In most cases, such regression models consist of both historical data and external elements, including seasonality, climate, and even public holidays.
Another use case for predictive models in logistics is predictive maintenance. The goal is to train neural networks on big data to anticipate potential failures and schedule repairs.
Apart from predictive maintenance, sensor data is also helpful for achieving an entire fleet's real-time tracking. Thanks to IoT sensors, it is possible to monitor vehicles' speed, location, and direction in real-time.