Using big Data in the Supply Chain Context: Opportunities and Challenges

With the globalization of the entire economy the need for improved logistics and supply chain management processes has become inevitable. The success of any supply management program is also largely dependent upon the ability to access, organize, and analyze data. A new supply chain management ecosystem is needed which would focus on the building of core strengths, the provision of real-time information, the globalization of service demand, the visibility of key performance indicators, the collaboration in supply chain operation and e-commerce development. Businesses are trying to use automated processes to exchange information, to understand and filter out customer reviews, and to operate in a more efficient and effective way across multiple industries and co-working with several partners. The growth in the quantity and diversity of data has led to data sets larger than is manageable by the conventional hands on management tools. To manage these new and potentially invaluable data sets, new methods of data science and new applications in the form of predictive analytics, have been developed. At a conceptual level, there are several applications of big data to logistics and supply chain in order to improve procurement strategy, to help manufacturers to early respond to extremely negative customer sentiment provided by customers at a real time or, to rapidly provide a prize/discount to those customers who provided the most numerous positive online reviews for their products. At the downstream level retailers could benefit from big data analysis in order to improve their inventory accuracy, updating their sales and their products availability located in different stores/warehouses. The aim of our conceptual paper is to explore the research opportunities that link big data and supply chain dynamics in a B2B context.