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Knowledge recommendation for product development using integrated rough set-information entropy correction

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Abstract

New product development is knowledge intensive as it needs the work teams and design engineers located at various locations to constantly share, update, and re-use knowledge. As such, improving the efficiency of acquiring knowledge and coping with the challenge of frequently retrieving related knowledge have become a key factor to managing knowledge in new product development. This paper combines rough set theory and information entropy to establish a new knowledge recommender technique to address the issue of knowledge reuse for new product development. Our method enhances knowledge acquisition and reuse, as it provides a realistic framework for knowledge acquisition and reuse, encompassing the entire process from what the design and work teams need, to recommending what they should have. To validate the proposed approach, we perform experiments on a case study to demonstrate the benefit and performance.

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Acknowledgements

The authors thank the editor and the anonymous reviewers for their helpful comments and suggestions on the drafts of this paper. We thank Dr. Zhang Wei for his contribution in improving the rough set-based model. The authors thank Guangxi University for funding this research. The work described in this paper was supported by the Natural Science Foundation of Guangxi Province (No. 2016GXNSFBA380184).

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Authors and Affiliations

  1. School of Mechanical Engineering, Guangxi University, Nanning, China

    Zhenyong Wu

  2. NUS Business School and TLI-AP, National University of Singapore, Singapore, Singapore

    Mark Goh

  3. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China

    Lina He

  4. Faculty of Engineering, National University of Singapore, Singapore, Singapore

    Zhenyong Wu & Yuan Wang

  5. Institute of Computer Integrated Manufacturing, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China

    Xinguo Ming

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  1. Zhenyong Wu
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Correspondence to Yuan Wang.

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Wu, Z., He, L., Wang, Y. et al. Knowledge recommendation for product development using integrated rough set-information entropy correction. J Intell Manuf 31, 1559–1578 (2020). https://doi.org/10.1007/s10845-020-01534-9

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