A Comparative Study of Supply Chain Configurations for Fast Fulfillment in E-commerce: Evidence from JD.com and Amazon
Main Article Content
Keywords
fast fulfillment, supply chain configuration, last-mile delivery, fulfillment network, logistics governance
Abstract
In e-commerce, delivery speed has become a core dimension of customer experience and platform competitiveness. Same-day and next-day delivery services do not rest solely on the efficiency of last-mile transportation. Their performance also depends on how warehousing, inventory positioning, order fulfillment, and delivery resources are coordinated with one another. JD.com and Amazon have developed large-scale fast fulfillment systems in China and the United States, respectively. Both firms seek to shorten the spatial distance between inventory and customer demand. Multi-node warehousing and inventory localization are the common means, yet the ways in which fulfillment networks are organized and logistics resources are allocated diverge considerably between the two firms. The study draws on a literature review and a comparative case study approach. JD.com' s mainland China operations and Amazon's U.S. market are placed side by side. The comparison is organized around three analytical dimensions: fulfillment network and inventory positioning, logistics governance, and resource configuration. Order density, labor costs, and the broader logistics service environment enter the analysis not as explanatory variables in a strict sense. They function instead as contextual conditions that help interpret the observed differences. The case evidence points toward a stronger reliance on internally coordinated fulfillment operations at JD.com. Amazon, by contrast, follows a hybrid governance approach, combining in-house infrastructure with external logistics resources. There is little ground, based on the two cases examined here, for claiming that a single fast fulfillment configuration can suit all market conditions. Speed, cost efficiency, and resource flexibility have to be weighed against one another. By bringing together supply network configuration and logistics governance perspectives, the study provides an analytical framework for understanding why fast fulfillment configurations vary across markets.
References
- [1]Zhang, J., & Chen, Y. Y. (2015). A review of the “last mile” logistics problem. China Business and Market, 29(4), 23–32. (in Chinese).
- [2]Lim, S. F. W. T., Jin, X., & Srai, J. S. (2018). Consumer-driven e-commerce: A literature review, design framework, and research agenda on last-mile logistics models. International Journal of Physical Distribution & Logistics Management, 48(3), 308–332.
- [3]Boysen, N., Fedtke, S., & Schwerdfeger, S. (2021). Last-mile delivery concepts: A survey from an operational research perspective. OR Spectrum, 43, 1–58.
- [4]Mangiaracina, R., Perego, A., Seghezzi, A., et al. (2019). Innovative solutions to increase last-mile delivery efficiency in B2C e-commerce: A literature review. International Journal of Physical Distribution & Logistics Management, 49(9), 901–920.
- [5]Lim, S. F. W. T., & Srai, J. S. (2018). Examining the anatomy of last-mile distribution in e-commerce omnichannel retailing: A supply network configuration approach. International Journal of Operations & Production Management, 38(9), 1735–1764.
- [6]Bai, Y., Rusmevichientong, P., & Topaloglu, H. (2025). Joint placement, delivery promise, and fulfillment in online retail. Management Science, 71(11), 9171–9192.
- [7]Zhou, L., Zhu, F. B., Dai, Y., et al. (2022). Location-routing problem for last-mile delivery driven by diversified personalized demands. Control and Decision, 37(10), 2745–2752. (in Chinese).
- [8]Luo, H., & Wang, L. F. (2021). The impact of third-party logistics integration on supply chain performance. Chinese Journal of Management, 18(3), 448–454. (in Chinese).
- [9]Shi, C. Y., Chen, G. H., Wang, Y., & Wang, R. B. (2023). Logistics service strategies for fresh e-commerce supply chains from a big-data perspective. Journal of Agrotechnical Economics, (10), 129–144. (in Chinese).
- [10]Liu, Y. G., Xie, S. M., & Li, T. T. (2024). Differences in performance between Chinese and U.S. e-commerce enterprises and their formation mechanisms: A case study of JD.com and Amazon. Communication of Finance and Accounting, (6), 3–11+63. (in Chinese).
- [11]Boyer, K. K., Prud’homme, A. M., & Chung, W. (2009). The last mile challenge: Evaluating the effects of customer density and delivery window patterns. Journal of Business Logistics, 30(1), 185–201.
- [12]Williamson, O. E. (1985). The Economic Institutions of Capitalism. New York: Free Press.
- [13]Ketokivi, M., & Mahoney, J. T. (2020). Transaction cost economics as a theory of supply chain efficiency. Production and Operations Management, 29(4), 1011–1031.
- [14]Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532–550.
- [15]Hu, H., Qi, Y., Lee, H. L., et al. (2024). Supercharged by advanced analytics, JD.com attains agility, resilience, and shared value across its supply chain. INFORMS Journal on Applied Analytics, 54(1), 54–70.
- [16]Shen, Z. J. M., Sun, S., Qi, Y., Hu, H., Kang, N., Zhang, J., Wang, X., & Lin, X. (2025). JD.com improves fulfillment efficiency with data-driven integrated assortment planning and inventory allocation. INFORMS Journal on Applied Analytics, 55(5), 386–398.
- [17]Sinha, A., Agte, J., Allgor, R., et al. (2026). Regionalize and scale: Amazon’s fulfillment network design for faster and cheaper delivery. INFORMS Journal on Applied Analytics, 56(1), 23–41.
- [18]Amazon.com, Inc. (2026). Annual Report 2025 (Form 10-K).
- [19]Fan, H., Hu, Y., & Tang, L. (2021). Labor costs and the adoption of robots in China. Journal of Economic Behavior & Organization, 186, 608–631.
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