Big Data Analytics in Supply Chain Management: A Systematic Literature Review and Research Directions

Big Data Analytics in Supply Chain Management: A Systematic Literature Review and Research Directions

SUBMITTED BY

Aman Kumar

Ad. No.- 23GSOB2040039

MBA 2023-2025

GALGOTIAS UNIVERSITY

UNDER THE GUIDANCE OF

Prof. Dr. Aijaz Khan

ABSTRACT

The increasing complexity and dynamism of global supply chains have intensified the demand for advanced decision-making tools and real-time insights. Big Data Analytics (BDA) has emerged as a transformative force capable of reshaping traditional supply chain management (SCM) by enabling enhanced visibility, agility, sustainability, and overall operational performance. This study investigates the role of BDA in supply chains by conducting a systematic literature review, drawing insights from 60 high-quality peer-reviewed journal articles published between 2011 and 2021. The review adopts an interdisciplinary lens—combining organizational and technical perspectives—to provide a comprehensive understanding of BDA's integration and impact within SCM.

From the organizational viewpoint, the study examines how BDA contributes to dynamic capabilities, resource optimization, strategic alignment, and sustainable practices. It explores various theoretical frameworks such as the Dynamic Capabilities View (DCV), Organizational Information Processing Theory (OIPT), and Resource-Based View (RBV) to analyze how BDA enables superior firm performance and supply chain resilience. BDA is found to significantly support supply chain agility, responsiveness to disruptions, and the development of sustainable and circular practices.

Technically, the study categorizes BDA applications according to the SCOR model—Plan, Source, Make, Deliver, Return, and Enable—and evaluates types of analytics employed (descriptive, predictive, and prescriptive). A wide array of techniques including machine learning algorithms, text mining, sentiment analysis, and optimization models are discussed. Predictive analytics emerged as the most dominant, supporting functions such as demand forecasting, customer behavior prediction, risk assessment, and operational efficiency.

The study also addresses implementation challenges such as data privacy, infrastructural limitations, lack of skilled talent, and top management resistance. It emphasizes the need for integrated architectures involving cloud computing, IoT, blockchain, and AI to overcome these barriers and unlock the full potential of BDA in SCM.

Key findings suggest that while BDA offers significant value in terms of strategic and operational outcomes, successful implementation requires alignment between technical infrastructure and organizational readiness. The study concludes with practical recommendations for supply chain professionals and managers, including investment in talent development, adoption of interoperable systems, and a focus on data governance and security.

In light of rapid digitalization and evolving global supply chain ecosystems, this thesis provides both scholars and practitioners with a roadmap for harnessing big data to drive competitive advantage, sustainability, and innovation in supply chain management.