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Selection of Manufacturing Enterprise Innovation Design Project Based on Consumer’s Green Preferences

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  • Jie Yang

    (College of mechanical engineering, Chongqing University of Technology, Chongqing 400054, China)

  • Jiafu Su

    (Chongqing Key Laboratory of Electronic Commerce & Supply Chain System, Chongqing Technology and Business University, Chongqing 400067, China)

  • Lijun Song

    (College of mechanical engineering, Chongqing University of Technology, Chongqing 400054, China)

Abstract

For enterprise, how to quickly realize the selection of green innovative design projects has become a key issue for improving innovation performance. Based on an analysis of enterprise product innovation and customer green preferences, an indicator set for innovation performance in enterprise was established. Considering the fuzziness of the correlation between indicators for innovation performance in enterprise and consumer’s green preferences, a fuzzy clustering method was used to identify the internal relations among the indicators for innovation performance with green preferences of customers. Then a wavelet neural network was used to select the innovation design project for various green preferences of customers. Finally, a case study was proposed to verify the feasibility and effectiveness of the method. This work can help the enterprise to develop green design, products, and serve uniformly, which can effectively shorten green product development cycles, reduce cost, and improve enterprise innovation performance greatly.

Suggested Citation

  • Jie Yang & Jiafu Su & Lijun Song, 2019. "Selection of Manufacturing Enterprise Innovation Design Project Based on Consumer’s Green Preferences," Sustainability, MDPI, vol. 11(5), pages 1-16, March.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:5:p:1375-:d:211311
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    References listed on IDEAS

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    Cited by:

    1. Wenfei Xia & Baizhou Li & Shi Yin, 2020. "Identification of Influencing Factors for Sustainable Development: Evaluation and Management of Regional Innovation Performance in Heilongjiang, China," Sustainability, MDPI, vol. 12(7), pages 1-55, March.
    2. Xi Yang & Maozeng Xu & Wanleng Zhang, 2020. "Can Design for the Environment be Worthwhile? Green Design for Manufacturers Brands When Confronted with Competition from Store Brands," Sustainability, MDPI, vol. 12(3), pages 1-18, February.
    3. Baodong Li & Yu Yang & Jiafu Su & Zhichao Liang & Sheng Wang, 2020. "Two-sided matching decision-making model with hesitant fuzzy preference information for configuring cloud manufacturing tasks and resources," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 2033-2047, December.
    4. Xingwei Li & Jinrong He & Yicheng Huang & Jingru Li & Xiang Liu & Jiachi Dai, 2022. "Predicting the factors influencing construction enterprises’ adoption of green development behaviors using artificial neural network," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-12, December.
    5. Jiafu Su & Chi Li & Qingjun Zeng & Jiaquan Yang & Jie Zhang, 2019. "A Green Closed-Loop Supply Chain Coordination Mechanism Based on Third-Party Recycling," Sustainability, MDPI, vol. 11(19), pages 1-14, September.
    6. Hui Xu & Shuxiu Li & Yongtao Tan & Bin Xing, 2022. "Comprehensive Resilience Assessment of Complex Urban Public Spaces: A Perspective of Promoting Sustainability," Land, MDPI, vol. 11(6), pages 1-23, June.
    7. Libiao Bai & Kanyin Zheng & Zhiguo Wang & Jiale Liu, 2022. "Service provider portfolio selection for project management using a BP neural network," Annals of Operations Research, Springer, vol. 308(1), pages 41-62, January.
    8. Jie Jian & Yu Guo & Lin Jiang & Yanyan An & Jiafu Su, 2019. "A Multi-Objective Optimization Model for Green Supply Chain Considering Environmental Benefits," Sustainability, MDPI, vol. 11(21), pages 1-20, October.
    9. Yuanyuan Luo & Da Ren, 2021. "Influence of the enterprise’s intelligent performance evaluation model using neural network and genetic algorithm on the performance compensation of the merger and acquisition parties in the commitmen," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-16, March.

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