Big Data Analytics for Supply Chain Transformation: A Systematic Literature Review Using SCOR Framework

Recent developments in information technology generating massive amount of data are referred to as big data. Such data with variety and velocity pose a challenge to supply chain management (SCM) practitioners on how to deal with them to draw valued insights for enhanced decision-making. The analysis of big data can offer unique intuitions into supply and market dynamics like understanding the customer preferences, developing new products, demand forecasting, supplier selection and evaluation, process improvements, quality control, capacity planning, managing delivery schedules, order management, etc., to reduce the supply chain costs and improve product availability. Thus, this chapter reviews and classifies the literature on big data analytics (BDA) application in SCM. We extracted and reviewed about 200 academic journal and conference articles from 2010 to 2017 from various research databases to know the extent of BDA applications in different supply chain domains (plan, source, make, deliver and return). The papers were also classified based on analytics (descriptive, predictive and prescriptive) and the supply chain resources utilized (financial, human, technological, organizational and intangibles). Based on the review results, we propose a supply chain (SC) visibility framework that identifies SC visibility as a key driving force for SC transformation, achieved through strong BDA capability. The findings of this review and future research directions will help the academics, researchers and practitioners to focus on the BDA opportunities and challenges.