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.

Suggested Citation

  • Shanhe Lou & Yixiong Feng & Hao Zheng & Yicong Gao & Jianrong Tan, 2020. "Data-driven customer requirements discernment in the product lifecycle management via intuitionistic fuzzy sets and electroencephalogram," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1721-1736, October.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:7:d:10.1007_s10845-018-1395-x
    DOI: 10.1007/s10845-018-1395-x
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    References listed on IDEAS

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    1. Rochdi Sarraj & Eric Ballot & Shenle Pan & Driss Hakimi & Benoit Montreuil, 2014. "Interconnected logistic networks and protocols: simulation-based efficiency assessment," Post-Print hal-01112138, HAL.
    2. Steven Saar & Valerie Thomas, 2002. "Toward Trash That Thinks: Product Tags for Environmental Management," Journal of Industrial Ecology, Yale University, vol. 6(2), pages 133-146, April.
    3. Andrew Kusiak, 2017. "Smart manufacturing must embrace big data," Nature, Nature, vol. 544(7648), pages 23-25, April.
    4. Dariush Khezrimotlagh & Yao Chen, 2018. "The Optimization Approach," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 107-134, Springer.
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    Cited by:

    1. Yicong Gao & Shanhe Lou & Hao Zheng & Jianrong Tan, 2023. "A data-driven method of selective disassembly planning at end-of-life under uncertainty," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 565-585, February.
    2. Qingfei Tong & Xinguo Ming & Xianyu Zhang, 2023. "Construction of Sustainable Digital Factory for Automated Warehouse Based on Integration of ERP and WMS," Sustainability, MDPI, vol. 15(2), pages 1-22, January.

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