Big Data Analytics in Supply Chain Management

Dealing with varying numbers of suppliers, manufacturers, logistic providers, etc. creates big data sets that can be used for optimization projects in a supply chain. Big data analytics improves demand forecasts, reduces the safety stock, and improves a supplier's management practices. It has been shown that big data predictive analytics can be combined with other methods such as enterprise resource planning to improve the performance of supply chains. Oncioiu et al. studied the role of big data analytics applications in improving Romanian supply chain companies' performance and implementing assessment processes. Big data analytics has also been used by Boone et al. to improve the practices of service parts management. In another study, Hofmann shows that the velocity of big data can be used to reduce the bullwhip effect (increasing the safety stock levels in upward echelons) in supply chains. Working with omni-channel supply chains generates a huge amount of data from different sources. Big data analytics can make more accurate sales predictions in different channels and develop optimal delivery plans to minimize transportation costs. In another application, sharing the data through a big data framework can reduce the uncertainty cost in a supply chain.