PROSPECTS OF USING BIG DATA FOR LSCM

Abstract: Big data analytics is the process of examining big data to uncover hidden patterns, unknown correlations and other useful information that can be used to make better decisions. With big data analytics, data scientists and others can examine huge volumes of data that conventional analytics and business intelligence solutions can't touch. Using high-performance data mining, predictive analytics, text mining, forecasting and optimization on big data enables you to continuously drive novelty and make the best possible decisions. With the use of big data becoming more and more important to businesses, it is even more crucial for them to find a way to analyze the ever (faster) growing dissimilar data coursing through their environments and give it meaning. From past few years we are hearing more and more about the use of data analytics in the supply chain & logistics function. Supply chain leaders are transforming their view of the data they collect, and how they analyze it. The work is not about inside-out and the deployment of traditional technologies. The problem is that supply chains today catch orders and shipments and assume that they are representative of the market. They do not allow for systems to manage the channel from the outside in. The analytics and traditional systems are not able to effectively use channel data. One of the advantages of predictive analysis and data science is that they may provide better insights than it would be possible with traditional business intelligence systems. Thus, the high quality decision support becomes attainable, and usage of predictive analysis and data science methods will enable extending its application in decision making.