Using machine learning for tracking in Logistics
The latest trends of researchers of our generation, data mining and machine learning, have finally gotten the attention of logistics companies and fleet owners. Machine learning algorithms such as neural networks, computer systems configurated around functions of the human brain and nervous system, and support vector machines are generally used to predict an unknown value based on previous data. Data mining techniques can be used to extract relations from the data. For instance, if one has weather and grass data, upon applying data mining techniques one would find that the grass gets wet whenever it rains. Using data mining techniques and machine learning algorithms, businesses are now able to find the expected optimal values for fuel usage, travel time, and driver fatigue.
How useful are these algorithms?
With the help of machine learning algorithms, companies are able to have more control over the field they are working on. Applied to the logistics concept, businesses can make decisions regarding their drivers, trucks or the units they are carrying based on computer generated facts, rather than making them blindly. The term computer generated facts used to be unreliable. However, especially after 2016 and Google's progress on deep learning,), these algorithms started to produce more and more accurate results.
Given the success rate of the machine learning algorithms, most companies are now using them to calculate the expected cost of a trip. This generally means measuring fuel and time cost. These values are very responsive to some features that are not very predictable like trip time and day, weather conditions, driver's experience and/or fatigue and so on. Therefore, if companies were able to train their model with such different features, they would get a prediction model that is more capable of producing more specific answers.
These algorithms may then also result in decreased fuel and time consumption. They will be able to identify the less cost intensive routes and therefore generate the fastest and/or the cheapest route between two locations.
So what's next?
Currently, machine learning algorithms are performing poorly in identifying and producing answers to cases that are foreign to them. The next step for researchers is to improve general performance of algorithms in these subject. In addition to that, companies using Autonomous trucks would benefit the most from these algorithms because their trucks and routes can be tweaked more easily compared to regular trucks.