Big Data Analytics in Supply Chain Management: A Qualitative Study

Abstract

This work explores the leading supply chain processes impacted by big data analytic techniques. Although these concepts are being extensively applied to supply chain management, the number of works that examine and classify the main processes in the current literature is still scarce. This article, therefore, provides a classification of the current literature on the use of big data analytics and provides insight from professionals in the field in relation to this topic. A well-established set of practical guidelines was used to design and carry out a systematic literature mapping. A total of 50 primary studies were analysed and classified, chosen from a sample of 5, 437 studies after careful filtering to answer six research questions. In addition, a survey was prepared and applied by professionals working in the area. In total, 25 professionals answered a questionnaire with eleven questions, ten of which seek to explore the importance of big data analytics for the areas of the supply chain addressed in this work, and one intends to list the three areas where BDA can be more shocking. More than 60% of the studies are directly linked to the area of chain management; most studies performed empirical studies but rarely classified or detailed methodological procedures; almost 50% bring models to optimize some process or forecasts for better decision-making; more than 50% of professionals working in the area believe that the processes where big data analytics can effectively contribute are related to inventory and stockout management. This study serves as a basis for further research and future work, as it reviews the literature, pointing out the main areas that are being addressed and making a relationship with understanding these areas in practice.