Big Data in Supply Chain Management: A Systematic Literature Review

Johan Krisnanto Runtuk (Industrial Engineering Study Program, Faculty of Engineering, President University, Indonesia)
Filson Sidjabat (Environmental Engineering Study Program, Faculty of Engineering, President University, Indonesia)
Jsslynn (Industrial Engineering Study Program, Faculty of Engineering, President University, Indonesia)
Felicia Jordan (Industrial Engineering Study Program, Faculty of Engineering, President University, Indonesia)

Green Intelligent Systems and Applications
Vol. 2 Iss. 2 (2022)

Big data analytics (BDA) have the potential to improve upon and change conventional supply chain management (SCM) techniques. Using BDA, organisations need to build the necessary skills to use big data effectively. Since BDA is relatively new and has few practical applications in SCM and logistics, a systematic review is needed to emphasise the most significant advancements in current research. The objectives are to evaluate and categorise the literature that addresses the big data potential in SCM and the current practises of big data in SCM. The Systematic Literature Review (SLR) was conducted to analyse several published papers between 2017 and 2022. It follows four steps: the literature collection, descriptive analysis, category selection, and material evaluation in a systematic review. The finding reveals that BDA has been applied in many supply chain functions. Furthermore, integrating BDA in SCM has several advantages, including improved data analytics capabilities, logistical operation efficiency, supply chain and logistics sustainability, and agility. Finally, the study emphasises the importance of using BDA to support the success of SCM in businesses.