The circular supply chain has recently received more attention as a relevant solution to effectively tackle environmental issues while simultaneously achieving resource recovery and circular business strategy benefits. This study builds a hierarchical circular supply chain structure from big data including qualitative and quantitative information. This study uses data-driven analysis to clarify circular supply chain trends and opportunities in practice. A valid hierarchical circular supply chain structure is composed of a big dataset. However, the attributes of the hierarchical circular supply chain structure must be explored to identify the opportunities and challenges of the circular supply chain. A combination of data-driven content and cluster analysis, including the fuzzy Delphi method, fuzzy decision-making trials, evaluation laboratories, and the entropy weight method, is utilized to address this gap. The study analyzes a set of five attributes from the literature, and 23 criteria are validated. The results show that resource recovery implementation, Industry 4.0 and digitalization, and reverse supply chain practice pertain to the causal group, while circular business strategy and life cycle sustainability assessment are included in the effect group. The conclusive criteria comprise material efficiency, waste-to-energy, machine learning, e-waste, plastic recycling, and artificial intelligence. K E Y W O R D S circular business strategy, circular supply chain, data-driven analysis, Industry 4.0 and digitalization, resource recovery, reverse supply chain 1 | INTRODUCTION Resource paucity, biodiversity inadequacy, climate change, and other forms of environmental adversity underscore the need for firms to use fewer resources and identify cleaner production techniques. In this context, attention to the circular supply chain (CSC) concept is intensifying (Rweyendela & Kombe, 2021). The CSC concept offers sustainable advantages by separating value formation from resource usage, leading to lower resource consumption and higher economic value acquisition from waste flows (Bressanelli et al., 2021;Tseng, Tran, et al., 2021). Numerous studies have employed CSC principles to enhance resource efficiency by expanding the lifetime usage of a resource via a resource recovery procedure and improving the number of resources consumed along with the waste created (Krishnan et al., 2020;Munir et al., 2021). However, CSC initiation still involves an array of difficulties and complexities such as the diversification of the performance metrics of various opportunities and challenges in Abbreviations: CSC, circular supply chain; FDM, fuzzy Delphi method; FDEMATEL, fuzzy decision-making trial and evaluation laboratory; EWM, entropy weight method.