Big Data Analytics (BDA) has attracted significant attention from both academicians and practitioners alike as it provides several ways to improve strategic, tactical and operational capabilities to eventually create a positive impact on the economic performance of organizations. In the present study, twelve significant barriers against BDA implementation are identified and assessed in the context of Indian manufacturing Supply Chains (SC). These barriers are modeled using an integrated two-stage approach, consisting of Interpretive Structural Modeling (ISM) in the first stage and Decision-Making Trial and Evaluation Laboratory (DEMATEL) in the second stage. The approach developed provides the interrelationships between the identified constructs and their intensities. Moreover, Fuzzy MICMAC technique is applied to analyze the high impact (i.e., high driving power) barriers. Results show that four constructs, namely lack of top management support, lack of financial support, lack of skills, and lack of techniques or procedures, are the most significant barriers. This study aids policy-makers in conceptualizing the mutual interaction of the barriers for developing policies and strategies to improve the penetration of BDA in manufacturing SC.

大数据分析（BDA）吸引了学术界和从业者的极大关注，因为它提供了多种提高战略，战术和运营能力的方法，从而最终对组织的经济绩效产生了积极影响。在本研究中，在印度制造业供应链（SC）的背景下，确定并评估了阻碍BDA实施的十二个重大障碍。使用集成的两阶段方法对这些障碍进行建模，包括第一阶段的解释性结构建模（ISM）和第二阶段的决策试验与评估实验室（DEMATEL）。所开发的方法提供了已识别结构及其强度之间的相互关系。此外，应用模糊MICMAC技术分析高影响力（即 高驱动力）的障碍。结果表明，最重要的障碍是缺乏最高管理层支持，缺乏财务支持，缺乏技能以及缺乏技术或程序这四种结构。这项研究帮助政策制定者将障碍的相互影响概念化，以制定政策和策略以提高BDA在制造业SC中的渗透率。