电商团队协作模式对运营绩效的优化研究
The Optimization of E-Commerce Team Collaboration Model on Operational Performance
摘要: 电商团队在面对日益复杂的市场环境和客户需求时,传统的协作模式逐渐暴露出效率低下、信息孤岛严重、决策滞后等问题。本研究提出新型电商团队协作模式。本研究首先通过集成企业内部信息系统(ERP, CRM等)以及外部电商平台API,建立统一的数据中心,确保所有相关数据能够实时同步更新,消除信息孤岛;采用微服务架构设计团队协作平台,将订单处理、库存管理、客户服务等功能模块化,提高系统的可扩展性和灵活性。此外,引入ARIMA-XGBoost对销售数据进行预测分析,优化库存管理和营销策略,提高销售额。新型协作模式缩短了订单处理时间、提高了库存周转率并降低了滞销品占比。本文为电商企业在激烈的市场竞争中提升运营效率和业务收益提供了理论依据和实践指导。
Abstract: When e-commerce teams face increasingly complex market environments and customer demands, traditional collaboration models gradually expose problems such as inefficiency, serious information islands, and delayed decision-making. This study proposes a new e-commerce team collaboration model. This study first integrates the internal information systems of the enterprise (ERP, CRM, etc.) and the external e-commerce platform API to establish a unified data center to ensure that all relevant data can be updated in real-time and eliminate information islands; the team collaboration platform is designed using a microservice architecture to modularize functions such as order processing, inventory management, and customer service to improve the scalability and flexibility of the system. In addition, ARIMA-XGBoost is applied to predict and analyze sales data, optimize inventory management and marketing strategies, and increase sales. The new collaboration model shortens the order processing time, improves inventory turnover, and reduces the proportion of unsalable products. This paper provides a theoretical basis and practical guidance for e-commerce companies to improve operational efficiency and business benefits in the fierce market competition.
文章引用:马赫. 电商团队协作模式对运营绩效的优化研究[J]. 电子商务评论, 2025, 14(6): 588-593. https://doi.org/10.12677/ecl.2025.1461779

参考文献

[1] 朱伟明, 章钟瑶. 基于数据驱动的跨境电商运营机制创新研究——以F品牌为例[J]. 丝绸, 2024, 61(10): 15-26.
[2] 程昂, 潘帆, 周兴建. 考虑消费者退货的服装企业电商运营决策分析[J]. 武汉纺织大学学报, 2024, 37(4): 86-90.
[3] 郭丽彬, 尚玉箫, 王宜举. 海外仓模式下考虑交付时间和平台信息共享的跨境电商运营模式选择[J]. 工程管理科技前沿, 2023, 42(4): 89-96.
[4] 李斯媛. 数字经济背景下的农产品电商运营[J]. 农村经济与科技, 2023, 34(15): 247-250.
[5] 王宣, 刘凯玉, 刘金美. 快手电商运营模式的问题与对策探究[J]. 市场周刊, 2023, 36(2): 72-74+85.
[6] Nurcahyo, R. and Putra, P.A. (2021) Critical Factors in Indonesia’s E-Commerce Collaboration. Journal of Theoretical and Applied Electronic Commerce Research, 16, 2458-2469. [Google Scholar] [CrossRef
[7] Gomes, A.C., De Lima Junior, F.B., Soliani, R.D., de Oliveira, D.A., De Oliveira, D.A., Siqueira, R.M., et al. (2023) Logistics Management in E-Commerce: Challenges and Opportunities. Revista de Gestão e Secretariado (Management and Administrative Professional Review), 14, 7252-7272. [Google Scholar] [CrossRef
[8] Aulawi, H., Suseno, N.S. and Abdullah, K.H. (2023) Trends in E-Commerce and Social Media Research in Asia: Five Years of Scientometric and Content Analysis. Journal of Applied Engineering and Technological Science (JAETS), 5, 58-72. [Google Scholar] [CrossRef
[9] Santoso, E. (2022) Opportunities and Challenges: E-Commerce in Indonesia from a Legal Perspective. Jurnal Penelitian Hukum De Jure, 22, 395-410.
[10] Kedah, Z. (2023) Use of E-Commerce in the World of Business. Startupreneur Business Digital (SABDA Journal), 2, 51-60. [Google Scholar] [CrossRef
[11] Khahro, S.H., Hassan, S., Zainun, N.Y.B., et al. (2021) Digital Transformation and E-Commerce in Construction Industry: A Prospective Assessment. Academy of Strategic Management Journal, 20, 1-8.
[12] Chang, D., Gui, H.Y., Fan, R., Fan, Z.Z. and Tian, J. (2019) Application of Improved Collaborative Filtering in the Recommendation of E-Commerce Commodities. International Journal of Computers Communications & Control, 14, 489-502. [Google Scholar] [CrossRef
[13] Wulfert, T., Woroch, R., Strobel, G., Seufert, S. and Möller, F. (2022) Developing Design Principles to Standardize E-Commerce Ecosystems. Electronic Markets, 32, 1813-1842. [Google Scholar] [CrossRef] [PubMed]
[14] Jalil, F., Yang, J., Al-Okaily, M. and Rehman, S.U. (2024) E-Commerce for a Sustainable Future: Integrating Trust, Green Supply Chain Management and Online Shopping Satisfaction. Asia Pacific Journal of Marketing and Logistics, 36, 2354-2370. [Google Scholar] [CrossRef
[15] Loukili, M., Messaoudi, F. and Ghazi, M.E. (2023) Machine Learning Based Recommender System for E-Commerce. IAES International Journal of Artificial Intelligence (IJ-AI), 12, 1803-1811. [Google Scholar] [CrossRef