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The Progress of Business Analytics and Knowledge Management for Enterprise Performance Using Artificial Intelligence and Man-Machine Coordination

The Progress of Business Analytics and Knowledge Management for Enterprise Performance Using Artificial Intelligence and Man-Machine Coordination

Qian Liu, Jiayi Li
Copyright: © 2022 |Volume: 30 |Issue: 11 |Pages: 21
ISSN: 1062-7375|EISSN: 1533-7995|EISBN13: 9781668464434|DOI: 10.4018/JGIM.302642
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MLA

Liu, Qian, and Jiayi Li. "The Progress of Business Analytics and Knowledge Management for Enterprise Performance Using Artificial Intelligence and Man-Machine Coordination." JGIM vol.30, no.11 2022: pp.1-21. http://doi.org/10.4018/JGIM.302642

APA

Liu, Q. & Li, J. (2022). The Progress of Business Analytics and Knowledge Management for Enterprise Performance Using Artificial Intelligence and Man-Machine Coordination. Journal of Global Information Management (JGIM), 30(11), 1-21. http://doi.org/10.4018/JGIM.302642

Chicago

Liu, Qian, and Jiayi Li. "The Progress of Business Analytics and Knowledge Management for Enterprise Performance Using Artificial Intelligence and Man-Machine Coordination," Journal of Global Information Management (JGIM) 30, no.11: 1-21. http://doi.org/10.4018/JGIM.302642

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Abstract

This study aims to explore the integration of human-computer interaction (HCI) technology and platform ecosystem in artificial intelligence (AI) environment, thus providing a practical basis for the intelligent development of strategic management of platform ecosystem. With clothing e-commerce as an example, first, the business model of brand clothing is simply analyzed. Then, the fashion knowledge management method is adopted to build the fashion data warehouse. The platform intelligent clothing ecosystem is innovatively put forward through the research of business analytics and management mode of clothing e-commerce industry. The optimized genetic algorithm is used to solve the objective function of the model, and a flexible production scheduling model with multiple constraints and maximum cost-saving is established. Finally, the questionnaire results of voice interaction users are analyzed by HCI customer trust model.