Big Data analytics and its applications in supply chain management: A literature review using SCOR model

As we move further into the 21st century, technology has played an essential role in people lives inevitably, and generated massive information called ‘Big Data (BD)'. With the ability to manage massive dataset, big data analytics can usefully extract the insight from big data and support the firm to leverage decision making. Hence, the interest in big data applications has spread to comprehends many areas of study, including Supply Chain Management (SCM). However, the academic articles studying the employment of Big Data Analytics (BDA) in SCM is limited. Besides, most of those academic papers offer less interest in the entire SC systems. Most of them prefer to study in an individual SC area. Thus, this study aims to investigate state of the art in this domain through Systematic Literature Review (SLR) and discuss future research opportunities. We found that optimisation, simulation, and visualisation tend to be the most appropriate BD tools to apply in SCM. Also, linear programming, statistics, association rule mining, fuzzy logic, and decision tree are likely to be the most suitable BDA techniques for SC operations