Big data analytics: Implementation challenges in Indian manufacturing supply chains

TLDR

Big Data Analytics offers strategic, tactical, and operational benefits that can positively impact organizational economic performance. The study identifies and assesses twelve key barriers to BDA implementation within Indian manufacturing supply chains. Barriers were analyzed using a two‑stage ISM‑DEMATEL framework, supplemented by Fuzzy MICMAC to evaluate high‑impact drivers. The analysis revealed interrelationships among barriers, with top management support, financial resources, skills, and procedures emerging as the most critical obstacles, informing policy and strategy development.

Abstract

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.