Big data analytics-A review of data-mining models for small and medium enterprises in the transportation sector <s> What Data Mining Can Mean for SMEs? <s> Big data, the authors write, is far more powerful than the analytics of the past. Executives can measure and therefore manage more precisely than ever before. They can make better predictions and smarter decisions. They can target more-effective interventions in areas that so far have been dominated by gut and intuition rather than by data and rigor. The differences between big data and analytics are a matter of volume, velocity, and variety: More data now cross the internet every second than were stored in the entire internet 20 years ago. Nearly real-time information makes it possible for a company to be much more agile than its competitors. And that information can come from social networks, images, sensors, the web, or other unstructured sources. The managerial challenges, however, are very real. Senior decision makers have to learn to ask the right questions and embrace evidence-based decision making. Organizations must hire scientists who can find patterns in very large data sets and translate them into useful business information. IT departments have to work hard to integrate all the relevant internal and external sources of data. The authors offer two success stories to illustrate how companies are using big data: PASSUR Aerospace enables airlines to match their actual and estimated arrival times. Sears Holdings directly analyzes its incoming store data to make promotions much more precise and faster. <s> BIB001 </s> Big data analytics-A review of data-mining models for small and medium enterprises in the transportation sector <s> What Data Mining Can Mean for SMEs? <s> We illuminate the myriad of opportunities for research where supply chain management intersects with data science, predictive analytics, and big data, collectively referred to as DPB. We show that these terms are not only becoming popular but are also relevant to supply chain research and education. Data science requires both domain knowledge and a broad set of quantitative skills, but there is a dearth of literature on the topic and many questions. We call for research on skills that are needed by SCM data scientists and discuss how such skills and domain knowledge affect the effectiveness of a SCM data scientist. Such knowledge is crucial to developing future supply chain leaders. We propose definitions of data science and predictive analytics as applied to supply chain management. We examine possible applications of DPB in practice and provide examples of research questions from these applications, as well as examples of research questions employing DPB that stem from management theories. Finally, we propose specific steps interested researchers can take to respond to our call for research on the intersection of supply chain management and DPB. <s> BIB002
The explosion of data is deemed critical for SMEs because, during the DM process, organisations can radically learn more about their business and translate the new knowledge into better decision making and performance BIB001 . In other words, DM has the potential to transform traditional SMEs to organisations with a competitive advantage. For instance, suppose an SME aims to mine its customer data, the potential benefit would entail creating cross-selling avenues at a higher margin, improving its customer retention and satisfaction rates, identifying the most profitable customer group and last but not least, enhancing the SME's marketing and sales strategy  . In the mining of inventory data, on the other hand, SMEs can gain an advantage by forecasting of the inventory that will help to reduce the total value of stock held. This would create a positive implication in allowing timely inventory purchase from the supplier, creating a supplier lock-in, leading the SME to a better trading agreement BIB002 . It is therefore evident that DM has the capability to create business opportunities to enable SMEs to stay ahead of their competition and leverage on the possibilities.