5 Ways to Enhance Your Freight Operations With Machine Learning

A  few years ago, there was a time when technology in the cargo industry was limited to only matching shippers with drivers. It was just about a simple tool that conducts a few operations online to ease freight management. 

“The logistics/trucking sector is followed by passenger ground (26%), shipping (17%), passenger air (14%), and railway (4%).” 

However, the situation is not the same at the current time. Freight operations are becoming seamless with the involvement of various technologies like Machine Learning, Artificial Intelligence, the Internet of Things, and more. 

You might have read it in the magazine or somewhere in the headlines about the implementations of Machine Learning and Artificial Intelligence in cargo management. And, how these technologies are revolutionizing the whole supply chain operations. 

The fact is just by going through a small article in the magazines or headlines you cannot get in-depth knowledge on the working of the ML in the freight industry. It may not cover the major points like what Machine Learning is, how it is implemented, what are the benefits, and so on.

In this article, we have broken down all the aforementioned points into the concerned topics. Keep reading the article to know about everything included in the truckload freight industry with the involvement of Machine Learning.

Let’s begin with an overview on Machine Learning

A Brief on Machine Learning

Machine Learning – also known as ML excels at gathering and analyzing a lot of data, various working patterns, and taking a better decision with the help of the information.    

Besides that, a part of machine learning is also called a model. The models are a pile of codes used to perform a set of operations or recognize the data for a specific pattern. Machine Learning works on the two primary aspects, 

  • With instruction created by the engineer
  • By analyzing the data of their own

When both of them are combined, the abilities of Machine Learning are explored to a new level. Some similar combinations are made in freight operations for better management. Here we’ve mentioned the best implementations of Machine Learning in drop and hook trucking.   

Top Ways That Shows Machine Learning is a Perfect Match for Freight Industry         

ML comes with a few capabilities to outperform the operations that were done manually in the traditional; drop and hook trucking management. The five strengths of Machine Learning are given below.     

The global logistics market is expected to grow at a CAGR of 3.48% from 2016 to 2022.

1. Gathers Colossal of Information

The freight industry comes with a gargantuan amount of data that can only be handled by Machine Learning. 

If you think of the US alone, there are more than 400 million classes for 8 shipments. For each case, there can be a thousand types of information to be taken care of like fuel used, shipment loading and unloading time, price, location, fuel used, GPS coordinates, and so on. 

Let me share with you an example for a better understanding. Imagine you’re operating a marketplace with approximately 1000 shipments per day. If you calculate this 1000 shipments with its 1000 pieces of micro information – every day 1 billion data is generated. And, it is impossible for any human to organize or analyze this amount of data. 

While Machine Learning is capable of handling all the data generated. 

2. Multiple Operations at a Time

When people opt to perform multiple tasks, there is a chance of mistakes like missing data or anything similar. Supply chain professionals work with dozens of data in the course of a day. However, if there is a team of professionals working on shipments then also it may not be enough to move the leads faster.  

On the other hand, Machine Learning models are enabled to handle millions of data samples in less than 20 milliseconds. So, now you can imagine how effectively ML handles the multiple operations in the freight industry. 

3. Freight & ML Work Around the Clock

 Almost every cargo operation works 24/7 every day. For humans it is not possible to work for the whole day, a shift of employees keep changing the shift to continue to work for 24 hours. This could sometimes lead to errors or misunderstandings. 

While the case is a bit different with ML. The Machine Learning models can work continuously without taking a break. They keep tracking the freight to avoid any clashes or issues. 

“The global contract logistic market is approximately 213.85 bn across the globe.  ” 

  4. No Error in Repetitive Task With ML

Every cargo shipment performs a repetitive task that involves people. Searching for ideal carriers who keeps track of every repetitive activity like driver record, confirming shipment, pricing of the shipment, resolving the issues raised, and so on. 

Machine Learning is capable of catching repetitive tasks. Whenever a repetition comes, ML catches and forms a pattern. Later, it will make automatic decisions by recognizing the pattern. Thus, this avoids any bugs while performing any task. 

  5. Machine Learning is Improving Continuously

The human learning rate slows down with the passing of time. However, Machine Learning is continuously improving with a faster learning rate. 

They come with the capability to analyze everything in a better way than humans. Every day, one or other new algorithms are implemented in Machine Learning. One of the best parts is when ML is implemented with the combination of other technologies like Artificial Intelligence, the Internet of Things, and more.

 The Concluding Thought    

At last, we would say before banging into Machine Learning for your freight operations, have a look at the data of your company. Analyze each freight operation of your company, and then go for the final call to implement ML.  

One thing that is for sure is machine learning is helping drop and hook like operations to enhance productivity and efficiency. It will definitely bring tons of fortune to your freight business. 

The post 5 Ways to Enhance Your Freight Operations With Machine Learning appeared first on AiiotTalk – Artificial Intelligence | Robotics | Technology.

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