AI and logistics: How machine learning reshapes the industry

Many believe that the concept of logistics implies moving goods from point A to point B. That’s true, but in fact, there’s much more — for business, it’s a multi-billion dollar industry, which — if to consider the latest technology achievements — brings even more benefits.

Logistics is a fast-growing industry in the world economy. Over the past five years, the industry has been growing by 7–10% annually. In 2017 leading companies in shipping industry earned more than $125 billion. At the same time, the amount of data accumulated in the course of logistic operations is also increasing . According to the estimates, the overall amount of big data will double every two years, and by 2020 will reach about 44 zettabytes (44 trillion GB).

Think about this for a moment. And just imagine how much these vast amounts of data can tell us after applying machine learning algorithms to its analysis and interpretation! The primary purpose of machine learning is to make predictions based on previously defined trends. Using ML algorithms, we can analyze massive databases without involving a person into this process. In other words, machine learning is a way to speed up the big data analytics. Using this technology, computers can be taught to identify certain patterns, and then perform determined actions when coming across these patterns. For instance: calculation of the shortest delivery route, instant calculation of the transportation cost, schedules optimization, etc.

The point is that machine learning algorithms are universal and don’t depend on a specific business or industry. All of these tasks — such as client base monitoring to predict customer churn rate, traffic flow analysis and possible places of traffic jams occurrence — can be solved by the similar mathematical tools for machine learning.

In the same way as any other industry, logistics often faces challenges related to the enterprise profitability improvement. There are three main ways of how it can be done.

1. Price increase — under free and open market conditions, competitors can provide the same services, but at lower prices. As a consequence, customers will stop paying an unreasonable price and will take advantage of the services provided by other companies.

2. Staff optimization — under the growing demand for logistics services, companies should be ready to increase their own resources load and give a timely response to changes. In the short term, you can gain some time by reducing the staff, but after a while, you’ll receive a substantial personnel gap, and the existing staff will not be able to cope with the increased workload.

3. Cost optimization — this method is designed to minimize transportation costs through the intelligent allocation of resources rather than reducing the number of employees.

Want to see how it works right now with machine learning? By combining customer data, economic indicators, and geolocations, logistics companies can predict demand, and the accuracy of such prediction will grow over time due to progressive learning based on the information received. This practice allows optimizing the distribution of loads on vehicles and efficiently plan delivery routes. And there’s more: through the usage of neural network and machine learning technologies, such analysis adapts and learns from his past experiences.

Let’s take an example: a logistics company received an urgent order for some goods transportation, and this should be done on the same day. In such a short period of time, it is unlikely to find a return load, and therefore the profitability of this service drops off, while the delivery cost increases. This is not acceptable either for customer or performer. But what if we take a solution offered by big data analysis? Based on past historical data, the system can predict that one particular customer will likely place an order on Monday, while the likelihood of placing an order on Tuesday is lower. On the other hand, for the way back, the algorithm will create a schedule when the vehicles are fully loaded, and will suggest the best route and departure time. Sounds impressive, right?

The similar scheme can be applied to other economic factors logistics and transportation businesses are affected by: the demand for transportation services, the cost of fuel, the type of vehicle, internal and external communications, the reliability of supply chains, etc. For their comprehensive analysis, processed and structured big data is required. After being processed by machine learning algorithms, this data becomes a basis for improving separate business indicators and the profitability of the enterprise in general.

Here are just some ideas of how machine learning tools can help in logistics:

• Aggregation of an enormous number of orders and their analysis. This practice allows optimizing the distribution of load on vehicles.

• Predicting the profitability and cost of transportation services using machine learning algorithms.

• Efficient route planning due to the integration of neural networks technology, which is trained on the transportation data from the previous periods.

• Schedules optimization through patterns based on algorithmically defined properties and trends.

• Accidents prediction, also based on data obtained in previous periods.

• Computer vision to ensure the security, monitor balances in the warehouse, control the shipments, ensure employee recognition and data collection. These are just some examples of how the computer vision can be applied.

• Speech recognition for customer service.

And the list goes on. There is no doubt that the most competitive logistics companies of the future will be those who’re able to integrate machine learning into their processes and make the best use of big data analysis. It doesn’t necessarily mean that the whole enterprise system should be changed right now — such technology implementation is an evolutionary process. But if you start taking advantage of machine learning today by optimizing some parts of the entire system — you’ll get the necessary experience that will facilitate the adoption of more complex solutions in future.