“Big Data” refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage and analyse. Typically, big data today will range from a few dozen terabytes to multiple petabytes. Data is being pervasively churned out in every aspect of manufacturing and supply chains; from transactional data of customers, suppliers and operations to networked sensors (e.g. RFID, GPS or more inclusively IoT) to “social” media, where consumers communicate, browse, buy, share, search and consequently, create their own huge trails of data. With big data, comes the need for better analytical capabilities to provide insightful information for informed decisions in a timely manner as reported in many industry surveys. The advent of Big Data is an open challenge in providing better insights or information (patterns) in supply chain services/applications, or data analytics. Information like hidden patterns and correlations uncovered can provide critical decision support to companies, both operationally and strategically. In this respect, responsive and cognitive analytics techniques in identifying useful patterns in right time and right context to support decision making is important as increasing scale and complexity of supply chain leads to large pool of shared information and complex information sharing processes.