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Customer demand analysis of the electronic commerce supply chain using Big Data

Author

Listed:
  • Lei Li

    (Tianjin University)

  • Ting Chi

    (Tianjin University)

  • Tongtong Hao

    (Tianjin University)

  • Tao Yu

    (Mmdata Marketing Company)

Abstract

With the advent of the Internet and the flourishing of connected technology, electronic commerce has become a new business model that disrupts the traditional transactional model and is transforming the consumer’s lifestyle. Electronic commerce leads to constantly changing customer needs, therefore quick action and collaboration between production and the market is essential. Meanwhile, the abundant transactional data generated by electronic commerce allows us to explore browsing behaviors, habits, preferences and even characteristics of customers, which can help companies to understand their customer’s needs more clearly. Traditional supply chain management (SCM) simply cannot keep up with electronic commerce because demand forecasts are constantly changing. Customer demands create and affect the whole supply chain. The purpose of SCM is to satisfy the customers who support the company by paying for the products; so meeting changing customer needs should be incorporated into SCM by developing demand chain management (DCM). In this paper, we explore how DCM can perform better in the electronic commerce environment based on studying website behavior data and using data analytics tools. The results show that DCM performs much better when paired with the benefits of electronic commerce and Big Data than traditional SCM methods.

Suggested Citation

  • Lei Li & Ting Chi & Tongtong Hao & Tao Yu, 2018. "Customer demand analysis of the electronic commerce supply chain using Big Data," Annals of Operations Research, Springer, vol. 268(1), pages 113-128, September.
    • Handle: RePEc:spr:annopr:v:268:y:2018:i:1:d:10.1007_s10479-016-2342-x
    DOI: 10.1007/s10479-016-2342-x
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    References listed on IDEAS

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    Cited by:

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    3. Núñez-Merino, Miguel & Maqueira-Marín, Juan Manuel & Moyano-Fuentes, José & Castaño-Moraga, Carlos Alberto, 2022. "Industry 4.0 and supply chain. A Systematic Science Mapping analysis," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    4. Cui, Yongfeng & Liu, Wei & Rani, Pratibha & Alrasheedi, Melfi, 2021. "Internet of Things (IoT) adoption barriers for the circular economy using Pythagorean fuzzy SWARA-CoCoSo decision-making approach in the manufacturing sector," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    5. Sanaz Ghorbanloo & Sajjad Shokouhyar, 2023. "Consumers' attitude footprint on sustainable development in developed and developing countries: a case study in the electronic industry," Operations Management Research, Springer, vol. 16(3), pages 1444-1475, September.
    6. Guangyong Yang & Guojun Ji & Kim Hua Tan, 2022. "Impact of artificial intelligence adoption on online returns policies," Annals of Operations Research, Springer, vol. 308(1), pages 703-726, January.
    7. Fatao Wang & Lihui Ding & Hongxin Yu & Yuanjun Zhao, 0. "Big data analytics on enterprise credit risk evaluation of e-Business platform," Information Systems and e-Business Management, Springer, vol. 0, pages 1-40.
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    9. Haoyu Liu & Kim Hua Tan & Xianfeng Wu, 2023. "Who’s watching? Classifying sports viewers on social live streaming services," Annals of Operations Research, Springer, vol. 325(1), pages 743-765, June.
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