Using rising consumer expectations and pricing challenges, competing in a dynamic and global business environment with traditional supply chain execution techniques is becoming increasingly difficult. That is why and where analytics has a bright future.
Due to traffic, rising fuel prices, driver shortages, weather conditions, and government rules, the logistics transportation process has gotten more complicated. As a result, the use of Supply Chain Big Data Analytics in transportation may eventually aid in the simplification of all transportation services.
Data availability, whether it is data from within the organisation or data from outside the organization's walls, and the coordination of activities to break down "silos" within the enterprise are some of the obstacles limiting analytics adoption.
The entire process of transporting materials from start to finish may be tracked with the use of big data. Previously, the delivery procedure could not be tracked. Despite the fact that the delivery truck may have left on time to deliver the supply, there may be reasons why the delivery person is unable to deliver the product on time.
If analytics aren't deployed in accordance with the supply chain's integration and maturity stage, they won't produce the intended results. Applying multi-echelon inventory optimization to semi-functional or even integrated organisations, for example, will not always yield the desired results. Meanwhile, using advanced demand forecasting skills in Supply Chain Big Data Analytics without capturing demand signals shared among different supply chain partners but not shared through collaboration may result in suboptimal results.
