Supported by ever-increasing amounts of data and maturing technologies, big data analytics offers viable, promising improvements for various fields and applications. Supply chain analytics (SCA), the application of big data analytics to supply chain management, can enhance and innovate supply chain processes and services in most companies. To reap such benefits, supply chain managers must overcome various obstacles, including the identification of appropriate methods, data, and application cases. The degree to which the potential value of SCA actually is being harnessed by practitioners remains uncertain. The study aims to synthesize scientific and practical perspectives regarding the SCA dimensions: goal and motivation, method, data, and application area. For this purpose the research applies a multi-vocal literature review (MLR) and a survey approach. The study reviews over 1481 publications and consults 278 respondents to reveal six different goals and seven motivations for SCA. Moreover, descriptive, predictive, and prescriptive analytics and many different data types enabling SCA within different application areas are examined. The cross-analysis between scientific and practical perspectives identifies several gaps, such as lack of specific data usage, low practical SCA maturity, or undersaturated research areas that show future paths of academic research.
