Shipping AI and its Possibilities
Shipping AI is taking transportation-related topics and solving them using artificial intelligence.
With shipping AI, we’re able to take a problem such as connecting carriers to shippers and:
- Apply complex algorithms using artificial intelligence
- Make those problems simpler for the users to be able to get the results that they’re looking for
This becomes more relevant when we’re talking about a complex situation. Where a standard algorithm, a yes/no type algorithm, doesn’t give us what we’re looking for.
But when we use artificial intelligence, we’re able to take and look at various solutions. With this, we can weigh the different solutions to find which is more likely to solve our problem.
That’s what AI does as compared to a standard algorithm. It gives us complex things and helps us decide which one is more likely to help solve our problem. After, we can test it many times in the background. Meanwhile, the user isn’t aware and we can respond with the strongest result.
We do this using reactive machines and machine learning. These are technologies that get used and words that we hear all the time, but what do they mean? And with machine learning, what it is a way to solve a problem that gives us more than a true or false?
Shipping AI VS Standard Algorithm
So if we take a standard algorithm, if you’re on one zip code to another zip code, we’ll get these five carriers as options. After we see those five carriers, we’ll use another algorithm to decide which gives us the best price. And that’s how we send the solution back. Or we’ll send it back to the user and let the user pick out of the five carriers. That’s a very traditional way to solve this kind of problem. With shipping AI or with machine learning applied to do the same, we’re able to look into a bit more information.
This way we can then look at:
- The history of what that shipper or carrier has done
- If the carrier is successful in this lane
- If the transit time is important to this customer
- The histories of customers
- The whole pool of information that’s available to us
With this, we can predict a percentage of likelihood which carrier will get selected. Then, we can take and return that one carrier and solve the problem by picking the best carrier for that lane. This will match up the carrier to the shipper without a user ever interacting.
We can also apply the same kind of machine learning and make those suggestions to a user. After, we can inform them why the system picked a specific carrier. With this, we can show five or six carriers to the user. Then, put them in order of what that user is most likely to select.
We do this by considering these things:
- Transit times
- Availability of the carrier
- Likelihood of damage
The machine learning algorithm also suggests to the user which carrier to choose. So that’s an example of a real-world kind of situation where we can apply machine learning and shipping AI today.
Where to Use Shipping AI
There are many different places to use AI to solve shipping and transportation problems. For example, selecting the right ports and terminals for warehousing. Tractor manufacturers are using it now to help for autonomous trucks. The autonomous driving is where we hear about AI the most. And it doesn’t have to be driver-less machine learning or vehicles. But, we can help the driver with machine learning and artificial intelligence in shipping.
We also see it in warehousing and analyzing system data. These are areas where shipping AI and machine learning become relevant and valuable. As we move into the future, data becomes very powerful in everything that we do. We want to have an organization that’s run off of data. Not only to feel like we have the right things or are moving in the right direction. We want to use data to analyze that.
As we move into the future:
- Using AI
- Shipping AI
- Solving shipping-related problems with AI
gives us that flexibility and the ability to take huge amounts of data and records. Then we can apply them to our business so that we can go off of data and use that to drive our future.
Reducing Costs, Not Employees
As we do that, it’ll help us with the reduction of costs, both in staff and in the effort of the staff. So we don’t have to say, “Oh, shipping AI is going to replace most of my employees.” That’s not what we’re trying to do with shipping AI. One of the better ways to use shipping AI is to take the tools and help the users keep from making mistakes. We still want people talking to your customers, and have that interaction. But we can help the staff reduce mistakes and understand how to handle them best.
With this, shipping AI becomes most valuable and helpful in:
- Reducing costs
- Increasing efficiencies
- Predictability in the supply chain and the company
This makes the opportunities available for efficiencies. And, predictability across the organization makes us a much better organization. As well as making it easier to manage and run our organization.
Now we can also get rid of simple tasks. These are things that we’ve been using automation and computers for a long time. But when we take and use shipping AI, we can automate some of these more complicated tasks. This brings me back to predicting pick-ups and delivery times, for example.
Predicting the Future
This is something that isn’t a straightforward algorithm. Although, it’s something that we can do using a little bit of machine learning. This helps the process of having your staff try to figure out what the delivery time will be for the next shipment. We can predict that using machine learning, we can reduce the time it takes to do that menial task. Then we can expand on that.
Not only can we predict transit times, but we also can predict service failures. For example, let’s say a driver’s allowed a certain number of miles per day before he gets out of compliance. We can make sure that we’re looking at those numbers. We can tell you ahead of time if a driver is behind schedule, and how many miles behind they are. Whatever that distance might be, we can predict if they’ll be there on time by looking at more than the raw data.
Making things Practical
There are also some more practical applications of shipping AI as well. One of those is in helping users understand the data that’s shown to them. That’s where it becomes a little more practical. We talk about these things on a higher level. And there are some ways, like autonomous vehicles, for example, and self-driving cars. These are big problems that are being solved and worked on today. But, that doesn’t bring it to a practical place of,” how does this work for me today?” And that’s where we can use AI to help interpret the available data.
For example, showing a giant report to a user. We have to train that user on how to look at the report and how to deal with the information that’s in that report. Whereas if we use machine learning, we can take and summarize that data or suggest things out of that data. This can help the user without them having to know that information, or remember it. Remember that they may have their way of interpreting it, but it may not be how the company wants to interpret it.
So we can take that data then, and offer suggestions to the user. We can let them know “Hey, this is the best way to deal with that for the organization.” And it may not be the most intuitive way, which is where we can add value to the end-user and staff.
At Teknowlogi, we’re committed to using AI for shipping-related problems. That’s what we focus on.
We want to take problems in:
- Supply chain
and solve them by using AI. As well as using traditional algorithms, to help improve the way your business is running. This will help logistics providers offer better solutions and technology. In turn, this will help drive technology and machine learning forward as we move into the future.