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Machine learning in freight transport should not be seen as a trend, but seen as a necessity for carriers that want to remain competitive
It's hard not to think that technology has reached all sectors and that it uses modern resources. Machine learning in freight transport brought innovations and allowed companies to have a new look. This happened because machine learning ensures greater efficiency in the process and makes the organization more competitive. However, not all technology can be considered machine learning. See below and learn about its benefits.
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What is machine learning?
Machine learning in freight transport is machine learning. It is related to artificial intelligence, as the machine can learn by itself.
Through a large amount of data, the computer can analyze statistical models and learn from them. Observing the patterns, learning takes place that allows machines to make decisions without major human intervention.
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This is a model where the algorithms are not statistical, they learn to react according to the data they receive. This allows each machine to follow a different path in its learning.
This is a trend that has been adopted in different forms of transport, including the public.
The benefits of machine learning in freight transport
It is not by chance that organizations are investing in machine learning in freight transport. This technology has brought a number of advantages and benefits, including:
improved pricing
The more transports are made, the more data the machine has access to. This improves service pricing.
It is possible to determine values that allow the organization to have the expected billing and that it manages to cover all costs. It also allows you to have competitive prices when analyzing how to reduce waste, for example, taking two loads on the same trip.
Machine learning optimizes freight transport
Machine learning can understand how cargo will be transported. He can determine the best route to take, the type of vehicle to be used, operating costs and improvements to be implemented.
A complete survey of the entire delivery and reverse logistics transport chain is carried out in order to achieve optimization.
Anticipation of demands
The statistical model created can be used to predict demand and understand idle periods. In vacant moments it is possible to look for alternatives so that there is no waste of time and resources.
However, when there is an increase in demand, a restructuring must be carried out. It may be necessary to incorporate new trucks, take out cargo transport insurance for the new fleet and have moh drivers.
This forecast will ensure that the company is ready to meet demand and does not miss opportunities.
Improved truck maintenance
It is possible to take care of the preventive maintenance of trucks, vans and other vehicles used in the fleet. As a complete follow-up happens, you know the wear of the parts, when you need to send them for maintenance and the necessary care.
For example, if engine oil has a useful life of 10 km, machine learning will warn you when it is getting close to that. The exchange can be provided without generating mechanical problems due to lack of care.
More assertive decisions
Managers will have a lot of data to base their decisions on. Machine learning in freight transport generates data for the machine to learn, but also produces reports.
Managers are able to know what is happening in real time, what are the forecasts and all the history that allows them to make better decisions about what the next steps will be. It also allows for interventions at the right time.
Cost reduction
If there is complete monitoring of the logistical process and the bottlenecks are known, they can be worked on. This means that it is possible to minimize losses by optimizing the process and making the right decisions.
A cost reduction it is a consequence, since the investment takes place in the right field and losses are minimized. At the same time that there is a gain in productivity, there is an improvement in the financial sector.
Satisfied customers with machine learning application
One of the final results of applying machine learning in freight transport is customer satisfaction. They pay a fair price for a process that works.
They will have their loads received within the deadline without any unforeseen occurrences. A service provided as expected generates satisfaction and trust.
Machine learning in freight transport should not be seen as a trend, but a necessity for carriers that want to remain competitive. Machines and people need to work in a complementary way for this.
By: Jeniffer Elaina, from SeguroAuto.org.
