Supply chain analytics is the application of mathematics,statistics,predictive modeling and machine-learning techniques to find meaningful patterns and knowledge in order,shipment and transactional and           sensor data.An important goal of supply chain analytics is to improve forecasting and efficiency and be more responsive to customer needs.For example,predictive analytics on point-f sale terminal data stored     in a demand signal repository can help a business anticipate consumer demand,which in turn can lead to cost-saving adjustments to inventory and faster delivery.
Globalization and complexity have put supply chains in the spotlight like never before Supply chains are a rich place to look for competitive advantage,partly because of their complexity and partly because of     the significant role they play in a company’s cost structure.And with the power of new analytics,companies can now fine-tune their supply chains in ways that simply weren’t possible in the past.If your               supply chain management models are based only on past demand,supply,and business cycles,you could be missing big opportunities to put analytics to work.
1.Transform data into real-time
2.Logistics controls and Parcel Tracking
3.Predictive insights Commodity volatility
4.Freight Cost Optimization