There's no need for "predictive modeling" when you have real-time data being transmitted from sensors attached to your cargo informing you of the location and condition of your freight.
Do you have the data sets you need to do the type of supply chain analysis you want? So often, analysts only have basic data points such as origin, destination, lane, carrier and pick-up/delivery dates and time. Limited data sets lead to many assumptions being made in your supply chain analysis. And, now you have so-called “predictive modeling” that takes these limited data sets and tries to recognize patterns that might indicate future behavior. However, the only thing predictive modeling can do at this point is automate assumptions about the performance of your supply chain. With sensor-based logistics, each piece of cargo that has a sensor attached to it will be able to provide a new and unique set of data points. Information on temperature, humidity, light exposure, dwell time, route deviations, real-time location and movements like shock and tilt can all be gathered from sensors allowing you the rich data points necessary for the in-depth supply chain analysis you deserve.