Empirical Study on Big Data Analysis for Supply Chain Management

Abstract

with globalization, outsourcing is reaching beyond continents. Design is done in one part of the world, manufacturing in another low-cost country and distribution to other countries in the world. Procurement is illuming as a central focus that requires to be synchronized with all other business functions. As a matter of course, a sizeable amount of a firm’s revenue goes for its supply chain that interprets the significance of the supply chain lays in a firm’s bottom-line. So, the supply chain has a tremendous opportunity to get used of data. Nowadays, the supply chain is attracting much and more attention because in terms of analytics it is behind other functions of a firm.  Specifically, this paper will (1) redefine, by research on scientific work, what BDA means in the context of Supply Chain Management, and how it differs and has evolved from analytics technologies; (2) evolve taxonomy of Big Data within SCM that identifies and classifies the different sources and types of data arising in modern supply chains and (3) suggest some applications of BDA and show the potential high value of this technology offers to solve intricate SCM challenges.  This research tries to explore how the behavior of Big Data can succor procurement and SCM in greater decision making. Big data can be a lightening of a resilient environment while managing suppliers in global SCM is a challenging task. Another studied aspect is having access to a greater pool of data and what kind of potential data can render benefit SCM. SCM professionals were interviewed to understand what they expect from their logistics, procurement and marketing systems and how Big Data can contribute to that. What type of transparency is needed? What requires to be automated? What delineation of data is useful? Furthermore, how Big Data can help with SCM risk management.