Supply Chain E-Commerce Through Predictive Analytics: A Conceptual Review With Tokopedia As An Illustrative Case
Abstract
The rapid expansion of Indonesia’s e-commerce sector has intensified the need for supply chain systems that are more accurate, adaptive, and data-driven. As competition grows, companies increasingly rely on digital tools to manage uncertainty and support faster operational decisions. This conceptual paper examines how predictive analytics could influence Indonesia’s e-commerce supply chain landscape, using Tokopedia only as a contextual reference. Drawing from a narrative review of recent literature on predictive analytics, digital supply chain transformation, and the Technology Acceptance Model (TAM), the study identifies several pathways through which data-driven prediction can strengthen operational performance. The review highlights that predictive analytics enhances demand forecasting accuracy, improves inventory synchronization, and supports more efficient logistics coordination. Insights from TAM further suggest that user acceptance of advanced analytics tools is influenced by perceived usefulness, perceived ease of use, and an organization’s overall digital readiness. Methodologically, this paper synthesizes existing scholarly work published within the past five years to build a theoretical understanding without collecting primary data. The conceptual findings underline the potential of predictive analytics to support more resilient and responsive e-commerce operations. The paper concludes by outlining theoretical implications, proposing managerial considerations for digital adoption, and recommending future empirical studies to validate the conceptual propositions presented.
Keywords: Big Data Analytics; Digital Transformation; Predictive Supply Chain; Platform-Based Commerce; User Acceptance
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HUMANIS (Humanities, Management and Science Proceedings) is a national peer reviewed and open access journal that publishes significant and important research from all area of agroindustry.
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