Artificial intelligence has started to impact the logistics industry, along with the supply chain. We are seeing innovations such as smart roads and autonomous vehicles. In this article, we’ll look at five promising AI use cases in logistics. The potential value to be gained is huge. Research shows it can generate from $1.3 trillion to $2 trillion per year.
The primary purpose of many AI implementations in the logistics industry is to automate time-consuming actions and save money. Many tech enterprises (e.g. Google, Amazon) are heavily invested in this technology and leading the field.
Use Case 1: Automated Warehouses
Artificial intelligence technology changes many warehousing operations, e.g. data collection, inventory processes, and more. As a result, companies can increase revenues. AI in warehouse automation is being used for predicting the demand for particular products. Based on this data, orders can be modified and the in-demand items can be delivered to the local warehouse. This predicting of demand, and planning of logistics well in advance, means lower transportation costs.
Use Case 2: Autonomous Vehicles
Self-driving cars get a lot of press today, and for obvious reasons. The use of automated vehicles in the logistics industry promises to save time and money, and could reduce accident rates. There is a lot of work still to do, as currently, drivers are required to be at the wheel of autonomous vehicles, and it will take some time before the technology and regulations allow for fully autonomous vehicles to drive on roads without human supervision.
Use Case 3: Smart Roads
Another AI use case in logistics is smart roads. Examples of this technology include highways with solar panels powered LED lights. Solar panels assist in producing the electricity while LED lights are used to alert drivers about the road conditions. Additionally, solar panels prevent the road from being slippery in winter. Another application is fiber optic sensors that can sense traffic volumes and patterns and alert drivers to road conditions ahead. They can also sense when vehicles leave the road or are involved in accidents, and alert the appropriate emergency services and authorities. This makes for faster deliveries and safer road conditions.
Use Case 4: Back Office
Artificial Intelligence in combination with Robotic Process Automation (RPA) provides the workers with an opportunity to increase their quality of work. For instance, everyday repetitive tasks can be automated. This lowers costs and improves the accuracy and timeliness of data for logistics companies.
Use Case 5: Demand Prediction
One critical business need affecting most businesses, is the need to predict the amount of supplies and goods it needs in future. Running short of inventory means lost sales, lost revenue, and often lost customers who may defect to a competitor’s product.
There are many ways to implement AI into the supply chain and into the logistics sector. It improves logistics processes and reduces costs. It also plays a major role in automating routine tasks to improve the speed and accuracy in numerous back office applications.
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