Traditionally, a business had to perform logistical tasks manually. A company had to ensure physical documents like shipping orders were available while the drivers transported freight from one location to another/while railways transported the cargo. Moreover, instructions had to be given to truck drivers about places where they could park their truck after reaching the city, the point where the unloading/loading of freight would take place, etc. The business manually instructed the time and date for freight delivery through road and rail; multiple phone calls and messages enabled cargo allocation to drivers. These processes were highly time-consuming, so the need for artificial intelligence to expedite logistical processes came into the picture.

Artificial intelligence has disrupted the traditional logistics network with changing times and technological advancements.

What is artificial intelligence, and what is its application in the logistics industry? Let’s explore.

What is artificial intelligence?

Artificial intelligence is a branch of computer science that involves a robot/computer to perform tasks otherwise done by humans. The robot/computer similarly performs the tasks to that of a human. Did you know that there has been a rise in the use of artificial intelligence in the current era? The size of the artificial intelligence market is estimated to reach $641.3 billion by the year 2023. (Source: Verified Market Research)

Application of artificial intelligence in the logistics industry

• AI-based ANPR and Computer Vision

AI-based ANPR(automatic number plate recognition) and computer vision involve optical character recognition (OCR), a technology that captures high-speed images of vehicle registration plates/number plates and utilizes image processing software to detect characters and their sequence. This image of the number plate is then converted to text using OCR. ANPR replaces RFID (radio frequency identification to identify goods) readers with ANPR modules and brings in machine learning/artificial intelligence abilities that help identify the truck and the number image as the trucks move across locations. Hence, it prevents frauds related to the freight, like cargo not being unloaded at the proper location.

• Big Data

Vast chunks of data are generated daily in a business. The larger the organization grows in size, the more is the amount of data generated. This data is also known as ‘’big data’’, and traditional logistics management does not allow for a mechanism to analyze the data or help identify trends.

There needs to be a way to make sense of all the generated data, which is where data analysis can help. Examples of logistical data businesses can analyze include the number of trucks allocated for a specific quantity of freight, the time taken to transport shipments, deliveries fulfilled by each freight transporter, etc. Analysis of such data over a long time helps business decision-makers take corrective action and improve the efficiency of their logistical operations.

• Demand forecasting

Real-time data analysis can help forecast demand, such as the number of trucks required to transport cargo from one city to another. Demand forecasting ensures timely availability of cargo and trucks, proper coordination to ensure freight is present on time for transportation through road/rail, etc. It also helps ensure that the shipment reaches the endpoint on time, improving customer satisfaction.

• Predictive risk management

In an organization, multiple risks/hazards may arise from time to time related to a company’s logistics network. In this scenario, predictive risk management can come to the rescue. Predictive risk management helps identify potential risks for an organization before they occur, hence ensuring logistical processes streamline and there are no interruptions.

In the logistics industry, predictive risk management helps identify potential problems, like sufficient trucks not being available to transport products, non-timely availability of products to be transported from one place to another, etc. Artificial intelligence capabilities enable the risk trends like non-availability of products to be analyzed, helping plan logistical operations better within the organization.

• Route optimization

In the logistics network, the business cannot ignore the importance of selecting the correct route for transporting freight from one location to another. Whether it is road or rail, the right route is the one that minimizes the travel time from one city to another. Therefore, the business can analyze the best path that prevents transportation roadblocks by applying artificial intelligence in the logistics industry.

• Theft detection

In today’s time, theft detection is easily possible with the help of artificial intelligence. For example, when cargo moves from one point to another, AI mechanisms help track the driver moving across locations and the cargo the driver carries. Hence, if the number of cargo changes unexpectedly, the business can detect theft. Furthermore, access to data, like the time of the robbery, frequency of theft, and the type of goods stolen, can also be obtained.

With AI-based theft detection, alerts can be shown to a company that wishes to transport cargo from one location to another. Hence, the business can take corrective action to minimize such theft in the future by understanding who was responsible for the robbery.

• Warehouse automation

Warehousing is the process of storing inventory for sale or distribution. In the logistics industry, warehouse automation refers to techniques that help ensure the correct quantity of stock is available in the warehouse, at the right time and in the right quantities. The size of the warehouse automation market is expected to reach USD 30.15 billion by the year 2026. (Source: Statista)

Warehouse automation systems can automatically count the number of orders that need to be fulfilled and estimate the level of stock required to fulfill the demand/number of orders. In addition, a business can also leverage the power of mechanized warehouse automation, where automated shelf loaders/robots lift products directly from the warehouse and give them to the human warehouse managers, who can then arrange the items.

Putting it all together…

You can start leveraging artificial intelligence in your logistics network for improved business efficiency. Are you ready to start experiencing the power of AI?