How is Machine Learning Reshaping Logistics?

Machine Learning

Those dreaded 3 week-long delivery wait times are a thing of the past and they have long been replaced with one-click online ordering and same-day deliveries. In fact, year after year, these types of services just keep getting more and more advanced. And the one thing that drives all of this progress is what is known as machine learning.

However, purchasing products online has become so simple and commonplace that customers generally overlook all of the work that goes into even making that possible.

So, in this article, we are going to shed some light on machine learning. We are going to take a look at what it is, how it is tested and most importantly... how it is reshaping the logistics industry.


What is Machine Learning?


Self-education is, I firmly believe, the only kind of education there is.

― Isaac Asimov


The concept of machine learning was the foundation of the work of the earliest computer makers such as Alan Turing. The concept is exactly as the name implies; a computer is tasked with learning logic to an extent that allows them to make more calculated decisions in the future.

And as the hardware has advanced, so has the ability for computers to make exceedingly fast and accurate decisions in less than a second.

That lightning speed is critical in today’s fast-paced market!

The actual learning process begins when an algorithm is given a set of equations and is instructed to run simulations for all possible outcomes. When a successful result is achieved it is logged as “true” and is likely to be checked again, whereas a failure will be logged and the entire solution will be discarded. Once the “true” and “false” results are complete, then the good paths are checked and rechecked until the absolute peak efficiency answer is found.  

This entire session is logged by the system. When the algorithm is run on another task of a similar nature, it relies on those data sets from previous tasks to help them decide on the best and most logical course of action.

This is machine learning in a nutshell.


Ants and Analytics

For millions of years, eusocial insects such as ants and bees have been able to coordinate rather complex logistical operations. Logistics that not only work but which also account for the extremely high populace and growth in their colonies.

As it turns out, the almost effortless precision which these insects display with almost every task they carry out as a unit is a wonderful model for humans to aspire to. And thanks to the current state of computing speeds, we can now use programs to mimic that behavior and learn to make the most out of every delivery route almost instantly.

The appropriately named Ant Colony Optimization Algorithm (ACO) was designed for exactly that! Using road and weather data, the ACO calculates in minutes what used to take humans hours to days to complete. This allows for much more flexibility and fewer delays all around. Using this data to the fullest extent is allowing for better routes for drivers and is also quite useful is the often chaotic realm of the warehouse.


Robots in the Workplace

The hustle and bustle of a warehouse can often feel like a literal beehive, and the application of modern robotics isn’t shying away from the idea, either.

Some companies are already testing and using “swarms” of droids, from small currier bots to heavy crate movers and everything in-between. While still essentially pack mules, the complexity of tasks that can be accomplished by an automated workforce is making quantum leaps.

Many of the earlier versions of robotically integrated warehouses saw the use of equipment on fixed railing. These include Automated Storage and Retrieval Systems (AS/RS) and Automated Guided Vehicles (AGVs). This requires no navigation software and works well with larger loaders on repetitive tasks. 

Autonomous Mobile Robots (AMRs) are the cutting edge of science today, as they require no set track. Instead, AMRs use mapping data and onboard sensors to determine the best route in real-time. This information is stored on the unit, thus it learns as it works.

However, the wheels of progress never stop spinning, and newer generations of machines are being developed every day! Once the self-deciding system was perfected on smaller AMRs, naturally the next step was to go bigger.   


High-Tech Highway Haulers

Long the fever dream of fantasy, a completely driverless delivery truck is a nearly complete reality! While several companies are testing the real-world application of these machines Amazon, in particular, is running a 650-mile route from Texas to California with great success.

Currently, these “driverless” vehicles are required to have a human sitting at the wheel as a failsafe, which is comforting to know during this phase of trials.

Eventually, the reliability of these AMRs will make the need for a human backup unnecessary and that could mean a drop in shipping costs up to 30%. Another bonus is that machines can run longer periods without the need for rest which will decrease overall shipping time. And aerial drones are also still being tested but those are for local delivery, not the long haul.


Learning Through Doing

Working around the clock has another benefit for our new robotic workforce. The data they collect is transmitted back to data banks and is then used in the future, and it very quickly becomes a self-sustaining inflow of new information to be tried and sorted. The more points of data available, the more accuracy lent to the solutions.   

And with all this new and constantly growing knowledge, it is becoming easier to handle all aspects of logistical needs with higher accuracy. Computers are getting better by the day meaning that the amount of information that can be processed and the speed at which it’s done are still not at maximum potential. The machines follow the program and the program collects data from the machines, making the potential for machine learning almost limitless!  


Looking Towards the Future

With gleaming eyes, we now look at a future of logistics where the optimal route for global shipping is calculated in a matter of seconds. Using the help of machines that learn using programs like ACO and the input of the ever-increasing number of active robots, that world is nearly upon us. Now if we could only get a laundry folding android!

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